Environmental P/qtectior^Technology Series
Pesticide Transport and Runoff
Model for Agricultural  Lands
                          Office of Research and Development
                          U.S. Environmental Protection Agency
                          Washington, D.C. 20460

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             PESEARCK EXPORT I KG SERIES
Research reports of the  Office  of  Research  and
Monitoring,  Environmental  Protection Aqency, have
been  grouped into five series.   These  five  broad
'categories  were established  to facilitate further
development  and  application   of   environmental
technology.   Elimination   of traditional grouping
was   consciously  planned   to  foster   technology
transfer   aiid  a  maximum  interface  in  related
fields.   The five series are:

   1.  Environmental Health Effects Research
   2.  Environmental Protection Technology
   3.  Ecological Research
   H.  Environmental Monitoring
   5.  Socioeconomic Environmental Studies

This  report has been assigned to the ENVIRONMENTAL
PROTECTION   TECHNOLOGY   series.    This   series
describes*  ^research   perfoJif  to  develop  and
                        t^KOT,"
demontrate j  ^^troxnentKOT,"    'equipment    and
methodolog?/   to  rs^i^r  or  prevent environmental
degradation from point and  non-point  sources  of
pollution.  This work provides the new or  improved
technology  required for the control and treatment
of pollution  sourtoes to meet environmental quality
standards.
                    EPA REVIEW NOTICE
This report has been reviewed by the Office of Research and
Development, EPA, and approved for publication. Approval does
not signify that the contents necessarily reflect the views
and policies of the Environmental Protection Agency, nor does
mention of trade names or commercial products constitute
endorsement or recommendation for use.

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                 PESTICIDE TRANSPORT

                  AND RUNOFF MODEL

               FOR AGRICULTURAL  LANDS
                                                     EPA-660/2-74-013

                                                     December 1973
                         By
                 Norman H. Crawford
               Principal Investigator
                        and
              Anthony S. Donigian, Jr.
                   Project Manager
               Contract No. 68-01-0887
               Program Element  1BB039
                   Project Officer

                 Dr,  George W. Bailey
     Southeast  Environmental Research  Laboratory
             Environmental Protection Agency
                 Athens, Georgia  30601
                    Prepared for

        OFFICE  OF RESEARCH AND DEVELOPMENT
       U.S.  ENVIRONMENTAL 'PROTECTION  AGENCY
           WASHINGTON, D,C.   20460
For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402 - Price $2.40

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                           ABSTRACT

     The development and testing of a mathematical  model   to  simulate
the  loss  of  pesticides  from  agricultural lands is presented.   The
Pesticide Transport and Runoff (PTR) Model  is  composed  of  submodels
concerned  with  hydrology, sediment loss,  pesticide-soil interaction,
and pesticide  attenuation  functions.   The  Model  'piggybacks'   the
applied  pesticide onto the movement of water through the soil  profile
and the loss of  water  and  sediment  from  the  land  surface.   The
pesticide-soil    interaction    is    based    on     the   Freundlich
adsorption-desorption isotherm, which provides  the  division  between
the  adsorbed  and  dissolved  states  of  the pesticide.  Attenuation
functions of volatilization and degradation are provided  but were   not
tested due to lack of data.
     An extensive sampling  and  data-gathering  program   at  the   EPA
Southeast Environmental Research Laboratory (Athens, Georgia) provided
observed  data  on  pesticide  loss  from  experimental  plots   in the
Southern Piedmont.  Comparison of si moated and  recorded  runoff   and
sediment loss showed considerable agreement.  Simulated pesticide  loss
agreed  reasonably  well  with  recorded  values  for those pesticides
completely adsorbed on sediment particles.   The Freundlich  adsorption
model did not accurately predict the division between the adsorbed and
dissolved  states for those pesticides which are transported by runoff
and sediment loss.  Recommendations for future  work  include  further
calibration and testing of the PTR Model, and additional  refinement of
the  pesticide  adsorption/desorption  and  attenuation functions.   The
regulation of pesticide releases to the environment  are   explored  as
possible eventual uses of the PTR Model.
     This  report  was  submitted  in  fulfillment   of Contract   No.
68-01-0887 by Hydrocomp, Inc.  under the sponsorship of the
Environmental Protection Agency.  Work completed as of December, 1973.
                                - ii -

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                             CONTENTS
                                                                    Page

Abstract  	    i
List of Figures 	
List of Tables  	
Acknowledgments 	

Section
I.    Conclusions 	    1
II.   Recommendations 	    3
III.  Introduction	    5
           The Pesticide Problem  	    5
           Pesticide Regulation 	    7
IV.  Mechanisms of Pesticide Loss and Transport in the
        Environment	10
           Pesticide Cycling in the Environment 	   10
           Mechanisms of Loss from Agricultural Lands 	   10
V.   .Pesticide Transport and Runoff Model  Components  	   13
           Hydrologic Model 	   13
           The LANDS Subprogram 	   17
           Modification to HSP LANDS	20
           Model  Description and Operation	22
                                - ill -

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                       CONTENTS (Continued)
           Sediment Loss Program
                The Erosion Process	23
                Sheet Erosion	24
                Sediment Loss Simulation  	   26
           Pesticide Adsorption-Desorption Model
                Mechanism of Pesticide Adsorption-Desorption  ...   27
                Model Description	28
           Pesticide Volatilization and Degradation Model  	   32
                Volatilization of Soil-Incorporated Pesticides.  .  .   32
                Volatilization of Surface Applied Pesticides.  ...   35
                Pesticide Degradation  	   38
VI.   PTR Model  Structure and Operation	39
           Model Structure	39
           Model Operation	39
           Model Input and Output
                Model Input	40
                Model Output	43
           Parameter Evaluation and Calibration Procedures   ....   48
                Operational Parameters  .  .  ,	59
                LANDS Parameters	59
                Watershed and Pesticide Application Parameters  .  .   59
                SEDT Parameters	60
                Pesticide Adsorption-Desorption and VOLDE6
                   Parameters	61
                                -  iv  -

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                       CONTENTS (Continued)
                                                                   Page

                Conclusion	61
VII.  Experimental Program* and Modeling Methodology  	  63
           Experimental Program 	  63
           Modeling Methodology 	  65
VIII. PTR Model Results and Discussions	67
           General  	67
           PI Watershed Results
                Calibration - Runoff and Sediment Loss	67
                Pesticide Loss	75
           P3 Watershed Results
                Simulation with PI Parameters	86
                Sources of Error and Data Problems	86
           Discussion of Simulation Results 	  89
IX.   Recommendations for Future Research 	  97
X.    References    	100
XI.   Appendices    	105
                                   v -

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                              FIGURES
No.

1
2
3
4
5
6
8
9
10
11

12
13
14
15
16
17
                                                              Page
Pesticide Cycling in the Environment 	  11
Flowchart of Pesticide Movement in PTR Model 	  14
Assumed Soil Depths for Pesticide Storage  	  15
The Hydro!ogic Cycle 	  16
LANDS Flowchart	19
Cumulative Frequency Distribution of Infiltration Capacity
   Showing Infiltrated Volumes, Interflow and Surface Detention 21
Source-Zones Superimposed on the Infiltration Capacity
   Function	21
Pesticide Adsorption-Desorption Model   	  30
PTR Model Structure and Operation  	  41
Location of Experimental Watersheds  	  64
PI Watershed:  Monthly Summary of Rainfall, Runoff, and
   Sediment Loss, (1972-73)  	
69
PI Watershed:  Storm of July 28, 1972	71
PI Watershed:  Storm of July 31, 1972	72
PI Watershed:  Storm of August 10, 1972	73
PI Watershed:  Storm of December 14, 1972  ,	74
Wl Watershed:  Monthly Runoff Volumes, (1941-42) 	   76
Wl Watershed:  Storms of July 11, 1974 and May 15, 1942  .' .  .   77
                                - VI -

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                     FIGURES (Continued)
No.                                                                Page

18   Wl Watershed:  Storm of August 16 and 17,  1942	78
19   PI Watershed:  Monthly Pesticide Loss (1972-73)	79
20   PI Watershed:  Paraquat in Water and Sediment During
        Storm Events	81
21   PI Watershed:  Diphenamid in Water and Sediment  During
        Storm Events	82
22   PI Watershed:  Rate of Sediment and Pesticide Loss  on
        Sediment - July 28, 1972	83
23   PI Watershed:  Rate of Sediment and Pesticide Loss  on
        Sediment - August 10, 1972	84
24   PI Watershed:  Rate of Diphenamid Loss in  Water,  July
        28 and August 10, 1972	85
25   P3 Watershed:  Summaries of Rainfall, Runoff, and Sediment
        Loss (1972)   	87
26   P3 Watershed:  Monthly Summaries of Pesticide Loss  (1972)   .  .   88
27   LANDS Flowchart	107
28   Schematic Frequency Distribution of Infiltration  Capacity
        in a Watershed	110
29   Cumulative Frequency Distribution of Infiltration Capacity .  .  Ill
30   Application of Cumulative Frequency Distribution  of
        Infiltration Capacity in HSP	Ill
31   Mean Watershed Infiltration as a Function  of Soil Moisture .  .  112
                                - vii -

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                      FIGURES (Continued)
No.                                                                Page
32   Cumulative Frequency Distribution of Infiltration Capacity
        Showing Infiltration Volumes, Interflow and Surface
        Detention	114
33   Interflow c as a Function of LZS/LZSN	114
34   Components of HSP Response vs.  Moisture Supply 	 115
35   Surface Denention Retained in the Upper Zone	116
36   HSP Overland Flow Simulation	H9
37   HSP Overland Flow Simulation	119
38   Hydrograph Simulated (0.26 square miles)   	 120
39   Hydrograph Simulation (18.5 square miles)  	 120
40   Infiltration Entering Groundwater Storage  	 122
41   Groundwater Flow	123
42   Potential and Actual Evapotranspiration  	 125
43   Examples of Hydrograph Response with Indicated Corrections
        to INFILTRATION Parameter 	 128
44   Example of the Response to the  INTERFLOW Parameter	129
                               - vlii -

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                             TABLES
No.                                                                 Page
1    Hydrologic Model (LANDS) Parameters 	  18
2    PTR Model Subprograms 	  40
3    Sequence of Input Data	42
4    PTR Model Input Parameters in 'NAMELIST' Format   	  44
5    Sample Input and Format for Evaporation, Temperature, and
        Wind Data	45
6    PTR Model Rainfall  Input Data Format  	  46
7    Sample Output:  LANDS and SEDT Calibration Run  	  47
8    Sample Output:  Pesticide Calibration Run 	  49
9    Sample Output:  Production Run-Daily Output Interval   	  50
10   Sample Output:  Calibration Run - Monthly Summary 	  52
11   Sample Output:  Production Run - Monthly Summary  	  53
12   PTR Model Input Parameter Description 	  54
13   PTR Model Input Parameter Attributes  	  57
14   Parameter Values and Initial  Conditions From PI Calibration .   .  62
15   1972 Summary of Rainfall, Runoff, Sediment and Pesticide Loss
        From the PI Watershed	70
16   Rainfall, Runoff, and Sediment Loss for SP3 and P3 Watersheds   .  90
17   1972 Summary:  Paraquat Simulation on PI Watershed  	  92
18   1972 Summary:  Diphenamid Simulation on PI Watershed  	  93
19   Pesticide Mass-Balance on PI  Watershed On October 30, 1972  .   .  95
                                - ix -

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                         ACKNOWLEDGEMENTS

     A  number  of  people  have  contributed  to   the   satisfactory
completion  of  this  project.   Primary acknowledgement must go to the
assistance and data provided by the Pesticide  Runoff  Modeling  staff
under  the  direction  of  Dr.    George  Bailey  at  the EPA Southeast
Environmental  Research Laboratory  (SERL)  in  Athens,  Georgia.   For
their  efficient  establishment  and  operation of the extensive field
experimental program, Dr.  Bailey and his staff  received  EPA  bronze
medals  for  their dedication to the project; a fitting witness to the
support provided in this modeling effort.
     The USDA Southern Piedmont Conservation Research  Center  (SPCRC)
in  Watkinsville, Georgia co-sponsored the experimental data gathering
program and provided the test  watersheds.   Dr.   Ralph  Leonard  and
others .on the staff of SPCRC were instrumental in supplying hydrologic
data  and assistance throughout the project.  For their efforts in the
development and  operation  of  the  runoff  sampling  equipment,  Dr.
Leonard  and  his  staff received certificates of recognition from the
ARS Incentive Awards Program.                                       i
     Associated  researchers  of  SERL  provided  support   on   model
development,  particularly  in  regards  to  adsorption/desorption and
pesticide attenuation processes.  Dr.  Walter  Farmer  (University  of
California  -  Riverside)  supplied  the  basic  theory  on  pesticide
volatilization  and  provided  data  for  model  testing.   Dr.  James
Davidson (Oklahoma State  University)  advised  on  the  mechanism  of
pesticide  adsorption-desorption  and  vertical movement, and reviewed
the proposed model algorithms.
     Many individuals on the Hydrocomp  staff  were  involved  in  the
project. '  Dr.   Norman H.  Crawford (principal investigator) provided
the guiding philosophy and general direction of the  modeling  effort.
Ray  K.   Linsley advised on methodology and hydrologic problems.  The
project manager, Mr.  Anthony Donigian Jr., was also  responsible  for
project  administration  and  completion of the final' report.  Mr.  W.
Henry Waggy assisted in model development and programming.  Mr.  James
Hunt was involved with  program  development  and  model  calibration,
while  Mrs.   Joan  Eyster  provided  assistance in data reduction and
drafting.  Clerical assistance throughout the project was supplied  by
Ms.   Sherri  Ellis, Miss Dea Bell, Mrs.  Carol Mendoza and Miss Carol
Sinclair.
                                   - x -

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                               SECTION I

                              CONCLUSIONS
     1.  The PTR Model was developed  to  simulate  the  transport  of
pesticides  in  solution and on sediment.  The Model  uses physically -
based   submodels   to   calculate   runoff   volumes   and   sediment
concentrations.  Initial model tests show good results  for  transport
of  pesticides  on  sediment and fair-to-good results for transport in
solution.

     2.  Surface  runoff  from  agricultural   lands  in  the  Southern
Piedmont can be simulated with reasonable accuracy with the PTR Model.
The  hydrologic  submodel  has been used extensively in other studies,
and past experience indicates that  similar  simulation  accuracy  for
runoff volumes can be expected in other geographical  regions.

     3.  Simulation of monthly sediment loss   agrees  adequately  with
recorded volumes; however, sediment concentrations during storm events
vary  somewhat  from  the  observed values.  The general  nature of the
sediment submodel shows promise of  applicability  to  other  regions,
although experience is limited at the present time.

     4.  The PTR Model has demonstrated the  capability  of  providing
reasonable   estimates  of  surface  runoff  and  sediment  loss  from
agricultural watersheds in the Southern Piedmont.   These  routes  are
the  major modes of transport of pesticides and other non-point source
pollutants to waterbodies.  Consequently, further  refinement  of  the
pesticide   functions   (adsorption/desorption,   volatilization,  and
degradation) will upgrade the capability of the model to  predict  the
pesticide  input  to  waterbodies from surface washoff.  Moreover, the
PTR Model can provide the basis for the simulation of other  non-point
source  pollutants  (nutrients,  fertilizers, etc.), and thus estimate
the water quality of surface runoff from agricultural lands.

     5.  The loss of  paraquat  from  the  experimental  watershed  is
simulated   reasonably  well  by  assuming  complete  adsorption  onto
sediment particles.  Pesticides with  a  similar  attraction  to  soil
particles would likely produce similar results.

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     6.  The single-valued (reversible)  Freundlich adsorption isotherm
appears to be inadequate in simulating the division  between  adsorbed
and  dissolved  phases  of  diphenamid  in runoff from the watersheds.
This was also evidenced by the  inability  to  simulate  the  observed
vertical movement of the pesticides.

     7.  The observed variations in  pesticide  concentrations  during
runoff  events appears to be of little consequence in predicting total
pesticide loss; total mass movement of pesticide  (grams/minute)  past
the  gage  during  a  storm  event  is a more valid comparison between
simulated and observed pesticide loss.

     8.  Although simulated and recorded pesticide  amounts  remaining
on  the  watersheds  agreed reasonably well,  concentrations within the
soil profile were in error.  The assumed depths of the soil  zones  is
largely   responsible  for  this  discrepancy  because  the  pesticide
concentration is dependent on the total  mass  of soil  in the zone.
                             - 2 -

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                             SECTION II

                            RECOMMENDATIONS
     From the results and conclusions of the present research  effort,
and  considering future uses of the PTR Model  in pesticide regulation,
the following recommendations are presented:

     1.  Continuous simulation of pesticide transport,  as  opposed  to
static  steady-state  investigations,  has  been  shown  to be a valid
methodology for performing a materials balance of  pesticides  applied
to  agricultural  lands.   The dynamic nature  of continuous simulation
allows the full accounting of:  (a) pesticides remaining on  the  land
surface,  (b)  pesticide  concentrations  and  volume lost during storm
events, and (c) accumulated amounts of pesticide lost to  the  aquatic
ecosystem  during a growing season.  Consequently this  approach to the
investigation of pesticide transport warrants  further refinement.

     2.  An understanding of the  mechanisms  of  surface  runoff  and
sediment loss is paramount to the study of the importance of non-point
source  pollutants  on  water quality.  The PTR Model has demonstrated
the capability of representing these  mechanisms.   Consequently,  the
coupling  of  the  PTR  Model  with  additional pollutant attenuation,
adsorption, and degradation functions could provide the structure  for
modeling the transport of plant nutrients, fertilizers, animal wastes,
and  other  non-point source pollutants.  The  effects of silviculture!
and agricultural management  techniques  on  water  quality  could  be
evaluated  through  the  PTR  Model  by  their effect on the transport
mechanisms of the non-point source pollutants.   Development  of  such
submodels  needs  to  be  undertaken  in  order  to  realize  the full
potential of the Model as a management tool.
     3.  For further refinement of  the  existing  PTR  Model,  future
research needs to be concerned with:
     a.  additional testing and calibration of the hydrologic model  to
     more  accurately  evaluate  model  algorithms  and  land  surface
     parameters.
     b.  calibration and possible  refinement  of  the  sediment  loss
                             - 3 -

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     model  to better reproduce recorded sediment concentrations and to
     gain  experience  with  the  sensitivity  of  the  sediment  loss
     parameters.
     c.  refinement and testing of the adsorption-desorption model   to
     better  determine the division between the adsorbed and dissolved
     phases of pesticides which are transported on both  sediment  and
     water.  The  inclusion of a nonsingle-valued adsorption-desorption
     model   warrants  further  investigation.   These  refinements  are
     critical to  the reliable prediction of pesticide  lost  in  water
     and on sediment during storm events.
     d.  additional development and testing of the volatilization  and
     degradation   models  on  actual   field  data, so that an accurate
     pesticide materials balance can  be  performed.    The  effects   of
     environmental   factors   on   these    mechanisms   needs  to   be
     investigated.

     4.  To determine the general applicability of the PTR Model,  the
following tasks are recommended:
     a.  Calibration and testing of the Model for runoff and  sediment
     loss on watersheds in various regions of the country.  This would
     allow  investigation  of changes in parameter values with varying
     soil and climatic  characteristics,  and  would  demonstrate  the
     behavior of  the Model under varying conditions.
     b.  Evaluation of model performance on watersheds ranging from 20
     to 200 hectaries in order to determine required  improvements   in
     the Model for larger watersheds.  This would provide insight into
     the effects  of channel processes on runoff and  sediment loss,  and
     demonstrate  the efficacy of existing  model algorithms to simulate
     the hydrologic and erosion processes.

     5.  If the PTR Model is to be considered as a tool for regulating
the  release  of   pesticides,  the  following   areas   need   to   be
investigated:
     a.  determination and definition of  control-size  watersheds   in
     various  regions  of  the country which would be most amenable to
     pesticide release regulations.
     b.   classification  and  grouping  of  pesticides  according   to
     toxicity, transport, and persistence  characteristics
     c.  establishment of  maximum  seasonal  releases  of  pesticides
     within  each  classification  which  would  not  inflict  serious
     consequences  on  man  or the aquatic ecosyste.  The fractions of
     applied  pesticides  which  reach  waterbodies   could   then   be
     evaluated by  the  PTR  Model and provide a basis for regulating
     pesticide releases.
                               -  4  -

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                            SECTION III

                           INTRODUCTION
     In 1962, the  publication  of  Rachel  Carson's  "Silent  Spring"
exposed  a  problem  which  had  been smoldering for a number of years
within the confines of the academic and scientific communities --  the
indiscriminate  use  of  toxic  pesticides  in  the  environment.  The
vituperative dialogue which  resulted  between  the  ecologically  and
agriculturally   minded   sectors   of  society  has  since  gradually
diminished.  At the  present  time,  it  appears  that  the  pesticide
situation  has  attained  a  more  rational and more productive level.
Enforcement of pesticide registration has been expanded  at  both  the
state  and  federal  levels.   Certain  persistent and extremely toxic
pesticides have been banned except in emergencies involving the public
health.  Studies on the transport, attenuation, accumulation and toxic
effects of pesticides have proliferated and are continuing by  various
governmental  agencies.   To  adequately  understand problems posed by
pesticide usage, it is  necessary  to  comprehend  the  arguments  put
forward  ,by  both  sides  of the conflict.  In this way, the requisite
data  and  research  can  be  determined  and  instigated  so  that  a
methodology can be developed to equitably regulate pesticide  releases
to  the  environment.   Such  a  regulatory  system  must  necessarily
consider  the  values  of  all  sectors  of society concerned with the
pesticide problem.
THE PESTICIDE PROBLEM

     Pesticide is a  general  term  for  all  forms  of  insecticides,
herbicides,    fungicides,    fumigants,    nematocides,    algacides,
rodenticides, etc.  A pesticide is often described as a substance used
to  control objectionable forms of life.  Ecologists prefer to use the
term  "biocide",  denoting  the  destruction  of  life  forms.    Many
governmental agencies will employ the term "economic poison" to stress
the monetary gains obtainable.  In any case, the total  production  of
these  chemicals  in  1971  amounted  to  600,000 tonnes 1    involving
nearly 1,000 registered chemicals produced in over 32,000 formulations
However,  of  the  1971  production,  65%  (385,000  tonnes)  can   be
attributed  to  only  24  different  pesticidal chemicals l  , i.e.  a
                               - 5 -

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relatively small number of pesticides is produced in large quantities.
     In 1939, the discovery of the insecticide! properties of DDT  was
hailed  as  a  great  advance  in the science of public health.  Swiss
chemist Paul Muller was awarded the Nobel Prize in 1948 for  his  part
in the discovery.  With the advent of low-cost production methods, the
production  of  DDT  climbed steadily in the post World War II period.
This fact initiated intensive research into chemicals with  structural
properties similar to DDT, known as the chlorinated hydrocarbons.  It
was this group of pesticides which absorbed the brunt of the attack by
Rachel Carson in Silent Spring.  The persistence and  biomagnification
of these pesticides in the environment are the main factors which have
led  to  the  decline  in  their  use.  The detection of DDT in remote
regions of the globe, and the discovery that marine  fish  are  almost
universally  contaminated with DDT residues 3   are testimony to these
problems  of  persistence  and  biomagnification.   In   some   cases,
pesticidal  chemicals will lodge and accumulate in fat tissues.  Since
lower animals serve as food  for  the  higher  animals,  the  chemical
content  of the fat tends to increase in concentration as one moves up
the food chain.  The discovery of DDT in mother's milk "*     indicates
that man, the finale of the food chain, is not immune to this problem.
     There is no need to cite the numerous accidents and catastrophies
which  have  resulted  due  to   pesticidal   contamination   of   the
environment.  This has been done quite eloquently elsewhere  4' 5' 6  .
The  problem  of  pesticide  contamination  is  a unique environmental
problem in  that  pesticides  are  deliberately  introduced  into  the
environment  for  beneficial purposes and/or monetary gains.  They are
not waste products as is  characteristic  of  the  large  majority  of
environmental  contaminants.   Consequently,  both  the  opponents and
advocates of pesticide usage  are  numerous  and  steadfast  in  their
beliefs.   In  the  past, this has raised the argument to an emotional
pitch, and, at present, continues to complicate the issue.
     The effects of pesticide contamination are both  short  and  long
term.   The  short  term  effects  often  involve  kills of non-target
organisms due to ingestion of toxic  levels  of  the  pesticide.   The
long-term  effects  can  be  classified  as  carcinogenic, teratogenic
(birth defects), mutagenic (genetic alteration), etc.  Pesticides that
accumulate in fat tissues can be spread throughout the  organism  when
the  energy in the fat tissues is required by the organism in times of
hunger or stress.  Thus, the time lag between  the  exposure  and  the
onset  of  symptoms  can  be quite variable.  The loss or reduction in
certain species can have  a  considerable  impact  on  the  ecosystem.
Ecologists  generally  agree  that  the presence of highly diversified
species within an ecosystem increases its stability.  Thus,  the  loss
of  individual   species  upsets  predator-prey  relationships  causing
imbalance  and  instability  in the ecosystem.  In sum, the effects of
pesticide contamination are varied and complex, and are a major  topic
of research at the present time.
     To equitably present the problem of pesticide contamination,  one
cannot  ignore  the benefits obtained from and the need for pesticides
for weed and insect control.  Although it is unlikely that  a  ban  on
                               - 6 -

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pesticide usage would lead to starvation in the U.  S., such an action
would  have  severe  economic implications.  Production would decrease
and prices of agricultural products would increase as the food  supply
diminished.   It  has  been  suggested  that more agricultural land be
brought into production to offset production  losses  from  a  ban  on
pesticide usage   7 .   There is no guarantee that unchecked pests would
quickly spread to the new land in production.  On the  contrary,  this
is  highly  likely.   In  many undeveloped countries, the situation is
entirely different.  Starvation and disease are still  major  problems
even with all available agricultural land in  production.   Pesticides
are invaluable in many such countries.  It would be sheer folly to try
to  convince  the  underdeveloped  countries  that  pesticide usage is
harmful and that  restrictions  should  receive  consideration.   With
regard   to   other   problems   facing   these  countries,  pesticide
contamination has very low priority.   In  other  words,  concern  for
environmental   pollution   is  strictly  a  luxury  of  the  affluent
countries.
     The inescapable  conclusion  is  that  chemical  pesticides  will
remain  a  permanent  tool  of the agriculturist and the public health
scientist for some time to come.   The  programs  of  Integrated  Pest
Management of the U.   S.  Department of Agriculture emphasize the need
to  use  a  variety  of  control methods.  This would include chemical
pesticides, although on a much lower level than at present.  Thus,  it
is  clear  that  pesticide regulations must be designed to balance the
detrimental and beneficial effects of  pesticide  usage.   The  recent
shift in pesticide usage away from the chlorinated hydrocarbons to the
less persistent pesticides is an encouraging trend.  However, the less
persistent  carbonates  and  organophosphates  which  are  gaining  in
popularity, are also more toxic.  Consequently, it  is  important   to
understand   the   routes   of  pesticide  loss  from  the  field  and
the  transport  processes  to  the  aquatic  environment.    Such   an
understanding,  along  with  a knowledge of the effects of  pesticides
on  non-target   species,   can   provide   an  equitable   basis  for
regulating the amount of pesticide to be released to the environment.
PESTICIDE REGULATION
     Prior to October , 1972, federal authority on the  regulation  of
pesticides  was  derived  from the Federal Insecticide, Fungicide, and
Rodenticide  Act  (FIFRA)  of  1947  requiring  registration  of   all
pesticides  shipped in interstate commerce.  Registration applications
were filed with the Environmental Protection Agency (EPA) and  renewed
at  5 year intervals.  EPA retained the authority to suspend or cancel
registrations if use of  the  pesticide   1    presented  an   'imminent
hazard1,  of   2  resulted in injury to man or animals, when applied in
accordance with label directions.  This authority has been exercised a
number of times.  In 1971 registration cancellation  proceedings  were
initiated against companies producing DDT, Mirix, 2,4,5-T, aldrin, and
dieldrin.   The  subsequent ban on nearly all uses of DDT demonstrated
the extent of authority of the EPA administrator under FIFRA  in  cases
of  extreme  risk  to  man  and the environment.  The establishment of
                                -  7  -

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statutory limits on pesticide residues in foods remains an  additional
EPA regulatory tool.
     In October, 1972, the Federal  Environmental  Pesticide Control  Act
(FEPCA)  was  written  into  law  (PL92-516).    FEPCA   substantially
increases  federal   authority  over  pesticide  regulation  from  that
provided under FIFRA.  FEPCA allows the EPA Administrator  to classify
registered pesticides according to  general or restricted use, or both.
Moreover,  FEPCA  requires  that all   pesticides be registered; thus,
requiring that state registration laws conform to present Federal  law.
Stop sale and seizure orders for pesticides in violation  of  the  new
legislation  are  also  authorized.   In  sum,  the level of pesticide
regulation has advanced considerably with the new law.
     With the passage of FEPCA, individual state  pesticide regulatory
agencies must revamp their existing regulations to assure conformance.
At present, state pesticide laws vary in both severity and scope    of
regulation.   With  the  increase   in   concern   for   environmental
contamination,  recent  years  have witnessed a proliferation of state
pesticide laws.  Generally these regulations involve one  or  more   of
the following:

     1.  Pesticide registration.
     2.  Permits for sale and/or use of pesticides.
     3.  Licensing of pesticide applicators.
     4.  Prohibition of certain hazardous pesticides, or,  compilation
     of acceptable ones.
     5.  Regulations for handling and  transport  of  pesticides,  and
     disposal of pesticide containers.

Due to its inherent nature, the enforcement of  pesticide  regulations
is  performed  most easily at the state or local  levels.  However,  the
non-uniformity of state regulations has led to jurisdictional disputes
and enforcement problems.
     Obvious by their absence are regulations stipulating  the  amount
of  pesticides  which  can  be  applied  without  detrimental external
effects.  The reasons for this are  largely technological.  Research is
presently being conducted on the physical,  chemical,  and  biological
effects  of  various  pesticide  concentrations  on  plant  and animal
species.  The complex interactions  of pesticides when released to  the
environment   have   hindered   the  quantitative  definition  of  the
mechanisms  involved   in   pesticide   transport   and   attenuation.
Laboratory research, aided by field experimentation, is progressing on
these topics.
     The present research effort is a study of the loss of  pesticides
from  agricultural   lands  and  an   attempt to model the mechanisms by
which pesticides  reach  a  nearby   watercourse.   Available  evidence
points  to  surface  runoff  and sediment loss as the primary modes by
which pesticidal contamination  inflicts  waterbodies.   The  reliable
modeling  of these mechanisms along with the pesticide interactions on
soil and water can result in estimates  of  the  fraction  of  applied
pesticide  reaching the stream channel.  Such information, accompanied
                              - 8 -

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with  estimates  of  allowable   pesticide   concentrations   in   the
environment,  can provide a basis for establishing allowable pesticide
application rates.  These rates could be determined for  each  of  the
various   edaphic  and  climatic  regions  of  the  country.   Such  a
methodology would allow regulation of pesticide use so as to  minimize
the  detrimental  environmental effects and still achieve the purposes
of pesticide application.
     A reliable pesticide transport model could provide the basis  for
formulation  and  evaluation of various management systems to restrict
and control pesticide movement to  receiving  waters.   Adjustment  of
model parameter values would represent the effects of proposed systems
and  techniques  while analysis of model output (e.g., pesticide loss)
would provide the basis for  system  evaluation.   In  this  way,  the
relative   efficiacy   of   terracing,   coutour   farming,  pesticide
regulations, and combinations of these and  other  management  systems
could  be  analyzed  and evaluated.  The end result would be a greater
understanding of the proposed management system.
                               - 9 -

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                             SECTION IV

     MECHANISMS OF PESTICIDE LOSS AND TRANSPORT IN THE ENVIRONMENT


PESTICIDE CYCLING IN THE ENVIRONMENT

     Upon release to the environment, pesticides  move by a  myriad  of
transport  mechanisms.    Figure  1  attempts   to   portray  the various
pathways by which pesticides cycle through the environment.   The  term
'cycle'    is  quite  appropriate  here.   Initially  applied  by  Man,
pesticide residues return to the human body through the  ingestion  of
meat  and  harvested  crops.   The  complexity of the maze of pathways
shown in Figure 1 emphasizes the need to approach the problem  at  its
origin  -- the initial  pesticide application.   The President's Council
on Environmental  Quality has  recognized  that multi-media   pollution
problems,  such  as  pesticides which involve  air, land and  water, are
best analyzed by a materials balance approach  P     A full  accounting
of the movement and loss of  pesticides, subsequent to application, is
necessary.   Knowing  the  quantities  of  pesticide  transported  and
retained  by  the  various  media,  one  could  then  attack the major
mechanisms responsible for the detrimental effects of  pesticide  use.
Figure 1 demonstrates that performing a materials balance on pesticides
is not a simple matter.  The mechanisms of transport and loss from the
various  media must be quantitatively understood,  so  that  amounts  of
pesticide  in  transport  can be predicted.  Climatic, hydrologic, and
edaphic  conditions of the region plus the chemical properties  of  the
pesticide  will  have  an  effect on the various  mechanisms.  Although
many of  these mechanisms are not completely understood at the  present
time,  research  is  underway  to  explain  the  transport behavior of
pesticides upon release to the environment.


MECHANISMS OF LOSS FROM AGRICULTURAL LANDS

     Pesticides are introduced into the environment by  a  variety  of
sources.   Agricultural  applications  are obviously the major source.
Spillage and accidents, industrial effluent,  and  municipal sewage  are
other   instruments   of   pesticide   pollution.   These  are  either
non-deliberate releases or point sources of pesticides, both of  which
must   be   handled   in  a  different  manner  than  applications  to
agricultural lands.   A  number  of  states  have  passed  legislation
                              - 10 -

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                   Figure 1.  Pesticide  cycling  in  the   environment

                                          PESTICIDE   APPLIED
                            Drift
                                             SPRAY   GRANULES
                                           PELLETS. FUMIGANTS
                                                                                                             Degradat ioi:

                                                                                                             loss
   Injection
   pellets, etc.
Inject! on,
soil incorpation
             Volatility

             Codistillation
                                                                                               L_Absorption ". - ' . . '."
Degradation loss
                                                                                        Degradation loss
PESTICIDE
RUNOFF
Hyd
r o c o m p

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regulating the intrastate transport of pesticides, and the disposal  of
pesticide  containers  9' 10      These  two areas have proven to be a
substantial source of spillage and accidential  releases of  pesticidal
chemicals.   Enforcement  and regulation of these components is easier
because of their point-source nature.
     Because agricultural applications of pesticides  constitutes  the
major release of pesticides  to the environment, this area has received
the   greatest  attention  in  the  curbing  of  pesticide  pollution.
Following application, pesticide loss  from agricultural  lands  occurs
through  surface  runoff,  sediment  loss,  volatilization,  organisms
(plant  and animal) uptake,  and degradation (microbial, photochemical,
chemical).  The relative significance  of  the  various  mechanisms  is
highly dependent on environmental  conditions and pesticide properties.
Movement of pesticides to a  water course is of  primary  environmental
concern  because  of  possible  effects on the aquatic ecology.  Other
than direct application, surface runoff and  sediment  transport  have
been  recognized  as  the major routes to the aquatic environment11'12

     Fortunately, a very  small  portion  of  the  amount  of  applied
pesticide will reach a watercourse.  In general, the large majority of
the  application  amount  will  volatilize  or  degrade  by  chemical,
photochemical,  and  microbial agents.  In some cases, pesticides will
degrade into other chemicals more  toxic  and/or  detrimental  to  the
ecology  of  the  area.   Volatilization  during and after application
accounts for  the  escape  of  pesticides  to  the  atmosphere.   Upon
entering  the  atmosphere, the area!  extent of pesticide contamination
is essentially unlimited.  Atmospheric transport has  been  recognized
as a major mechanism of widespread DDT contamination 13
     In summary, the movement of pesticides from agricultural lands is
the major component of pesticidal  contamination  of  the  fresh  water
aquatic  environment.   Performing  a   materials balance on pesticides
applied to agricultural lands would help to understand,  qualitatively
and quantitatively, the mechanisms involved in the loss of the applied
pesticides.  This report is  part of  a  research  effort  designed  to
evaluate  and model these mechanisms.   The general goal is to estimate
the amount of pesticide lost from agricultural  levels that will  reach
and  contaminate a watercourse.  The remainder of the report describes
the accompanying modeling program  and  preliminary  results  of  that
research effort.
                              - 12 -

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                              SECTION V

           PESTICIDE TRANSPORT AND RUNOFF MODEL COMPONENTS
     Figure 2  illustrates  the  movement  of  applied  pesticides  as
visualized  by  the  Pesticide Transport and Runoff (PTR)  Model.   Four
pesticide storage zones within the soil profile are assumed.   Figure 3
presents the assumed depths of the various pesticide storage   zones  -
surface  zone,  upper  zone,  lower  zone,  and groundwater zone.   The
assumed zone depths are necessary to specify the mass of soil  involved
in the pesticide-soil interactions.
     The PTR Model estimates the loss  of  pesticides  from  the   land
surface by simulating the mechanisms of surface runoff, sediment  loss,
pesticide  adsorption-desorption,  and  pesticide  volatilization   and
degradation.   This  chapter describes the various loss mechanisms and
submodels included within the PTR  Model.   Initially  the  hydrologic
model  responsible  for  the  determination of surface runoff and  soil
moisture storage is discussed.   The  sediment  loss  model  estimates
sediment  production from the land surface based on input rainfall and
surface runoff provided by the  hydrologic  model.   The  division  of
applied  pesticide  among the various phases (adsorbed, dissolved, and
crystalline) is determinated by  the  pesticide  adsorption-desorption
model.   This  model,  in conjunction with the hydrologic and sediment
loss models, determines the amount of pesticide removed from the   land
surface  by  surface runoff and sediment loss.  This section  concludes
with a discussion on  the  modeling  of  the  loss  of  pesticides  by
volatilization and degradation.  The actual structure and operation of
the PTR Model is presented in Section VI.
HYDROLOGIC MODEL

The Hydrologic Cycle

     The hydrologic cycle can be defined as follows:  "The circuit  of
water  movement  from  the  atmosphere  to the earth and return to the
atmosphere through various stages or processes such as  precipitation,
interception, runoff, infiltration, percolation, storage, evaporation,
and   transpiration.    Also   called   water   cycle"13.    Figure  4
                              - 13 -

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Figure  2.   Flowchart   of  pesticide   movement in PTR  Model
      Soil
    I incorporated
     latil izationy
    beg radatiory
   Volatilization)
    Degradation/
/Pesti
\Applic
cide\
ation/
	 /Application ]
V^Morfe J

HSur)
Pesti
Sto

Surface
Applied Pesticide on
/ Sediment
' 	 _^y/r
ace 	 jy^Surface^< Pesticide
rirln I Pncitlrjrln ' reSllCIQe
rage * 	 \lnteract ion^/ Crystals
/ x Pesticide in
/ Overland Flow
/ Infiltration
_ Upper Zone 	 ./Upper ZoneN Pesticide in
Pest c de I Pesticide I 	 , ,. 	 •
	 Storage * 	 ^Interaction/ Interflow

-+ to Stream
-> to Stream
f to St rea r

                                   Percolat ion
                                          ,ower Zone\
                                          Pesticide J
                                          nteraction/
                                   Losses   to
                                 Ground water
Legend


  -Data Input



  — Function



  — Storage
PESTICIDE
RUNOFF
Hyd
r o c o m p
                                     - 14 -

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                            Figure  3.   Assumed  soil   depths  for  pesticide  storage
en

i
BERTH
(Parameter
(tiinput
 variable)
                                                  ^
                                                                                                                liirf-irr
                                         ............ ....... .._, _ _.  • ..:.. ........  .


                              / [•;. ':/' V !;V .':) ^-•"•t"\ •-: " :' >'-' ! v'rV-^v-^'.-V-:';--' " Y. ? -v''.*---; V •:V-v-"'V:;>'"'.:---:>-:-:;:'>' '  uPPer Zor
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     Figure 4.    The  hydrologic   cycle
11   I   I  1  I  1  1  I  I  1   M
      Precipitation
                       Interception
                          w
Evapotranspiration

      t
     , Groundwater
PESTICIDE
RUNOFF
H y d r o c o
m p
                            - 16

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schematically shows the interactions and mechanisms that comprise  the
hydro!ogic  cycle.   The  hydrograph of streamflow from a watershed is
the  end  product  of  variable  time  and  areal    distributions   of
precipitation,   evapotranspiration,  soil  moisture  conditions,  and
physical watershed  characteristics.   Streamflow  eventually  reaches
large  water  bodies,  such  as rivers, lakes, and oceans, evaporation
from which is the major source of atmospheric moisture.  The cycle  is
completed  when atmospheric moisture condenses and returns to the land
surface in the form of precipitation.
     Since the  movement  of  water  is  cyclical,  without  permanent
sources  or  sinks, a pollutant entering any phase of the cycle can be
transported by the water; hence, other phases  of  the  cycle  can  be
contaminated.   Thus,  an understanding and an ability to simulate the
hydrologic cycle is instrumental  in  the  study  of  the  cycling  of
pollutants in the environment.  Since pesticide movement from the land
surface  is  the major mechanism of aquatic contamination, the present
study is concerned largely with the land  surface  components  of  the
hydrologic cycle.
The LANDS Subprogram

     Within  the  PTR  Model,  the  LANDS  subprogram  simulates   the
hydrologic  response  of the watershed to inputs of precipitation  and
evaporation.  LANDS simulates runoff continuously  through  a  set  of
mathematical   functions   derived   from  theoretical   and  empirical
evidence.   It is basically a moisture accounting procedure on the land
surface for water in each major component  of  the  hydrologic  cycle.
Parameters  within the mathematical functions are used  to characterize
the land surface and soil profile characteristics  of  the  watershed.
These  parameters  must be selected, tested and modified when LANDS is
applied to a new watershed.  Calibration is the  process  whereby  the
parameters  are  modified as a result of a comparison of simulated and
recorded  streamflow data  for  the watershed.   Section  VI  on model
structure and operation will describe the calibration process.
     The mathematical foundation of the LANDS  subprogram  is  derived
from   the  Stanford  Watershed  Model  (SWM)  developed  at  Stanford
University by Crawford and Linsley  lk .  Subsequent improvements  and
refinements  in  the  simulation  algorithms  of  the  SWM  have  been
incorporated  into  the  Hydrocomp  Simulation  Program (HSP).  HSP is
composed of three components whose functions involve  data  management
and  handling (named LIBRARY), hydrologic response of the land surface
(also named LANDS), and kinematic channel routing of the land  surface
runoff  (named CHANNELS).  The LANDS subprogram of the  PTR Model, with
certain modifications is identical to the HSP LANDS module;  moreover,
                              - 17 -

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the calibration parameters, listed in Table 1, are identical.   For the
sake  of  brevity,  the  basis  and  description  of  the mathematical
functions of LANDS have been abstracted from the HSP Operations Manual
and have been included in Appendix A.  Figure 5 presents the flowchart
of  LANDS  showing  the  interactions  of  the  various  land  surface
components that are described in Appendix  A.   Modifications  to  HSP
LANDS  which  are  incorporated  into  the  PTR  Model  are  explained
subsequently.

     Table 1.     HYDROLOGIC MODEL (LANDS) PARAMETERS
A - A fraction representing the impervious area in a segment.
EPXM - The interception storage parameter, related  to  vegetal  cover
       density.
UZSN - The nominal  storage index for the upper soil  zone.
LZSN - The nominal  lower zone soil  moisture storage  parameter.
K3 - Index to actual  evaporation (a function of vegetal  cover).
K24L  &  K24EL  -  Parameters  controlling  the  loss  of  water  from
     groundwater storage. K24L is the fraction of groundwater recharge
     that percolates  to deep groundwater tables. K24EL is the fraction
     of the segment area  where shallow water  tables  put groundwater
     within reach of  vegetation.
INFIL -  This parameter is a function of soil characteristics defining
      the infiltration characteristics of the watershed.
INTER - This parameter defines the  interflow  characteristics  of the
      watershed.
L - Length of overland flow plane.
SS - Average overland flow slope.
NN - Manning's "n"  for overland flow.
IRC & KK24 - The interflow and groundwater recession parameters.
KV - The parameter  KV is used to allow a variable recession  rate  for
      groundwater discharge.
                              - 18 -

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Figure 5.      Lands   flowchart
                                                               KEY
                     PRECIPITATION  \
                      POTENTIAL   \
                   .JVAPOTRANSPIIIATIOH/
PESTICIDE
RUNOFF
Hydrocomp
                   -  19 -

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Modification to HSP LANDS

     The major concern in modifying the HSP LANDS module for pesticide
transport was the desire to accommodate the expected  area!   variation
in  pesticide  concentration  over  the land surface.   It is generally
accepted in hydrology that infiltration is time  and  area  dependent;
infiltration  capacity  will  vary  even  within small watersheds with
reasonably homogeneous soil characteristics.  This area! variation  in
infiltration  results in source areas, or zones, with  low infiltration
capacity within the  watershed,  contributing  a  large  component  of
overland  flow.   Sediment  loss will  vary with the area! variation in
overland flow.  Since overland flow and sediment loss   are  the  major
mechanisms  of  pesticide  transport  to  the  water  course,  the low
infiltration source areas will  also  experience  a  greater  loss  of
pesticide  than  the  remainder  of  the watershed.  Consequently, the
pesticide concentration on the land surface will vary, in spite of  an
initially uniform application.  The pesticide concentration within the
soil   profile  will  also  vary  as  a  function  of   the  volume  of
infiltration.  Obviously, the  extent  of  pesticide  areal   variation
depends  upon  the  solubility  and  transport  characteristics of the
specific  pesticide  applied,  and  upon  topography   and   watershed
characteristics.    Natural   hydrologic   conditions    and  watershed
characteristics are sufficiently  non-uniform  to  justify  the  above
described  mechanisms  leading to areal variations in  infiltration and
pesticide concentrations.
     HSP  LANDS  employs  a  cumulative  frequency  distribution   on
infiltration  capacity  to  account for the areal variation.  Figure 6
graphically presents the infiltration function of HSP  LANDS  described
in  Appendix  A.  A mean infiltration capacity, f, is  calculated and a
linear approximation to the actual cumulative distribution is assumed.
Interflow is determined as a function of infiltration  and  lower  zone
moisture  storage.   It  is  evaluated  in Figure 6 as a second linear
cumulative distribution denoted by f (c-1) (see Appendix A for a  full
description).   Since  the  X-axis  is  unity (i.e.  100% of watershed
area), the area of each wedge in Figure 6 represents  the  portion  of
the  moisture  supply  allocated  to  each component.   During any time
interval, the available moisture  supply  is  distributed  to  surface
detention,  interflow  detention  and infiltration. Overland flow and
interflow  are  determined  as  losses  from  surface   detention   and
interflow  detention  respectively.   Lower  zone moisture storage and
groundwater components are derived from the infiltration component.
     The  LANDS  subprogram  of  the  PTR  Model  employs   the   same
infiltration  function  as  HSP  LANDS,  with  one  modification.  The
watershed is divided into five zones, each  representing  20%  of  the
total  area.   The  zonal  division is based on infiltration capacity.
Schematically, Figure 7 shows that zone 1 will  infiltrate  much  less
water  than zone 5; conversely, zone 5 will provide less overland flow
than zone 1.  Thus, the areal variation in  infiltration  capacity  is
approximated.   Zones  with  lower infiltration capacity will serve as
the major source areas for overland flow and  sediment  and   pesticide
                              -  20 -

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MOISTURE

 SUPPLY

   (mm)  X
              Increment  to
              Surface
             (-Detention
          Increment to
          Interflow
         -Detention  „
7 (c-1)
             INFILTRATION

              CAPACITY


                 (mm)
                   IF AREA WITH INFILTRATION CAPACITY
                 LESS THAN OR EQUAL TO INDICATED VALUE

     Figure 6.  Cumulative frequency distribution of infiltration
                capacity showing infiltrated volumes, interflow
                and surface detention
MOISTURE

 SUPPLY

  (mm)
_ Zone  1   Zone 2    Zone 3/^Zone 4    Zone 5
                        INFILTRATION

                          CAPACITY

                            (mm)
                   20
                  40
  60
80
100
               % OF AREA WITH INFILTRATION CAPACITY
               LESS THAN OR EQUAL TO INDICATED VALUE
     Figure 7.   Source-zones  superimposed  on  the  infiltration
                capacity function
PESTICIDE
RUNOFF
Hyd
r o c o m p
                             - 21 -

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loss.   Generally,  zones  with  high  infiltration  will  contain  more
pesticide in the  soil   profile  because  of  the  greater  amount  of
infiltrated water.
     Conceptually,  the  zones   are   not   necessarily   concentric,
continuous,  or  contiguous.   Each is connected directly to the stream
channel by the overland flow  plane.   As  with  any  simulation  model,
this  source  zone  concept  is an approximation.  It is an attempt to
portray  mechanisms which are known  to occur, but  are  impossible  to
simulate in detail.
Model Description and Operation

    The LANDS subprogram operates on a  5   or 15 minute time interval
at the discretion of the user.   Daily potential  evapotranspiration and
time-interval  precipitation  are  required  inputs.   During each time
interval, precipitation, in inches, is input and first encounters  the
interception  function,  as  shown  in  Figure  5.   Interception is a
storage  function  dependent  on  vegetation  and  crop  canopy.    For
agricultural lands, this will   vary  over  the  growing  season.    The
change  in  crop canopy is administered by the MAIN program of the PTR
Model which oversees input-output  operations,  time   accounting,  and
general  management of the component submodels.   The  MAIN program will
be described in a subsequent section.
     Once interception storage  is filled, any remaining  precipitation
is  added  to  the  moisture supply of the infiltration function.  The
infiltration  function,  performs  the  basic  division  of  available
moisture  into  the  components  of   surface   detention,   interflow
detention,   and   infiltration.    Surface   detention  includes  the
components of overland flow  and  an  increment  to  upper  zone   soil
moisture   storage.    Interflow   detention   is  a   delay  mechanism
controlling the release of interflow to the stream.  Infiltration  and
percolation  from  the  upper zone provide the means  by which moisture
reaches lower zone storages.  From lower zone storage,  the  available
moisture  proceeds  to  active   groundwater  storage   from  which  the
groundwater   component   of   streamflow   is derived.  Other    than
evapotranspiration, inactive groundwater (groundwater recharge) is the
only other means of release of  water from active groundwater  storage.
The  mathematical  functions  describing the divisions involved in the
LANDS subprogram are fully explained in  Appendix  A.   The  necessary
input parameters are also defined.
     Other  than  streamflow  and  losses  to  inactive   groundwater,
evapotranspiration  is  the  only  remaining  loss  component  in  the
moisture  balance  performed  in  LANDS.  Evapotranspiration occurs at
different rates from each of the various moisture  storages  shown  in
Figure  5.   Daily  potential   evapotranspiration values are input and
transformed to  hourly  values   by  an  empirical  diurnal  variation.
Actual  evapotranspiration  is   calculated  on  an  hourly  basis from
interception, upper zone, and lower zone storages, and on  an  average
daily  basis  from  groundwater  storage.   From interception storage,
                              -  22  -

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evapotranspiration  occurs  at  the  potential  rate.   Any  remaining
potential is satisfied initially from the upper zone and then from the
lower   zone,   depending    on    existing    moisture    conditions.
Evapotranspiration  from  groundwater storage is controlled through an
input parameter, K24EL, delineating  the  percent  of  watershed  area
where  the  groundwater  table is close enough to the surface to allow
evapotranspiration to occur  (e.   g.   marshes,  swamps,  deep-rooted
vegetation).  Appendix  A  also  describes  in  detail  the  functions
simulating evapotranspiration from the various moisture storages.
     The basic algorithms of the LANDS  subprogram,  as  part  of  the
Stanford  Watershed  Model,  and the HSP, have been tested on over 200
watersheds in various regions of the country.   However,  its  use  on
watersheds in the range of 4 - 20 hectares has been limited. Also, the
modifications, described above, and its use  in  simulating  pesticide
transport   are  new  endeavors.   Problems  arising  from  these  new
applications, and results of model testing are  discussed  in  Section
VIII - Model Results and Discussions.
SEDIMENT LOSS SUBPROGRAM

The Erosion Process

     The process of erosion  has  been  in  operation  throughout  the
history  of  the earth.  The monuments of its glory surround the globe
and shape its surface.  Man marvels at the dichotomy of the minuteness
of the Colorado River within the majestic environment it  has  shaped.
Yet, despite the millions of acres and centuries of geologic evidence,
the mechanism of erosion has not been mastered to the point where soil
loss  can  be reliably predicted.  Perhaps this will remain another of
Mother Nature's secrets to which man's ingenuity can only  achieve  an
approximation.   However,  attempts  to  understand  and  simulate the
process continue in order to attain the best approximation possible.
     Erosion is generally thought to consist of the detachment of soil
particles, and movement of the particles to a channel  in  which  they
are   transported   to   their   ultimate  destination.   Our  present
understanding of the process involves its  breakdown  into  the  three
mechanisms  of  sheet  erosion,  gully  erosion,  and channel erosion.
Sheet erosion refers to the relatively uniform loss of topsoil  across
the  soil  surface.   The  impact of falling raindrops is an important
process in this mechanism, detaching soil particles,  or  fines,  from
the soil aggregates.  The fines are then available to be picked up and
transported by overland flow.
     Initially occurring as sheet flow, the overland  flow  will  soon
begin to concentrate into small rivulets due to the topography  of the
surface.  Gully erosion becomes operative  when  the  flow  turbulence
creates  local  forces sufficient to dislodge particles from the sides
and head of the gully.  As the  gully  grows  deeper  and  wider,  the
momentum and inertia of the flow become significant factors in shaping
the  streambed  and  stream  course.   Here, channel erosion begins to


                              - 23  -

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influence the direction of the stream  resulting  in  changes   in  the
stream cross-section and meandering of the streambed.
     In reality, the erosion process is gradual   and  continuous  from
the  raindrop  impact  and  overland  flow  pickup to  the transport of
sediment in the stream.  There is no definitive dividing line  between
the  mechanisms  of sheet, gully and channel erosion.   This conceptual
model of the erosion process helps our overall understanding.
Sheet Erosion

     With regard  to  pesticide  loss  from  agricultural  lands,  our
interest  in the erosion process is directed to the mechanism of sheet
erosion.  Included in this mechanism is the formation of  small  rills
or  rivulets  which  signify  the  primitive  stages of gully erosion.
Although gully erosion is significant  in  total  sediment  loss,  the
occurrence of gullies will not have a significant effect on  pesticide
loss  because  pesticide  application  is  generally restricted to the
surface or top few inches of soil.  In  this  region,  sheet  erosion,
including small rills, is the critical mechanism.
     The hydrologic factors governing sheet erosion include the impact
of raindrops and the occurrence of overland flow.  As raindrops strike
the land surface, soil aggregates are broken down  and  detached  soil
particles  are dispersed in all directions.  On a sloping surface, the
raindrop splash will tend to move particles downhill.   The  angle  of
incidence  of  the  raindrops will also be a function of wind velocity
and direction.  In any case, the soil movement by raindrop  splash  is
quite  small.   Its  main effect is the production of soil fines which
are transported downslope by overland flow.  The soil fines are either
picked up from the surface by the overland flow or are  added  to  the
flow  by    the   soil     splash.   In  this  way, the soil fines are
transported  downslope  until  a  reduction  in  velocity  allows  the
particles to settle.   Subsequent  overland  flow  will  pick  up  the
deposited  particles  and  transport  them another distance downslope.
This process occurs repeatedly until the soil particles reach a  major
gully or stream channel where they become part of the sediment load of
the stream.
     The quantitative evaluation of the above processes  is  far  from
obvious.   Theoretically,  one would expect the soil detachment due to
raindrop splash to be dependent on soil properties and on the  kinetic
energy  of  the  raindrop in its collision with the soil surface.  The
kinetic energy, in turn, is  a  function  of  the  mass  and  terminal
velocity  of the raindrop.  Since the mass is the accumulated depth of
rainfall, one  could  determine  the  theoretical  kinetic  energy  if
terminal velocities  were known.  Wischmeier and Smith  16  have shown
that raindrop terminal velocities will vary  with  drop  diameter  and
rainfall  intensity.   Natural  rain  storms  will generally include a
range of drop sizes and rainfall intensities.  Moreover, wind velocity
and crop canopy will have  a  significant  effect  on  these  factors.
                              - 24 -

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Consequently,  a  strict theoretical approach would be impractical  for
field conditions.
     Up to the present time, gross soil loss estimates have been based
on either the Musgrave equation  17  or the Universal Soil Loss  (USL)
equation, developed by the Agricultural Research Service of the U.   S.
Department  of  Agriculture.  These equations are similar in form,  and
both contain empirical components relating to soil  credibility,  crop
cover and management, overland slope and length and rainfall.  The  USL
equation  18  is presented as follows:
     A = B x  F
                  SL  x  P
(1)
where:  A = average annual soil loss, tons per acre

        B = relative soil credibility factor

        F = relative farming practice factor

        G = relative cropping-management factor

        SL = relative slope-length factor

        P = rainfall erosion factor

The above equation yields the average annual soil loss  per  acre  for
the  watershed.   The    factor   which    attempts   to  account  for
hydrologic  conditions  is  P,  the  rainfall erosion factor, which is
determined by:
     P = (E I30)/100
                                                           (2)
where:
E = Total storm kinetic energy, in foot-tons/acre-inch.
130 = Maximum 30-minute intensity during the storm,  in/hour.
Wischmeier and Smith  16  have evaluated E as a function  of  rainfall
intensity,  and  have  further  determined  that  the  factor   E  Ion
demonstrated the best correlation for soil loss per  storm  among  the
factors tested.  For this reason, the EI30 factor has been employed to
evaluate  P,  the  rainfall erosion factor in the USL equation.  Negev
19   has noted that neither the Musgrave equation nor the USL equation
contains a factor specifically accounting for the effects of runoff on
soil   loss.   Since  the  sheet  erosion  mechanism  depends  on  the
occurrence of overland flow, applying these equations  to  a  specific
short  time  interval  can lead to gross errors.  Attempts to simulate
the sheet erosion process on small watersheds must employ a short time
interval.  For this reason, neither the Musgrave nor the USL equations
are applicable to continuous simulation of sediment  loss  from  small
watersheds.
                                25

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Sediment Loss Simulation

     The simulation of sheet erosion  as  performed  by  the  sediment
subprogram  is  based  on a sediment model  developed by Moshe Negev at
Stanford University  19  . Negev simulated the entire spectrum  of  the
erosion   process   including   sheet,  gully,  and  channel  erosion.
Simulation was performed on an hourly basis.    The  accumulated  daily
sediment loads from Negev's model  compared well with  recorded  values
on an 81 square mile watershed in  California.  The simulated suspended
sediment loads also agreed reasonably well  with the recorded values.
     The  sediment  subprogram  of  the  PTR  model  employs   Negev's
functions  to  simulate  the mechanism of raindrop impact and overland
flow pick up and transport of soil particles.  As opposed  to  Negev's
model,  rill  formation  and  erosion is assumed to be included within
sheet erosion process.  On the small test  watersheds,  gully  erosion
did  not  appear  to be significant, and consequently was not included
within the model.
     The production of soil fines  from raindrop splash is modeled  per
unit area for each time interval  as follows:
                                         JRER
     RER(t) = (l-COVER(T)) x KRER  x PR(t)                          (3)

where:  RER(t) = soil fines produced during time interval, T.

        COVER(T) = percent vegetal cover as a function of the relative
                     time within the growing season.

        KRER = coefficient of soil properties.

        PR(t) = precipitation during time interval, t.

        JRER = exponent.

     The term 'soil fines' is not  defined according to  particle  size
within the Model.  Negev assumed that the 'wash load1 portion of total
sediment  loss  included  particles  less than 0.062 mm in diameter 19.
The  'soil fines' would include the finer particles of the  wash  load,
i.e.,  approximately the silt and  clay fraction.  Implicitly, the soil
fines can be defined as those particles disrupted and detached by  the
force of raindrop impact.
     The soil fines produced by the raindrop  impact  are  immediately
available for transport by overland flow if overland flow is occurring
within the  time interval.  If overland flow is not occurring, such as
during the initial or final stages of the storm period, the soil fines
accummulate on the soil  surface.    Thus,  a  reservoir  of  fines  is
deposited  on  the  surface  available  for  pick  up and transport by
subsequent overland flow.  The pick up of deposited soil  fines  is  a
function  of  the overland flow occurring during the time interval and
the  total amount of deposited fines on the surface.  This mechanism is
modeled per unit area for each time interval  by the relationship:
                              - 26 -

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     SER(t) = KSER x SRER(t-l) x ROSB(t) JSER                      (4)

where:  SER(t) = fine pick up during time interval, t.

        KSER = coefficient of pick up.

        SRER(t-l) = reservoir of deposited fines existing at the
                    beginning of time interval, t.

        ROSB(t) = overland flow occurring during time interval,  t.

        JSER = exponent.

Thus, the total contribution to sediment loss during any time interval
is the sum of the soil fines production from raindrop impact plus  the
fines  pickup  by  overland  flow.   The subprogram also includes soil
washoff  component  from  impervious  areas,  although   this  is   not
significant for agricultural lands.
     The  source-zone  concept  described  in  Section   V  allows  the
calculation of the overland flow component from each zone  within  the
watershed.   The zones with lower infiltration will experience greater
overland flow and, thus, greater sediment  loss.   The   overland  flow
component  from  each  zone is used to calculate the soil fines  pickup
for that zone.  Consequently, during each time interval, the  sediment
production from each zone is determined from the above  equations.  The
sum  of  the  zonal  contributions  is  the total sediment load  to the
stream.  This zonal concept attempts to simulate the  areal  variation
in sediment loss due to the overland flow variation.


PESTICIDE ADSORPTION - DESORPTION MODEL

Mechanism of Pesticide Adsorption-Desorption

     The interactions between pesticides and the soil  to  which  they
are  applied  are  undoubtedly the  most critical  mechanisms  in  the
determination  of  pesticide loss from agricultural lands.  Bailey and
White20    have  concluded that pesticide adsorption-desorption,   the
attraction  of  pesticidal  molecules  to  soil particles, directly or
indirectly effects  all  factors  known  to  influence   the  fate  and
behavior of pesticides in soil systems.  Pesticide adsorption provides
the  route  of  transport  to  stream  channels  for  many pesticides.
Moreover, the strength of this adsorption will affect  the  importance
of  surface runoff as a mechanism of transport for soluble pesticides.
The mechanisms of volatilization, degradation,  organism  uptake,  and
movement  of  pesticides are all affected by the adsorption-desorption
interactions which  occur.   Consequently,  in  order  to  attempt  to
simulate  the  loss  of  pesticides from the soil, one  must adequately
understand this complex phenomenon.
                              - 27 -

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     A  number  of  factors  determine   the   extent   of   pesticide
adsorption-desorption  in  a soil-water system.  King and McCarty  21
have classified these factors into three groups relating  to  (1)  the
nature  of  the  soil,  (2)  the  nature of the pesticide, and (3)  the
influence of environmental  factors.   Soil   characteristics  of  major
importance  include  clay  content,  major   clay mineral type, organic
matter content, and cation  exchange capacity.   Surface charge  density
and  surface  area  will   also have an effect  on pesticide adsorption.
The  significant  pesticidal  properties  include  molecular   weight,
chemical   structure,  water  solubility,  acidity  or  basicity,  and
polarity.  In general, the  extent of pesticide adsorption will  depend
upon  the specific combination of soil and  pesticide properties in  any
given situation.  Also, since many  of  the determining  factors  are
interrelated,  the  individual  effect  of  separate soil and pesticide
characteristics is extremely difficult to investigate.  An elaboration
of the possible effects of  all these factors is beyond  the  scope   of
this  report.   A  number of articles  20'  22' 23  have summarized  the
existing state of knowledge and research on the determinant factors of
pesticide adsorption-desorption.  The  reader   is  referred  to  these
references for additional information.
     The influence of environmental factors is  believed  to  be  less
significant    than    pesticide    and   soil   properties   in   the
adsorption-desorption  reaction.   Soil  temperature,  soil   moisture
content,  and  climatic  conditions  are  usually considered to be  the
major environmental factors.  Although  rainfall,  runoff,  and  other
climatic  conditions  are critical determinants of pesticide loss from
the soil, their importance  is limited  in  the  actual  pesticide-soil
interaction.
     Because of the lack of quantitative relationships describing  the
effects  of  the  above mentioned factors on pesticide adsorption,  the
model simulating this process is based on  a  number  of  assumptions.
Testing  the validity of these assumptions  from the model output was a
major  portion  of  the  present  research   effort.   The  model   and
accompanying assumptions are described subsequently.
Model Description

     The algorithm currently in use to model  pesticide adsorption  and
desorption on soil is as follows:
              X/M = K C       + F/M                                 (5)

where:  X/M = Pesticide adsorbed per unit soil, mg/gm,

       F/M = Pesticide adsorbed in permanent fixed state per unit soil,
             F/M is  less  than  or equal   to FP/M,   where FP/M is the
             permanent fixed capacity of soil  in mg/gm for pesticide.
             This can be approximated by the cation or anion exchange
             capacity for that particular soil type.
                            - 28 -

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       C   = Equilibrium  pesticide   concentration  in  solution, mg/ml

       N   = Exponent;
       K   = Coefficient;
                                                             24    25
     This model was derived from the work of Weber and Weed     '   "  ,
Faust and Zarins  26  and others.  It combines the standard Freundlich
model x/m = K C (1/N) with an  empirical  term.   The  empirical  term
describes pesticide which is adsorbed so strongly that the surrounding
dissolved   pesticide  concentration  is  too  small  to  be  measured
experimentally.  Weber and Weed  2Lf  have shown  that  this  initially
adsorbed pesticide will not desorb under repeated washing; hence,  this
initial   adsorption   is  called  the  permanently  fixed  pesticide.
Pesticide adsorbed into the permanent  fixed  state  will  be  assumed
unavailable  for  desorption.  The permanent fixed state is assumed to
have   a  maximum capacity, FP/M, which  must  be   determined    from
experimental  adsorption  isotherms for the pesticide-soil combination
under consideration.  All available dissolved pesticide is assumed  to
be adsorbed into the permanent fixed state until FP/M is reached.   The
remaining   dissolved   pesticide   is   then  subject  to  reversible
equilibrium adsorption as governed by Freundlich equation.  The  model
is presented graphically in Figure 8.
     The basic assumptions underlying this model are as follows:

     (1) Permanent fixed adsorption is irreversible.

     (2) Single-valued reversible adsorption  occurs  above the   FP/M
         concentration.

     (3) Reaction is pH independent.

     (4) No competing ion effect occurs.

     (5) Adsorption is time independent, i.e.  equilibrium is assumed.

     Assumption 1 appears to be substantiated by the work of Weber and
Weed  2k . For the  majority  of  pesticides,  the  permanently  fixed
portion   will   be  negligible.   However,  the  existing  model   can
accommodate those pesticides, such as paraquat, which are so  strongly
adsorbed  to soil particles that their mode of transport from the land
surface is exclusively by sediment loss.
     Assumption 2 states that the division of  the  pesticide  between
the  water  and sediment phases will follow the same curve in both the
adsorption and desorption cycles, i.e.   a  single-valued,  reversible
adsorption-desorption  relationship is assumed.  Davidson and McDougal
Van Genuchten, Davidson, and Wierenga  28 , and Davidson, Mansell, and
Baker  29 , have shown that  the  reaction  is  non-reversible  for  a
number  of  pesticides.   The  end  result  of  assumption  2  is that
predicted pesticide concentration  on  sediment  would  be  less  than
recorded values.  The field data is inconclusive.
                              - 29 -

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       Figure 8.  Pesticide  adsorption-desorption  model
E
o>

01
E
O
z
O
O
CO
Q

O
r:  FP
?  M
CO
LLI
a.
              PESTICIDE SOLUTION  CONC.(C), mg/ml
PESTICIDE
RUNOFF
Hyd
r o c o m p
                        - 30 -

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However, the existing laboratory evidence suggests that  the  addition
of a non-reversible adsorption-desorption model warrants further study
and  consideration  (see  Section  IX  -  Recommendations  for  Future
Research).
     The third and fourth assumptions are more the result of necessity
than of experimental evidence.  On the contrary, both the pH  and  the
availability  of  competing ions will affect the adsorption-desorption
reaction.  The lack of  quantitative  relationships  describing  these
effects  is  the  reason  for  the assumptions.  However, the existing
model clearly requires  laboratory  determination, of  the  Freundlich
adsorption  constants  for  the  specific  pesticide  and soil system.
Consequently,  the  adsorptton-despfl-ptton  constants  will  inherently
correct for the pH and competing ion conditions of the  specific  soil
system.   Variations  fn  ph over the growing season are thought to be
negligible.  Also, adsorption sites are considered much more prevalent
than available  pesticide  ions.   Therefore,  the  third  and  fourth
assumptions appear to be reasonable, and will likely introduce minimal
error in the final results.
     Assumption/5 states that the  adsorption-desorption  reaction  is
essentially  instantaneous when the time -required to reach equilibrium
is  compared  with  infiltration  times  through  soils.    Thus,   in
accordance  with  the  Freundlich isotherm, equilibrium conditions are
assumed to exist within any time interval.  Time intervals employed in
the model include five or fifteen minutes  during  storm  events,  and
daily  intervals otherwise.  Hornsby and Davidson  30   have shown that
under  saturated  conditions,  adsorbed   and   solution   phases   of
fluometuron  are not in equilibrium at an average infiltration rate of
5.5 cm/hr. (2.165 in/hr.).  whereas equilibrium  does  exist  at  0.59
cm/hr ( 0.23 in/hr).  Obviously, the range of infiltration values will
depend on the pesticide and specific soil system.  However, in natural
soil  systems,  infiltration  rates will generally fall near the lower
and middle values of  this  range.   Consequently,  the  existence  of
equilibrium  conditions appears to be a reasonable assumption that may
be violated infrequently during some storm events.  Further  study  on
the  significance  of  these  events  is  needed  as more data becomes
available.
     In summary, a  model  is  only  an  approximation  to  real-world
conditions.  The above assumptions have been adopted to provide a more
manageable framework to the problem of pesticide adsorption in natural
soil  systems.   These assumptions are based on mechanisms and factors
which are known to be operative under laboratory conditions, but whose
significance under field  conditions  is  unknown.   The  vagaries  of
nature have yet to be reproduced in laboratory experiments.  This is a
familiar  problem in the application of laboratory research to natural
conditions.  The results of continuing testing of the PTR Model should
provide a basis for evaluating the validity of the  above  assumptions
under natural  field conditions.  Those assumptions found to be invalid
will provide the basis for further Model improvements.
                              - 31 -

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PESTICIDE VOLATILIZATION AND DEGRADATION MODEL

     The loss of  pesticides  by  volatilization  and  degradation  is
generally  the  major  component  in  the establishment of a materials
balance of the applied pesticide.   A number of studies   31>    »
have  shown  that these mechanisms are operative and generally account
for the largest portion of pesticide loss subsequent  to  application.
The relative significance of volatilization and  degradation is highly
dependent on the chemical properties of the pesticide.  Although  some
selected   pesticides  are  non-volatile  and/or  non-degradable,  the
majority of available pesticides are vulnerable in varying degrees  to
these mechanisms.
     Volatilization  is  most  significant  during   and   immediately
following  application.   Loss  during  application is highly variable
depending on climatic conditions,  pesticide formulation,  and  methods
of application  31  .  Immediately following application, volatilization
losses  remain  at  high levels for a number of days and then decrease
rapidly to low but continuous levels.  During this time, variations in
volatilization rate are due to soil and pesticide characteristics, and
environmental conditions  34' 35 .
     Degradation  of  pesticides  is  accomplished  through  chemical,
photochemical, and microbial mechanisms.  The relative significance of
the   various   mechanisms   is   highly   dependent   on    pesticide
characteristics  and  environmental  conditions.   Although laboratory
studies have been performed on the various degradation mechanisms, the
extrapolation  of  the  results  to   field   conditions   is   highly
questionable.
     The volatilization and degradation models used in the  PTR  Model
are  derived  from  theoretical considerations and laboratory results.
Consequently, the use of these models to  predict  volatilization  and
degradation  losses from natural watersheds has not been substantiated
or verified.    Hopefully  future   work  will  provide    data     on
which   calibration   of  the  models  can  be  performed.   Pesticide
concentration  on  the  land  surface  is  partially  a  function   of
accumulated   losses   from   volatilization  and  degradation.   Also
pesticide loss from surface runoff is dependent on  surface  pesticide
concentrations. The  present  volatilization models have been included
in order   to  be  available  if  sufficient  data for calibration  is
developed.  The   degradation  model,  a simple first-order decay, was
included to reflect  a  decrease  in surface  concentration  over  the
growing  season  in order to more realistically simulate the pesticide
lost by surface runoff.
Volatilization of Soil-Incorporated Pesticides

     The   sub-model    for   determining   the    volatilization    of
soil-incorporated  pesticides  is  based  on  work performed by Mayer,
Letey, and Farmer36      In that investigation, an  analogy  is  drawn
between  heat  flow  and  pesticide  diffusion through the soil.  Five
                              - 32 -

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submodels were  presented  for  differing  boundary  conditions.   All
submodels  consider  volatilization  a  diffusion-based phenomenon and
ignored transport  via mass flow.  Thus .diffusion is assumed to be the
rate limiting mechanism for pesticide flux  occurring  from  the  land
surface  for soil-incorporated pesticides.  The basis for our model is
the following equation:

         F = DCo/ /irDtT                                            (6)

where:  F = pesticide flux
        D = diffusion coefficient
       Co = initial pesticide concentration
        t = time since application

     Modification of  the  value  of  D,  the  diffusion  coefficient,
accounts  for  the  effects  of  various environmental  factors such as
temperature, soil moisture, and physical soil properties.   Ehlers  et
al      have investigated the effects of these environmental   factors
on lindane diffusion in soils. The results of that work have been used
in  our  model to correct the value of D for environmental  conditions.
The actual equation used in the model is an approximation to  equation
(6)  so  that  the  mechanism  can  be described in a time-independent
manner.  This was necessary in order to avoid problems  associated with
a continuous bookkeeping of  time  since  application,   and  pesticide
concentrations due to reapplication.

Model Assumptions - The validity of equation (6) is dependent  on  two
basic assumptions:

     (1) Pesticide concentration above the soil surface remains at
     zero, and

     (2) The initial pesticide concentration, Co, remains essentially
     constant at the lowest depth of soil incorporation.
In the heat flow analog, the first assumption would correspond to  the
case   of  determining  the  heat  flux  from  an  infinite  (constant
temperature) heat source, where the surface temperature is  maintained
at  a constant lower temperature.  Farmer et al  34  and Mayer, Letey,
and Farmer  36  have shown that extremely small  air  flow  rates  are
needed  to  maintain  a  negligible  pesticide concentration above the
surface.  Thus, assumption (1)  above  appears  reasonable  except  in
those cases where vegetal cover and crop canopies stagnate the air and
allow  a  buildup  of  pesticide  concentration  to  occur at the soil
surface.  The importance of this situation will need to be  considered
and investigated when sufficient data is available for model testing.
     Assumption (2) is obviously an approximation since the  pesticide
concentration  at the lowest depth of incorporation will decrease from
                               -  33 -

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pesticide degradation and mobility.   However, the approximation  seems
reasonable   for   an   initial   model   upon  which  improvements  and
modifications can be made.  Calculations for dieldrin by Mayer, Letey,
and Farmer  36  using equation (6),  have shown that 2,000  days  would
be  required  for  Co  to  be  reduced  by 1% at the bottom of the soil
column 11 cm deep (assuming a constant  D = 2.3 mm 2 /wk).  Thus in the
initial weeks following pesticide application, the approximation would
be valid.  However, the long-term volatilization rates may be too high
and thus require adjustment.
Environmental Factors - The major environmental conditions which  will
have  an  effect  on  volatilization  of  soil incorporated pesticides
include  surface  temperature,  soil moisture,  and   physical   soil
properties.   The  work  of  Ehlers  et  al  37  on lindane diffusion is
used  to  adjust  the   diffusion   coefficient   for   the   existing
environmental conditions.
     Working with  organochlorine  insecticides,  Farmer  et  al   34
reported  that  vapor  pressure  was  the  most  significant factor in
volatilization losses, and that pesticide concentration was  the  next
most important.   Igue et al  35   and Spencer, Claith, and Farmer   38
have  shown that the vapor density (calculated from the ideal gas law,
vapor density =  P x M/RT)  of  dieldrin  will increase  with  relative
humidity  and soil  water content until  the equivalent of one molecular
layer of water molecules is held in  the soil.  With further  increases
in  soil  water   content,  the  vapor  density  remains constant.  The
conclusion to be drawn is that vapor density, and therefore, pesticide
volatilization,  will  remain  constant   with  respect  to  soil  water
content  once a  minimum value of soil moisture is obtained.  Ehlers et
al  37  has reported a similar relationship between soil water content
and the diffusion coefficient of lindane.  For a  soil  water  content
greater  than  4-5%,  the  diffusion  coefficient  of  lindane remains
essentially  constant  Consequently,  the  model  assumes   that   the
diffusion  coefficient  in  equation  (6)  will be independent of soil
moisture for our agricultural lands.
     Farmer  39   has noted that this is a reasonable assumption except
for the variation in soil moisture with depth.  The  problem  here  is
that  during  dry  periods,  pesticide   will  diffuse  to the dry soil
surface and accumulate there because volatilization will be  inhibited
by   the  lack  of  soil  moisture.    Consequently,  this  accumulated
pesticide will volatilize rapidly immediately following a storm period
when moisture is available.  The model  neglects this  mechanism  since
it  is  possible  that  the  total  amount  lost  will not be affected
significantly.
     Variation of the diffusion coefficient with  temperature  changes
has been  investigated by Ehlers et al   37  for the case of lindane in
Gila  salt  loam.   Ehlers  divided   the  diffusion  coefficient  into
components representing diffusion in the vapor and non-vapor phases of
the  pesticide.    The  relationship  between   the   total   diffusion
coefficient  (sum  of  vapor and non-vapor phases) and temperature was
shown to be exponential, and of the  following form:
                              - 34 -

-------
                D = AeBT                                           (7)

where:  D = Total diffusion coefficient, in mm2  /wk
        T = Temperature in degrees, C
        A, B = Constants

The values of A and B for lindane in Gila silt loam are 0.42 and 0.11,
respectively.  Equation  (7)  is  employed  in  the  soil-incorporated
pesticide volatilization  model.  The values  of  A and  B  need to be
determined  for  the  specific pesticide and field conditions,  or from
experimental values.
     At present, the influence of  physical  soil   properties  on  the
diffusion coefficient has not been quantified to a significant  extent.
Ehlers  et  al   37 has reported an almost linear relationship  between
bulk density and the diffusion coefficient for lindane  in  Gila  silt
loam.   However,  additional  evidence  has not been uncovered  at this
time.  Consequently, the model requires a diffusion coefficient    for
the  specific  soil  conditions.    Future  improvements in  the model
could  be aimed at possible  adjustments in the diffusion   coefficient
for the physical soil properties.
Volatilization of Surface Applied Pesticides

     Surface applied pesticides present a slightly  different  problem
than soil incorporated pesticides.  The main differences relate to the
higher  concentrations  of  the  surface  applied  pesticide,  and the
greater  direct  exposure  to  environmental   conditions,   such   as
temperature  and  wind.  The  model is  based   on   investigations by
Farmer  Lf°  and on conventional empirical  relations  for  determining
water evaporation.  The resulting equation which is used is:

              F = C x U x (EVC/M1/2)                               (8)

where:  F = pesticide flux
        U = Wind velocity
         EVC= Equilibrium vapor concentration
        M = Molecular weight of the pesticide
        C = Constant

The  equilibrium  vapor  concentration,  EVC  ,  is  a  function   of
pesticide concentration and surface soil temperatures.  The value of C
is determined through calibration for the specific watershed.

Model Assumptions - Farmer investigated pesticide  flux  from  treated
sand  for  four different pesticides at three different air flow rates
across the surface of the  sand.   He  obtained  reasonable  agreement
between experimental values and those values calculated from:
                               1 /?
                F = K x (SVC/M  ' )                           (9)
                               -  35  -

-------
where:  F = Pesticide flux
        SVC  = Saturation vapor concentration (equivalent to EVC
                in equation 8)
        M = Molecular weight
        k = Constant

Values of k were determined experimentally for each pesticide for each
flow rate.  An average value of k for all four pesticides was used  to
determine  the calculated flux at each flow rate.  The main conclusion
here is that pesticide flux  can  be  adequately  represented  as  the
product  of the pesticide vapor concentration and a constant, k, which
is a function of the air flow rate.  This relationship is  similar  to
the following equation often used to determine water evaporation:

                   E = C x U x (ew - ea)                           (10)

where:  E = Evaporation
        U = Wind velocity
        ew = Vapor pressure of water
        ea = Vapor pressure of water in air
        C = Constant

Equation (8) which is  used  initially  in  the  model  was  developed
conceptually from equations (9) and (10).  The value of k was replaced
by wind velocity times a  constant,  C,  which  is  evaluated  through
calibration and testing.
     An obvious problem with the model is that volatilization will not
occur if wind velocity is zero.  This problem was also  recognized  in
the  case  of  water  evaporation.   The  original  Meyer equation for
evaporation, from which equation (11) was derived, is as follows:

                   E = CT (1 + C2U) (ew - ea)                      (11)

     C] and C2 are constants.   Thus, if wind velocity  is  zero,  some
evaporation  will   still  occur  through  the  mechanism  of molecular
diffusion.    It  was argued  that this component was insignificant in
water evaporation.  The occurrence of zero wind movement in a 24  hour
day  is  quite rare, e.g.  in Denver only 2 days of zero wind movement
occurred in 20 years of record.  For  pesticide  volatilization,  this
adjustment  for  zero  wind  movement  may  be quite significant since
volatilization will likely occur in the  absence  of  wind.   However,
this  adjustment  is presently neglected until calibration and testing
results necessitate its inclusion.  The evaporation equation is  based
on  a  vapor  pressure (or vapor concentration) difference between the
water and air.  Our model essentially assumes that wind velocity  will
be  non-zero  and large enough to disperse pesticide vapor at the soil
surface.  Thus, the vapor concentration of the air at the soil surface
is assumed to be zero because of the wind component.
     A major assumption in the model relates to the dependence of  the
equilibrium  vapor  concentration  on  pesticide  soil  concentration.
                               -  36  -

-------
Spencer,   Claith   and   Farmer    *tl    have  shown  that  the  vapor
concentration (or density) of dieldrin increases  with  dieldrin  soil
concentration  until  a  value  of 25 ppm is reached in the soil.  For
pesticide  soil  concentrations  greater  than  25  ppm,   the   vapor
concentration   is   essentially  constant  and  equal  to  the  vapor
concentration  of  pure  dieldrin.   Thus,  it  appears  that  surface
applications of pesticides will initially volatilize from the soil  as
quickly  as  from  the  pure  pesticide.   Farmer et al  ^2   reached a
similar conclusion when comparing lindane and DDT to dieldrin,  except
that the long-term rates of volatilization would be smaller because
the pesticide loss would  reduce  the  pesticide  soil  concentration.
However,  the  assumptions  in  our model will be that the equilibrium
vapor  concentration  in  equation  (8)  will  be  that  of  the  pure
substance.  Long-term volatilization rates will have  to  be  adjusted
when  data  is  available  to  prove that this assumption is no longer
valid.

Environmental Factors - Temperature, wind, soil moisture, and physical
soil properties all  have  an  effect  on  volatilization  of  surface
applied  pesticides.   Wind velocity is included directly in the model
equation.  Soil moisture and  physical  soil  properties  will  affect
volatilization  largely  by their influence on the competing mechanism
of pesticide-soil adsorption.  The  greater  the  adsorption  to  soil
particles, the less opportunity for volatilization.  The model assumes
that volatilization of surface applied pesticides will be
similar to volatilization from the pure pesticide.  Consequently, soil
properties should not have a major effect.  If calibration and testing
prove this assumption to be  invalid,  then  further  adjustments  and
improvements will be required.
     The major effect of surface temperature will  be  to  change  the
pesticide  vapor  pressure which in turn affects the equilibrium vapor
concentration.  Spencer and Claith    43,44    nave  investigated  the
effects of temperature on the vapor pressures of dieldrin and lindane.
The general form of the relationship is:

                Log 1Q P = A - B/T                                (12)

where:  P = Vapor pressure, mm of Hg.
        T = Temperature, in degrees, K
        A, B = Constants

The results of Spencer and Claith  are    used  to  adjust  the  vapor
pressure  of  the  pesticide  for  temperature changes.  The resulting
equilibrium vapor concentration   is    calculated from the ideal  gas
law as follows:

                    EVC    = P x M/RT                              (13)

This value of EVC   is then used in  Equation  (8)  to  determine  the
pesticide  flux  for  any time interval.  The values of A and B  in the
                                - 37  -

-------
temperature adjustment are determined either  experimentally  or  from
the literature.
Pesticide Degradation

     As  mentioned  previously,  pesticide  degradation  occurs  by  a
variety of mechanisms involving chemical, photochemical, and microbial
agents.  As with volatilization and adsorption, environmental  factors
have  significant  impact  on  the various degradation processes.  The
quantification of the various degradation mechanisms and the effect of
environmental conditions is a current research  topic  of  substantial
importance.    However,   no  definitive  models  of  total  pesticide
degradation  are  currently  available  which  would  be  amenable  to
inclusion in the PTR Model.  The staff at SERL is  presently  studying
various  degradation  mechanisms  with  the  goal  of  developing such
models.
     A few studies have  shown  that  total  degradation  for  certain
pesticides  can  be  approximated by a first-order decay function
Fractional order or Michaelis-Menton kinetics have shown  to  be  more
accurate  in  other  situations          Considering such inconclusive
evidence, a first-order degradation function has been assumed for  the
PTR  Model.   This  assumption  is  necessary  so  that  the amount of
pesticide available for transport by surface runoff and sediment will
be gradually diminished over time.  Thus, more realistic estimates  of
the  concentration  and  volume  of pesticide in surface runoff can be
obtained.  As research progresses in this area, the first-order  decay
can  be  replaced  by  a  model  which  would  be capable of adjusting
degradation rates for the specific soil, pesticide, and  environmental
conditions.
     In the PTR Model, the user supplies a daily  degradation  factor,
which  reduces  the  amount of pesticide in storage at the end of each
day.  The same factor is applied to  each  of  the  pesticide  storage
zones  within  the  soil  profile-surface, upper zone, and lower zone.
Since the majority of pesticides never reach the  groundwater  storage
zone, no pesticide degradation is considered to occur from that zone.
                              - 38 -

-------
                              SECTION VI

                  PTR MODEL STRUCTURE AND OPERATION
MODEL STRUCTURE
     The PTR Model is structured about a main, or  executive  program,
which controls the operation of six (6) accompanying subprograms.  The
MAIN  program  also  controls input and output operations, manages the
time-keeping operations, and performs the  transfer  of  data  between
subprograms.   The MAIN program and subprograms are listed in Table 2,
along with the time intervals and functions which are performed.   The
six  subprograms  simulate  pesticide  volatilization and degradation,
surface runoff,  sediment  loss,  and  pesticide  interaction  in  the
various  soil  storages.   A  complete  listing  of  the  PTR Model is
included in Appendix C.
MODEL OPERATION

     The Model operates continuously on a  number  of  different  time
intervals.   The  basic manner of operation is shown in Figure 9.  The
VOLDEG subprogram adjusts the pesticide storages on a daily basis  for
loss  of active pesticide by volatilization and degradation.  For days
in which rain occurs, Model operation follows the solid line in Figure
9, operating on a five or fifteen minute interval for the  subprograms
LANDS,  SEDT,  ADSRB1,  ADSRB2,  and  ADSRB3.  The choice of a five or
fifteen minute time interval depends on  the  time  intervals  of  the
input rainfall.  For days in which rainfall does not occur, the  model
operation  follows  the  dashed  line  in Figure 9:  SEDT is bypassed;
LANDS operates on five or fifteen minutes;  and  ADSRB1,  ADSRB2,  and
ADSRB3  operate  on  a  daily  basis.   The  MAIN program monitors the
passage  of  real  time  and  keys  the  operation  of  the   separate
subprograms at the proper time intervals.
     The PTR Model is written in the IBM FORTRAN IV language  and  was
developed and run on the Stanford University IBM 360/67 computer.  The
Model  operates  most  efficiently in a two-step procedure.  The first
step involves the compilation of the program and the  storage  of  the
compiled  version on disk or magnetic tape.  In step two, the compiled
Model is provided the necessary input data and is executed.  Thus, the
                              - 39 -

-------
Model can operate a number of types of different  input  data  with  a
single  compilation.   The format of the input and output is described
below.
                    Table 2.  PTR MODEL SUBPROGRAMS
      Name
      Function
Operation Time
   Intervals
      MAIN

      LANDS

      SEDT

      ADSRB1


      ADSRB2


      ADSRB3



      VOLDEG
Executive program

Hydrologic model

Sediment loss model

Surface Pesticide
   Interaction

Upper Zone Pesticide
   Interaction

Lower Zone and Ground-
    water Pesticide
    Interaction

Pesticide Volatilization
    and Degradation
Not Applicable

5-min., 15-min,

5-min., 15-min,

5-min., 15-min,
    Daily

5-min., 15-min.
    Daily

5-min., 15-min.
    Daily


    Daily
MODEL INPUT AND OUTPUT

Model Input

     Input data is accepted by the Model   on  a  sequential   basis  in
English   or   Metric   units.    Model   parameters,  daily   potential
evapotranspiration,  average  daily  soil   temperature,   daily   wind
movement,  and rainfall  data are input in  that order as shown in Table
3.   To  simplify  input  procedures  and   reduce   computer   storage
requirements,  the  hydrometeorologic data is input on a calendar year
cycle.   Each block of data in table 3 represents all daily values  for
the  full year or for that portion of the  calendar year which is to be
simulated.  Thus, if simulation begins on  July 1,  1972  and  ends  on
February    15,    1973,    the    hydrometeorologic   data    sequence
(evapotranspiration, soil   temperature,  wind,  rainfall)  for  July  1
through  December  31,  1972 is followed by the hydrometeorologic data
for>January 1 through February 15, 1973.
                              - 40 -

-------
Figure  9.    PTR  Model  structure  and  operation
PESTICIDE
RUNOFF
Hyd
r o c o m p

-------
         Table 3.   SEQUENCE OF INPUT DATA










|_  PTR Model  Input Parameters





   Potential  Evapotranspiration            - 1st Year





   Soil Temperature                        - 1st Year







   Wind Movement                           - 1st Year
   Rainfall                                 - 1st Year
   Potential Evapotranspiration            - 2nd Year





   Soil Temperature                        - 2nd Year







   Wind Movement                           - 2nd Year
:   Rainfall                                - 2nd Year
r
etc.
                    -  42  -

-------
     Model  parameters are input in FORTRAN 'namelist1  format as  shown
in  Table  4.   (Parameter  evaluation  is  discussed  in Section 6.4).
Daily potential  evapotranspiration, average  daily  soil  temperature,
and  daily  wind movement are input under identical  format conditions.
Daily values are required for  each  day  of  simulation.    The  input
format consists  of a 31 X 12 matrix, i.e.  12 columns  of 31 rows each,
representing the 12  months with a maximum  of 31 days each. The input
format is shown  in Table 5.  Input daily evapotranspiration  and  wind
movement  are  in integer form while average daily soil  temperature is
in real number form correct to the first decimal  place.   Rainfall  data
is input in five or fifteen minute intervals according to   the  format
described  in Table  6.  A complete listing of the input data sequence
is included in Appendix B.
Model Output

     A number of options are available for the type of  output  to  be
obtained  from  the  Model.   The  form and substance of the output is
controlled by the input parameters  HYCAL,  PRINT,  and  UNIT.   HYCAL
specifies whether the simulation is a calibration run (with or without
pesticide  simulation),  or  a production run providing concentrations
and amounts of pesticide  remaining  in  the  various  soil  storages.
PRINT  specifies  the  interval  for  the  printing  of output; daily,
hourly,  and  five  or  fifteen  minute  intervals  are  allowed.   In
addition, monthly and yearly summaries are provided  with  all  output
options.   UNIT  specifies  whether  the  units  of the output will be
English, Metric, or both.  These three parameters are integers and can
only have values of -1, 0, or +1.  The  values  for  each  ootion  are
specified  in  the  portion  entitled  'Parameter Evaluation and Model
Calibration1.
     Since each of the parameters can take on three  possible  values,
the  possible  output  options  numbers  27.   In practice, only a few
combinations are used regularly and will  produce specific output forms
with  any  degree  of  reliability.   These  combinations  and   their
description and use are as follows:

HYCAL = +1 PRINT= -1 UNIT = -1, 0, +1

     This   combination is used primarily for calibration of the LANDS
     and SEDT subprograms.  The output, as shown in Table  7, provides
     flow  and  sediment  loss  values  for  five  or  fifteen  minute
     intervals  during storm  events.   Monthly  and  yearly summaries
     provide  totals  so  that  recorded  and simulated volumes can be
     compared.  The input  parameter  HYMIN specifies the minimum flow
     for which output is to be printed.

HYCAL = -1  PRINT =  -1  UNIT = -1, 0, +1
                               -  43 -

-------
Table 4.  PTR MODEL  INPUT PARAMETERS IN 'NAMELIST1 FORMAT
SHYCL
&PRNT
GSTRT
CENCD
6TRVL
GLN01
&LND2
SLND3
£LND4
&PEST
&NAWE
&CRGP
&SMOL
SAMOL
6VOL1
SVOL2
SQEG1
HYCAL^
PRINT=
BGNCAY=
ENDDAY=
INTRVL =
UZSN=0.
IRC=0. 0
LZS = 20.
ICS=0.0
SSTR=5*
PNAME=«
COVMAX=
JRER=3.
CMAX=0.
D1FC=30
MOLEWT =
DEGCON=
                     BGNYR=1972  &ENO
                     »  ENDYR=1973 6 END
0*  HYMIN=0.001«  UNIT=-1, INPUT=-1

1, BGNMON=7,
15,  ENDMCN=
5  SEND
05. LZSN=18.0,  INFIL=0.5, INTER=0.7
  NN=0.20,  L=160.»  SS=0.05, A=0.00,
0, SGW=0.0, GWS=0.0,  KV=0.0, K24L= 1
» OFS=0.0,  IFS=0.0,  K24EL=0.0, K3=0
13.4, APMODE=0,  DEPTH=6.125  SEND
                                              SEND
                                             UZS=0.05
                                               6ENC
                                             40,  EPXM=0.12  SEND
0.60,TIMST=182.,TIMAP=182.,TIMAT=274.,TIMHAR^334.
0. KRER=0.09,  JSER=1.0, KSER=1.5,  SRERI=9.0 &END
00001,DD = 0.0003,8ULKD=103.0,K=120.,N=2.,AREA=6.7
.0, TD!FC=30.0,  CBDIF=0.11   SEND
335., APFAC=0.0, BPFAC=0.0,  WCFAC=1.0  (LEND
0.0001  £END
                                                           SEND
                                                           SEND

-------
                                               Table  5.   SAMPLE INPUT  AND  FORMAT  FOR  EVAPORATION,
                                                              TEMPERATURE,  AND WIND  DATA
tn
 i
                             COLUMN
                             NUMBER


MONTH
EVAP72
EVAP72
EVAP72
f!VAP72
EVA P 72
E IMP 72
CVAP72
CVAP72
HVAP72
EVAP72
EVAP72
EVAP72
EVAP72
EVAP72
CVAP72
EVA P 72
EVAP72
EVAP72
EVAP72
EVAP72
EV/AP72
€VAP72
C VAP72
EVAP72
EVAP72
EVAP72
f VAP72
EVAP72
EVAP72
EVAP72
EVAP72
>,
rd
C
•"3
27
27
43
41
41
70
43
119
54
54
54
54
54
59
108
124
103
0
49
11
11
11
65
59
97
97
22
0
27
27
27
t
3
t.
ji
Ol
32
176
410
252
44
63
32
139
57
0
132
63
76
69
189
63
76
151
265
277
69
88
31
38
32
69
101
76
113



o
I-
2!
96
141
148
118
74
192
163
126
155
148
155
141
215
126
126
89
118
67
74
S9
141
178
200
74
111
96
96
81
89
141
74

^
CL
154
49
84
91
105
140
140
154
109
161
70
112
126
147
252
175
280
224
210
168
196
42
189
102
238
112
98
•252
63
140



1
167
175
190
190
198
251
198
91
122
228
220
175
84
243
205
236
152
144
137
84
219
129
106
167
205
289
243
106
68
53
122


-------
          Table 6.   PTR MODEL  RAINFALL  INPUT  DATA  FORMAT
Column No.                    Description  and  Format

    1           -     Blank
    2-7        -     Year,  Month,  Day  (e.g., January  1,  1940  is  400101).
    8          -     Card Number  - each  card represents  a  3-hr,  period,  e.g.
                        Card #1  - Midnight to  3:00  AM
                              #2  - 3:00 AM  to  6:00  AM
                              #3  - 6:00 AM  to  9:00  AM
                              #8  - 9:00 PM  to  Midnight
                    All eight cards are required  if  rain  occurred  anytime
                    during the day.   A  card number of 9 signifies  that  no
                    rain occurred during  the  entire  day,  and  no other
                    rainfall  cards are  required for  that  day.

    9-80       -     Rainfall  data (OOO's  of millimeters (OO's of inches).
                    15-minute intervals:
                    6  column  per  each 15-minutes  in  the 3-hour  period of
                    each card.   Number  must be  right justified, i.e.
                    number must  end in  the 6th  column for the 15-minute
                    period.

                    5-minute  intervals:
                    2  columns per each  5-minute interval  - i.e., the 15-
                    minute period still occupies  6 columns,  but it is
                    broken down  into  3  5-minute intervals.
    NOTES      -     1.   Appendix  B  contains  a  sample  of  input data.
                    2.   At  least  one  rainfall  card  is  required  for each
                        day of  simulation.
                    3.   Blanks  are  interpreted as zeros  by  the  Model.
                        Consequently,  zeros  do not  need  to  be input.
                               - 46 -

-------
SAMPLE  OUTPUT:  LA.IDS AND SEDT CALIBRATION  RUN
DATE
FLOW(CFS - CMS)
                           SEDIMENKLBS -  KG - GM/L)
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
20: 5
20: 10
20:15
20:20
20:25
20: 30
20:35
20:40
20:45
20:50
20:55
21: 0
21: 5
0:20
0:25
0:30
0:35
0:40
0:45
0:50
0:55
l: 0
l: 5
1:10
1:15
1:20
1:25
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.o
0.5
0.7
0.5
0.3
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.000
0.003
O.OD2
0.002
0.002
0.002
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.013
0.020
0.013
0.010
0.006
0.004
0.003
0.002
O.OC1
0.001
0.001
0.001
0.000
4.45
14.58
5.99
0.92
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.07
159.26
201.62
88.51
46.74
19.03
5.42
0.53
0.0
0.0
0.0
0.0
0.0
0.0
2.02
6.62
2.72
0.42
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
0.0
7.30
72.30
91.54
40.19
21.22
8.64
2.46
0.24
0.0
0.0
0.0
0.0
0.0
0.0
24.62
6.82
3.80
0.74
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.24
18.81
15.37
10.49
7.42
4.74
2.02
0.27
0.0
0.0
0.0
0.0
0.0
0.0

-------
     The output from  this combination  of values  is identical  to the
     one above,  except  that pesticide volumes  and concentrations in
     water and  on sediment  are also  provided.   The format for this
     combination is shown in Table  8.

HYCAL = 0 PRINT = +1 UNIT = -1 or +1

     This   output  format  is referred  to as a production run.  The
     amount  and concentration  of pesticide remaining in the various
     soil   storages is   provided  along  with    the total volume of
     pesticide   lost  by   surface   runoff,   sediment   loss,  and
     volatilization  and  degradation.  The format  is shown in Table
     9.     A PRINT   value  of -1 or 0 allows   the investigation of
     pesticide   concentration   and   movement    during  short  time
     intervals.   However,  if PRINT is -1 or 0  for a long simulation
     run,  the  volume of output would be enormous.  Also if the UNIT
     option  is specified as 0, the volume of   output  would  double
     because the format in Table 9 would  simply  be  reproduced   in
     metric units  during each time interval.  Consequently,  caution
     should  be used when specifying values of PRINT and UNIT   other
     than shown above for a production run (HYCAL  = 0),

     The format of the monthly and yearly  summaries  for  calibration
and  production  runs is shown in Tables 10 and  11.  The use of a UNIT
value of 0 would simply reproduce these tables in   metric  units,  and
thus, double the amount of output for the summaries.
PARAMETER EVALUATION AND CALIBRATION PROCEDURES

     Parameter  evaluation  and  calibration   are   closely   related
processes.   The  parameters  of  the PTR Model are determined for the
most  part  by  physical  and/or  chemical    characteristics.     Those
parameters   which  are  not  fixed  by  natural   characteristics  are
evaluated through calibration.  Initial estimates of these  parameters
are used in the first calibration run of the Model.  The comparison of
simulated and recorded values provides the basis  for re-evaluating the
parameter  values  for  subsequent calibration trials.   The process is
re-iterated until the desired accuracy  is  achieved.
     The following  sections  discuss  the  evaluation   of  the  input
parameters  for each of the subprograms of the PTR Model.   Calibration
procedures  are  also  discussed  as  they  pertain  to  the  specific
subprograms.  A  complete  list  of  the  Model  input   parameters  is
provided  in  Table  12,  while  Table  13  includes  namelist  names,
                              - 48 -

-------
                                  Table 8.   SAMPLE OUTPUT:  PESTICIDE CALIBRATION RUN
               DATE
TIME
FLOWICFS  -  CMS)
SEDIMEWTILBS  - KG - GM/L)
    PESTICIDE  (GM - PPM)
WATER             SEDIMENT
UD
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
JULY
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
20: 5
20: 10
20:15
20:20
20:25
20: 30
20:35
20:40
20:4-5
20:50
20:55
21: 0
21: 5
0: 20
0:25
0:30
0: 35
0:40
0:45
0:50
0:55
1: 0
l: 5
1:10
1:15
1:20
1:25
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.7
0.5
0.3
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.000
0.003
0.002
0.002
0.002
0.002
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.013
0.020
0.013
0.010
0.006
0.004
0.003
0.002
O.OC1
0.001
0.001
0.001
o.oco
4.45
14.58
5.99
0.92
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
16.07
159.26
201.62
88. 51
46.74
19.03
5.42
0.53
0.0
0.0
0. 0
0.0
0.0
0.0
2.02
6.62
2.72
0.42
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7.30
72.30
91.54
40.19
21.22
8.64
2.46
0.24
0.0
0.0
0.0
0.0
0.0
0.0
24.62
6.82
3.80
0.74
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.24
18.81
15.37
10.49
7.42
4.74
2.02
0.27
0.0
0.0
0.0
0.0
0.0
0.0
2.729
9.446
3.255
0.501
0.0
0.0
0.0
0.0
0.0
0,0
0.0
0.0
0.0
1.471
9.862
12. 505
5.750
2.979
1.230
0.352
0.035
0.0
0.0
0.0
0.0
0.0
0.0
33.230
9.732
4.553
0.883
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.876
2.564
2.098
1.500
1.040
0.675
0.288
0.038
0.0
0.0
0.0
0.0
0.0
0.0
0.032
0.106
0.040
0.006
0.0
0.0
0.0
o.c
0.0
0.0
0.0
0.0
O.P
0.036
0.278
0.343
0. 147
0.078
0.032
O.C09
0.001
0.0
0.0
0.0
0.0
0.0
0.0
16.043
16.036
14.852
14.849
0.0
0.0
0.0
n.o
0.0
0.0
0.0
0.0
0.0
4.902
3.842
3. 744
3.664
3.659
3.656
3.656
3.656
0.0
0.0
0.0
0.0
0.0
0.0

-------
   Table 9.   SAMPLE OUTPUT:   PRODUCTION  RUN-DAILY  OUTPUT  INTERVAL
                   24; 0	ON
                                  _JiUJL_
                                          JL222-
                             ZONE 1
                                       ZONE 2
                                                 ZONE 3
                                                           ZONE
                                                                     ZONE 5
                                                                                TOTAL
   fcATER,  INCHES

      PRECIPITATION

      RUNOFF
        OVERI_AND_FLOW
        INTERFLOW
        IMPERVIOUS
        TOTAL

      8ASE_FLOU
      CRQ*ATER_RECHARGE

      EVAPORATION
        POTENTI AL
        NET

      STORAGES
        UPPtR_ZONE
        LOt,ER_ZONE
        GROLNDWATER
        INTERCEPTION
        QVEBLAND_FLOW
        IMTfRFLOW

      WATF.R_BALANCE =  0.0

   SEDIMENT,  TONS
      TCTAL  SEDIMENT LOSS
      FINES  DEPOSIT
      IMPERVIOUS EROSION

SURFACE LAYEP PESTICIDE

   PESTICIDE , LBS
      ACSOPBEO
      CRYSTALLINE
      DISSOLVED

   PESTIC IDE , PPM
      ACSORSED
      CRYSTALLINE
      D ISSOLVED

   REMOVAL.  LBS
      SED IMENT
      OVERLAND  FLOW
      PERCOLATION

UPPER ZCNF LAYER PESTICIDE

   PESTICIDE, LBS
      ACSORBED
      CRYSTALLINE
      DISSOLVED
0.0
          0.0
                    0.0
                              0.0
                                        0.0
                                                   0.0
0.0
0.0

0.0


0.0
0.0

0.0


0.0
0.0

0.0


0.0
0.0

0.0


0.0
0=0

O.G


0.0
0.0
0.0
0.0
0.0
0.0
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
o.iei
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0. 161
0.0
0.0
2.568
2. 568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
l.BOO
0.161
C.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
0.245
0.202
0.003
19.845
0.0
0.0
0.0
0.0
0.0
1.800
0.161
0.161
0.0
0.0
2.568
2.568
0.0
0.0
0.0
0.0
0.0
0.0
3.859
3.858
0.0
0.002
                                                   0.245
                                                   0.202
                                                   0.003
                                                  19.845
                                                   0.0
                                                   0.0
                                                   0.0
                                                   0.0
                                                   0.0
                                                   9.000
                                                   0.0
                                                    0.804
                                                    0.804
                                                    C.O
                                                    0.0
                                                    2.568
                                                    2.568
                                                    0.0
                                                    0.0
                                                    0.0
                                                    0.0
                                                    0.0
                                                    0.0
                                                   19.296
                                                   19.288
                                                     0.0
                                                     0.008
                                        50

-------
                             Table 9.    (continued)
   PESTICIDE. PPM              4.335     4.335     4.335     4.335     4.335      4.335
      ADSORBED                 2.566     2.566     2.566     2.566     2.566      2.566
      CRYSTALLINE              0.0       0.0       0.0       0.0       0.0        0.0
      DISSOLVED                1.768     1.768     1.768     1.768     1.768      1.768

   REMOVAL, LBS                0.0       0.0       0.0       0.0       0.0        0.0
      INTERFLOW                0.0       0.0       0.0       0.0       0.0        0.0
      PERCOLATION              0.0       0.0       0.0       0.0       0.0        0.0

LOWER ZCNE LAYCR PESTICIDE

   PESTICIDE, L8S                                                                 0.0
      ADSORBED                                                                    0.0
      CRYSTALLINE                                                                 0.0
      DISSOLVED                                                                   0.0

   PESTICIDE . PPM
      ACSCRBEO                                                                    C.O
      CRYSTALLINE                                                                 0.0
      DISSOLVED                                                                   0.0

   REMOV4L.  LBS                                                                   0.0
      PERCCLATION                                                                 0.0

GRCLNC^ATER  LAYER PESTICIDE

   PESTICIDE, LBS                                                                 0.0
      ADSORBED                                                                    0.0
      CRYSTALLINE                                                                 C.O
      DISSOLVED                                                                   0.0

PESTICICE VOLATILIZATION LOSS. LBS.
   TCTAL                                                                          0.540
   F«CM SURFACE                                                                   0.0
   FROM UPPEB /ONE                                                                0.540

PESTICIDE OECRAOATION LOSS. LBS.
   TCTAL                                                                          0.219
   FRCP SURFACE                                                                   0.009
   FRCM UPPEfi ZONE                                                                0.210
   FRCM LOWEF ZONE                                                                0.0
                                     -  51  -

-------
Table  10.   SAMPLE  OUTPUT:   CALIBRATION RUN -  MONTHLY  SUMMARY
WATERf  INCHES

   PBECIPITATION

   RUNOFF
      OVERLAND_FLOW
      INTERFLOW
      IMPERVIOUS
      TOTAL

   BASE FLOW
   GRDWATER_RECHARGE

   EVAPORATION
      POTENTIAL
      NET

   STORAGES
      UPPER_ZONE
      LOWEP_ZQNE
      GROUNDWATER
      INTERCEPTION
      OVERLANO_FLOW
      INTERFLOW

   WAT£R_BALANCE=  0.0006

SEDIMENT, TONS
   TOTAL SEDIMENT LOSS
   FINES DEPOSIT
   IMPERVIOUS EROSION

PESTICIDE REMOVAL, LBS.
   OVERLAND FLOW REMOVAL
   SEDIMENT REMOVAL
   INTERFLOW REMOVAL
                           ZONE 1     ZONE 2
          2.190
0.834
0.117

0.952
          0.519
          0.673
                    1972

                    ZONE 3



                    2.190
0.307
0.145

0.452
                                                         ZONE 4
                              2.190
0.177
0.124

0.301
                                                                  ZONE 5
                    2.190
0.094
0. 146

0.240
5.259
3.574
0.096
17.432
0.0
0.039
.0.0
0.0
2.068
0.633
0.148
0.143
0.005
0.0
5.259
3.574
0.095
17.432
0.0
0.039
0.0
0.0
1.642
1.058
0.042
0.040
0.002
0.0
5.259
3. 574
0.095
17.432
0.0
0.039
0.0
0.0
1.161
1.539
0.016
0.015
0.001
0.0
5.259
3.574
0.095
17.432
0.0
0.039
0.0
0.0
0.731
1.970
0.008
0.008
0.301
0.0
5.259
3.574
0.094
17.432
0.0
0.039
0.0
0.0
0.416
2.284
0.005
0.004
0.000
0.0
                                                                             TOTAL
                               2.190
0.386
0.137
0.0
0.523

0.0
0.573
                                                  5.259
                                                  3.574
                                                  0.096
                                                 17.432
                                                  0.0
                                                  0.039
                                                  0.0
                                                  0.0
                                                  6.019
                                                  7.483
                                                  0.0

                                                  0.220
                                                  0.210
                                                  0.009
                                                  0.0
 PESTICIDE VOLATILIZATION LOSS, LBS.
    TOTAL
    FROM SURFACE
    FROM UPPER ZONE

 PESTICIDE DEGRADATION LOSS, LBS.
    TOTAL
    FROM SURFACE
    FROM UPPER ZONE
    FRUM LOWER ZONE

    PESTICIDE_BALANCE- -0.0033
                                                  0.0
                                                  0.0
                                                  0.0
                                                  5.756
                                                  0.751
                                                  5.005
                                                  0.000
                                    -  52 -

-------
Table  11.   SAMPLE  OUTPUT:   PRODUCTION  RUN  - MONTHLY  SUMMARY
                    .SiiM,MAB.X_F_CR._M.aNIli_CE

                              ZONE 1    ZONE 2
kATER, INCHES

   PRECIPITATION

   RUNOFF
      OVEPL4KD_FLOW
      INTERFLOW
      IMPFRVIOUS
      TOTAL
    BASF_FLUH
    GRCWAT FR._RFChARGE
                              2.190
0.834
0.117
                              0.9f>2
                                       2.190
                                        0.519
                                        0.15*
                                       0.673
                                                 L2J2

                                                 ZONE  3



                                                 2.190
                                                 0.307
                                                 0.1*5

                                                 0.*52
                                                             ZONE  *
                                                           2.190
0.177
0.12*

0.301
                                                                       ZONE  5
                                                                     2. 190
O.C9*
0. 1*6

0.2*0
      HATFR_tALANCE=  0.3006

   SECIf-ENT, TONS
      TOTAL «EOIMENT LOSS     2.066
      FINES 1)1 POSIT           0.63*
      I«P£R'J IPUS ER1SION

PESTICICE. PCUNOS

   SURFACE LAYER
      Arsnp BED
      CRYSTALLINE
      DISSOLVED

   IPPER ZONE LAYER
      ACSORBED
      CRYSTALLINE
      DISSOLVED

   LOHER ZONE LAYER
      ACSORBEO
      CRYSTALLINE
      DISSOLVED

   GPCUNDWATER LAYER
      ATSORPED
      CRYSTALL INE
      0 ISSCLVFO

   PESTICICE REMOVAL. LBS.
      OVERLAKO FLOW REMOVAL
      SEDIMENT REMOVAL
      INTERFLOW REMOVAL       0

   PESTICIDE VOLATILIZATION LCSS,  LBS.
      TCTAL
      FRTM SLRFACE
      FROM UPPEK ZONE

   PESTICIDE DEGRADATION LOSS, LBS.
      TOTAL
      FROM SURFACE
      FROM UPPER ZOJF
      FROV LCWER ZU'JE

      PFSTICIDE_BALANCE= -0.0033
          1.6*2
          1.058
                                                1. 161
                                                1.539
0.731
1.970
0.416
2.28*
                                                    TCTAL
                                                                                  2.190
0.386
0.137
0.0
0.523

0.0
0.573
EVAPORATION
PCTFNT1 AL
NET
STORAGES
UPPFR_/fNE
LOW Fk_ZONE
GHOLNOWATER
INT FPCFPT ION
OVFPt.AND_FLOW
INT GRFLOW

5.259
3.57*

0.096
17. ',32
0.0
0. 039
0.0
0.0

5.259
3.57*

0.095
17. *32
0.0
0.039
0.0
0.0

5.259
3. 57*

0.095
17.*32
0.0
0.039
0.0
0.0

5.259
3.57*

0.095
17.*32
0.0
0.039
0.0
0.0

5.259
3.57*

0.09*
17.*32
0.0
0. 039
0.0
0.0

5.259
3.57*

0.096
17.*32
0.0
0.039
0.0
0.0
                                                                                6.017
                                                                                7.*85
                                                                                0.0
0.021
0.021
0.0
0.0
2.699
2.719
0.0
0.010








.1*8
.1*3
.005
.0
0.020
0.020
0.0
0.0
2.807
2.826
0.0
0.011








0.042
0.0*0
0.002
0.0
0. 019
0.019
0.0
0.0
2.833
2.852
0.0
0.011








0.016
0.015
0.001
0.0
0.019
0.019
0.0
0.0
2.8*1
2.661
0.0
0.011








0.008
0.008
0.001
0.0
0.018
0.019
0.0
0.0
2. 8*5
2.865
0.0
0.011








0.005
0.00*
0.000
0.0
0.097
0.098
C.O
0.0
14.02*
1*.12*
0.0
0.055
0.000
0.0
0.0
C.O
0.0
0.0
0.0
0.0
0.220
0.210
0.009
0.0
                                                                                0.0
                                                                                0.0
                                                                                0.0
                                                                                5.756
                                                                                0.751
                                                                                5.005
                                                                                0.000
                                       53

-------
              Table 12.     PTR MODEL INPUT PARAMETER DESCRIPTION

HYCAL:  Indicates mode of operation
      -1 - Calibration run with pesticide simulation
       0 - Production run
      +1 - Calibration run without pesticide simulation
HYMIN:  Minimum flow for printing output during a time interval.
INPUT:  Input units; English (-1), Metric (1).
UNIT:  Output units; English (-1), Metric (1),  Both (0).
PRINT:  Denotes frequency of printing output; Each interval  (-1),
        each hour (0), or each day (1).
BGNDAY, BGNMON, BGNYR:  Date simulation  begins; Day, month,  year
ENDDAY, ENDMON, ENDYR:  Date simulation  ends; Day, month, year.
INTRVL:  Time interval of operation (5    or 15 minutes).
UZSN:  Nominal  upper zone storage.
LZSN:  Nominal  lower zone storage.
INFIL:  Index infiltration rate.
INTER:  Interflow parameter, alters runoff timing.
IRC:  Interflow recession rate.
NN:  Manning's  n for overland flow.
L:  Length of overland flow to channel.
SS:  Average overland flow slope.
A:  Fraction of area that is impervious.
UZS:  Initial upper zone storage.
LZS:  Initial lower zone storage.
SGW:  Initial groundwater storage.
GWS:  Initial slope of the groundwater table.
                              -  54  -

-------
KV:   Parameter to allow variable recession rate for groundwater
        discharge.
K24L:  Fraction of groundwater recharge percolating to deep
        groundwater.
KK24:  Groundwater recession rate.
ICS:  Initial interception storage.
OFS:  Initial overland flow storage.
IPS:  Initial interflow storage.
K24EL:  Fraction of watershed area where groundwater is within
         reach of vegetation.
K3:   Index to actual evaporation.
EPXM:  Maximum interception storage.
PNAME:  Pesticide name (8 characters, maximum).
WSNAME:  Watershed name (8 characters, maximum).
SSTR:  Total pesticide application (on zonal  basis).
APMCDE:  Mode of application; surface applied (0),  soil  incorporated
        (1).
DEPTH:  Depth of soil incorporation and/or upper zone depth.
COVMAX:  Maximum surface area covered by fully matured vegetation.
TIMST:  Time simulation starts (Julian day,  i.e.,  day  of  the year
         (e.g., January 1 is 1, December 31,  is 365 or 366, etc.).
TIMAP:  Time of pesticide application (Julian day).
TIMAT:  Time of crop maturity (Julian day).
TIMHAR:  Time of harvest (Julian day).
JRER:  Exponent of rainfall intensity in soil splash equation.
KRER:  Coefficient in soil splash equation.
JSER:  Exponent of overland flow in surface scour equation.
KSER:  Coefficient in surface scour equation.
                               -  55  -

-------
SRERI:  Initial  fines deposit.

CMAX:  Maximum solubility of pesticide in water.

DD:  Permanently fixed capacity (Ib pesticide/lb  soil).

BULKD:  Bulk density of soil.

K:  Coefficient in Freundlich adsorption curve.

N:  Exponent in Freundlich adsorption curve.

AREA:  Watershed area.

DIFC:  Pesticide diffusion coefficient.

TDIFC:  Temperature at which DIFC was measured.

CBDIFC:  Exponent of temperature correction for diffusion coefficient.

MOLEWT:  Molecular weight of pesticide.

APFAC, BPFAC:  Constants for temperature adjustment of pesticide vapor
         pressure.

WCFAC:  Wind calibration factor.

DEGCON:  First order daily pesticide decay rate.
                               -  56  -

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Table 13.   PTR MODEL INPUT PARAMETER ATTRIBUTES
Namelist
Name
HYCL





PRNT
STRT


ENDD


TRVL
LND1




LND2





LND4





LND4


* - dashes
Parameter
Name
HYCAL
HYMIN


UNIT
INPUT
PRINT
BGNDAY
BGNMON
BGNYR
ENDDAY
ENDMON
ENDYR
INTRVL
UZSN
LZSN
INFIL

INTER
IRC
NN
L
SS
A
UZS
LZS
SGW
GWS
KV
K24L
KK24
ICS
OFS
IFS
denote dimensionless
Type
Integer
Real


Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Real
Real
Real

Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
values.
English* Metric*
Units Units
.
Cubic feet per Cubic meters
second (CFS) per second
(CMS)
-
-
-
_ _
-
-
_ _
-
-
Minutes (min) Minutes (min)
Inches (in) Millimeters (mm)
Inches (in) Millimeters (mm)
Inches per Millimeters per
hour (in/hr) hour (mm/hr)
-
_ «
_ -
feet (ft) meters (m)
-
-
inches (in) millimeters (mm)
inches (in) millimeters (mm)
inches (in) millimeters (mm)
-
-
-
-
inches (in) millimeters (mm)
inches (in) millimeters (mm)
inches (in) millimeters (mm)

                     - 57 -

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PEST


NAME

CROP




SMDL




K24EL
K3
EPXM
SSTR
APMODE
DEPTH
PNAME
WSNAME
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
KSER
SRERI
Real
Real
Real
Real
Integer
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
AMDL
VOL1
VOL2
DEG1
CMAX
                 DD
  DEGCON
Real

 Real
 Real
                                            inches(in)   millimeters (mm)

                                            pounds (Ib)   kilograms (kg)

                                            inches(in)   millimeters (mm)
                                                tons
                                               tonnes (t)

BULKD


K
N
AREA
DIFC
TDIFC
CBDIF
MOLEWT
APFAC
BPFAC
WCFAC

Real


Real
Real
Real
Real
Real
Real
Real
Real
Real
Real
(lb/lb)
pounds per
cubic foot
(Ib/ft3)
-
_
acres (ac)


-

_
-
-
pounds per    kilograms per
pound (lb/lb) kilogram (kg/kg)
pound pesticide kilogram pesticide
per pound soil  per kilogram soil
                  (kg/kg)
                grams per cubic
                centimeter
                 (g/cc)


               hectares (ha)

               millimeters square
               per week (mm2/wk) +
               degrees Celcius
                                          grams per mole
                                           (g/mole)+
+only allowable input units
                              - 58 -

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Operational Parameters

     The operational parameters  are  employed  largely  in  the  MAIN
subprogram,   and   are   involved  with  real-time  and  input/output
operations of  the  Model.   These  parameters  are  included  in  the
namelists HYCL, PRNT, STRT, ENDD, and TRVL as shown in Table 13.  They
are essentially self-explanatory, and are not involved in calibration.
LANDS Parameters

     The LANDS subprogram parameters are included in  namelists  LND1,
LND2,  LND3,  and  LND4.   The  majority  of  the LANDS parameters are
explained in detail in Appendix A.  In fact, the only  parameters  not
described  in  Appendix A are the initial storage parameters UZS, LZS,
SGW, GWS, ICS, OFS, and IPS, which are defined in  Table  12.   Except
for UZS and LZS, the remaining storage parameters are generally set to
zero.   Since  these  parameters  refer  to  initial conditions, their
effect on long-term simulation is negligible.   The  initial  storages
UZS  and  LZS  are  directly  related  to  the surface response of the
watershed during the initial months of simulation.  Consequently their
values have a direct impact on surface runoff for three to six  months
after  simulation  begins.   Their  effect  in  the  long term is also
negligible.
     The values for UZS and LZS are generally close to  their  nominal
values,  UZSN  and  LZSN.   If simulation begins in a wet season their
values are either equal to or greater than the nominal values.  On the
other hand,  the  initial  storages  will  be  less  than  nominal  if
simulation begins in a dry season.
     Calibration  of  the  LANDS  subprogram  initially  involves  the
adjustment of the parameters  so  that  recorded  annual  and  monthly
runoff  volumes are simulated as closely as possible.  Once the volume
simulation is attained, simulated and recorded storm  hydrographs  are
compared.  Peak flow rates, timing, and hydrograph shape are the major
characteristics    investigated.    Appendix   A   contains   numerous
suggestions on the effects of varying the different  parameters.   The
interactions  of the 15 major parameters (excluding moisture storages)
are quite complex.  A detailed reading of Appendix A plus a number  of
calibration  trials  should provide the user with a certain 'feel1 for
the parameters involved.
Watershed and Pesticide Application Parameters

     This group of parameters is used throughout the PTR Model for the
special purposes  of  pesticide  application  nomenclature,  and  crop
cover.   Calibration does not involve  these  parameters  as  they are
completely defined.
     The namelist, PEST, includes parameters which apply to the amount
of pesticide applied (SSTR), the method of application (APMODE),   and
                              - 59 -

-------
the depth of the upper zone  and/or  the  soil-incorporated  pesticide
(DEPTH).   SSTR  is  programmed  to  allow  an  areal   variation   in
application  across  the five surface zones of the watershed.  Thus, a
uniform application of 100 pounds can be specified either as  'SSTR  =
20.,  20.,  20.,  20.,  20.' or 'SSTR = 5 * 20.' , within the namelist
format.  The DEPTH parameter specifies both the bottom  depth  of  the
upper  zone  and of the soil-incorporated pesticides.  Thus, a uniform
pesticide concentration throughout  the  upper  zone  is  assumed  for
soil-incorporated  pesticides,  and total upper zone moisture (UZS) is
employed when determining pesticide loss by interflow and percolation.
     The namelist CROP includes parameters which define  the  variable
interception  of  rainfall by the crop canopy over the growing season.
COVMAX defines the maximum percent of the watershed  area  covered  by
the  crop  canopy  when  fully  matured.  The parameters TIMST,  TIMAP,
TIMAT, and TIMHAR define the first day of simulation, and the days  of
application,  crop  maturity,  and harvesting, respectively.  The MAIN
program, which monitors the passage of real time,  provides  a  linear
increase  in  crop  cover  from  TIMAP  to TIMAT up to a maximum value
defined by COVMAX.  Crop cover remains at COVMAX until harvesting time
when it is returned to a value of zero.  The crop canopy affects  both
the  interception of rainfall in LANDS, and the soil splash production
of fines in SEDT.
SEPT Parameters

     The SEDT parameters are included within the namelist  SMDL.   The
only  experience  with  the  SEDT  parameters,  other than the subject
research effort, has been presented by Negev 19 .  His work was used as
a guide in calibrating  the  sediment  loss  model  to the experimental
watersheds.  Much additional research is needed into  the  sensitivity
of the SEDT parameters and their relationship to soil properties.   The
values  obtained  from  calibration  on  the  experimental  watersheds
provides some guidance for use of the model on watersheds with similar
characteristics.
     Calibration procedures for SEDT are similar to those  for  LANDS.
Recorded  annual and monthly volumes are compared  to simulated values.
The soil splash parameters, JRER and KRER, appear   to  have  the  most
significant  impact  on  the  seasonal  distribution of sediment loss.
Increasing  JRER  provides  a   greater   relative    impact   to   the
high-intensity  summer  storms  than  the  low-intensity long duration
winter storms.  KRER and SRERI, the initial surface  storage  of  soil
fines,  were  adjusted  so  that the surface fines  deposit did not get
progressively larger or smaller throughout the simulation period.   The
surface scour and pick-up parameters, JSER and  KSER,  have  a  direct
effect  on  sediment  loss  concentrations  during   the  storm events.
Adjustment of both JSER and KSER is required in order to reproduce the
rate of sediment loss recorded during storm events.
     In summary, the above guidelines are the  result  of  calibration
trials  on  the  experimental  watersheds  in  this  research  effort.

                              - 60 -

-------
Application  on  other  watersheds  will  require separate calibration
trials, and caution should be used when transferring calibrated values
from one watershed to another.
Pesticide Adsorption-Desorption and VOLDEG Parameters

     The parameters in this group need to be determined  largely  from
laboratory  experiments.   Consequently  very little adjustment during
calibration is justifiable.  Some variation in parameters for  natural
environmental  conditions  different  from  laboratory  conditions  is
expected.    However,  this  should  be  minimal  for  the  parameters
involved.
     The   pesticide   adsorption-desorption   parameters   used    in
subprograms ADSRB1, ADSRB2, and ADSRB3 are contained in namelist AMDL.
These  are described sufficiently in Tables 12 and 13.  CMAX and BULKD
can be obtained from experimental results or published  reports.   DD,
K,  and  N  must  be evaluated by an experimental determination of the
adsorption isotherm for the specific pesticide and  soil  system.   DD
would  be  the intercept at zero solution concentration, while K and N
describe the isotherm in terms of the Freundlich  equation  (  Section
V).
     The namelists, VOL1 and VOL2, include the parameters for  surface
and soil-incorporated pesticide volatilization.  The majority of these
parameters  must  also  be  determined  from laboratory experiments or
published reports.  For soil-incorporated pesticides,  the  parameters
DIFC,  TDIFC, and CBDIF define the temperature dependence of the total
diffusion coefficient as described in Section III.  For surface
applied  pesticides, APFAC and BPFAC define the temperature dependence
of the pesticide vapor pressure (Section  III ), while WCFAC  provides
a  calibration tool for the effect of wind movement on volatilization.
Since no data was available for calibration or verification  of  these
models,  they  were  not operated during calibration trials of the PTR
Model.  To bypass the volatilization models, DIFC (soil-incorporated),
and both APFAC and BPFAC (surface-applied) are set equal to zero.
     The namelist,  DEG1  contains  the  soil  degradation  parameter,
DEGCON,  which  defines the daily first-order degradation rate for the
pesticide.   DEGCON  was  calculated   by   applying   a   first-order
degradation  to  the  initial  application,  and determining that rate
which would  reproduce  the  amount  of  pesticide  remaining  on  the
watershed  as  determined  by the October 30 sampling analysis.  Thus,
the value is purely  empirical  and  is  used  to  allow  a  realistic
reduction  in  pesticide concentration on the watershed as a result of
degradation.
Conclusion

     Calibration  initially  involves  only   the   LANDS   and   SEDT
subprograms  (HYCAL=  +1)  until  an adequate simulation of runoff and
                               -  61  -

-------
sediment loss is obtained.    Then  pesticide  simulation  is  included
(HYCAL=  -1)  and a number of trials may be run for minute adjustments
in the pesticide parameters.   Finally production runs (HYCAL = 0)  are
performed  to obtain pesticide concentrations and amounts remaining on
the land surface.
     In  summary,  efficient   calibration  and  parameter   evaluation
requires a thorough knowledge of the operation of the PTR Model.  This
is  best acquired through repeated use of the Model and its components
under'varying climatic and edaphic conditions.  Additional  experience
on use of the model is necessary.
     The parameter values for the final  trials on the PI watershed are
shown in Table 14.  Separate  values for Paraquat and  Enide  are  also
included.  Section VIII presents the results obtained from application
of the PTR Model to the experimental watersheds.
          Table 14.  PARAMETER VALUES AND INITIAL CONDITIONS
                         FROM PI CALIBRATION
                           (English Units)
Parameter   Value
UZSN
LSZN
INFIL
INTER
IRC
NN
L
SS
A
KV
K24L
KK24
K24EL
K3
EPXM
APMODE
DEPTH
DEGCON

0.05
18.0
0.5
0.7
0.0
0.20
160.0
0.05
0.0
0.0
1.0
0.6
0.0
0.40
0.12
0.0
6.125
(P) 0.0001
(D 0.0109
                Parameter   Value
Parameter
Value
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
KSER
CMAX (P)
(D)
DD (P)
(D)
BULKD
K (P)
(D)
N (P)
(D)
AREA
0.60
182.0
182.0
274.0
334.0
3.0
0.09
1.0
1.5
0.00001
0.00026
0.0003
0.0
103.0
120.0
1.8
2.0
1.6
6.7
DIFC
TDIFC
CBDIF
MOLEWT
APFAC
BPFAC
WCRAC

Initial Conditions
UZS
LZS
SGW
GWS
ICS
OFS
IFS
SRERI
0.0*
1.0*
1.0*
0.0*
0.0*
0.0*
1.0*

Value
0.05
20.0
0.0
0.0
0.0
0.0
0.0
9.0
SSTR (P) 5x13.4
(D)
5x4.02
(P)
CD)
Paraquat
Diphenamid
    - These values are input to bypass  the volatilization models
                              - 62 -

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                             SECTION VII

           EXPERIMENTAL PROGRAM* AND MODELING METHODOLOGY
EXPERIMENTAL PROGRAM

     The  PTR  Model  development  was  conducted  in  support  of  an
experimental data gathering  program  at  the  U.   S.   Environmental
Protection Agency's Southeast Environmental Research Laboratory (SERL)
in  Athens,  Georgia.  The program was a joint effort between the SERL
and the U.  S.   Agricultural  Research  Service's  Southern  Piedmont
Conservation  Research  Center  (ARS-SPCRC)  in Watkinsville, Georgia.
Experimental watersheds at the SPCRC in Watkinsville (Figure 10)  were
instrumented  for the continuous monitoring and sampling of runoff and
sediment during the 1972 growing  season.   Samples  collected  during
storm  events  were  analyzed for pesticide concentrations at the SERL
laboratories.  A gas-liquid chromatograph was used  in  the  pesticide
analysis.
     The watersheds  are  located  on  the  Piedmont  Plateau  of  the
Southeastern   U.    S.    in   Oconee  County,  Georgia.    The  soils
classification is a Cecil  sandy  loam  with  high  acidity  and  clay
content  and  low organic matter content.  The moderate slopes, two to
six percent, provide  a  slight  to  moderate  erosion  hazard,  while
surface   runoff,  infiltration  and  water  capacity  are  considered
moderate.
     The 1972 program included two large watersheds (PI - 2.7  ha  and
P3 - 1.2 ha), two small runoff plots (SP1 (9x22 meters) and SP3 (26x39
meters)),  and  12  20x30  ft.  (6x9 meters) attenuation plots (Figure
10).  The experimental areas were planted with soybeans for  the  1972
growing  season.  The herbicides, paraquat (1,1'-dimethyl-4,4'-bipyri-
dinium ion),   diphenamid  (N,N-dimethyl-2,2-diphenylacetamide),   and
trifluralin (a,a,ortrifluoro-2,  6-dinitro-N,  N-dipropyl-p-toluidine)
were applied at ten, three and one pound per acre (11.2, 3.4, and 1.1)
kg/ha, respectively.  These herbicides were chosen to  facilitate  the
study  of  pesticide transport in the adsorbed phase (paraquat) in the
dissolved phase (diphenamid), and  discrete  particles  (trifluralin).
Runoff  and sediment samples were automatically collected during storm
events at short time intervals, and  refrigerated  on-site  for  later
analysis   at   the  SERL.   Core  samples  were  taken  and  analyzed
      Contact Dr. George W. Bailey at the SERL, Athens,  Georgia   for
      further details on the continuing experimental program.
                                   -63-

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I

cr>
                               Figure 10.    Location   of experimental  watersheds
                              Experimental
                               Area
                      GEORGIA
          SP1
  PI     (0.02 ha)
(2.71 ha)
                                                    (0.10 ha)1
                                                      SP3
                                                          2000
PESTICIDE
RUNOFF
Hyd
r o c o m p

-------
immediately after pesticide application and  periodically  during  the
growing  season  to  determine the extent of vertical movement and the
remaining volumes of the applied  pesticides.   Recording  rain  gages
were  established  at  each major watershed, and a weather station was
set up at  the  attenuation  plots  to  record  air  temperature,  pan
evaporation,   and  wind  data.   Also,  the  attenuation  plots  were
instrumented to record soil moisture and temperature at  various  soil
depths,  wind  velocity and direction, solar intensity, net radiation,
air temperature, and relative humidity at different heights above  the
soil  surface.   This data was automatically recorded on magnetic tape
by a PDP-8 computer.  The extensive data gathered during  the  program
is  to  be  included  in  the  EPA  STORET  data  system, available to
interested agencies.
MODELING METHODOLOGY

     Hydrocomp's support of the experimental program  involved  review
of  the  data gathering procedures and development of the PTR Model  to
simulate the recorded loss of  pesticide  from  the  watersheds.    The
guiding  philosophy  in  Hydrocomp's  modeling effort was to develop a
model capable of:

     (1) Simulating the pesticide lost from, and remaining on the land
     surface.

     (2) Reproducing the gross movement of pesticide within  the   soil
     profile.

     (3) Performing a mass-balance of the applied pesticide.

     (4) General application in various regions of the country.

     (5) Performing continuously for up to 1 or 2 years at  reasonable
     computer cost.

     Although  the  existing  Model  is  not  capable  of   completely
satisfying  all  of  these  goals, the basic framework of the Model  is
designed to perform these functions.
     The modeling approach was on an area-wide watershed  basis,   with
an attempt to 'piggyback1 the pesticide onto the movement of water and
sediment  from  the  land  surface.   Detailed  consideration  was not
directed to the microscale movement and  minute  interactions  of  the
pesticide within numerous layers of the soil profile.  The reasons for
this   approach   are   three   fold:    first,   variations  in   soil
characteristics, sediment movement, and pesticide  movement  across  a
watershed  are  too  complex to simulate in detail; second, a detailed
model  would  be  so  watershed-dependent  as  to  lose  its   general
applicability;  third,  a useful model must employ only parameters and
constants which are generally available  with  reasonable  effort  for
watersheds to which it is to be applied.

                              - 65 -

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    With this approach, the modeling effort was concerned largely with
the data collected on the larger watersheds, PI and P3.  Data from SP1
and   SP3   was   investigated   for    comparison    purposes,    and
hydrotneteorological  data  was  provided by the instrumentation at the
attenuation plots.  Also, historical rainfall and runoff data for  the
period 1940-44 for the ARS Wl (7.77 ha ) watershed at Watkinsville was
provided  by  the  SPCRC  for  calibration  purposes.  Calibration for
runoff and sediment was performed on the PI watersheds.  Results  from
simulation  on  the  Wl  watershed with the calibrated parameters were
used to check the validity of the PI calibration.  Both the PI and  P3
watersheds  were  run  with  the  calibrated  parameters  to determine
pesticide loss.  Since P3 is a terraced watershed, some deviation  was
expected  from recorded values of runoff, sediment and pesticide loss,
because  the  parameters  were  calibrated  on  the  PI   non-terraced
watershed.    The   results   of  the  simulation  runs  and  problems
encountered in calibration are discussed in Section VIII.
                              -  66

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                            SECTION VIII

                   PTR MODEL RESULTS AND DISCUSSIONS
GENERAL
     As in most research endeavors, the results obtained from the  PTR
Model  development  and  testing  were both good and bad.  A number of
questions have been answered but many  more  have  been  raised.   The
initial portion of this section discusses calibration trials on PI and
Wl,  and  presents  results  of  pesticide  trials  on PI.  Analytical
problems  in  obtaining  an  adsorption  isotherm   for   trifluralin,
prevented  simulation  of  that  herbicide.   The  results  of runoff,
sediment,  and  pesticide  trials  on  the  P3  watershed  using   the
parameters  calibrated on PI are provided.  Certain inconsistencies in
the recorded data are also discussed.  Finally, a  general  discussion
of the simulation results is presented.
PI WATERSHED RESULTS

Calibration - Runoff and Sediment Loss

     Seven  and  one-half  months  of  continuous  rainfall,   runoff,
sediment,   and   pesticide   residue  data  provided  the  basis  for
calibration of the PI watershed.  During this period, only four
significant runoff-producing events  occurred,  three  of  which  were
within  one  and  one-half months from the beginning of the simulation
period.   Generally,  calibration  is  performed  continuously  for  a
minimum of about two  years.   The  reasons  for  this  are  two-fold:
first,  two years of data will likely include both dry and wet periods
so that the hydrologic response can be calibrated under a  variety  of
soil  moisture  conditions;  secondly,  a  long  period of record will
overcome the effects of initial soil  moisture  and  surface  sediment
conditions which can have a significant effect for three to six months
into the calibration period.
     The available data for the PI calibration violated  both  of  the
above  conditions.   The  lack  of  a long period of data could not be
corrected.  Initially, the  starting  soil  moisture  conditions  were
obtained  by  simulating  the  three  month period prior to the July  1
starting date.  Rainfall data from the Athens Weather  Service  Office
                               - 67 -

-------
was  used.  Because of the variability of thunderstorms in Georgia and
resulting soil moisture conditions, the  results  of  the  three-month
simulation  yielded  starting soil moisture conditions much lower than
expected.  Consequently, the values  of  initial  soil  moisture  were
adjusted  in  the  final  calibration  trials  in order to reflect the
recorded runoff volumes.
     Table 14 in Section VI  presented the calibration  parameters  of
the  PI  watershed.   The  simulated  and  recorded monthly volumes of
runoff and sediment are shown in Figure 11 and Table  15,  along  with
recorded  rainfall  values.   Simulated  runoff  volumes are generally
within 10 percent of recorded values.   The  error  in  the  simulated
runoff  for January is partially the result of the January 7-8 storm
during which snowmelt occurred.   Also, rainfall data on the  storm  of
January  21-22 was received too late to be included in the simulation.
This would have an effect  on  antecedent  soil  moisture  conditions.
Simulated  sediment  volumes closely follow the recorded values except
for the month of December.
     Rainfall  intensity,  and  recorded  and  simulated  runoff   and
sediment  concentrations  for  the  four major storm events during the
calibration period are shown in Figures 12, 13, 14, and 15.  Simulated
peak  runoff  and ' sediment  concentrations  are  reasonably  close to
recorded values.  The high intensity,  short  duration  summer  storms
(Figures 12, 13, 14) appear to be more accurately simulated than   the
long low-intensity winter storm (Figure 15).  Note the time  variation
between the summer and winter storms.  For  the  summer  storms,  peak
runoff  occurs  within five minutes of peak rainfall intensity.  Also,
peak sediment concentration occurs immediately prior to  peak  runoff.
For  the  winter  storm,  the recorded hydrologic response is somewhat
sluggish with peak runoff occurring up to 15  minutes  following  peak
rainfall  intensity.   Also, detailed  sediment  concentrations    are
lacking at critical moments during the winter storm.  One would expect
the peak sediment concentration to  occur  between  22:30  and  22:10.
Unfortunately  no  sediment  values  were  recorded  during  this time
period.
     It should be noted that a weir pond formed in  front  of  the  PI
gage  during runoff events.  This pond likely had a significant effect
on recorded runoff and sediment  loss,  and  the  resulting  pesticide
loss.   The  storage of water provided by the pond would tend to delay
the rising portion of storm hydrographs and extend the recession  side
of  the  hydrograph.   The  recorded peak flow would be less than that
which actually occurred from the watershed.  The estimated  volume  of
the  PI  weir  pond  appears  to be sufficient to change the simulated
hydrograph of the July 28 storm (Figure 12) to closely approximate the
recorded hydrograph.
     The effect of the pond on sediment loss would be to:

     (1) more evenly distribute sediment concentrations recorded at
          the gage, and
     (2) allow larger sediment particles to settle out and not pass
          through the gage.
                               -  68 -

-------
F  160  -
              JULY   AUG.   SEPT.   OCT.    NOV.    DEC.   JAN.
                                                              FEB.
              JULY
                  AUG.
SEPT.  OCT.  NOV.   DEC.    JAN.     FEB.
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                  AUG.    SEPT.   OCT.   NOV.   DEC.
                             JAN.   FEB.
        Figure  11.   PI watershed:   monthly summary of  rainfall,  runoff,

                    and  sediment  loss,  (1972-73)
PESTICIDE
RUNOFF
Hyd
r o c o m p
                               - 69  -

-------
Table  15.   1972 SUMMARY OF RAINFALL, RUNOFF,  SEDIMENT
            AND PESTICIDE LOSS FROM THE PI WATERSHED
                    (RECORDED AND  SIMULATED)
    July
August
September     October     November     December     Total
Rainfall (mm)
Runoff (rum)
Recorded
Simulated
Sediment loss
Recorded
S imu lated
Diphenamid
On- water
Recorded
Simulated
On sediment
Recorded
Simulated
Total
Recorded
Simulated
Paraquat loss
On water
Recorded
Simulated
On Sediment
Recorded
Simulated
Total
Recorded
Simulated
55.6

14.7
13.3
(tonnes)
6.00
5.47

0.057
0.095
0.004
0.004
0.061
0.099
(kg)
0.001
0.0
1.211
1.145

1.212
1.145
54.4

12.7
10.8

3.79
3.28

0.018
0.005
0.003
0.000
0.021
0.005

0.0
0.0
0.883
0.664

0.883
0.664
36.8

0.1
0.4

0.01
0.05

0.000
0.000
0.000
0.000
0.000
0.000

0.0
0.0
0.001
0.051

0.001
0.051
59.9

0.2
1.9

0.03
0.28

0.000
0.000
0.000
0.000
0.000
0.000

0.0
0.0
0.001
0.055

0.001
0.055
91.4

0.1
1.0

0.00
0.07

0.000
0.000
0.000
0.000
0.000
0.000

0.0
0.0
0.000
0.013

0.000
0.013
216.4

22.0
23.9

0.42
1.73

0.000
0.000
0.000
0.000
0.000
0.000

0.0
0.0
0.021
0.329

0.021
0.329
514.5

49.8
51.3

10.25
10.88

0.075
0.100
0.007
0.004
0.082
0.104

0.001
0.0
2.117
2.257

2.118
2.257
                     -  70 -

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        Figure  12.   PI  watershed:   storm of July 28, 1972
                                             _  RECORDED

                                             --  SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
                            -  71  -

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         Figure 13.   PI watershed:   storm of July  31,  1972
                                                         1730

                                                        	 RECORDED

                                                        	 SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
                             -  72 -

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                                                                   2300
PESTICIDE
RUNOFF
Hyd
r o c o m p
                             - 74  -

-------
The existence of  the  pond  biased  the  calibrated  LANDS  and  SEDT
parameters,  but  the  extent of the effect is impossible to determine
with the existing data.
     In general, the calibration appears to adequately  reproduce  the
hydrologic  response  to  the high-intensity summer thunderstorms, but
overestimates both runoff  and  sediment  volumes  for  the  one  long
duration, low-intensity winter storm.
     To test  the  calibration  on  PI,  the  nearby  Watkinsville  Wl
watershed  (Figure  10)  was  run continuously for two years (1941-42)
with the PI parameters.  The  PI  parameters  will  not  apply  to  Wl
without  modification,  but  conclusions  can  be drawn about regional
stability of parameters and the problem  of  extending  parameters  to
ungaged  streams.   Simulated  and recorded monthly runoff volumes are
shown in Figure 16, while Figures 17 and 18 present the simulated  and
recorded  hydrographs  for  the  four major storms during the two year
period.  Although  monthly  volumes  are  underestimated  during  high
runoff  periods,  storm hydrograph shape and flow peaks are accurately
reproduced.  Since Wl is approximately three  times  larger  than  PI,
subsurface  flow  and  groundwater components are likely to contribute
more  significantly  to  runoff  on  the  Wl   watershed.    Parameter
adjustments  to  increase  these subsurface flows on Wl would increase
runoff volumes  (Figure 16) but would  not  significantly  change  peak
flows  (Figure  17 and 18).  The existence of subsurface components are
not reflected in the PI calibration parameters, as is typical of  very
small watersheds.  Also the PI infiltration characteristics are likely
to be somewhat  inaccurate because calibration period was extremely dry
and  too short  to perform a precise water balance.  In summary, the Wl
trial with the  PI  calibration  parameters  accurately  simulates  the
surface  hydrologic  response  but  is  somewhat inaccurate on monthly
runoff volumes  because of the data and conditions for calibration.  It
shows encouraging evidence of the ability to transfer parameters  that
control  the  surface  hydrologic  response which in turn controls the
transport of sediment and pesticide.
     The lack of detailed sediment loss data prevented any  comparison
of  simulated   and recorded values for the Wl watershed.  Estimates by
the Soil Conservation Service of annual soil loss from  a  135  square
meter  (1450  square  feet)  plot near the watershed amounted to 21.89
tonnes/ha /yr (9.75 tons/ac/yr) k6 .  Simulated annual loss  from  the
Wl watershed was 21.40 tonnes/ha /yr (9.53 tons/ac/yr).  Consequently,
simulated  volumes  appear  to  be  within  range  of expected values.
However, the validity of the simulated sediment concentrations and the
monthly distribution of sediment loss on Wl is unknown.   Redeposition
and  channel processes, which are neglected in the model, might become
a significant source of error on larger watersheds.
Pesticide Loss

     Monthly vi        ,
runoff   (water  and  sediment)  are presented  in Figure  19.  Paraquat,


                                 75 -
 Monthly values of paraquat and diphenamid  collected  in  surface
ff  (water  and  sediment)  are presented ii

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                       Figure  16.  Wl watershed:   monthly runoff volumes, (1941-42)
PESTICIDE
RUNOFF
Hyd
r o c o m p

-------
            1100      1120     1140      1200

                                   JULY 11, 1941
                            1220
                             1240
   1250
10

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   0.8  -
   0.6  _
   0.4  -
   0.2  -
             240
300
320       340       400

   MAY 15, 1942
    440
RECORDED

SIMULATED
        Figure  17.   Wl watershed:
        storms  of  July 11,  1941  and
        May  15,  1942
PESTICIDE
RUNOFF
Hydrocor
n p
                                       -  77 -

-------
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    1.2
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        2200   2220      2240      2300       2320      2340

                                   AUGUST  17-18, 1942
2400
0200
       Figure 18.  Wl watershed:

       storm of August 16 and 17, 1942
   RECORDED

   SIMULATED
PESTICIDE
RUNOFF
H y d r o c o
m p
                                    -  78 -

-------
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-------
quite predictably, closely follows the monthly  sediment  loss  values
presented  previously  in  Figure  11.   Again,  the  overestimate  of
sediment  loss in December is reflected in an overestimate of paraquat
loss.  The variations  in  diphenamtd   loss  are  more  difficult  to
explain.   The  first-order degradation function included in the Model
may possibly underestimate diphenamTd   degradation  during  July  and
overestimate  the  rate  in  later  periods; hence, the deviation from
recorded values.                                           ,
     Paraquat and diphenamid  concentrations on  runoff  and  sediment
during  the  three summer storm events are shown in Figures 20 and 21.
These were  the  only  major  events  for  which  extensive  pesticide
sampling  was performed.   The fluctuations of pesticide concentrations
recorded on sediment during storm events are essentially  unexplained.
The  pesticide concentrations do not correlate with variations in flow
or  sediment  concentration.   Also,  analysis  of  sediment   samples
collected  during  the  storm produced no obvious relationship between
pesticide concentration and clay content  or  particle  surface  area.
The  accuracy  of  laboratory  analytical  procedures corroborates the
existence of the  concentration variations.  Thus, the variations  must
result  from  the  complex interaction of the pesticide exposed to the
natural environment.
     Although  the  concentration  variations  are  unexplained  their
significance may  be  negligible.   The  loss  of  pesticide  from  the
watershed  is  demonstrated  more  clearly  by  the  mass  movement of
pesticide past the gage during the storm event.   Figures  22  and  23
show the rate of  sediment and pesticide loss on sediment (paraquat and
diphenamid)   for the July 28 and August 10 storms, respectively.   The
area under each curve represents  the  total  volume  of  sediment  or
pesticide  lost  during  the  event.   These  figures  demonstrate the
significance of sediment loss as a mechanism of  pesticide  transport.
This is especially true  for  paraquat,  but  is  also  important  for
diphenamid.   A closer simulation of sediment loss would have improved
the simulated pesticide loss.  The error in simulated diphenamid  loss
appears  to  be greater than the errors in sediment or paraquat.  This
possibly indicates inaccuracies in the pesticide adsorption model   and
may warrant further investigation.
     The rate of  diphenamid  loss on water is presented in  Figure  24
for  the  July  28  and  August  10  storms.   These figures should be
compared with the storm hydrographs of Figures 12  and  14.    Although
the  simulated  runoff  agrees  reasonably  well with recorded values,
substantial errors exist between recorded  and  simulated   diphenamid
loss  as  shown in Figure 24.  The amount of pesticide lost by surface
runoff is calculated  in  the  pesticide  adsorption-desorption  model
which  determines  the  division  between  the  adsorbed and dissolved
phases of the pesticide.   The discrepancies in Figure  24  demonstrate
that     further      investigation    and    modification    of    the
adsorption-desorption model is warranted.
                               - 80 -

-------
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                                                       „.   WATER
            2000     2010      2020     2030

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                                                  2040
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1730
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            2020      2030      2040       2050     2100

                                    AUGUST 10, 1972
                                                              2110      2120
                                                              RECORDED
                                                              SIMULATED
          Figure 21.  PI watershed:
          Diphenamid  in water and
          sediment during storm events
PESTICIDE
RUNOFF
Hydrocom
P
                             - 82  -

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             Figure 22.   PI watershed:
             rate of sediment and pesticide
             loss on sediment - July 28, 1972
          2050
             RECORDED
             SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
                                 -  83  -

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             Figure 23.   PI watershed:

             rate of sediment  and pesticide
             loss on sediment  - August 10,1972
                                                                        RECORDED

                                                                        SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
                                 - 84 -

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-------
 P3 WATERSHED RESULTS

 Simulation with PI Parameters

     The parameters calibrated  on  the  PI  watershed  and  partially
 verified  on  the  Wl watershed were used in simulation runs on the P3
 watershed.   Calibration  was  not  performed  separately  on  the  P3
 watershed because of data inconsistencies (explained below).   The  P3
 watershed  is terraced, has a smaller slope and a grass-lined channel,
 and is approximately one-half the size of the PI watershed.  Simulated
 and recorded monthly runoff and sediment loss is shown in  Figure  25,
 along  with  recorded  rainfall.   The simulation period extended over
 five months from July to November.  There is considerable  discrepancy
 between  recorded  and  simulated volumes of runoff and sediment loss.
 This discrepancy is also reflected in the monthly pesticide loss shown
 in Figure 26.
     Monthly runoff volumes are generally  underestimated,  while  the
 sediment   loss  values  are  reasonable,  except  for  the  month  of
 September.   Paraquat  loss  consistently  followed  the  volumes   of
 sediment  lost, while diphenamid  loss was overestimated in July as on
 the PI watershed.
Sources of Error and Data Problems

     A  number  of  possible  sources  of  error  could  explain   the
discrepancies  in the P3 simulation results.  The PI and Wl watersheds
are of similar shape and  both  experience  natural   drainage.   Their
average  land  slopes  are  similar  (0.05  for  PI, and 0.07 for Wl).
Although PI is one-third the area of Wl, the PI calibration parameters
behaved reasonably well on the Wl watershed.  The P3 watershed, on the
other hand, is a terraced watershed; it has a grass-lined channel  and
its  average  slope  is  0.03.   Consequently,  one  should expect some
variation in the hydro!ogic response of the two watersheds.  Both  the
Wl and P3 simulation runs  underestimated  volumes  during high runoff
periods.   For  the  Wl  watershed,   inaccuracies   in   infiltration
characteristics and the lack of a groundwater component were mentioned
as  possible  sources  of  error.  Although groundwater is unlikely to
significantly affect the P3 watershed,  an overestimate of infiltration
capacity could explain the discrepancies in runoff volumes.  Also, the
grass-lined channel could have a significant effect  on  runoff  timing
and sediment loss.
     Although high runoff volumes were  underestimated on both  the  Wl
and  P3 watersheds, the Wl simulated hydrographs accurately reproduced
recorded  values;  whereas,  the  P3  simulated   hydrographs   varied
considerably from the observed.  From the terracing  and smaller slope,
one  would  expect  less runoff to occur than simulated.  However, the
exact opposite occurred.  Thus, the hydrologic data   was  investigated
as  another source of error.  The P3 watershed is rectangular in shape
and is separated from the  surrounding   land  by  a   soil  berm.   The
                               -  86 -

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                                 AUG.   SEPT.  OCT.
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        Figure 25.   P3 watershed:

        summaries of rainfall, runoff,

        and sediment loss (1972)
                                                              RECORDED


                                                              SIMULATED
PESTICIDE
RUNOFF
Hydrocor
n p
                           -  87 -

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          JULY
                  AUG.  SEPT.   OCT.
       NOV.
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   0.4
GO
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          JULY    AUG.
                         SEPT.
OCT.  NOV.
        Figure  26.   P3  watershed
        monthly summaries  of
        pesticide  loss  (1972)
                                                                    RECORDED

                                                                    SIMULATED
PESTICIDE
RUNOFF
Hyd
r o c o m p
                                      - 88  -

-------
probability  is  small  that  subsurface  drainage  conforms  to  this
watershed   boundary.    Consequently,  both  surface  and  subsurface
drainage that crosses the boundary would introduce  substantial  error
in  the  recorded  runoff  data.   A  small  0.1  ha (0.25 ac) surface
drainage plot, SP3, is located adjacent to the P3 watershed.  Table 16
presents the recorded rainfall, runoff, and sediment loss for the  SP3
and  P3  watersheds.   It  can  be  seen that recorded depth of runoff
exceeds rainfall on SP3 for three storms (7/28/72,  8/10/72,  9/4/72).
This  can  only  occur  if  outside  drainage  is  entering SP3.  This
drainage likely originates  in  the  P3  watershed  which  is  located
upslope.    The  possibility  of  outside  drainage  crossing  the  P3
watershed boundary is another possible source  of  error  which  could
explain the discrepancies in simulated and recorded values.
     In summary, the  error  in  the  simulation  results  on  the  P3
watershed  is due to both surface characteristics not reflected in the
calibrated  parameters  and  inconsistences  in  the  recorded   data.
Further calibration trials and additional inspections of the watershed
will help to reduce these errors and improve simulation results.
DISCUSSION OF SIMULATION RESULTS

     In  spite  of  inconclusive  results  from   the   P3   watershed
simulation,   the   Model  results  from  the  PI  and  Wl  watersheds
simulations  provide  ample   evidence   of   the   capabilities   and
inadequacies  of  the  PTR  Model.   It  is  evident  that  a reliable
hydrologic  and  sediment  transport  simulation   is   paramount   to
simulating pesticide loss from agricultural lands.  The calibration of
the  PI  watershed  and  the  verification of the parameters on the Wl
watershed demonstrates that the LANDS subprogram can reliably simulate
surface runoff from agricultural watersheds.  Moreover, the calibrated
parameters are applicable to the  geographic  region  rather  than  to
specific  individual  watersheds.   Based  on surface runoff and input
rainfall, the SEDT subprogram estimates of monthly sediment  loss  are
reasonably  close  to recorded values.  However, simulated hydrographs
and  sediment  concentrations  appear  to  recede  more  rapidly  than
recorded during the storm events.  The weir pond formed  in  front  of
the PI gage may be partially responsible.
Also,  lack  of  experience  with  sediment  loss  calibration  and/or
inaccuracies in the basic algorithms could be partially at fault.
     The pesticide functions of adsorption-desorption, volatilization,
and degradation performed reasonably well for  Paraquat  but  were  in
error  when  simulating diphenamid  loss.  The reasons for this are as
follows:

     (1)  Volatilization  and  degradation   are   major   attenuation
mechanisms  for diphenamid  but are not significant for paraquat.  The
volatilization functions were  not  operated  during  simulation  runs
because of the lack of necessary parameters and verification data.
Degradation was assumed to be first order.
                               -  89  -

-------
                     Table  16.  RAINFALL, RUNOFF, AND SEDIMENT LOSS FOR SP3 AND P3 WATERSHEDS
o
I
                                                  SP3 Watershed
                                                    (0.10 ha)
P3 Watershed
  (1.25 ha)
Storm date
7/02/72
7/02/72
7/28/72
7/31/72
8/09/72
8/10/72
8/23/72
9/04/72
9/30/72
10/27/72
Rainfall
(mm)
19.05
6.10
21.84
11.43
8.13
10.92
17.27
49.28
12.70
35.56
Runoff
(mm)
6.43
0.51
38.35
7.87
2.08
13.56
11.30
75.18
0.64
16.69
Sediment loss
(kg/ha)
1121
113
5426
794
191
1157
327
1856
"
_
Runoff
(mm)
3.40
0.02
10.67
6.60
1.14
5.33
5.33
21.08
0.01
0.52
Sediment loss
(kg/ha)
454
2
611
395
25
140
134
350
-
_

-------
     (2) For degradable pesticides,  rates  of  degradation  are  high
during  the  first  days and/or weeks following application.  In later
periods, the rate usually decreases to essentially  constant  baseline
values.   The  first-order  rate  used  in the Model was determined by
calculating the daily  rate  which  would  allow  remaining  pesticide
levels  to  be approximately those determined by the soil core samples
of October 30, 1972.   This  first-order  rate  likely  underestimates
losses  immediately  after  application,  and  overestimates losses in
later periods.  This would introduce error into the monthly  pesticide
losses.

     (3) The attraction of paraquat to soil particles is such that the
entire paraquat application is adsorbed  in  the  surface  layer.   In
soils  with a high capacity for adsorption, the Model assumes that all
the paraquat is adsorbed  and  consequently  bypasses  the  Freundlich
adsorption curve.

     (4) Since diphenamid  is not permanently fixed to soil particles,
the distribution between adsorbed and dissolved phases  is  determined
by    the    Freundlich    adsorption    curve.     Errors    in   the
adsorption/desorption      parameters      and/or      the       basic
adsorption/desorption  algorithm  would  thus have a greater effect on
diphenamid  loss than on paraquat.  The effect of the adsorption model
on diphenamid  loss is shown most dramatically in Figures 22, 23,  and
24.   Although  recorded  and  simulated flow and sediment loss are in
substantial agreement, simulated diphenamid  loss  deviates  from  the
observed.

     The source-zone approach, described in  Section  V,  produced  an
areal variation in runoff, sediment and pesticide loss.  Tables 17 and
18  summarize simulation results for 1972 for paraquat and diphenamid,
respectively.  The Model predicts that 50% or more  of  total  runoff,
sediment,  and  diphenamide  loss  is  derived  from  20% of the total
watershed  area.   Paraquat  loss  is  more  widespread;  50   percent
originating  from approximately 30 percent of the watershed area.  The
variable loss of pesticide from  the  source-zones  also  produced  an
areal  variation  in pesticide remaining as shown in Tables 17 and 18.
The sampling on October 30 determined that the  remaining  amounts  of
paraquat and diphenamid were 29.96 kg(65.91 Ib) and 2.33 kg (5.13 Ib);
corresponding  simulated  values  were 28.23 kg (62.11 Ib) and 2.39 kg
(5.26 Ib).  This  close  agreement  is  a  result  of  the  calculated
degradation  constant.   The  main  emphasis  is  that  although total
volumes agree, the recorded and simulated  concentrations  across  the
watershed   deviate   considerably.   Since  concentration  is  highly
dependent on the mass of soil available, the depths of the soil  zones
in  the  Model  would have a major effect on simulated concentrations.
Additional work with a variety of  soil  zone  depths  would  help  to
determine the importance of this factor.
                              - 91 -

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         Table  17.   1972  SUMMARY:
   PARAQUAT SIMULATION ON
   PI  WATERSHED
   WATER,  INCHES

      PtECIPITATION
                              ZONE  1
                             20.2oO
                                        /ONE  2
                                       20.260
                                                  ZONE  3
         20.260
                                                            ZONE  4
                   20.260
                                                                      ZONE  5
                             20.260
                                                                                 TUTAL
                                        20.260
      RUNUFF
         OVKRL ANO_FLOW
         INTERFLOW
         IMPERVIOUS
         TOTAL
      tiRiJrt'ATER_RECHARGE

      EVAPORATION
         POTENTIAL
         NET

      STORAGES
         UPPER_ZONE
         LUdtR_ZONE
         GKOUNDWATER
         INTERCEPTION
         CWORLAND_FLOH
         INTERFLOW

      WATLR_BALANCE=   0.0011

   SEDIMENT,  TONS
      TOTAL  SEDIMENT LOSS
      FINFS  DEPOSIT
      IMPERVIOUS EROSION

PESTICIDE,  POUNDS

   SURFACE  LAYER
      ADSORBED
      CRYSTALLINE
      DISSOLVED

   UPPER ZONE LAYER
      ADSORBED
      CRYSTALLINE
      01 SSOLVtD

   LOWER ZONE LAYER
      ADSORBED
      CRYSTALLINE
      01 SSOLVED

   GROUNDWATER  LAYER
      ADSORBED
      CRYSTALLINE
      DISSOLVED
A. 275
1.2J2

5.478


1.417
0.804

2.221


0.683
0.576

1.259


0.360
0.372

0.732


0. lol
0.259

0.420


1.379
0.6
-------
          Table  18.   1972  SUMMARY:
                                               DIPHENAMID   SIMULATION  ON
                                               PI  WATERSHED
                    SUMMARY  FOR  1977
VATER.  INCHES

   PRECIPITATION

   RLNOFF
      CVECLAND.FLOW
      INTERFLOW
      IMPERVIOUS
      TOTAL

   BASE_Finw
   GRO*ATER_RECHARGE
                              ZONE  1
                             20.260
4.275
1.202

5.478
                                        ZONE  2
                                       20.260
                                     1.417
                                     0.804

                                     2.221
                                                  ZONE  3
                                                 20.260
                                                  0.683
                                                  0.576

                                                  1.259
                                                            ZONE 4
                                                           20.260
          0.360
          0.372

          0.732
                                                                      ZCNE 5
                                                                     20.260
          C.161
          0.259

          0.420
   HAT ER_ BALANCE*   0.0011

JEDIfENT.  TONS
   TOTAL SEDIMENT  LOSS
   FINES CEPOSIT
   IMPERVIOUS EROSION
4.069
0.063
                                     3.374
                                     0.759
2.325
1.807
1.456
2.677
0.748
3. 385
                                                                                TOTAL
                                                                               2C.260
           1.379
           0.643
           0.0
           2.022

           0.0
           5.241
EVAPORATION
POTENTIAL
NET
STORAGFS
UPPEP_ZCNE
LO^ £R_ZGNE
GRCUNDWATER
INTERCEPTION
OVEPLANO_FLOW
INTERFLOW

19.717
14.588

0.085
18.417
0.0
0.0
0.0
0.0

19.717
14.588

0.017
18.417
0.0
0.0
0.0
0.0

19.717
14.588

0.0
18.417
0.0
0.0
0.0
0.0

19.717
14.588

0.0
18.417
0.0
0.0
0.0
0.0

19.717
14.588

O.C
18.417
O.C
O.C
O.C
0.0

19.717
14.588

0.022
16.417
0.0
0.0
0.0
0.0
                                                                                11.971
                                                                                 8.691
                                                                                 0.0
PESTICIDE. PCLMDS
   SURFACE L AYFR
      CRYSTALL INE
      DISSOLVED

   LPPER ZONE LAYER
      ADSORBED
      CRYSTALLINE
      DISSOL VEO

   LChER ZONf LAYER
      ADSOSHFO
      CRYSTALLINE
      DISSOLVED

   CRCUNDWATFR  LAYER
      ATSORBED
      CRYSTALLINE
      DISSOLVED

   FESTICIDF  REMOVAL,  LBS.
      OVERLAP  FLOW  REMOVAL
      SEDIMENT  REMOVAL
      INTERFLOW REMOVAL
   PESTICIDE VOLATILIZATION LCSS, LBS.
      TCTAL
      FROM SURFACE
      FROM UPPER ZONE

   PESTICIOF DEGRADATION LOSS. LBS.
      TCTAL
      FRCM SURFACE
      FPCH UPPER ZONE
      FROM LCwFR ZONF

      PESTIC IDE_8ALANCe» -0.0484
0.000
0.000
0.0
0.0
0.419
0.425
0.0
0.000








.153
.148
>.005
.0
0.000
0.000
0.0
0.0
0.528
0. 533
0.0
0. 000








0.045
0.042
0.003
0.0
0.000
0.000
0.0
0.0
0.555
0.560
0.0
0.0








o.oie
0.017
0.002
0.0
0.000
0.000
0.0
0.0
0.563
0.569
0.0
0.0








0.010
0.009
0.001
0.0
0.000
0.000
0.0
0.0
0.568
0.574
0.0
0.0








0.005
0.004
0.001
0.0
0.001
0.001
0.0
0.0
2.633
2.662
0.0
0.001
0.000
c.o
0.0
0.0
0.0
0.0
c.o
0.0
0.231
0.220
0.011
0.0
                                                                              0.0
                                                                              0.0
                                                                              0.0
                                                                             17.223
                                                                              0.778
                                                                             16.445
                                                                              0.000
                                     -  93

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     Table 19 presents an approximate mass-balance of  the  pesticides
applied  to  the  PI  watershed.   Less  than 1 percent of the applied
diphenamid  was lost by runoff and sediment loss; almost 7 percent  of
the  paraquat  was  lost  by  these  mechanisms.   Since  paraquat  is
essentially  non-degradable,  the  discrepancy  in the mass-balance is
likely due to errors in the application equipment and/or  problems  in
obtaining   representative   samples   across   the   watershed.   For
diphenamid  the mass balance error in  Table  19  is  due  to  various
mechanisms   of  degradation.   Table  19  demonstrates  the  relative
importance of the various loss mechanisms for each of the pesticides.
     The  simulated  vertical  movement  of  the  pesticides  can   be
determined from Tables 17 and 18.  Paraquat remained completely in the
surface  zone,  whereas  diphenamid  was  moved  to  the upper zone by
percolating water.  The observed vertical movement of  the  pesticides
was much greater than simulated.  Paraquat was recorded at depths down
to  10  cm  (4 inches) or more, while diphenamid  was much more evenly
distributed and observed  at  even  lower  depths.   This  is  further
evidence  that  the  adsorption-desorption routine requires additional
investigation.
     In summary, the basic conclusions that can be observed  from  the
PTR Model results are as follows:

     1.  The PTR Model was developed  to  simulate  the  transport  of
pesticides  in  solution and on sediment.  The Model uses physically -
based   submodels   to   calculate   runoff   volumes   and   sediment
concentrations.  Initial model tests show good results  for  transport
of  pesticides  on  sediment and fair-to-good results for transport in
solution.

     2.  Surface  runoff  from  agricultural  lands  in  the  Southern
Piedmont can be simulated with reasonable accuracy with the PTR Model.
The  hydrologic  submodel  has been used extensively in other studies,
and past experience indicates that  similar  simulation  accuracy  for
runoff volumes can be expected in other geographical regions.

     3.  Simulation of monthly sediment loss  agrees  adequately  with
recorded volumes; however, sediment concentrations during storm events
vary  somewhat  from  the  observed values.  The general nature of the
sediment submodel shows promise of  applicability  to  other  regions,
although experience is limited at the present time.

     4.  The PTR Model has demonstrated the  capability  of  providing
reasonable   estimates  of  surface  runoff  and  sediment  loss  from
agricultural watersheds in the Southern Piedmont.   These  routes  are
the  major modes of transport of pesticides and other non-point source
pollutants to waterbodies.  Consequently, further  refinement  of  the
pesticide   functions   (adsorption/desorption,   volatilization,  and
degradation) will upgrade the capability of the model to  predict  the
pesticide  input  to  waterbodies from surface washoff.  Moreover, the
                               -  94  -

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         Table 19.  PESTICIDE MASS-BALANCE ON PI WATERSHED

                    ON OCTOBER 30, 1972  (kg)
                              PARAQUAT
                          DIPHENAMID
                          Field     Simulation     Field     Simulation
                       Measurement              Measurement
Application Amount*

Pesticide Lost by
 Runoff and Sediment

Degradation Loss**

Pesticide Remaining on
 Watershed

Mass Balance Error
30.45
2.097
_
-
1.915
0.356
9.14
0.082
_
-
0.104
6.690
29.96*

+1.607
28.23
 2.33*

-6.728
2.39
          * These values are affected by errors in equipment operation
            for application and problems of obtaining representative
            samples over the watershed.

         ** These values assume first order degradation with an empirical
            constant based on the pesticide remaining on the watershed  on
            October 30, 1972.
                               -  95 -

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PTR Model can provide the basis for the simulation of other  non-point
source  pollutants  (nutrients,  fertilizers, etc.), and thus estimate
the water quality of surface runoff from agricultural lands.

     5.  The loss of  paraquat  from  the  experimental   watershed  is
simulated   reasonably  well  by  assuming  complete  adsorption  onto
sediment particles.   Pesticides with  a  similar  attraction  to  soil
particles would likely produce similar results.

     6.  The single-valued (reversible) Freundlich adsorption isotherm
appears to be inadequate in simulating the division  between  adsorbed
and  dissolved  phases  of  diphenamid  in runoff from the watersheds.
This was also evidenced by the  inability  to  simulate   the  observed
vertical movement of the pesticides.

     7.  The observed variations in  pesticide  concentrations  during
runoff  events appears to be of little consequence in predicting total
pesticide loss; total mass movement of pesticide  (grams/minute)  past
the  gage  during  a  storm  event  is a more valid comparison between
simulated and observed pesticide loss.

     8.  Although simulated and recorded pesticide  amounts  remaining
on  the  watersheds   agreed reasonably well, concentrations within the
soil profile were in error.  The assumed depths of the soil  zones  is
largely   responsible  for  this  discrepancy  because  the  pesticide
concentration is dependent on the total mass of soil in  the zone.
                               -  96  -

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                             SECTION IX

                RECOMMENDATIONS FOR FUTURE RESEARCH
     The subject research effort has  produced  a  model  which  shows
considerable  promise  in  simulating  the  pesticide  contribution to
waterbodies by agricultural runoff.  The PTR Model, developed on small
agricultural watersheds, requires additional development  and  further
refinement  before  it can be utilized in the regulation of pesticides
released to the environment.  In addition  to  internal  improvements,
suggested  in  the  conclusions  of  Section I, complimentary research
needs to be conducted to support the Model, if it is  to  be  used  in
regulation.
     One philosophy of regulation might involve the  use  of  the  PTR
Model  to  estimate the fraction of applied pesticide that would reach
the aquatic environment.  Standards could be established denoting  the
maximum  amount (or concentration) of pesticide that could be accepted
by the aquatic environment without damage to  man  or  the  ecosystem.
Such standards in conjunction with the fractions obtained from the PTR
model  could establish a maximum allowable pesticide application rate.
To  develop  a  viable  methodology,  such  rates  would  have  to  be
determined  for  groups  of  pesticides  with  similar  toxicity   and
transport characteristics.  Also, the rates would need to be evaluated
under  varying edaphic and climatic conditions across the country.  In
water quality management, a 'control point1 or 'point of use1 is often
specified to establish standards  of  pollutant  concentrations  at  a
critical point in the watershed.  Similarly in pesticide regulation, a
control  point  would be necessary to specify at what point pesticides
leave  the  agricultural  field  environment  and  enter  the  aquatic
environment  where  substantial   harm  is  possible.   This  would  be
necessary for regulation purposes.  Intermittent streams,  topography,
and climatic conditions all contribute to the complexity of specifying
a  control  point  and the corresponding drainage area.  The PTR Model
would need to be run in various regions  in  order  to  determine  the
pesticide  fractions  for  these  control  size watersheds which would
likely vary across the country.   The pesticide fractions, control-size
watersheds, and  pesticide  standards  would  provide  the  basis  for
regulating pesticides releases to the environment.
     Other philosophies of regulation  might  emphasize  the  need  to
control   the   concentrations  of  pesticides  entering  the  aquatic
                               - 97  -

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ecosystem.   The  PTR  Model  could  be  used  to  estimate  pesticide
concentrations in  agricultural  runoff  from  certain  storm  events.
Extended  simulation  runs  of  20 or 30 years could provide frequency
distributions of pesticide concentrations, which, in turn, would  help
to  evaluate the risk of contamination.  Hopefully, methodologies such
as these would help  to  equitably  compromise  the  concerns  of  the
agriculturist  and  the  ecologist.  The use of pesticides needs to be
regulated to provide efficient pest control without undue harm to  the
environment.
     From the results and conclusions of the present research  effort,
and  considering future uses of the PTR Model in pesticide regulation,
the following recommendations are presented:

     1.  Continuous simulation of pesticide transport, as  opposed  to
static  steady-state  investigations,  has  been  shown  to be a valid
methodology for performing a materials balance of  pesticides  applied
to  agricultural  lands.   The dynamic nature of continuous simulation
allows the full accounting of:  (a) pesticides remaining on  the  land
surface,  (b)  pesticide  concentrations  and volume lost during storm
events, and (c) accumulated amounts of pesticide lost to  the  aquatic
ecosystem  during a growing season.  Consequently this approach to the
investigation of pesticide transport warrants further refinement.

     2.  An understanding of the  mechanisms  of  surface  runoff  and
sediment loss is paramount to the study of the importance of non-point
source  pollutants  on  water quality.  The PTR Model has demonstrated
the capability of representing these  mechanisms.   Consequently,  the
coupling  of  the  PTR  Model  with  additional pollutant attenuation,
adsorption, and degradation functions could provide the structure  for
modeling the transport of plant nutrients, fertilizers, animal wastes,
and  other  non-point source pollutants.  The effects of silvicultural
and agricultural management  techniques  on  water  quality  could  be
evaluated  through  the  PTR  Model  by  their effect on the transport
mechanisms of the non-point source pollutants.   Development  of  such
submodels  needs  to  be  undertaken  in  order  to  realize  the full
potential of the Model as a management tool.

     3.  For further refinement of  the  existing  PTR  Model,  future
research needs to be concerned with:
     a.  additional testing and calibration of the hydrologic model to
     more  accurately  evaluate  model  algorithms  and  land  surface
     parameters.
     b.  calibration and possible refinement of the sediment loss
     model to better reproduce recorded sediment concentrations and to
     gain  experience  with  the  sensitivity  of  the  sediment  loss
     parameters.
     c.  refinement and testing of the adsorption-desorption model  to
     better  determine the division between the adsorbed and dissolved
                              - 98 -

-------
     phases of pesticides which are transported on both  sediment  and
     water.  The inclusion of a nonsingle-valued adsorption-desorption
     model   warrants  further  investigation.   These  refinements are
     critical  to the reliable prediction of pesticide  lost  in  water
     and on sediment during storm events.
     d.   additional  development and testing of the volatilization  and
     degradation  models  on  actual  field  data, so that an accurate
     pesticide materials balance can be  performed.   The  effects  of
     environmental  factors  on   these   mechanisms   needs   to   be
     investigated.
     4.   To determine the general applicability of the PTR Model,  the
following tasks are  recommended:
     a.   Calibration and testing of the Model for runoff and  sediment
     loss on watersheds in various regions of the country.  This would
     allow  investigation  of changes in parameter values with varying
     soil and climatic  characteristics,  and  would  demonstrate  the
     behavior of the Model under varying conditions.
     b.   Evaluation  of model performance on watersheds ranging from 20
     to 200 hectaries in order to determine required  improvements  in
     the Model for larger watersheds.  This would provide insight into
     the effects of  channel processes on runoff and sediment loss, and
     demonstrate  the  efficacy  of  existing   model   algorithms  to
     simulate the hydrologic and erosion processes.

     5.   If the PTR  Model is to be considered as a tool for regulating
the  release  of  pesticides,  the  following   areas   need   to   be
investigated:
     a.   determination and definition of  control-size  watersheds  in
     various  regions  of  the country which would be most amenable to
     pesticide release regulations.
     b.    classification  and  grouping  of  pesticides  according  to
     toxicity, transport, and persistence characteristics
     c.   establishment of  maximum  seasonal  releases  of  pesticides
     within  each  classification  which  would  not  inflict  serious
     consequences  on  man  or the aquatic ecosyste.  The fractions of
     applied  pesticides  which  reach  waterbodies  could   then   be
     evaluated  by  the  PTR  Model and provide a basis for regulating
     pesticide releases.
                               - 99 -

-------
                              SECTION  X

                              REFERENCES
(1) The Pollution Potential   in   Pesticide   Manufacturing.    Pesticide
Study  Series  Volume  #5.   Environmental Protection  Agency,  Office  of
Water Programs, Washington,  D.   C.   June  1972.   p.  5-2.

(2) Integrated Pest Management.   Council   on   Environmental   Quality,
Washington, D.  C.   November 1972.   p.  3.

(3) Man's Impact on  the  Global  Environment.    Mass.    Institute   of
Technology.  Cambridge, Mass.   1970.   p.  127-128.

(4) Rudd, Robert L.  Pesticides  and  the Living  Landscape.   University
of Wisconsin Press, 1964.   p.   153.

(5) Carson, Rachel.  Silent  Spring.   New  York,  N.Y.    Fawcett   World
Library Press, 1962.  p.  304.

(6) Graham, Frank Jr.  Since Silent  Spring.  New York,   N.Y., Fawcett
World Library Press, 1970.   p.   288.

(7) Langham, Max R.,  Joseph  C.   Headley,  and  W.    Frank   Edwards.
Agricultural   Pesticides:     Productivity   and  Externalities.    In:
Environmental Quality Analysis,  Edited by  A.V.   Kneese  and B.    T.
Bower.  RFF, John Hopkins  Press,  1972.  p.   181-212.

(8) President's Council on  Environmental  Quality.   Washington, D.    C.
Annual Report, 1972.  p.  27.

(9) Laws and  Institutional   Mechanisms   Controlling  the  Release   of
Pesticides  into  the  Environment,  Pesticide Study Series  Volume #11.
Office of Water Programs,  Environmental Protection  Agency,  Washington,
D.  C.  1972.  p.  140.

(10)  The  Effects   of  Agricultural   Pesticides   in    the   Aquatic
Environment, Irrigated Croplands, San Joaquin Valley.   Pesticide  Study
Series, Volume #6.   Office  of Water  Programs, Environmental Protection
Agency.  Washington, D.  C.   June 1972.   p.  210-268.
                              -  100  -

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(11) Nicholson,  H.   P.   The  Pesticide  Burden  in  Water  and  its
Significance,  In:   Agricultural   Practices  and  Water Quality, Iowa
State University Press.  Ames, Iowa, 1970.   p.  183-193.

(12)  Pesticide  Use  on  Non-irrigated  Croplands  of  the   Midwest.
Pesticide  Studies  Series,  Volume  #4.    Office  of  Water Programs,
Environmental Protection Agency.  Washington, D.   C.  June  1972.   p.
5-D.

(13) Glossary - Water and Wastewater Control Engineering.  APHA,  ASCE,
AWWA, WPCF.  1969.  p.  168.

(14) Crawford, N.  H.  and R.  K.    Linsley.   Digital  Simulation  in
Hydrology;   Stanford   Watershed    Model  IV.   Department  of  Civil
Engineering, Stanford University.   Technical  Report  No.   39.   July
1966.  p.  210.

(15) Hydrocomp Simulation Programming:   Operations Manual.   Hydrocomp
Incorporated.  2nd Edition.  Palo  Alto, California.  1969.  p.   1-1  to
1-27, 3-5 to 3-16.

(16) Wischmeier, W.  H., and D.  D.  Smith.  Rainfall Energy  and  Its
Relationship  to  Soil  Loss.   Transactions  AGU.   April  1958.   p.
285-291.

(17) Gottschalk, L.C.   Reservoir   Sedimentation.   In:    Handbook  of
Applied  Hydrology,  V.T.   Chow  (ed.).   New York, N.Y., McGraw-Hill,
1964.  p.  17-27.

(18)  Wischmeier,   W.    H.,   and   D.     D.    Smith.    Predicting
Rainfall-Erosion  Losses  from  Cropland   East of the Rocky Mountains.
Agr.  Handbook No.  282.  USDA.  1965.   p.   47.

(19) Negev, M.  A Sediment Model on a Digital Computer.   Department  of
Civil Engineering, Stanford University.   Technical  Report  No.    76.
March 1967.  p.  109.

(20) Bailey, G.  W.  and  J.   L.    White.    Factors  Influencing  the
Adsorption,  Desorption,  and Movement of Pesticides in  Soil.   Residue
Reviews.  32:29-92, 1970.

(21) King, D.  H.  and P.  L.  McCarty.  The Movement of Pesticides  in
soils.  Presented  at  Purdue  Industrial  Wastes  Conference,   Purdue
University.  May 3-5, 1966.  p.  25.

(22) Edwards, C.  A.  Insecticide  Residues in Soils.  Residue Reviews.
13:83-132, 1966.

(23) Edwards, C.  A.  Persistent Pesticides in  the  Environment,  CRC
Critical Reviews in Environmental  Control, 1(1):   6-67,  February 1970.
                              -  101  -

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(24) Weber, J.  B., and S.   B.   Weed.   Adsorption  and  Desorption  of
Diquat,  Paraquat, and Prometone by Montmorillonite and Kaolinite Clay
Minerals.  Soil Sci.   Soc.   Amer.  Proc.   32:485-487.  1968.

(25) Weed, S.  B., and J.   B.  Weber.   The Effect of  Cation  Exchange
Capacity  on  the  Retention  of  Diquat   (+2)  and  Paraquat  (+2) by
Three-layer type Clay Minerals:  I Adsorption and Release.  Soil   Sci.
Soc.  Amer.  Proc.  33:379-382.  1969.

(26) Faust, S.   D.,   and   A.   Zarins.   Interaction  of  Diquat  and
Paraquat  with  Clay  Minerals and Carbon  in Aqueous Solution.  Residue
Reviews.  29:151-170.  1969.

(27) Davidson,  J.   M.,  and  J.   R.    McDougal.    Experimental  and
Predicted  Movement  of Three   Herbicides  in a Water-Saturated  Soil.
Journal of Environmental Quality,  p.  2:428-433. 1973.

(28) Van Genuchten, M.  Th.f J.  M.  Davidson and  P.   J.   Wierenga.
An  Evaluation of Kinetic  and Equilibrium Equations for the Prediction
of Pesticide Movement through Porous Media.  Soil Sci. Soc. Amer. Proc.
Jan.-Feb., 1974.  (Accepted for publication).

(29) Davidson, J.  M., R.   S.  Mansell, and D.  R.   Baker.   Herbicide
Distributions   Within  a   Soil  Profile   and  Their  Dependence   Upon
Adsorption-Desorption.  Soil  and  Crop  Science  Society  of  Florida
Proceeding.  1973.

(30) Hornsby, A.  G., and  J. M.   Davidson.    Solution  and  Adsorbed
Fluometuron  Concentration   Distribution   in   a  Water-Sautrated  Soil:
Experimental  and  Predicted  Evaluation.  Soil Sci. Soc.  Amer.  Proc.
Nov.-Dec., 1973.  (Accepted for publication).

(31) Pionke,  H.   B.   and  G.   Chesters.   Pesticide-Sediment-Water
Interactions.  Journal of  Environmental Quality.  2(l):29-45.  1973.

(32) Hall, J.  K., M.  Paulus,  and   E.   R.   Higgins.   Losses  of
Atrazine  in Runoff Water  and Soil Sediment.   Journal of Environmental
Quality.  1(2):172-176, 1972.

(33) The Fate of Pesticides Applied to  Irrigated  Agricultural   Land.
California  Dept.  of Water Resources,  The Resources Agency.  Bulletin
No.  174-1.  May 1968.  p.   30.

(34) Farmer, W.  J.,  K.   Igue,  W.   F.    Spencer,  J.   P.   Martin.
Volatility  of  Organochlorine   Insecticides   from   Soil:  I Effect of
Concentration, Temperature, Air Flow Rate, and Vapor  Pressure.    Soil
Sci.  Soc.  Amer.  Proc.  36:443-447,  1972.
                             - 102 -

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(35) Igue, K.   W.   J.    Farmer,  W.    F.    Spencer,   J.    P.   Martin.
Volatility  of  Organochlorine  Insecticides   from Soil:   II  Effect  of
Relative Humidity and  Soil  Water Content  on Dieldrin  Volatility.   Soil
Sci.  Soc.  Amer.   Proc.  36:447-450,1972.

(36) Mayer, R., J.  Letey,  W.    J.    Farmer.    Models  for Predicting
Volatilization   of   Soil-Incorporated   Pesticides.   (Accepted  for
publication in 1974 by Soil Sci. Soc. Amer. Proc.).

(37) Ehlers, W., J.  Letey, W.  F.   Spencer,  W.  J.   Farmer.   Lindane
Diffusion  in  Soils:   II Water Content,  Bulk Density,  and Temperature
Effects.  Soil Sci.  Soc.  Amer.  Proc.  33:505-508,  1969.

(38) Spencer, W.  F.,  M.  M.  Claith, W.   J.   Farmer.   Vapor Density
of Soil-Applied Dieldrin as Related  to Soil-Water Content,  Temperature
and   Dieldrin   Concentration.    Soil   Sci.   Soc.   Amer.   Proc.
33:509-511, 1969.

(39) Farmer, W.  J.  (Personal Communication).

(40) Farmer, W.  J.  (Unpublished Material).

(41) Spencer, W.  F.,  M.  M.  Claith, W.   J.   Farmer.   Vapor Density
of Soil-Applied Dieldrin as Related  to Soil-Water Content,  Temperature
and   Dieldrin   Concentration.    Soil   Sci.   Soc.   Amer.   Proc.
33:509-511, 1969.

(42) Farmer, W.  J., K.   Igue,  W.    F.    Spencer,   J.    P.   Martin.
Volatility  of  Organochlorine  Insecticides   from  Soil:   I  Effect  of
Concentration, Temperature, Air Flow Rate, and Vapor   Pressure.    Soil
Sci.  Soc.  Amer.   Proc.  36:443-447, 1972.

(43) Spencer, W.  F.  and M.  M.  Claith.  Vapor Density of  Dieldrin.
Envir.  Sci.  and Tech.  3:670-674,  1969.

(44) Spencer, W.  F.  and M.  M.  Claith.  Vapor Density and  Apparent
Vapor  Pressure  of  Lindane  ( BHC).  Journal of Agr.   and Food Chem.
18:529-530, 1970.

(45) Zindahl, R.  L.,  V.  H.  Freed, M.  L.  Montgomery,  and W.    R.
Furtick.   The  Degradation of Triazine and Uracil Herbicides in Soil.
Weed Research.  10(1);18-26, March 1970.

(46) Burschel, P.   and V.  H.   Freed.  The Decomposition of Herbicides
in Soils.  Weeds.   7(2):157-161, April 1959.

(47) Hamaker, J.  W.,  C.  R.  Youngson, and C.  A.   I.   Goring.    Rate
of  Detoxification  of 4-Amino-3, 5, 6-tricloropiralinic acid in Soil.
Weed Research.  8:46-57, 1968.
                              -  103  -

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(48) Carreker, J.  R., and  A.   P.   Barnett.   Rainfall  and  Runoff
Characteristics  on  a Small Watershed in the Southern Piedmont.  USDA
Soil Conservation Service.  SCS-TP-114.  Washington,  D.   C.   August
1953.  p. 16.

(49) Phillips, J.R.  The Theory of Infiltration; 1.  The  Infiltration
Equation and its Solution.  Soil Science 83:  345-375, 1957.

(50) Linsley, R.K.   M.A.  Kohler, and J.L.H.  Paulus.   Hydrology  for
Engineers New York, N.Y., McGraw-Hill, 1958.  p.  99 - 119.

(51) Barnes, B.S.  Discussion of Analysis of  Runoff  Characteristics.
Trans.   ASCE 105:106, 1940.
                              - 104 -

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                               SECTION XI



                               APPENDICES
                                                                Page



A.  Description of HSP LANDS	106



B.  PTR Model Sample Input Data	129



C.  PTR Model Listing	146
                                - 105 -

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                              APPENDIX A

                        DESCRIPTION OF HSP LANDS*
Figure 27 presents the flowchart of the HSP LANDS program.   The hydro-
logic processes and functions sketched are discussed in the following
sections.

INTERCEPTION

The first loss to which falling precipitation is subjected  is inter-
ception, or retention on leaves, branches and stems of vegetation.
Interception in any single storm is small in amount and is  not important
in flood producing storms.  However, in the aggregate interception  may
have a significant effect on annual runoff.

In nature, interception is a function of the type and extent of vegeta-
tion and, for deciduous vegetation, the season of the year.  In HSP,
interception is modeled by defining an interception storage capacity
EPXM+ as an input parameter.  All precipitation is assumed  to enter
interception storage until it is filled to capacity.  Water is removed
from interception storage by evapotranspiration at the potential  rate.
Evapotranspiration may occur even during rain so that after the storage
is filled there is a continuing interception equal to the potential eva-
potranspiration.

IMPERVIOUS AREA

Precipitation on impervious areas that are adjacent to or connect with
stream channels will contribute directly to surface runoff.  An input
parameter"1" A in HSP represents this "impervious" fraction of the total
watershed area.  Precipitation minus interception is multiplied by the
impervious area fraction to determine the impervious area contribution
to streamflow.
*This appendix is abstracted directly from the Hydrocomp Operations
Manual'b
+Input parameters appear in uppercase roman letters.
                                - 106 -

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Figure  27    Lands  flowchart
                                                            KEY
                    PRECIPITATION
                     POTENTIAL
                  EVAPOTRAN5PIRATION
PESTICIDE
RUNOFF
Hyd
ro c o m p
                      -  107

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The impervious area is usually a very small  percentage of the total
watershed, except in urban areas.  In rural  watersheds impervious area
does not contribute large amounts of runoff.  However, for the light
rains with relatively dry soil impervious area may be the sole contri-
bution of runoff to the stream.   During the calibration phase the im-
pervious area term is usefull  in reproducing these small runoff events.
It enhances the detailed understanding of the hydrologic process during
simulation.  In urban areas the  "impervious  area" term becomes very im-
portant.

Lakes, swamps, and reservoirs  create a special class of impervious area.
"Runoff" results from all of the precipitation that reaches these surfaces
and potential (lake) evaporation occurs continuously.  The parameter A
does not include the surface area of major lakes and reservoirs, but does
include the surface area of small ponds and  stream channels.

Calculations in the land surface phase of MSP are carried in  terms of
water depth (inches or millimeters) over a unit area.  Only at the
beginning of the channel phase are these depths multiplied by area to
derive actual volumes of runoff.  This system is used to allow investi-
gation of various amounts of impervious area or reservoir surface area
without altering the response  from pervious  areas.

INFILTRATION

The process of infiltration is essential and basic to simulation of the
hydrologic cycle.  Infiltration  is the movement of water through the
soil surface into the soil profile.  Infiltration rates are highly var-
iable and change with the moisture content of the soil profile, and in-
filtration is the largest single process diverting precipitation from
immediate streamflow.  Usually more than half of the water which in-
filtrates is retained in the soil until it is returned to the atmosphere
by evapotranspiration.  However, not all infiltrated water is permanently
diverted from streamflow.  Some  infiltrated  water may move laterally
through the upper soil to the  stream channels as interflow, and some may
enter temporary storages and later discharge into the stream  channels as
base or groundwater flow.

Water which does not infiltrate  directly into the soil moves  over the
land surface and is subject to delayed infiltration and retention in
surface depressions.  The delayed infiltration is introduced  by the
upper zone function.

The infiltration capacity, the maximum rate  at which a soil will accept
infiltration, is a function of fixed characteristics of the watershed-
soil type, permeability, land  slopes and vegetal cover; and of variable
characteristics - primarily soil moisture content.  Soils containing
clay colloids may expand as moisture content increases, thus  reducing
pore space and infiltration capacity.   The actual infiltration rate at
a point at any time is equal to  the infiltration capacity or  the supply
rate (precipitation minus interception plus  surface detention), which-
                               - 108 -

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ever is least.

Traditionally infiltration has been represented by an infiltration
capacity curve in which the capacity is an exponential function of time.
This is in accord with experimental evidence provided the supply rate
always exceeds the capacity.  Since supply rates are frequently less than
infiltration capacity, the variation of infiltration capacity is con-
trolled by accumulation of soil moisture and may not be described by any
smooth function of time.

Infiltration relationships used for continuous simulation must:

     (1)  Represent segment mean infiltration rates continuously.
          Since variable moisture supply rates preclude continuous
          functions of time, expressions for infiltration as a
          function of soil moisture content are used.  (Note:  A seg-
          ment is a portion of a watershed with uniform climatic and
          hydrologic characteristics.)

     (2)  Represent the area! variation in infiltration - the
          distribution of infiltration capacities that will exist
          at any time about the segment average.

To meet requirement (1) HSP uses a method based on infiltration equations
developed by Phillips^.

                 F = st3* + at                                     (14)

                 f - sf ^ + a                                     (15)
Where F is cumulative infiltration, f is infiltration rate, and t is time
The  letters a and s are constants that depend on soil properties.

If the constant a is small Eqns.  (14) and  (15) can be written:

                 fF=ii                                          (16)
                      2
Since s2/2 is constant, Eqn.  (16) continuously relates infiltration rate
to cumulative infiltration or infiltrated  volume.  This is the type of
relation needed in hydrologic simulation.

Equation (16) will apply only approximately to intermittent infiltration
when the moisture distribution  in the soil profile adjusts between rains.
Homogeneous soil is also assumed  but a decrease  in permeability as depth
increases is more common.  Therefore Eqn.  (16) is modified to:

                fBb = constant                                    0?)
                                  109 -

-------
where b is a constant greater than one.  Numerous trials have resulted in
adoption of b = 2 as a standard value in HSP.

The second requirement listed above, representation of areal variations
in infiltration capacity, has not normally been considered in applications
of the infiltration concept.  Areal variation results from differences in
soil type and permeability and from differences in soil moisture which in
turn result from differing vegetal cover, precipitation, and exposure to
evaporation.  It can be expected that the
exist from point to point in a watershed
distribution about a mean value (Fig. 28),
infiltration capacity curve (Fig.  29) is of interest as a basis for run-
off volume calculations.  The solid line sketched in Fig. 29 is plotted
from the example of an actual  frequency distribution sketched in Fig. 28.
 infiltration  capacities  that
 at  a  given  time  will  have some
.  The corresponding  cumulative
        o-
        LlJ
              RELATIVE  INFILTRATION  CAPACITY
      Figure 28.  Schematic frequency distribution of infiltration
                  capacity in a watershed

The shape of the cumulative frequency distribution that will apply in a
watershed at any time is impractical to determine, and for mathematical
simplification the dashed line in Fig.  29, corresponding to the dashed
frequency distribution in Fig. 28, is assumed in HSP.  The assumption of
a linear variation is reasonably well verified by the limited experi-
mental data that is available, and experience indicates that the assump-
tion yields satisfactory results.
                                -  110  -

-------
o

D_

O

2:
O
I—I
h-

-------
       t
       >-
       o
       
-------
in a given time interval.  Infiltration occurs and the variable soil
moisture storage LZS increases.  In the next time interval f will de-
crease since LZS/LZSN in Fig. 31 has increased.  The combination of
functions represented by Figures 30 and 31 simulates the complex time
and area! variation of infiltration over a segment of a watershed.
Since different parameters may be used in each segment of a watershed
large variations in topography and soil properties can be represented.
Simulation algorithms make infiltration a function of the supply rate
and vary the area contributing to runoff continuously.

INTERFLOW

Infiltration may lead to interflow, runoff that moves laterally in the
soil for some part of its path toward a stream channel.  Interflow is
encouraged by any relatively impermeable soil layers and has been ob-
served to follow roots and animal borrows in the soil.  Interflow may
come to the surface to join overland flow if its flow path intersects the
surface.  Fig. 30 is extended (Fig. 32) to represent interflow by adding
a line to represent transient infiltration for the interflow process.
The variable c is defined by:

                   c= INTERFLOW x  2(LZS/LZSN)                  (19)
an empirical equation that results in the variation with soil moisture
sketched in Fig. 33.  INTERFLOW is an input parameter that governs the
volume assigned to interflow.  INTERFLOW is shortened to INTER in the
PTR LANDS sub-program.

This simulation scheme makes interflow a function of the local infiltra-
tion rate and of soil moisture.  That is, the higher the soil moisture
the greater the fraction of infiltration which becomes interflow.  The
combination of interflow and infiltration functions yields a smooth
response to variations in moisture supply in any time interval.  Fig. 34
illustrates this response.

UPPER ZONE

Moisture that is not infiltrated directly will increase surface detention
storage.  The increment to surface detention calculated from Fig. 32
will either contribute to overland flow or enter upper zone storage.
Depression storage and storage in highly permeable surface soils are
modelled by the upper zone.  The upper zone inflow percentage £ is inde-
pendent of rainfall intensity but upper zone storage capacity Ts low.
Moisture is lost from the upper zone by evaporation and percolation to
the lower zone and groundwater storages.
                                - 113 -

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MOISTURE
 SUPPLY


 (INCHES)


       I
INCREMENT TO
SURFACE
DETENTION
                    INCREMENT  TO
                    INTERFLOW
                    DFTFNTTnN
                                                             INFILTRATION
                                                              CAPACITY

                                                               (INCHES)
Figure 32.   Cumulative  frequency  distribution  of infiltration  capacity
            showing  infiltration  volumes,  interflow and  surface  detention
    4.0
    3.0
    2.0
    1.0
    0.0
                                               c vs LZS/LZSN

                                               for INTERFLOW =1.0
            0.2   0.4   0.6   0.8   1.0   1.2   1.4   1.6   1.8  2.0

                LOWER ZONE SOIL MOISTURE RATIO (LZS/LZSN)
Figure 33.  Interflow c as a funtion of LZS/LZSN
                            -  114 -

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    00
    •z.
    o
    D_
    OO
    Q_
    OO
    O

    oo
    o
    O-
    o
    C_J
                                     INCREASE IN OVERLAND
                                     FLOW SURFACE DETENTION
                                     INCREASE IN INTERFLOW DETENTION
                    NET
                    INFILTRATION
                                     I  ' i   .   i   .   i   .
          0.2    0.4   0.6    0.8   1.0    1.2    1.4   1.6

                               MOISTURE SUPPLY


        Figure 34.   Components  of HSP response vs.  moisture supply
The following expressions  are  used  to calculate the response of the
upper zone storage.   The upper zone has a nominal capacity given by  the
input parameter UZSN.   The percentage? of a potential addition to over-
land flow surface detention that  is held in the upper zone is a function
of the upper zone storage  UZS  and the nominal capacity UZSN (Fig.  35).
When the ratio UZS/UZSN is less than two,

 £-=10°  i1-0- 
-------
The upper zone storage prevents overland flow from a portion of the
watershed depending on the value of the ratio UZS/UZSN, but since the
nominal capacity UZSN is small, the upper zone retention percentage de-
creases rapidly with early increments of accretion.
PERCENT OF THE INCREASE IN
SURFACE DETENTION
RETAINED BY THE UPPER ZONE
i— *
ro -p> cri co o
0 0 0 0 0 0






	 ^




-\





\
\




\
\
\




\.
0.5 1.0 1.5 2.0 2,5 3.
                  UPPER ZONE SOIL MOISTURE RATIO  (UZS/UZSN)


         Figure 35.  Surface detention retained in the upper zone
Percolation (PERC) occurs from the upper zone to the groundwater and
lower zone storages when the upper zone storage ratio UZS/UZSN exceeds
the  lower zone storage ratio LZS/LZSN.  This is calculated as

PERC = 0.1 x INFILTRATION xUZSN x [(UZS/UZSN) - (LZS/LZSN)]  3     (24)
where INFILTRATION is the infiltration level input parameter and PERC is
the  percolation rate in inches/hour.  Evapotranspiration occurs from the
upper zone storage at the potential rate of UZS/UZSN is greater than
2.0.  If UZS/UZSN is less than 2.0 the portion of the potential evapo-
transpiration (PET) that is satisfied by upper zone is given by
              ET (actual) = 0.5 (UZS/UZSN) xPET
(25)
Potential evapotranspiration that is not assigned to the upper zone is
passed to the lower zone.  The assignment from Eqn. 25 models direct
evaporation from near-surface soil.  Moisture loss from the lower zone
models transpiration by vegetation.

The use of a nominal rather than an absolute capacity for the upper zone
storage permits a smooth increase in overland flow rates as upper zone
storage increases.  If an absolute capacity were used, there would be an
                                - 116 -

-------
abrupt increase in overland flow when the capacity was attained.  Such
an abrupt change is not consistent with experience nor with the obser-
vation that a truly "saturated" state is rarely, if ever, observed.
Because of the use of a nominal capacity it is not possible to define
upper zone storage in any rigorous physical sense.  It is best viewed as
an input parameter representing moisture retention at and near the soil
surface.

OVERLAND FLOW

The movement of water in surface or overland flow is an important land-
surface process.  Interactions between overland flow and infiltration
need to be considered since both processes occur simultaneously.  The
variations in rates of infiltration described above allow overland flow
in areas with low infiltration while preventing overland flow in other
areas.  During overland flow, water held in detention storage remains
available for infiltration.  Surface conditions such as heavy turf or
very mild slopes that restrict the velocity of overland flow tend to
reduce the total quantity of runoff by allowing more time for infiltra-
tion.  Short, high intensity rainfall bursts are attenuated by surface
detention storage reducing the maximum outlow rate from overland flow.

A wide range of methods for the calculation of unsteady overland flow
were considered.  The only rigorous general methods for simulating un-
steady overland flow are finite difference techniques for the numerical
solution of the partial differential equations of continuity and momentum.
These methods have a major disadvantage for continuous simulation since
substantial amounts of computer time are needed.  In a natural watershed
there are area! variations in the amount of runoff moving in overland
flow because of areal variations in infiltration.  Average values must be
used in the calculations for the length, slope, and roughness of over-
land flow.  Hence the accuracy gained by using finite difference methods
for overland flow is subject to question because of the limitations on
the input data.

In HSP, overland flow is treated as a turbulent flow process.  Since
continuous surface detention was chosen as the parameter to be related
to overland flow discharge.  Using the Chezy-Manning equation, the
relation between surface detention storage at equilibrium cy, the supply
rate to overland flow i, Manning'sn and the length L and slope S of the
flow plane is

                   D  = 0.000818i°-6n °'6  L 1'6                   (26)
                                   O
Using the ratio of detention depth at any instant  to detention depth at
equilibrium  D£ as an index of the distribution of flow over the overland
plane, an empirical expression relating outflow depth and detention
storage which fits experimental data quite well is
                                - 117 -

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                     =     x
                                              e  ->
                                  (27)
Susti tuting Eqn. (27) in the Chezy-Manning Equation the rate of discharge
from overland flow in ft^/sec/ft is
              = 0.6  (  -
                                                             5/3
                                                                 (28)
             n                                           e
where  Deis a function of the current supply rate to overland flow and is
calculated from Eqn. (26).  During recession flow when D is less than
 D the ratio  D/De is assumed to be one.  HSP continuous!/ solves a
continuity equation
D=D]
A D  -  q~
                                                                 (29)
where At is the time interval used, D2 is the surface detention at the
end of the current time interval, D, is the surface detention at the end
of the previous time interval, A D is the increment added to surface
detention in the time interval, and q is the overland flow into the
stream channel during the time interval.  The discharge  q~is a function
of the moisture supply rate and of (D^ + D2) /2, the average detention
storage during the time interval (D in Eq. (28).

The system of equations can be solved numerically with good accuracy if
the time interval of the calculation is sufficiently small so that the
value of discharge in any time interval remains a small fraction of the
volume of surface detention.  Calculations of discharge from overland
flow are made on a 15-minute time interval, but shorter time intervals
can be used if required by the characteristics of the flow plane, or
if justified by the input data.

The overland flow calculations enter the delayed infiltration process
through the fact that any water remaining in detention at the end of an
interval is added to the rainfall minus interception of the next period
to give the supply rate for the infiltration calculations.  Overland
flow detention is an important part of the total delay time in runoff on
small watersheds.  Figures 36 and 37 illustrate the "fit" of the HSP
simulation of overland flow to experimental data.  Fig. 38 shows that on
a watershed of 0.26 square miles, overland flow simulation closely
approximates the actual outflow hydrograph indicating that overland flow
delay is much more important than channel storage in controlling hydro-
graph shape.  Fig. 39 shows a similar comparison for a watershed of 18.5
square miles which is partly urbanized.  Here, the overland flow effects
on hydrograph shape are relatively small although the effect through
delayed infiltration is still present.
                                - 118 -

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C/5
UJ
   4.0
   3.0-
2.0.
   1.0-
           	 SIMULATED OVERLAND FLOW (WATERSHED MODEL)
           	 SIMULATED OVERLAND FLOW (MORGALI)
           	- OBSERVED OVERLAND FLOW
                                            SURFACE - TURF
                                            RAINFALL - 3.60 in/hr
                                            S = 0.04
                                            L = 72 ft.
      01   2  3  4  5  6  7  8  9  10 11 12 13 14 15  16 17 18
                           TIME (MINUTES)
                  Figure 36.  HSP Overland flow simulation
   4.0-
   3.0-
 oo
 UJ
   2.0"
 u_
 o
   i.o-
                SIMULATED OVERLAND FLOW  (WATERSHED MODEL)
                SIMULATED OVERLAND FLOW  (SCHAAKE)
                OBSERVED OVERLAND FLOW
                                         SURFACE  - CRUSHED SLATE
                                         RAINFALL- 3.68  in/hr
                                         S  -  0.04 ft./ft.
                                         L  -  72 ft.
      Figure 37,
                 6    8    10   12   14   16  18  20  22
                         TIME (MINUTES)
                HSP  Overland  flow simulation
                                                         24   26   28
                                - 119 -

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INTERFLOW STORAGE

The calculation of an increment to interflow detention storage SRGX was
described in Sec. 1.5.  Outflow from this storage to the stream is
calculated on a 15-minute time interval by the equation


                  INTF = LIRC4 xSRGX                             (30)

where


                  LIRC4 = 1.0  -  (IRC)1/96                      (31)


IRC, an input parameter, is the daily recession constant for the inter-
flow discharge at any instant to the interflow discharge twenty-four
hours earlier.

LOWER ZONE AND GROUNDWATER STORAGE FUNCTION

This function operates on the direct or immediate infiltration (Fig. 32)
and the percolation from upper zone storage (PERC in Eqn.  24).  The
available water is divided between the lower zone or soil  moisture
storage and the groundwater storage.  The division is based on the lower
zone storage ratio LZS/LZSN where LZS is the quantity of moisture in the
lower zone storage and LZSN is the lower zone nominal capacity.  The
percentage of the infiltration plus percolation that enters groundwater
storage (Fig. 40) is given by

                     p  =  inn  .k^S-     (    1>Q	}Z         (3?)
                     Kg    IUU  LZSN    (    1.0 + z  '          l^;

when LZS/LZSN is less than one and by

                                                      z
                     P   =  100   (l.O- (  i o+ z  )  }         (33)
                      y           v.          *           j
when LZS/LZSN is greater than one.  z is defined by
                     z = 1.5
LZS    -  1.0
+ 1.0        (34)
                                 LZSN

These relationships are plotted in Fig. 40.

LOWER ZONE STORAGE

The lower zone storage is the main moisture storage for the land surface
in HSP.  Like the upper zone storage it is defined in terms of a
nominal capacity LZSN, the storage level at which half of the incoming
infiltration enters the lower zone and half moves to groundwater.  This
use of a nominal rather than an absolute capacity serves the same
                                - 121 -

-------
      100
    CD
    
-------
Figure 41.  Groundwater flow
                            The outflow from active ground-
                            water storage at any time is
                            based on the simplified model  in
                            Fig.  41.  The discharge of an
                            aquifer is proportional to the
                            product of the cross-sectional
                            area  and the energy gradient
                            of the flow.  A representative
                            cross-sectional area of flow
                            is assumed proportional to the
                            groundwater storage level
                            computed by HSP.

                            The energy gradient is estim-
                            ated  as a basic gradient plus
                            a variable gradient that depends
                            on groundwater accretion.  The
                            groundwater outflow GWF at any
                            time  is given by
                      GWF = LKK4  x (1.0 + KV  GWS) x  SGW
                                                      (35)
where GWS is
variable GWS
storage and
 groundwater slope and SGW is
 is an antecedent index based
is calculated daily as
groundwater storage.  The
on inflow to groundwater
             GWS = 0.97 (GWS + inflow to groundwater storage)    (36)
Groundwater outflow is calculated on 15-minute intervals.  The identifier
LKK4 is defined as
             LKK4 = 1..0 -  (KK24)1/%
                                                     (37)
where KK24 is the minimum observed daily recession constant of ground-
water flow, the ratio of current groundwater discharge to the ground-
water discharge twenty-four hours earlier.  When the parameter KV is
zero and inflow to groundwater storage is zero, Eqn. (35) reproduces the
commonly used logarithmic depletion curve, i.e., the flow after a period
of n days decreases by (KK24) , and a semi-logarithmic plot of discharge
vs. time is a straight line.

KV is introduced to allow variable groundwater recession rates.  When
KV is non-zero a semi-log plot of discharge vs. time is not linear.  For
example, if the typical  daily dry-season recession rate in a stream is
0.99 and a recession of 0.98 is more typical when groundwater storages
are being recharged, the value of KK24 can be set to 0.99 and the value
of the parameter KV can be adjusted so that 1.0 + KVxGWS will reduce the
effective recession rate to 0.98 during recharge periods.  This added
flexibility in groundwater outflow simulation, introduced at the cost of
                                - 123 -

-------
an additional input parameter, is useful in many watersheds.

EVAPOTRANSPIRATION

The volume of water that leaves a watershed as evaporation and trans-
piration exceeds the total  volume of stream flow in most hydrologic
regimes.  Continuous estimates of actual evapotranspiration must there-
fore be made by HSP.  There are two separable issues involved in esti-
mating actual evapotranspiration.  Potential evapotranspiration must be
selected, and actual evapotranspiration must be calculated as a function
of moisture conditions and  the potential evapotranspiration.

Potential evapotranspiration is assumed to be equal to lake evaporation
estimated from U.S. Weather Bureau Class A pan records50.   This procedure
is more convenient than an  approach based on meteorological data since
input requirements are less stringent.   A single variable, adjusted pan
evaporation data, serves a  purpose that would otherwise require input of
several variables.  If pan  evaporation  data are not available, the input
data for potential evapotranspiration may be estimated by  an appropriate
method.  The relationship of actual evapotranspiration to  potential evapo-
transpiration over large areas should logically be a function of moisture
conditions.  Even if transpiration from vegetation is independent of soil
moisture until the wilting  point is reached, variable soil moisture will
cause wilting in some parts of a watershed but not in others.  Evaporation
from soil, a component of the total process, is dependent  on moisture
conditions.

When near surface storage is depleted,  the concept of evapotranspiration
opportunity is defined as the maximum quantity of water accessible for
evapotranspiration in a time interval at a point in the watershed.  It is
analogous to infiltration capacity and  would have a cumulative distri-
bution similar to that in Fig. 29.  The cumulative evapotranspiration
opportunity curve will be a function of watershed soil moisture conditions,
and will give estimates of  actual evapotra-nspi ration for any quantity of
potential evapotranspiration, just as the cumulative infiltration
capacity curve estimates net infiltration for any moisture supply.

Evapotranspiration occurs from interception storage at the potential
rate.  Evapotranspiration opportunity controls evapotranspiration from
the lower zone storage.  Evaporation from streams and reservoir surfaces,
and evapotranspiration from groundwater storages is also simulated.
Daily lake evaporation or potential evapotranspiration data, or average
daily rates for semi-monthly periods are used as input. HSP computes
hourly values from the daily totals using an empirical diurnal variation.

Potential evapotranspiration will result in a water loss or actual
evapotranspiration only if  water is available.  HSP first  attempts to
satisfy the potential from  interception storage and from the upper zone
in that order.  The contribution to actual  evapotranspiration of the
upper zone is limited if UZS/UZSN is less than 2.0 (Eq. 25).  Any
remaining potential enters  as E  in Fig. 42.  Since evapotranspiration
                                - 124 -

-------
opportunity in a watershed on a given day may be expected to vary
through a considerable range, a cumulative frequency distribution
similar to those found for infiltration capacity in Fig. 30 might be
reasonable.  Following the assumption made for  infiltration capacity
cumulative frequency distribution  of evapotranspiration opportunity
assumed to be linear (Fig. 42)
                                                                     the
                                                                    is
   Q_
   oo

   <=£ 00
   O
   D-
   O
   C_
                             EVAPOTRANSPIRATION
                                                              O
                                                               m
                                                               GO
                                                                  O  -a
                                                                  -o  o
                                                                  -a  —i
                                                                  o  72
                                                                  ;o  3=-
                                                                  —I  ^.
                                                                  d  GO
                                                                  z  -a
                    25
                                 50
75
100
          PERCENT OF AREA WITH A DAILY EVAPOTRANSPIRATION
          OPPORTUNITY EQUAL TO OR LESS THAN THE INDICATED VALUE

         Figure 42.  Potential and actual evapotranspiration
The quantity of water lost by evapotranspiration from the lower zone
when Ep Is less than  ris given by the cross-hatched trapezoid of Fig. 42.
The variable r is an index given by
                           0.25
                       170 - K3
                                  -) x  (-
                                           LZS
                                          LZSN
                                                                     (38)
Evapotranspiration is further limited when K3 is less than 0.5.  A
fraction of the segment area given by 1.0-2xK3 is considered devoid of
vegetation that can draw from the lower zone storage.  K3 is an input
parameter that is an index to vegetation density.

PARAMETER EVALUATION

The process of applying LANDS to a watershed requires a fitting or
calibration of parameters for the specific watershed.  Some parameters
are measured directly from topographic maps, or are easily found by
conventional hydrologic procedures.  Other parameters are established by
computer runs.  Numerical values for the LANDS parameters are required
for each simulation trial.  Methods for estimating these parameters
follow:
                                  -  125 -

-------
     A:
  EPXM:
  UZSN:
  LZSN:
    K3:
K24L,
K24EL:
            A is the fraction representing the impervious area in a
            segment.  Usually A will  be neglible for agricultural
            watersheds, except in cases of extensive outcrops along
            channel  reaches.

            This interception storage parameter is a function of cover
            density.
                 Grassland
                 Forest cover (light)
                 Forest cover (heavy)
                                               0.10 in.
                                               0.15 in.
                                               0.20 inc.
            The nominal  storage in the upper zone is related to LZSN and
            watershed topography.

                    Low  depression storage,
                    steep slopes,  limited
                    vegetation                        O.OSxLZSN

                    Moderate depression storage
                    slopes and vegetation             O.OSxLZSN

                    High depression storage,
                    soil fissures, flat
                    slopes, heavy  vegetation          0.14xLZSN

            The nominal  lower zone soil moisture storage parameter is
            related to the annual  cycle of rainfall  and evapotranspiration.
            Approximate  values range from 5.0 to 20.0 inches for most of
            the continental U.S. depending on soil  properties.   The
            proper value will need to be checked by computer trials.

            Index to actual evaporation.  Values range from 0.25 for open
            land and grassland to  0.7-0.9 for heavy forest.  The area
            covered by forest or deep rooted vegetation as  a fraction of
            total watershed area is an estimate of  K3.

            These parameters control  the loss of water from near surface
            or active groundwater  storage to deep percolation and trans-
            piration respectively.  K24L is  the fraction of the ground-
            water recharge that percolates to deep  groundwater table.
            Thus a value of 1.0 for K24L would preclude any groundwater
            contribution to surface runoff.   K24EL  is the fraction of
            watershed area where shallow water tables put groundwater
            within reach of vegetation.

INFILTRATION:   This parameter is also a function of soil characteristics.
            As for LZSN, approximate or initial values will need to be
            checked by computer trails.  INFILATRATION can  range from
            0.01 to 1.0  in/hr depending on the cohesiveness and permeabi-
            lity of the  soil.
                             - 126 -

-------
INTERFLOW: This parameter alters runoff  timing, and is closely related
           to  INFILTRATION and LZSN.  Examples of its effect are dis-
           cussed below.

        L: Length of overland flow is obtained from topographic maps
           and approximates the length of travel to a stream channel.
           Its value can be approximated by dividing the watershed area
           by  twice the length of the stream channel.

       SS: Average overland flow slope is also obtained from topographic
           maps.  The average slope can  be estimated by superimposing a
           grid pattern on the watershed, estimating the land slope at
           each point of the grid, and obtaining the average of all
           values measured.

       NN: Manning's n for overland flow.  Approximate values are:
                    Asphalt
                    Packed Clay
                    Turf
                    Heavy Turf and
                        Forest Litter
                                 0.014
                                 0.03
                                 0.25

                                 0.35
IRC; KK24: These parameters are the interflow and groundwater recession
           rates.  They can be estimated graphically^! or found by tria'
           from simulation runs.
              IRC =
             KK24 =
           Interflow discharge on any day
           Interflow discharge 24 hours earlier

           Groundwater discharge on any day
           Groundwater discharge 24 hours earlier
                 (39)
                                                                 (40)
       KV:
The parameter KV (Eqn. 35) is used to allow a variable
recession rate for groundwater discharge.  If KV = 1.0 the
effective recession rate for different levels of KK24 and
the variable groundwater slope parameter GWS is as follows:
                                          GWS
                   KK24    0.0
                          0.5
1.0
2.0
0.99
0.98
0.97
0.96
0.99
0.98
0.97
0.96
0.985
0.97
0.955
0.94
0.98
0.96
0.94
0.92
0.97
0.94
0.91
0.88
CALIBRATION

Among all of the parameters used in HSP, LZSN, INFILTRATION and INTER-
FLOW are three that are not clearly defined from physical watershed
characteristics.  LZSN and INFILTRATION affect both runoff volumes and
runoff timing.  INTERFLOW effects only runoff timing.
                                - 127 -

-------
   (a)
   (b)
   (c)
   (d)
        UJ
        CJ3
        a:
        
-------
LZSN and INFILTRATION:  The nominal  lower zone storage and the infiltr-
ation index control runoff volumes,  if  the parameter UZSN is based on
LZSN.  Runoff volumes over extended  periods are governed by the water
balance equation:

     Precipitation - Actual Evapotranspiration - Deep percolation
                       = Streamflow                              (41)

Thus when deep percolation is small  and precipitation is known, actual
evapotranspiration must be changed to cause a change in long-term run-
off volume   Increasing LZSN will increase actual evapotranspiration
loss, and decreasing LZSN will reduce actual evapotranspiration loss.
The parameter INFILTRATION is also directly related to actual  evapotrans-
piration.  Lowering INFILTRATION will usually reduce actual  evapotrans-
piration.

When correct annual runoff volumes are obtained, the seasonal  distribu-
tion of runoff should be checked using the monthly summaries  from the
LANDS load module.  INFILTRATION parameter adjustments are effective in
altering groundwater recharge for improved seasonal distribution.   When
detailed hydrographs from the CHANNEL load module are available the
INFILTRATION parameter can be closely checked.  Fig. 43 shows  the possible
results.   Cases (a) and (b) indicate that an increase and decrease
respectively is in order for the INFILTRATION parameter.   Cases (c) and
(d) give conflicting indications and unrepresentative input  data  is
likely.   These cases should be ignored in the fitting process.

Trial runs are used to determine if cases (a) and (b) predominate  during
the calibration period and the INFILTRATION parameter is  adjusted
accordingly.   Note that Fig.  43 (a) and (b) illustrate the effect  of
INFILTRATION on groundwater flows.   Groundwater flow is a very  useful
indicator of infiltration levels.

INTERFLOW:   The interflow parameter can be used effectively  to  alter
hydrograph shape after storm  runoff volumes have been correctly adjusted.
INTERFLOW has  no effect on runoff volumes.   Its effect is  illustrated
in Fig.  44 for which values of INTERFLOW were (a)  1.4,  (b) 1.8, (c)  1.0.
                         TIME
Figure 44.  Example of the response to the INTERFLOW parameter
                              - 129 -

-------
Appendix   B


//PESTICID JOB (0510,510,15,30),'TONY.SP1.PARAQUAT'
/* SERVICE CLASS=B, BLOCK=W
//JOBLIB DD DSNAME=C510.TONY.PEST10,DISP=(OLD,KEEP),
//       UNIT=2314,VOL=SER=SYS13
//STEP1 EXEC PGM=PEST
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT05F001 DD *
 &HYCL
 &PRNT
 &STRT
 &ENDD
 &TRVL
HYCAL= 0,  HYMIN=0.001, UNIT=-1, INPUT—I &END
PRINT= 1 &END
BGNDAY=1, BGNMON=75
ENDDAY=15.
INTRVL=5
          BGNYR=1972  &END
  ENDMON=2, ENDYR=1973 SEND
SEND
&LND1 UZSN=0.05, LZSN=18.0, INFIL=0.5, INTER=0.7 &END
&LND2 IRC=0.0, NN=0.20, L=160., SS=0.05, A=0.00, UZS=0.05 &END
&LND3 LZS=20.0, SGW=0.0, GWS=0.0, KV=0.0, K24L=1.0, KK24=0.6 &END
&LND4 ICS=0.0, OFS=0.0, IFS=0.0, K24EL=0.0, K3=0.40, EPXM=0.12 &END
&PEST SSTR=5*13.4, APMODE=0, DEPTH=6.125 SEND
&NAME PNAME= ' PARAQUAT ', WSNAME= ' PI ' &END
&CROP COVMAX=0.60,TIMST=182. ,TIMAP=182. ,TIMAT=274. ,TIMHAR=334. &END
&SMDL JRER=3.0, KRER-0.09, JSER=1.0, KSER=1.5, SRERI=9.0 SEND
&AMDL CMAX=0. 00001, DD=0.0003,BULKD= 103. 0,K=120. ,N=2. ,AREA=6.7 &END
&VOL1 DIFC=30.0, TDIFC=30.0, CBDIF=0.11 &END
&VOL2 MOLEWT=335., APFAC=0.0, BPFAC=0.0, WCFAC=1.0 &END
&DEG1 DEGCON=0.0001
EVAP72
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27
27
43
41
41
70
43
119
54
54
54
54
54
59
108
124
103
0
49
11
11
11
65
59
97
32
176
410
252
44
63
32
139
57
0
132
63
76
69
189
63
76
151
265
277
69
88
31
38
32
&END
96
141
148
118
74
192
163
126
155
148
155
141
215
126
126
89
118
67
74
89
141
178
200
74
111

154
49
84
91
105
140
140
154
189
161
70
112
126
147
252
175
280
224
210
168
196
42
189
182
238

167
175
198
190
198
251
198
91
122
228
220
175
84
243
205
236
152
144
137
84
219
129
106
167
205

185
200
162
285
116
239
231
231
216
162
285
185
200
231
216
200
216
185
131
154
154
377
370
223
216

245
262
93
93
23,4
234
141
141
125
177
177
148
171
158
199
206
67
152
92
272
211
195
227
158
141

124
194
219
242
200
89
259
280
283
157
112
132
107
129
164
119
115
156
174
150
204
205
59
133
137

145
139
138
238
140
169
92
75
203
106
133
114
113
151
149
159
73
61
129
152
108
112
129
108
84

93
116
116
67
45
124
142
144
109
172
97
29
112
82
89
146
114
56
52
156
148
66
86
208
17

7
52
61
61
61
107
71
81
29
53
53
53
47
97
68
29
78
78
78
53
20
30
30
30
30

30
30
30
34
19
19
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5
5
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16
21
21
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31
16
47
16
26
26
78
31
42
                                   130  -

-------
Appendix  B    (continued)
EVAP72 97 69 96
EVAP72 22 101 96
EVAP72 0 76 81
EVAP72 27 113 89
EVAP72 27 141
EVAP72 27 74
TEMP72
TEMP72
TEMP72
TEMP72
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112 289 277 141
98 243 123 111
252 106 39 322
63 68 246 99
140 53 285 158
122 109
24
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24
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.4
.6
.4
.6
.9
.4
.6
.2
.8
.2
.9
.7
.2
.9
.4
.1
.9
.0
.3
.3
.3
.9
.3
.9
.6
.3
.4
.4
.1
.2
.6
66
70
68
72
69
67
64
67
64
67
64
126
123
156
151
187
48
117
141
104
94
90
186
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58
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184
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.0
.0
.3
.1
.9
.9
.7
.5
.7
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.4
.3
.4
.4
.8
.7
.9
.4
.3
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.9
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.9
.5
.1
.0
.8
.0
.9
.9
.6
.4
.3
.1
.7
.4
.2
.7
.9
.3
.5
.1
.3
.9
.2
.3
.3
.3
.3
.6
.3
.8

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58
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.2
.8
.6
.3
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.8
.4
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.8
.8
.6
.6
.4
.4
41
48
45
44
16
60
72
48
47
78
25
30
71
4
43
102

14
16
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15
12
9
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12


14
15
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7
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6













.9
.9
.4


.2
.8
.3
.2
.1


.0
.6
.9
.7
.9
.5
.6
.2
.8
.8
.3
.3
.3
.5
.1
.6
.8
.3

14
79
57
58
58
88
70
83
16
46
46
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52
104
36
42
62
2.2


8.3
16.1
18.3
10.4
9.0























31
32
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48
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78
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88
                                 - 131

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Appendix   B    (continued)

 WIND72                                         67    15    12    29    47    17
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                                 - 132 -

-------
Appendix  B    (continued)

 7207129000000000000000000000000000000000000000000000000000000000000000000000000
 7207139000000000000000000000000000000000000000000000000000000000000000000000000
 7207149000000000000000000000000000000000000000000000000000000000000000000000000
 7207159000000000000000000000000000000000000000000000000000000000000000000000000
 7207169000000000000000000000000000000000000000000000000000000000000000000000000
 7207179000000000000000000000000000000000000000000000000000000000000000000000000
 7207189000000000000000000000000000000000000000000000000000000000000000000000000
 7207199000000000000000000000000000000000000000000000000000000000000000000000000
 7207209000000000000000000000000000000000000000000000000000000000000000000000000
 7207219000000000000000000000000000000000000000000000000000000000000000000000000
 7207229000000000000000000000000000000000000000000000000000000000000000000000000
 7207239000000000000000000000000000000000000000000000000000000000000000000000000
 7207241000000000000000000000000000000000000000000000000000000000000000000000000
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 7207243000000000000000000000000000000000000000000000000000000000000000000000000
 7207244000000000000000000000000000000000000000000000000000000000000000000000000
 7207245000000000000000000000000000000000000000000000000000000000000000000000000
 7207246000000000000000000000000020300000000000000000000000000000000000000000000
 7207247000000000000000000000000000000000000000000000000000000000000000000000000
 7207248000000000000000000000000000000000000000000000000000000000000000000000000
 7207259000000000000000000000000000000000000000000000000000000000000000000000000
 7207269000000000000000000000000000000000000000000000000000000000000000000000000
 7207279000000000000000000000000000000000000000000000000000000000000000000000000
 7207281000000000000000000000000000000000000000000000000000000000000000000000000
 7207282000000000000000000000000000000000000000000000000000000000000000000000000
 72072830000000000000000000000000000000000000000000000000QOOOOOOOOOOOOOOOOOOOOOO
 7207284000000000000000000000000000000000000000000000000000000000000000000000000
 7207285000000000000000000000000000000000000000000000000000000000000000000000000
 7207286000000000000000000000000020101010000000000000000000000000000000004110301
 7207287010000000000000000000000000000000000000000000000082121200200020001000000
 7207288000000000000000000000000000000000000000000000000000000000000000000000000
 7207299000000000000000000000000000000000000000000000000000000000000000000000000
 7207309000000000000000000000000000000000000000000000000000000000000000000000000
 7207311000000000000000000000000000000000000000000000000000000000000000000000000
 7207312000000000000000000000000000000000000000000000000000000000000000000000000
 7207313000000000000000000000000000000000000000000000000000000000000000000000000
 7207314000000000000000000000000000000000000000000000000000000000000000000000000
 7207315000000000000000000000000000000000000000000000000000000000000000000000000
 7207316000000000000000000000000000000000000002524010301010101040100000000000000
 7207317000000000000000000000000000000000000000000000000000000000000000000000000
 7207318000000000000000000000000000000000000000000000000000000000000000000000000
 7208019000000000000000000000000000000000000000000000000000000000000000000000000
 7208029000000000000000000000000000000000000000000000000000000000000000000000000
 7208039000000000000000000000000000000000000000000000000000000000000000000000000
 7208049000000000000000000000000000000000000000000000000000000000000000000000000
 7208059000000000000000000000000000000000000000000000000000000000000000000000000
 7208069000000000000000000000000000000000000000000000000000000000000000000000000
 7208079000000000000000000000000000000000000000000000000000000000000000000000000
 7208089000000000000000000000000000000000000000000000000000000000000000000000000
                                - 133 -

-------
Appendix  B    (continued)

 7208091000000000000000000000000000000000000000000000000000000000000000000000000
 7208092000000000000000000000000000000000000000000000000000000000000000000000000
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 7208094000000000000000000000000000000000000000000000000000000000000000000000000
 7208095000000000000000000000000000000000000000000000000000000000000000000000000
 7208096000000000000000000000000000000000000000000000000000000000000000000000000
 720809700000000000000000000000000000000000000000000000000 000000003070302050400
 7208098000000000000000000000000000000000000000000000000000000000000000000000000
 7208101000000000000000000000000000000000000000000000000000000000000000000000000
 7208102000000000000000000000000000000000000000000000000000000000000000000000000
 7208103000000000000000000000000000000000000000000000000000000000000000000000000
 7208104000000000000000000000000000000000000000000000000000000000000000000000000
 7208105000000000000000000000000000000000000000000000000000000000000000000000000
 7208106000000000000000000000000000000000000000000000000000000000000000000000000
 7208107000000000000000000000000000000000000000000000000000503010000000012162417
 7208108150900000000000000000000000000000000000000000000000000000000000000000000
 7208111000000000000000000000000000000000000000000000000000000000000000000000000
 7208112000000000000000000000000000000000000000000000000000000000000000000000000
 7208113000000000000000000001103010100000000000000000000000000000000000000000000
 7208114000000000000000000000000000000000000000000000000000000000000000000000000
 7208115000000000000000000000000000000000000000000000000000000000000000000000000
 7208116000000000000000000000000000000000000000000000000000000000000000000000000
 7208117000000000000000000000000000000000000000000000000000000000000000000000000
 7208118000000000000000000000000000000000000000000000000000000000000000000000000
 7208129000000000000000000000000000000000000000000000000000000000000000000000000
 7208139000000000000000000000000000000000000000000000000000000000000000000000000
 7208149000000000000000000000000000000000000000000000000000000000000000000000000
 7208159000000000000000000000000000000000000000000000000000000000000000000000000
 7208169000000000000000000000000000000000000000000000000000000000000000000000000
 7208179000000000000000000000000000000000000000000000000000000000000000000000000
 7208189000000000000000000000000000000000000000000000000000000000000000000000000
 7208199000000000000000000000000000000000000000000000000000000000000000000000000
 7208209000000000000000000000000000000000000000000000000000000000000000000000000
 7208219000000000000000000000000000000000000000000000000000000000000000000000000
 7208229000000000000000000000000000000000000000000000000000000000000000000000000
 7208231000000000000000000000000000000000000000000000000000000000000000000000000
 7208232000000000000000000000000000000000000000000000000000000000000000000000000
 7208233000000000000000000000000000000000000000000000000000000000000000000000000
 7208234000000000000000000000000000000000000000000000000000000000000000000000000
 7208235000000000000000000000000000000000000000000000000000000000000000000000000
 7208236000000000000000000000000000000000000000000000033090402000000000000000000
 7208237000000000000000000000000000000000000000000000000000000000000000000000000
 7208238000000000000000000000000000000000000000000000000000000000000000000000000
 7208249000000000000000000000000000000000000000000000000000000000000000000000000
 7208259000000000000000000000000000000000000000000000000000000000000000000000000
 7208269000000000000000000000000000000000000000000000000000000000000000000000000
 7208271000000000000000000000000000000000000000000000000000000000000000000000000
 7208272000000000000000000000000000000000000000000000000000000000000000000000000
                                -  134  -

-------
Appendix  B    (continued)

 7208273000000000000000000000000000000000000000000000000000000000000000000000000
 7208274000000000000000000000000000000000000000000000000000000000000000000000000
 7208275000000000000000000000000000000000000000000000000000000000000000000000000
 7208276000000000000000000000000000000000000000505070700000000000000000000000000
 7208277000000000000000000000000000000000000000000000000000000000000000000000000
 7208278000000000000000000000000000000000000000000000000000000000000000000000000
 7208289000000000000000000000000000000000000000000000000000000000000000000000000
 7208299000000000000000000000000000000000000000000000000000000000000000000000000
 7208309000000000000000000000000000000000000000000000000000000000000000000000000
 7208319000000000000000000000000000000000000000000000000000000000000000000000000
 7209019000000000000000000000000000000000000000000000000000000000000000000000000
 7209029000000000000000000000000000000000000000000000000000000000000000000000000
 7209039000000000000000000000000000000000000000000000000000000000000000000000000
 7209041000000000000000000000000000000000000000000000000000000000000000000000000
 7209042000000000000000000000000000000000000000000000000000000000000000000000000
 7209043000000000000000000000000000000000000000000000000000000000000000000000000
 7209044000000000000000000000000000000000000000000000000000000000000000000000000
 7209045000000000000000000000000000000000000000000000000000000000000000000000000
 7209046000000000000000203020303050701010004050901000000010300000000000000010203
 7209047000101030101010001030100000000000100000000000000000000000000000000000000
 7209048000000000000000000000000000000000000000000000000000000000000000000000000
 7209051000000000000000000000000000000000000000000000000000000000000000000000000
 7209052000000000000000000000000000000000000000000000000000000000000000000000000
 7209053000000000000000000000000000000000000000000000000000000000000000000000000
 7209054000000000000000000000000000000000000000000000001010201010202010000000000
 7209055000000000000000000000000000000000000000000000000000001010101000000000000
 7209056000000000000000000000000000000000000000000000000000000000000000000000000
 7209057000000000000000000000000000000000000000000000000000000000000000000000000
 7209058000000000000000000000000000000000000000000000000000000000000000000000000
 7209069000000000000000000000000000000000000000000000000000000000000000000000000
 7209079000000000000000000000000000000000000000000000000000000000000000000000000
 7209089000000000000000000000000000000000000000000000000000000000000000000000000
 7209099000000000000000000000000000000000000000000000000000000000000000000000000
 7209109000000000000000000000000000000000000000000000000000000000000000000000000
 7209119000000000000000000000000000000000000000000000000000000000000000000000000
 7209129000000000000000000000000000000000000000000000000000000000000000000000000
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 7209149000000000000000000000000000000000000000000000000000000000000000000000000
 7209159000000000000000000000000000000000000000000000000000000000000000000000000
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 7209172000000000000000000000000000000000000000000000000000000000000000000000000
 7209173000000000000000000000000000000000000000000000000000000000000000000000000
 7209174000000000000000000000000000000000000000000000004010000000000000000000000
 7209175000000000000000000000000000000000000000000000000000000000000000000000000
 7209176000000000000000000000000000000000000000000000000000000000000000000000000
 7209177000000000000000000000000000000000000000000000000000000000000000000000000
 7209178000000000000000000000000000000000000000000000000000000000000000000000000
                                 -  135  -

-------
Appendix  B    (continued)

 7209181000000000000000000000000000000000000000000000000000000000000000000000000
 7209182000000000000000000000000000000000000000000000000000000000000000000000000
 7209183050302000000000000000000000000000000000000000000000000000000000000000000
 7209184000000000000000000000000000000000000000000000000000000000000000000000000
 7209185000000000000000000000000000000000000000000000000000000000000000000000000
 7209186000000000000000000000000000000000000000000000000000000000000000000000000
 7209187000000000000000000000000000000000000000000000000000000000000000000000000
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 7209209000000000000000000000000000000000000000000000000000000000000000000000000
 7209219000000000000000000000000000000000000000000000000000000000000000000000000
 7209229000000000000000000000000000000000000000000000000000000000000000000000000
 7209239000000000000000000000000000000000000000000000000000000000000000000000000
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 7209269000000000000000000000000000000000000000000000000000000000000000000000000
 7209279000000000000000000000000000000000000000000000000000000000000000000000000
 7209289000000000000000000000000000000000000000000000000000000000000000000000000
 7209299000000000000000000000000000000000000000000000000000000000000000000000000
 7209301000000000000000000000000000000000000000000000000000000000000000000000000
 7209302000000000000000500000212040200000000000000000101020000000101000000000000
 7209303000100010204000000000000010103010000000000000000000000000000000000000000
 7209304000000000000000000000000000000000000000000000000000000000000000000000000
 7209305000000000000000000000000000000000000000000000000000000000000000000000000
 7209306000000000000000000000000000000000000000000000000000000000000000000000000
 7209307000000000000000000000000000000000000000000000000000000000000000000000000
 7209308000000000000000000000000000000000000000000000000000000000000000000000000
 7210019
 7210029
 7210039
 7210049
 7210051
 7210052                           2 1
 7210053             11        121               114         1133221
 7210054
 7210055
 7210056
 7210057
 7210058
 7210069
 7210079
 7210089
 7210099
 7210109
 7210119
 7210129
 7210131
 7210132
                                 - 136 -

-------
Appendix   B    (continued)

 7210133                                                                    7
 7210134
 7210135
 7210136
 7210137
 7210138
 7210149
 7210159
 7210169
 7210179
 7210189
 7210199
 7210209
 7210219
 7210229
 7210231
 7210232
 7210233
 7210234
 7210235
 7210236
 7210237                               5                   4         1111
 7210238         12431       131
 7210249
 7210259
 7210269
 7210271
 7210272
 7210273
 7210274                     1123112232 116 1     3141
 7210275 1           1           1     1112       1 7 814 4 3 4 2        141
 7210276 11551 41  2         113544321     13221
 7210277
 7210278                                                                     2 2
 7210281 2                     2
 7210282
 7210283
 7210284
 7210285
 7210286
 7210287
 7210288
 7210299
 7210309
 7210319
 7211019
 7211029
 7211031
                                 - 137 -

-------
Appendix    B  (continued)

 7211032                                                                1 4
 7211033     1 2 4
 7211034
 7211035
 7211036
 7211037
 7211038
 7211049
 7211059
 7211069
 7211071
 7211072
 7211073                                  1  1  1
 7211074   21131       236                          12
 7211075   2         121     111        2
 7211076                           2481                121134433111
 7211077 224632
 7211078
 7211089
 7211099
 7211109
 7211119
 7211129
 7211131
 7211132
 7211133
 7211134
 7211135
 7211136
 7211137
 7211138000017051800000101020000000101040000000003010101010000000000000000000000
 7211149
 7211159
 7211169
 7211179
 7211189
 7211191                                                                100000000
 7211192                                                    1
 7211193                                  102                1            101020000
 72111940001                       1                201          101     101010001
 7211195                                            10100020004040001010204020603
 72111960300020404000100010201010402010101                    403020301       101
 72111970101
 7211198
 7211209
 7211219
 7211229
 7211239
                                  -  138  -

-------
Appendix    B   (continued)
 7211249
 7211251
 7211252
 7211253                     101030101010201010101010101010202010101000000010101
 72112540101       1010102       202010101010101010101010101        1           1
 7211255020100050501000101010102020                          204
 7211256
 7211257
 7211258
 7211269
 7211279
 7211289
 7211299
 7211301
 7211302
 7211303                        50101        10101030302
 7211304
 7211305
 7211306
 7211307
 7211308
 7212019
 7212029
 7212039
 7212049
 7212051
 7212052
 7212053
 7212054
 7212055
 7212056
 7212057
 7212058
 7212061 11111311            2222
 7212062 1
 7212063
 7212064 1  1
 7212065
 7212066
 7212067
 7212068
 7212079
 7212081
 7212082
 7212083
 7212084                                        5  2  1
 7212085
 7212086
202010000030204010303030101   1010101
    222221   1121111111
                            2 2

                            2111
                      1111
                                  - 139 -

-------
Appendix  B    (continued)
7212087
7212088
7212099
7212109
7212119
7212129
7212139
7212141
7212142
7212143
7212144 232 1
7212145 1 1 1
7212146
7212147 722222222
7212148 122782792
7212151 111 11
7212152 11851 121
7212153
7212154 2 2 210 5 3 2 1 1
7212155
7212156
7212157
7212158
7212169
7212179
7212189
7212199
7212201
7212202
7212203
7212204
7212205
7212206
7212207
7212208
7212211 423511152
7212212 11111
7212213 1211 11 1
7212214 111 1123
7212215
7212216
7212217 11111
7212218 1111
7212221
7212222
7212223
7212224
7212225


1 111
1211
22222221
3 410 31212 899151 2 2 11 1
1101010 823232323237311121
1112 315 3114232411233411
1111 1 211
3
1112 11















7 3 1
811131 4432415181661, 1
11 11
1 1 1 1 1 1 1 111111
3133111211235213221111
333

11111 24
11111 1
1111111111








2247
111 4
1
11115
11111
334 2
















4111
1
1
1 I 3




1111





                                 - 140 -

-------
Appendix   B   (continued)
7212226
7212227
7212228
7212239
7212249
7212259
7212269
7212279
7212289
7212299
7212309
7212311
7212312
7212313 2
7212314
7212315
7212316
7212317
7212318
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73














1 1


1 1


108
54
43
49
76
27
27
27
0
54
27
27
32
43
43
38
22
38
54
86
76
81
97
86
43
70
43
59
65













1 1 1


1


50
31
132
31
82
19
63
44
88
63
50
44
31
19
69
120
76
25
57
57
101
145
132
101
82
25
82
88

                                                         322323112121
                     21421   212111   111      1111

                                                 2
                               11111     11111            11111
                                 -  141  -

-------
Appendix   B    (continued)
            53
             0
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 TEMP72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
 WIND72
24.4
25.6
24.4
25.6
23.9
21.4
20.6
22.2
22.8
22.2
23.9
24.7
27.2
25.9
27.4
26.1
25.9
26.0
26.3
25.3
26.3
28.9
28.3
28.9
26.6
26.3
25.4
25.4
25.1
23.2
21.6
66
70
68
72
69
67
64
67
64
67
64
67
70
30
0
23.0
25.0
24.3
26.1
25.9
24.9
26.7
26.5
24.7
24.7
23.4
25.3
24.4
24.4
25.8
26.7
24.9
27.4
27.3
27.7
24.9
23.6
22.7
24.4
25.2
24.6
24.8
24.4
25.4
23.8
22.7
28
79
46
58
59
31
75
71
71
40
35
15
26
18
20
22.9
22.5
24.1
25.0
20.8
20.0
19.9
20.9
24.6
24.4
19.3
21.1
21.7
23.4
25.2
24.7
24.9
23.3
25.5
26.1
22.3
23.9
24.2
23.3
23.3
23.3
25.3
24.6
26.3
20.8

25
24
21
40
47
32
9
26
42
48
24
12
13
43
17
14.5
14.2
16.6
18.4
19.7
17.7
18.8
16.9
17.6
19.2
16.1
17.5
21.7
20.6
21.7
19.4
22.2
18.8
16.6
11.3
6.9
13.8
17.4
20.9
13.8
14.8
14.8
13.6
16.6
15.4
16.4
41
48
45
44
16
60
72
48
47
78
25
29
28
33
25
14.9
16.9
21.4


15.2
12.8
9.3
9.2
12.1


14.0
15.6
9.9
7.7
5.9
7.5
8.6
6.2
7.8
7.8
5.3
3.3
8.3
7.5
6.1
10.6
8.8
6.3

14
79
57
58
58
88
70
83
16
46
46
47
83
93
63
2.2


8.3
16.1
18.3
10.4
9.0























31
32
32
37
48
88
78
44
44
45
88
17
73
69

                                  - 142 -

-------
Appendix B     (continued)
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
WIND72
7301019
7301029
7301031
7301032
7301033
7301034 111112
7301035 1 1
7301036
7301037
7301038
7301041 1 1
7301042
7301043
7301044
7301045
7301046
7301047
7301048
7301051
7301052
7301053
7301054
7301055
7301056
7301057 7 3 1
7301058 11111
7301061 1111111111
7301062 11111
7301063
7301064
73UJ.C3I
7301066
75
33
30
35
31
36
30
50
66
66
78
110
106
106
53
39




1 1 1
1 2




1 1 1













2 2
11111
311111223





39
49
26
21
39
34
28
27
18
15
19
20
26
24
44
52




1 1
1


























27
22
22
19
53
20
10
37
30
35
22
18
32
50
61






1

3
















1 1







46
48
39
99
73
39
46
40
60
27
78
73
16
103
38
31



1 1
2 1
1

1
















1 1 1

3 2





33
57
58
58
68
17
56
56
56
56
57
90
65
80
68




1
1 1


1


















1 1 1





                                                                          1 1
                                                                           1 1
                                 - 143 -

-------
Appendix  B    (continued)

 7301067
 7301068
 7301071
 7301072
 7301073
 7301074
 7301075                          11111    1    1111   1   1   1   1   1
 7301076             1            111111      111   112112121111.
 7301077 111111221222222222222222222222222222
 7301078 222222222222221111111112111111111111
 7301081 111212121212111111111111
 7301082
 7301083
 7301084
 7301085
 7301086
 7301087
 7301088
 7301099
 7301109
 7301119
 7301129
 7301139
 7301149
 7301159
 7301169
 7301179
 7301189
 7301191
 7301192             53   11    11112222111311414121
 7301193
 7301194
 7301195
 7301196
 7301197
 7301198
 7301209
 7301219
 7301229
 7301239
 7301249
 7301251
 7301252
 7301253
 7301254
 7301255
 7301256
 7301257
                                  - 144 -

-------
Appendix   B   (continued)

 7301258     4                                             111111111
 7301261 11334221111   1   11   11   11       11111232422
 7301262 4221122222                       4111111
 7301263
 7301264
 7301265
 7301266
 7301267
 7301268
 7301279
 7301281
 7301282
 7301283
 7301284
 7301285
 7301286
 7301287           111                                 11
 7301288   1 1 1
 7301299
 7301309
 7301319
 7302019
 7302029
 7302039
 7302049
 7302059
 7302061
 7302062
 7302063
 7302064                                             3211
 7302065
 7302066
 7302067
 7302068
 7302079
 7302081
 7302082
 7302083                 111               11
 7302084                                                 1 1
 7302085                                     1111133211111
 7302086
 7302087
 7302088
 7302099
 7302101
 7302102
 7302103
 7302104
                                 -  145 -

-------
Appendix  B    (continued)

 7302105
 7302106
 7302107
 7302108
 7302119
 7302129
 7302139
 7302149
 7302151
 7302152                                                  232111111111
 7302153 121121112111211111    1   1   1
 7302154
 7302155
 7302156
 7302157
 7302158
/*
                               - 146 -

-------
Appendix  C                 PTR MODEL  LISTING

//PESTICID JOB (C510,510,3,8),'TONY.PEST10.COMPILED1
/* SERVICE CLASS=B, BLOCK=NIGHT
//STEP1 EXEC FORTHCL,PARM.FORT='OPT=2,MAP,XREF'
//FORT.SYSIN DD *
C
C
C
C                            PESTICIDE MODEL -- MAIN PROGRAM
C
C
      IMPLICIT  REAL(L)
C
      DIMENSION  RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5), IEVAP(12,31)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
      DIMENSION  UZSBMT(5), RESBMT(5), SRGXMT(5), SRERMT(5)
      DIMENSION  STSMET(5), SASMET(5), SCSMET(5), SDSMET(5)
      DIMENSION  UTSMET(5), UASMET(5), UCSMET(5), UDSMET(5)
      DIMENSION  ERSNMT(5)
      DIMENSION  RAIN(288), IRAIN(288), MNAM(24), ROSB(5)
      DIMENSION  PRTTOT(5), PRTTOM(5), IWIND(12,31), ITEMP(12,31)
C
      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM,  RITOM, RINTOM, BASTOM, RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT,  RITOT, RINTOT, BASTOT, RCHTOT
      COMMON  ROBTOM, ROBTOT,  INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT,  PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
      COMMON  MNAM, IX, IZ, DAY, IFLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM,  ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
      COMMON  STS, STST, SAST, SCST,  SDST, UTS, UTST, UAST, UCST,  UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST,  RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS,  SGW1
      COMMON  A,  UZS, LZS, SGW, GWS,  KV, K24L, KK24, K24EL, TF,  EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS, UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
                                    -  147 -

-------
Appendix  c    (continued)
      INTEGER  BGNDAY, BGNMON, BGNYR, ENDDAY, ENDMON, ENDYR
      INTEGER  DYSTRT, DYEND, YEAR, MONTH, DAY, H, HYCAL, TIME
      INTEGER  YR, MO, DY, CN, TF, PRNTKE, PRINT, DA, APMODE, UNIT
      INTEGER  INPUT
C
      REAL  IRC, NN, KV, K24L, KK24, INFIL, INTER
      REAL  IPS, ICS, K24EL, K3, NEPTOM, NEPTOT
      REAL  JRER, KRER, JSER, KSER
      REAL  M, MM, N, NI, K, MUZ, MU, ML, MLZ
      REAL  INFTOM, INFTOT, MOLEWT, ITEMP
      REAL  MMPIN, METOPT, KGPLB, QMETRC
      REAL  UZSMET, LZSMET, SGWMET, SCEPMT, RESSMT, TWBLMT
      REAL  SRGXTM, SRRTMT, SASTMT, SCSTMT, SDSTMT, STSTMT
      REAL  UTSTMT, UASTMT, UCSTMT, UDSTMT, LSTRMT, LASMET
      REAL  LCSMET, LDSMET, GSTRMT, GASMET, GCSMET, GDSMET
      REAL  VLTMMT, TPBALM, ERSNTT, EIMMET, ERSNMT
      REAL*8  PNAME, WSNAME
C
      DATA  PRINT/I/
      DATA. COUNT, TIMAT, TIMST, TIMAP/4*0.0/
      DATA  ICS, OFS, TPBAL, DEGT/4*0.0/
      DATA  PRTTOM, PRTTOT, PRT, VOLT,  TOTPAP/13*0.0/
C
C
C                   DATA INPUT -- SINGLE-VALUED VARIABLES
C
      NAMELIST /HYCL/  HYCAL, HYMIN, UNIT, INPUT
      NAMELIST /PRNT/  PRINT
      NAMELIST /STRT/  BGNDAY, BGNMON,  BGNYR
      NAMELIST /ENDD/  ENDDAY, ENDMON,  ENDYR
      NAMELIST /TRVL/  INTRVL
      NAMELIST /LND1/  UZSN, LZSN, INFIL, INTER
      NAMELIST /LND2/  IRC, NN,  L, SS,  A, UZS
      NAMELIST /LND3/  LZS, SGW, GWS, KV, K24L, KK24
      NAMELIST /LND4/  ICS, OFS, IFS, K24EL, K3, EPXM
      NAMELIST /PEST/  SSTR, APMODE, DEPTH
      NAMELIST /NAME/  PNAME, WSNAME
      NAMELIST /CROP/  COVMAX, TIMST, TIMAP, TIMAT, TIMHAR
      NAMELIST /SMDL/  JRER, KRER, JSER, KSER, SRERI
      NAMELIST /AMDL/  CMAX, DD, BULKD,  K, N, AREA
      NAMELIST /VOL1/  DIFC, TDIFC, CBDIF
      NAMELIST /VOL2/  MOLEWT, APFAC, BPFAC, WCFAC
      NAMELIST /DEG1/  DEGCON
C
C        INPUT PARAMETER DESCRIPTION
C  HYCAL :  INDICATES WHAT FACTORS ARE TO BE SIMULATED
C        = -1  CALIBRATION RUN WITH PESTICIDE
                                    - 148 -

-------
Appendix C     (continued)
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
=

HYMIN
INPUT
UNIT
PRINT

BGNDAY
ENDDAY
INTRVL
UZSN
LZSN
INFIL
INTER
IRC
NN
L
SS
A
UZS
LZS
SGW
GWS
KV

K24L

KK24
ICS
OFS
IPS
K24EL

K3
EPXM
PNAME
WSNAME
SSTR
APMODE
DEPTH
COVMAX
TIMST
TIMAP
TIMAT
TIMHAR
JRER
KRER
JSER
           BGNMON, BGNYR
           ENDMON, ENDYR
           TIME INTERVAL
 0  PRODUCTION RUN
 1  CALIBRATION RUN, NO PESTICIDES
MINIMUM FLOW FOR OUTPUT DURING A TIME INTERVAL (CFS, CMS)
INPUT UNITS; ENGLISH(-l), METRIC(l)
OUTPUT UNITS; ENGLISH(-l), METRIC(l), BOTH(O)
DENOTES FREQUENCY OF OUTPUT; EACH INTERVAL(-l),
EACH HOUR(O), OR EACH DAY(l)
              :  DATE SIMULATION BEGINS
              :  DATE SIMULATION ENDS
              (  5 OR 15 MINUTES)
NOMIMAL UPPER ZONE STORAGE (IN, MM)
NOMINAL LOWER ZONE STORAGE (IN, MM)
INFILTRATION RATE (IN/HR, MM/HR)
INTERFLOW PARAMETER, ALTERS RUNOFF TIMING
INTERFLOW RECESSION RATE
MANNING'S N FOR OVERLAND FLOW
LENGTH OF OVERLAND FLOW TO CHANNEL (FT, M)
AVERAGE OVERLAND FLOW SLOPE
FRACTION OF AREA THAT IS IMPERVIOUS
INITIAL UPPER ZONE STORAGE (IN, MM)
INITIAL LOWER ZONE STORAGE (IN, MM)
INITIAL GROUNDWATER STORAGE (IN, MM)
GROUNDWATER SLOPE
PARAMETER TO ALLOW VARIABLE RECESSION RATE FOR GROUNDWATER
DISCHARGE
FRACTION OF GROUNDWATER RECHARGE PERCOLATING TO DEEP
GROUNDWATER
GROUNDWATER RECESSION RATE
INITIAL INTERCEPTION STORAGE (IN, MM)
INITIAL OVERLAND FLOW STORAGE (IN, MM)
INITIAL INTERFLOW STORAGE (IN, MM)
FRACTION OF WATERSHED AREA WHERE GROUNDWATER IS WITHIN
REACH OF VEGETATION
INDEX TO ACTUAL EVAPORATION
MAXIMUM INTERCEPTION STORAGE (IN, MM)
PESTICIDE NAME (8 CHARACTERS)
WATERSHED NAME (8 CHARACTERS)
PESTICIDE APPLICATION FOR EACH ZONE (LB, KG)
APPLICATION MODE; SURFACE APPLIED(O), SOIL INCORPORATED(l)
DEPTH OF SOIL INCORPORATION (IN, MM)
MAXIMUM SURFACE AREA COVERED BY VEGETATION
TIME SIMULATION STARTS (JULIAN DAY)
TIME OF PESTICIDE APPLICATION (JULIAN DAY)
TIME OF CROP MATURITY (JULIAN DAY)
TIME OF HARVEST (JULIAN DAY)
EXPONENT OF RAINFALL INTENSITY  IN SOIL SPLASH EQUATION
COEFFICIENT IN SOIL SPLASH EQUATION
EXPONENT OF OVERLAND FLOW IN SURFACE SCOUR EQUATION
                                    - 149 -

-------
Appendix C     (continued)
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
KSER
SRERI
CMAX
DD
BULKD
K
N
AREA
DIFC
TDIFC
CBDIFC
MOLEWT
APFAC,
WCFAC
DEGCON

C
C
C
           COEFFICIENT IN SURFACE SCOUR EQUATION
           INITIAL FINES DEPOSIT (TONS, TONNES)
           MAXIMUM SOLUIBILITY OF PESTICIDE IN WATER (LB/LB)
           PERMANENTLY FIXED CAPACITY (LB PESTICIDE/LB SOIL)
           BULK DENSITY OF PESTICIDE (6/CM(3))
           COEFICIENT IN FREUNDLICH ADSORPTION CURVE
           EXPONENT IN FREUNLICK ADSORPTION CURVE
           WATERSHED AREA (AC, HA)
           PESTICIDE DIFFUSION COEFFICIENT (MM(2)/WK)
           TEMPERATURE AT WHICH DIFC WAS MEASURED (DEGREES CELCIUS)
           COEFICIENT FOR DIFFUSION
           MOLECULAR WEIGHT OF PESTICIDE (G/MOLE)
          BPFAC :  CONSTANTS FOR PRESSURE AND TEMPERATURE ADJUSTMENT
           WIND CALIBRATION FACTOR
           FIRST ORDER PESTICIDE DECAY RATE (PER DAY)
      READ (5,HYCL)
      READ (5,PRNT)
      READ (5,STRT)
      READ (5,ENDD)
      READ (5JRVL)
      READ .(5.LND1)
      READ (5,LND2)
      READ (5,LND3)
      READ (5,LND4)
      READ (5,PEST)
      READ (5,NAME)
      READ (5,CROP)
      READ (5,SMDL)
      READ (5.AMDL)
      READ (5,VOL1)
      READ (5,VOL2)
      READ (5.DEG1)
              PRINTING OF INPUT PARAMETERS
 1001
 1002
C
C
IF (HYCAL)   1002,  1001,  1002

   WRITE (6,1091)
   WRITE (6,1092)
   GO TO 1003

   WRITE (6,1093)
   WRITE (6,1092)
 1003 WRITE (6,1107) WSNAME
      WRITE (6,1106) PNAME
                                   - 150 -

-------
Appendix  C    (continued)
      IF (INPUT .EQ. -1)  WRITE (6,1108)
      IF (INPUT .EQ.  1)  WRITE (6,1109)
C
C
C
      IF (APMODE .EQ. 1
      IF (APMODE .EQ. 0
      WRITE (6,1092)
                       WRITE (6,1105)
                       WRITE (6,1104)
   WRITE (6,STRT)
   WRITE (6,1092)
   WRITE  6.ENDD
   WRITE (6,1092)
   WRITE (6.TRVL)
   WRITE (6,1092)
   WRITE (6.LND1)
   WRITE (6,1092)
   WRITE (6.LND2)
   WRITE (6,1092)
   WRITE (6,LND3)
   WRITE (6,1092)
   WRITE (6,LND4)
   WRITE (6,1092)
   WRITE (6,PEST)
   WRITE (6,1092)
   WRITE (6,CROP)
   WRITE (6,1092)
   WRITE (6.SMDL)
   WRITE (6,1092)
   WRITE (6,AMDL)
   WRITE (6,1092)
   WRITE (6.VOL1)
   WRITE (6,1092)
   WRITE (6.VOL2
   WRITE (6,1092)
   WRITE (6,DEG1)
   WRITE (6,1092)
   WRITE (6,HYCL)
   WRITE (6,1092)
   WRITE (6,PRNT)
   WRITE (6,1092)
   IF (INPUT .EQ. -1) GO TO 950

CONVERSION OF METRIC INPUT DATA TO ENGLISH UNITS

   HYMIN= HYMIN*35.3
   UZSN = UZSN/MMPIN
   LZSN = LZSN/MMPIN
   INFIL= INFIL/MMPIN
   L    = L*3.281
   UZS  = UZS/MMPIN
                                   - 151 -

-------
Appendix  C    (continued)










LZS =
SGW =
ICS =
OFS =
IPS =
EPXM =
DEPTH=
SRERI=
AREA =
DO 501
LZS/MMPIN
SGW/MMPIN
ICS/MMPIN
OFS/MMPIN
IFS/MMPIN
EPXM/MMPIN
DEPTH/MMPIN
SRERI/METOPT
AREA*2.471
1 = 1,5
501 SSTR(I)=SSTR(I)/0.4536
C
C
C




ADJUSTMENT OF

950 H = 60/INTRVL
                                  CONSTANTS
      TIMFAC =  INTRVL
      INTRVL =  24*H
C
      KRER = KRER*H**(JRER-1.)
      KSER = KSER*H**(JSER-1.)
      NI = l./N
C
      MM = BULKD/96.
      MU = BULKD*((DEPTH   0.125)/12.)
      ML = BULKD*6.
      M = MM*43560.*AREA*0.2
      MUZ = MU*43560.*AREA*0.2
      MLZ = ML*43560.*AREA
C
      DO 1000   1=  1,5
 1000 TOTPAP =  TOTPAP + SSTR(I)
C
      IFLAG=1
      COUNT = 0.
      IF  (APMODE  .EQ. 0)  GO TO  1004
      CONCIU =  (TOTPAP*6./((MUZ + M)*5.))*1000000.*(BULKD/62.43)
C
      DO  999   1=  1,5
      USTR(I) = SSTR(I)
      SSTR(I) = SSTR(I)*(0.125/DEPTH)
  999 USTR(I) = USTR(I) - SSTR(I)
      CADIF = DIFC/(EXP(CBDIF*TDIFC))
C
 1004 FP = DD*M
      FPUZ = DD*MUZ
      FPLZ = DD*MLZ
                                    -  152 -

-------
Appendix  C    (continued)
 1005
DO 1005  1=1,5
   SRER(I) = SRERI*0.2
   UZSB(I) = UZS
   RESB(I) = OFS
   SRGX(I) = IPS
   CONTINUE

RESS1 = OFS
RESS = OFS
SCEP = ICS
SCEP1 = ICS
SRGXT = IFS
SRGXT1 = IFS
SGW1 = SGW
C
C
C
                       PROGRAM EXECUTION
  998
DO 1070  YEAR=BGNYR,ENDYR
   IF (YEAR .EQ. BGNYR) GO TO 998
   TIMST = 0.0
   TIMAP = 0.0
   TIMAT = TIMAT - 365.
   IF (TIMAT .LE. 0.0) TIMAT = 0.0
   TIMHAR = TIMHAR - 365.
   IF (TIMHAR .LE. 0.0) TIMHAR = 0.0
   COUNT = TIMST
   COVER =0.0
   MNSTRT = 1
   MNEND = 12
   IF (YEAR .EQ. BGNYR)  MNSTRT = BGNMON
   IF (YEAR .EQ. ENDYR)  MNEND = ENDMON
C
C
C  EVAPORATION, TEMP, AND WIND
C
       DO 1007  NO = 1,31
        DO 1006  MK = 1,12
        IEVAP(MK,NO) = 0
        ITEMP(MK,NO) = 0.0
 1006   IWIND(MK,NO) = 0
 1007  CONTINUE
                          DATA INPUT
C
C
 1008
 DO  1008  DA = 1,31
  READ (5,1264) (IEVAP(MN,DA), MN =1,12)
                                     -  153 -

-------
Appendix C     (continued)
        DO  1013 DA = 1,31
 1013   READ(5,1265) (ITEMP(MN,DA),  MN=1,12)
C
        DO  1014 DA = 1,31
 1014   READ(5,1264) (IWIND(MN.DA),  MN=1,12)
        IF (INPUT .EQ.  -1) GO TO 625
        DO 700 DA=1,31
           DO 650 MN=1,12
              IEVAP(MN,DA) = IEVAP(MN,DA)*3.937
              IWIND(MN,DA) = IWIND(MN,DA)*0.6214
  650         CONTINUE
  700      CONTINUE
C
C
  625 IF (INPUT .EQ. 1) GO TO 628
      DO 627 DA=1,31
         DO 626 MN=1,12
            ITEMP(MN,DA) = (ITEMP(MN,DA) - 32.)*.5556
  626       CONTINUE
  627     CONTINUE '
  628    DO 1060  MONTH=MNSTRT,MNEND
            IF (HYCAL .EQ. 0)  GO TO 1009
            WRITE (6,1263)
            WRITE (6,382)
            WRITE (6,1092)
 1009       DYSTRT = 1
            IF (MOD(YEAR,4))  1012,  1010, 1012
 1010          GO TO (31,29,31,30,31,30,31,31,30,31,30,31), MONTH
 1012          GO TO (31,28,31,30,31,30,31,31,30,31,30,31), MONTH
   28             DYEND = 28
                  GO TO 1015
   29             DYEND = 29
                  GO TO 1015
   30             DYEND = 30
                  GO TO 1015
   31             DYEND = 31
C
 1015       IF (YEAR .NE. BGNYR)  GO TO 1017
            IF (MONTH .NE. BGNMON)  GO TO 1017
            DYSTRT = BGNDAY
C
 1017       IF (YEAR .NE. ENDYR)  GO TO 1018
            IF (MONTH .NE. ENDMON)  GO TO 1018
            DYEND = ENDDAY
C
 1018       DO 1050  DAY=DYSTRT,DYEND
               IF ((MONTH .EQ. 1) .AND. (DAY .EQ. 1)) COUNT = 1.
                                    - 154 -

-------
Appendix  C    (continued)

               TIME = 0
               RAINT = 0.0
               EP = IEVAP(MONTH,DAY)/1000.
               TEMP = ITEMP(MONTH,DAY)
               WIND = IWIND(MONTH,DAY)
 1016          DO 1019   I=1,INTRVL
                  IRAIN(I) = 0
                  RAIN(I) = 0.0
 1019             CONTINUE
C
C      CROP CANOPY EFFECTS - ASSUMES LINEAR GROWTH TO MAX. % COVER
C
               COUNT = COUNT + 1.
               IF (COUNT  - TIMAP) 1047, 1047, 104'6
 1046          IF (COUNT  .LT. TIMAT)  GO TO 1045
               COVER = COVMAX
               IF (COUNT  .GE. TIMHAR)  COVER=0.0
               GO TO 1047
 1045            COVER =  COVMAX*((COUNT-TIMAP)/(TIMAT-TIMAP))
C
C
 1047          IF (INTRVL  .EQ. 288)  GO TO 1021
               DO 1020   J=l,8
                  JK = J*12
                  JJ - JK  - 11
                  READ (5,1094)   YR, MO, DY, CN,  (IRAIN(I),  I=JJ,JK)
                  IF (INPUT .EQ.  -1) GO TO 704
                  DO 702  I=JJ,JK
                     IRAIN(I) = IRAIN(I)*3.937 +  0.5
   702                CONTINUE
   704             IF (CN  .EQ. 9)  GO TO 1025
                  YR = YR  + 1900
                  IT = (YEAR-YR)  +  (MONTH-MO) +  (DAY-DY) + (J-CN)
                  IF (IT  .EQ. 0)  GO TO 1020
                  WRITE  (6,1090)  J, MONTH, DAY,  YEAR, CN, MO, DY, YR
                  GO TO  1080
 1020             CONTINUE
               GO TO 1023
C
 1021          DO 1022   J=l,8
                  JK = J*36
                  JJ = JK - 35
                  READ (5,1095)   YR, MO,  DY, CN,  (IRAIN(I),  I=JJ,JK)
                  IF (INPUT  .EQ.  -1) GO TO 708
                  DO 706  I=JJ,JK
                     IRAIN(I) =IRAIN(I)*3.937 +  0.5
   706                CONTINUE
   708             IF (CN .EQ. 9)  GO TO 1025
                                    - 155 -

-------
Appendix  C    (continued)

                  YR = YR + 1900
                  IT = (YEAR-YR) + (MONTH-MO) + (DAY-DY) + (J-CN)
                  IF (IT .EQ. 0)  GO TO 1022
                  WRITE (6,1090)  J, MONTH, DAY, YEAR, CN, MO, DY, YR
                  GO TO 1080
 1022             CONTINUE
C
 1023          DO 1024  I=1,INTRVL
                  RAIN(I) = IRAIN(I)/100.
                  RAINT = RAINT + RAIN(I)
 1024             CONTINUE
C
               IF (RAINT)  1025, 1025, 1026
C
C
C  USE RAIN LOOP IF MOISTURE STORAGES ARE NOT EMPTY
C
 1025  IF  ((RESS .GE. 0.001).OR.(SRGXT .GE. 0.001)) GO TO 1026
       GO TO 1040
C
C
C                  RAIN LOOP
C
 1026          DO 1036  I=1,INTRVL
                  TIME = TIME + 1
                  TF = 1
                  PR = RAIN(I)
C
                  IMIN = MOD(TIME,H)
                  IHR = (TIME - IMIN)/H
                  IMIN = TIMFAC*IMIN
                  PRNTKE = 0
                  IF (PRINT)  1027, 1028, 1029
 1027                PRNTKE = 1
                     GO TO 1030
 1028                IF (IMIN .LT. 1)  PRNTKE = 1
                     GO TO 1030
 1029                IF (IHR .EQ.  24)  PRNTKE = 1
C
 1030             IF (PRNTKE .EQ.  0)  GO TO 1031
                  IX = 2*MONTH
                  IZ = IX - 1
C
                  IF (HYCAL .NE. 0)  GO TO 1031
C
 1037             WRITE (6,1101)  IHR, IMIN, DAY,MNAM(IZ),MNAM(IX),YEAR
                  WRITE (6,1102)
                  WRITE (6,1103)
                                   -  156 -

-------
Appendix  C    (continued)

C
 1031             CALL LANDS
                  IF  ((RESS  .GE. 0.001).OR.(PR  -ST. 0.001))  GO TO 1034
                  DO  1033  J=l,5
                      ERSN(J) = 0.0
 1033                 CONTINUE
                  IF  (PRNTKE  .EQ. 0)  GO TO 1035
 1034             CALL SEDT
 1035        IF ((HYCAL .EQ.  1)  .OR.  (COUNT ,LT. TIMAP))  GO TO 1036
                  CALL ADSRB1
                  CALL ADSRB2
                  CALL ADSRB3
                  IF  (IHR  .EQ.  24)   GO TO  1038
                    VOLU =  0.0
                    VOLS =  0.0
                    DEGS =  0.0
                    DEGU =  0.0
                    GO TO  1036
 1038             CALL VOLDEG
 1036             CONTINUE
 C
               GO TO  1050
 C
 C                   NO RAIN  LOOP
 C
 C
 1040          TF = INTRVL
               PR = 0.0
               P3 = 0.0
               DO   1042   1=1,5
 1042             RESBl(I) = 0.0
               PRNTKE =  1
               IMIN = 00
               IHR  =  24
               IX = 2*MONTH
               IZ = IX -  1
               IF  (HYCAL  .NE. 0)   GO TO  1043
               WRITE  (6,1101)   IHR,  IMIN,  DAY,  MNAM(IZ), MNAM(IX), YEAR
               WRITE  (6,1102)
               WRITE  (6,1103)
 C
 1043          CALL LANDS
               SRERT  =0.0
               ERSNT  =0.0
               EIM  =  0.0
               DO  1041   J=l,5
                   SRERT  =  SRERT +  SRER(J)
                   ERSN(J)  =  0.0
                                    - 157 -

-------
Appendix  C    (continued)

 1041             CONTINUE
               IF (HYCAL  .NE. 0)  GO TO 1044
             IF (UNIT .EQ. 1) GO TO 1081
               WRITE (6,1209)
               WRITE (6,1210) ERSN, ERSNT
               WRITE (6,1211)  SRER, SRERT
               WRITE (6,1212) EIM
 1081          IF (UNIT .EQ. -1) GO TO 1044
C  METRIC CONVERSIONS FOR OUTPUT
               ERSNTT=ERSNT*METOPT
               SRRTMT=SRERT*METOPT
               EIMMET=EIM*METOPT
               DO 1163 1=1,5
                 ERSNMT(I)=ERSN(I)*METOPT
                 SRERMT(I)=SRER(I)*METOPT
 1163          CONTINUE
               WRITE (6,1208)
               WRITE (6,1210) ERSNMT, ERSNTT
               WRITE (6,1211) SRERMT, SRRTMT
               WRITE (6,1212) EIMMET
 1044       IF ((HYCAL .EQ. 1) .OR. (COUNT .LT. TIMAP))  GO TO 1050
               CALL ADSRB1
               CALL ADSRB2
               CALL ADSRB3
               CALL VOLDEG
C
 1050          CONTINUE
C
C                   MONTHLY SUMMARY
C
          DO 1051  1= 1,5
 1051     PRTTOM(I) = PRSTOM(I) + PROTOM(I) + UPITOM(I)
C
C
          VOLTOM = VOLSOM + VOLUOM
          VOLT = VOLT + VOLTOM
          DEGTOM = DEGSOM + DEGUOM + DEGLOM
          DEGT = DEGT + DEGTOM
C
            PRTM = SPROTM + SPRSTM + UPRITM
          PRT = PRT + PRTM
C
          PBAL = STST + UTST + LSTR + GSTR + PRT
     X           + VOLT + DEGT - TOTPAP
          IF ((PBAL .LE.  0.0).AND.(PBAL .GE.  -0.0009))  PBAL = 0.0
          TPBAL = TPBAL + PBAL
C
            IX = 2*MONTH
                                    - 158 -

-------
Appendix  C    (continued)
            IZ = IX - 1
            WRITE (6,1200)  MNAM(IZ), MNAM(IX), YEAR
            WRITE (6,1201)
            WRITE (6,1103)
IF (UNIT .EQ. 1)
WRITE (6,360)
                       GO TO 1053
WRITE (6,361
WRITE (6,362
WRITE (6,363
PRTOM, PRTOM, PR'

ROBTOM, ROSTOM
WRITE (6,364) INFTOM, RINTOM
WRITE (6,365) RITOM
WRITE 6,366 ROITOM, RUTOM
WRITE (6,380) BASTOM
WRITE 6,381
WRITE (6,367
WRITE (6,368
WRITE (6,369
RCHTOM

EPTOM, EPTOM, EP
NEPTOM.NEPTOM,
      WRITE  (6,370)
      WRITE  (6,371) UZSB.UZS
      WRITE  (6,372) LZS,LZS,LZS,LZS,LZS,LZS
      WRITE  (6,373) SGW,SGW,SGW,SGW,SGW,SGW
      WRITE  (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
      WRITE  (6,375) RESB.RESS
      WRITE  (6,376) SRGX.SRGXT
      WRITE  (6,377) TWBAL
             WRITE  (6,1209)
             WRITE  (6,1210) ERSTOM,
             WRITE  (6,1211)  SRER,
             WRITE  (6,1212)
             IF  (HYCAL  .NE.
             WRITE  (6,1220)
                                 STST
                                 SAST
                                 SCST
                                SDST
                                 UTST
                                 UAST
                                 UCST
                                 UDST
                              ERSNTM
                            SRERT
                      EIMTM
                     0)  GO TO 1052
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
(6
,1221)
,1222)
,1223)
,1227)
,1224)
,1222)
,1223
,1227)
,1228)
,1229)
,1230)
,1231)
,1232)
,1229)
,1230)
,1231)
STS,
SAS,
SCS,
SDS, :
UTS,
UAS,
UCS,
UDS,
LSTR
LAS
LCS
LDS
GSTR
GAS
GCS
GDS
                                    - 159 -

-------
c
c
c
Appendix C     (continued)
 1052        WRITE  (6,1240)   PRTTOM,  PRTM
            WRITE  (6,1241)   PROTOM,  SPROTM
            WRITE  (6,1242)   PRSTOM,  SPRSTM
            WRITE  (6,1243)   UPITOM,  UPRITM
          IF (HYCAL  .EQ.  1)   GO  TO  1053
            WRITE  (6,1244)
            WRITE  (6,1245) VOLTOM
            WRITE  (6,1246) VOLSOM
            WRITE  (6,1247) VOLUOM
            WRITE  (6,1248)
            WRITE  (6,1245) DEGTOM
            WRITE  (6,1246) DEGSOM
            WRITE  (6,1247) DEGUOM
            WRITE  (6,1252) DEGLOM
            WRITE  (6,1266) TPBAL

'l053  IF  (UNIT  .EQ.  -1) GO TO 1055
   CONVERSIONS  TO  METRIC
   NEW PARAMETERS  DEFINED FOR VARIABLES  NOT  RESET TO ZERO.

      PRTOM  =PRTOM*MMPIN
      ROSTOM=ROSTOM*MMPIN
      RINTOM=RINTOM*MMPIN
      RITOM  =RITOM*MMPIN
      RUTOM  =RUTOM*MMPIN
      BASTOM =BASTOM*MMPIN
      RCHTOM=RCHTOM*MMPIN
      EPTOM  =EPTOM*MMPIN
      NEPTOM =NEPTOM*MMPIN
      UZSMET=UZS*MMPIN
      LZSMET=LZS*MMPIN
      SGWMET=SGW*MMPIN
      SCEPMT=SCEP*MMPIN
      RESSMT=RESS*MMPIN
      TWBLMT=TWBAL*MMPIN
      SRGXTM=SRGXT*MMPIN
   SEDIMENT
      ERSNTM=ERSNTM*METOPT
      SRRTMT=SRERT*METOPT
      EIMTM=EIMTM*METOPT
   PESTICIDE
      STSTMT=STST*KGPLB
      SASTMT=SAST*KGPLB
      SCSTMT=SCST*KGPLB
      SDSTMT=SDST*KGPLB
      UTSTMT=UTST*KGPLB
      UASTMT=UAST*KGPLB
      UCSTMT=UCST*KGPLB
                                    - 160  -

-------
Appendix  C     (continued)

      UDSTMT=UDST*KGPLB
      LSTRMT=LSTR*KGPLB
      LASMET=LAS*KGPLB
      LCSMET=LCS*KGPLB
      LDSMET=LDS*KGPLB
      GSTRMT=GSTR*KGPLB
      GASMET=GAS*KGPLB
      GCSMET=GDS*KGPLB
      GDSMET=GDS*KGPLB
      PRTM  =PRTM*KGPLB
      SPROTM=SPROTM*KGPLB
      SPRSTM=SPRSTM*KGPLB
      UPRITM=UPRITM*KGPLB
      VLTMMT=VOLTOM*KGPLB
      VOLSOM=VOLSOM*KGPLB
      VOLUOM=VOLUOM*KGPLB
      DEGTMT=DEGTOM*KGPLB
      DEGSMT=DEGSOM*KGPLB
      DEGUMT=DEGUOM*KGPLB
      DEGLMT=DEGLOM*KGPLB
      TPBALM=TPBAL*KGPLB
C
C ARRAY METRIC MODIFICATIONS
      DO  1048 1=1,5
        ROBTOM(I)=ROBTOM(I)*MMPIN
        INFTOM(I)=INFTOM(I)*MMPIN
        ROITOM(I)=ROITOM(I)*MMPIN
        UZSBMT(I)=UZSB(I)*MMPIN
        RESBMT(I)=RESB(I)*MMPIN
        SRGXMT(I)=SRGX(I)*MMPIN
        ERSTOM(I)=ERSTOM(I)*METOPT
        SRERMT(I)=SRER(I)*METOPT
        STSMET(I)=STS(I)*KGPLB
        SASMET(I)=SAS(I)*KGPLB
        SCSMET(I)=SCS(I)*KGPLB
        SDSMET(I)=SDS(I)*KGPLB
        UTSMET(I)=UTS(I)*KGPLB
        UASMET(I)=UAS(I)*KGPLB
        UCSMET(I)=UCS(I)*KGPLB
        UDSMET(I)=UDS(I)*KGPLB
        PRTTOM(I)=PRTTOM(I)*KGPLB
        PROTOM(I)=PROTOM(I)*KGPLB
        PRSTOM(I)=PRSTOM(I)*KGPLB
        UPITOM(I)=UPITOM(I)*KGPLB
 1048 CONTINUE
      WRITE (6,460)
      WRITE (6,461) PRTOM,PRTOM,PRTOM,PRTOM,PRTOM,PRTOM
      WRITE (6,362)
                                    - 161 -

-------
Appendix  C    (continued)

      WRITE (6,463) ROBTOM,ROSTOM
      WRITE (6,464) INFTOM,RINTOM
      WRITE (6,465) RITOM
      WRITE (6,466) ROITOM,RUTOM
      WRITE (6,480) BASTOM
      WRITE (6,481) RCHTOM
      WRITE (6,367)
      WRITE (6,468) EPTOM,EPTOM,EPTOM,EPTOM,EPTOM,EPTOM
      WRITE (6,469) NEPTOM,NEPTOM,NEPTOM,NEPTOM,NEPTOM,NEPTOM
      WRITE (6,370)
      WRITE (6,471) UZSBMT,UZSMET
      WRITE (6,472) LZSMET,LZSMET,LZSMET,LZSMET,LZSMET,LZSMET
      WRITE (6,473) SGWMET,SGWMET,S6WMET,SGWMET,SGWMET,S6WMET
      WRITE (6,474) SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT
      WRITE (6,475) RESBMT,RESSMT
      WRITE (6,476) SRGXMT,SRGXTM
      WRITE (6,477) TWBLMT
      WRITE (6,1208)
      WRITE (6,1210) ERSTOM,ERSNTM
      WRITE (6,1211) SRERMT,SRRTMT
      WRITE (6,1212) EIMTM
        IF (HYCAL  .NE. 0) GO TO 1049
      WRITE (6,1207)
      WRITE (6,1221) STSMET,STSTMT
      WRITE (6,1222) SASMET,SASTMT
      WRITE (6,1223) SCSMET,SCSTMT
      WRITE (6,1227) SDSMET,SDSTMT
      WRITE (6,1224) UTSMET,UTSTMT
      WRITE (6,1222) UASMET,UASTMT
      WRITE (6,1223) UCSMET,UCSTMT
      WRITE (6,1227) UDSMET,UDSTMT
      WRITE (6,1228) LSTRMT
      WRITE (6,1229) LASMET
      WRITE (6,1230) LCSMET
      WRITE (6,1231) LDSMET
      WRITE (6,1231) GSTRMT
      WRITE (6,1229) GASMET
      WRITE (6,1230) GCSMET
      WRITE (6,1231) GDSMET
 1049 WRITE (6,1239) PRTTOM,PRTM
      WRITE (6,1241) PROTOM,SPROTM
      WRITE (6,1242) PRSTOM.SPRSTM
      WRITE (6,1243) UPITOM,UPRITM
        IF (HYCAL  .EQ. 1) GO TO 1055
      WRITE (6,1238)
      WRITE (6,1245) VLTMMT
      WRITE (6,1246) VOLSOM
      WRITE (6,1247) VOLUOM
                                    - 162 -

-------
Appendix C     (continued)
      WRITE (6,1249)
      WRITE (6,1245) DEGTMT
      WRITE (6,1246) DEGSMT
      WRITE (6,1247) DEGUMT
      WRITE (6,1252) DEGLMT
      WRITE (6,1266) TPBALM
                    ZEROING OF VARIABLES
C
C
 1055       PRTOM =0.0
      RUTOM =0.0
      NEPTOM =0.0
      ROSTOM =0.0
      RITOM =0.0
      RINTOM = 0.0
      BASTOM =0.0
      RCHTOM =0.0
            EPTOM =0.0
            ERSNTM-=  0.0
            EIMTM = 0.0
            PRTM =0.0
            SPROTM =0.0
            SPRSTM =0.0
            UPRITM =  0.0
            VOLSOM =  0.0
            VOLUOM =0.0
            DEGSOM =0.0
            DEGUOM =0.0
            DEGLOM =0.0
 C
            DO  1058   1=1,5
                ERSTOM(I)  =0.0
                ROBTOM(I)  = 0.0
                INFTOM(I)  = 0.0
                PRTTOM(I)  =0.0
                PROTOM(I)  =0.0
                PRSTOM(I)  =0.0
                UPITOM(I)  = 0.0
  1058          ROITOM(I)= 0.0
 C
  1060      CONTINUE

                     YEARLY SUMMARY
             DO 1061   1= 1,5
  1061       PRTTOT(I) = PRSTOT(I) + PROTOT(I) + UPITOT(I)

             VOLTOT = VOLSOT + VOLUOT
             DEGTOT = DEGSOT + DEGUOT + DEGLOT
 C
 C
                                     - 163 -

-------
Appendix C     (continued)
            PRTT = SPROTT + SPRSTT + UPRITT
C
C
         WRITE (6,1250)
         WRITE (6,1251)
         WRITE (6,1103)
YEAR
      IF (UNIT .EQ. 1) 60 TO 1066
      WRITE (6,360)
      WRITE (6,361) PRTOT,PRTOT,PRTOT,PRTOT,PRTOT,PRTOT
      WRITE (6,362)
      WRITE (6,363) ROBTOT, ROSTOT
      WRITE (6,364) INFTOT, RINTOT
      WRITE (6,365) RITOT
      WRITE (6,366) ROITOT, RUTOT
      WRITE (6,380) BASTOT
      WRITE (6,381) RCHTOT
      WRITE (6,367)
      WRITE (6,368) EPTOT,EPTOT,EPTOT,EPTOT,EPTOT,EPTOT
      WRITE (6,369) NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT
      WRITE (6,370)
      WRITE (6,371) UZSB,UZS
      WRITE (6,372) LZS,LZS,LZS,LZS,LZS,LZS
      WRITE (6,373) SGW,SGW,SGW,SGW,SGW,SGW
      WRITE (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
      WRITE (6,375) RESB,RESS
      WRITE (6,376) SRGX,SRGXT
      WRITE (6,377) TWBAL
         WRITE (6,1209)
         WRITE (6,1210) ERSTOT.
         WRITE (6,1211)
         WRITE (6,1212)
            IF (HYCAL .NE. 0)
         WRITE (6,1220)
         WRITE (6,1221)  STS, STST
         WRITE (6,1222)  SAS, SAST
         WRITE (6,1223)  SCS, SCST
         WRITE (6,1227) SDS, SDST
         WRITE (6,1224)  UTS, UTST
         WRITE (6,1222)  UAS, UAST
         WRITE (6,1223)
         WRITE (6,1227)
         WRITE (6,1228)
        ERSNTT
SRER, SRERT
EIMTT
      GO TO 1063
         WRITE (6,1229)
         WRITE (6,1230)
         WRITE (6,1231)
         WRITE (6,1232)
UCS, UCST
UDS, UDST
LSTR
LAS
LCS
IDS
GSTR
                                    -  164 -

-------
Appendix  C    (continued)
                         GAS
                         GCS
                         GDS
                         PRTTOT, PRTT
                         PROTOT, SPROTT
                         PRSTOT, SPRSTT
                         UPITOT, UPRITT
                         1)  GO TO 1066

                         VOLTOT
                         VOLSOT
                         VOLUOT
        WRITE  (6,1229)
        WRITE  (6,1230)
        WRITE  (6,1231)
 1063    WRITE  (6,1240)
        WRITE  (6,1241)
        WRITE  (6,1242)
        WRITE  (6,1243)
          IF  (HYCAL  .EQ
        WRITE  (6,1244)
        WRITE  (6,1245)
        WRITE  (6,1246)
        WRITE  (6,1247)
        WRITE  (6,1248)
        WRITE  (6,1245)  DEGTOT
        WRITE  (6,1246)  DEGSOT
        WRITE  (6,1247)  DEGUOT
        WRITE  (6,1252)  DEGLOT
        WRITE  (6,1266)  TPBAL
 1066  IF (UNIT  .EQ.  -1)  GO  TO 1065
:  CONVERSIONS
      PRTOT =PRTOT*MMPIN
      ROSTOT=ROSTOT*MMPIN
      RINTOT=RINTOT*MMPIN
      RITOT =RITOT*MMPIN
      RUTOT =RUTOT*MMPIN
      BASTOT=BASTOT*MMPIN
      RCHTOT=RCHTOT*MMPIN
      EPTOT =EPTOT*MMPIN
      NEPTOT=NEPTOT*MMPIN
      UZSMET=UZS*MMPIN
      LZSMET=LZS*MMPIN
      SGWMET=SGW*MMPIN
      SCEPMT=SCEP*MMPIN
      RESSMT=RESS*MMPIN
      TWBLMT=TWBAL*MMPIN
      SRGXTM=SRGXT*MMPIN
      ERSNTT=ERSNTT*METOPT
      SRRTMT=SRERT*METOPT
      EIMTT =EIMTT*METOPT
:  PESTICIDE
      STSTMT=STST*KGPLB
      SASTMT=SAST*KGPLB
      SCSTMT=SCST*KGPLB
      SDSTMT=SDST*KGPLB
      UTSTMT=UTST*KGPLB
      UASTMT=UAST*KGPLB
      UCSTMT=UCST*KGPLB
      UDSTMT=UDST*KGPLB
                                    - 165

-------
Appendix   C    (continued)

      LSTRMT=LSTR*KGPLB
      LASMET=LAS*KGPLB
      LCSMET=LCS*KGPLB
      LDSMET=LDS*KGPLB
      GSTRMT=GSTR*KGPLB
      GASMET=GAS*KGPLB
      GCSMET=GDS*KGPLB
      GDSMET=GDS*KGPLB
      PRTT  =PRTT*KGPLB
      SPROTT=SPROTT*KGPLB
      SPRSTT=SPRSTT*KGPLB
      UPRITT=UPRITT*KGPLB
      VLTMMT=VOLTOT*KGPLB
      VOLSOT=VOLSOT*KGPLB
      VOLUOT=VOLUOT*KGPLB
      DEGTMT=DEGTOT*KGPLB
      DEGSMT=DEGSOT*KGPLB
      DEGUMT=DEGUOT*KGPLB
      DEGLMT=DEGLOT*KGPLB
      TPBALM=TPBAL*KGPLB
C  METRIC MODIFICATION OF ARRAYS
      DO 1062 1=1,5
        ROBTOT(I)=ROBTOT(I)*MMPIN
        INFTOT(I)=INFTOT(I)*MMPIN
        ROITOT(I)=ROITOT(I)*MMPIN
        UZSBMT(I)=UZSB(I)*MMPIN
        RESBMT(I)=RESB(I)*MMPIN
        SRGXMT(I)=SRGX(I)*MMPIN
        ERSTOT(I) = ERSTOT(I)*METOPT
        SRERMT(I)=SRER(I)*METOPT
        STSMET(I)=STS(I)*KGPLB
        SASMET(I)=SAS(I)*KGPLB
        SCSMET(I)=SCS(I)*KGPLB
        SDSMET(I)=SDS(I)*KGPLB
        UTSMET(I)=UTS(I)*KGPLB
        UASMET(I)=UAS(I)*KGPLB
        UCSMET(I)=UCS(I)*KGPLB
        UDSMET(I)=UDS(I)*KGPLB
        PRTTOT(I)=PRTTOT(I)*KGPLB
        PROTOT(I)=PROTOT(I)*KGPLB
        PRSTOT(I)=PRSTOT(I)*KGPLB
        UPITOT(I)=UPITOT(I)*KGPLB
 1062 CONTINUE
C
      WRITE (6,460)
      WRITE (6,461) PRTOT,PRTOT,PRTOT,PRTOT,PRTOT,PRTOT
      WRITE (6,362)
      WRITE (6,463) ROBTOT,ROSTOT
                                    - 166 -

-------
Appendix  C    (continued)

      WRITE (6,464) INFTOT.RINTOT
      WRITE (6,465) RITOT
      WRITE (6,466) ROITOT.RUTOT
      WRITE (6,480) BASTOT
      WRITE (6,481) RCHTOT
      WRITE (6,367)
      WRITE (6,468) EPTOT,EPTOT,EPTOT,EPTOT,EPTOT,EPTOT
      WRITE (6,469) NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT,NEPTOT
      WRITE (6,370)
      WRITE (6,471) UZSBMT.UZSMET
      WRITE (6,472) LZSMET,LZSMET,LZSMET,LZSMET,LZSMET,LZSMET
      WRITE (6,473) SGWMET,SGWMET,SGWMET,SGWMET,SGWMET,SGWMET
      WRITE (6,474) SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT,SCEPMT
      WRITE (6,475) RESBMT.RESSMT
      WRITE (6,476) SRGXMT.SRGXTM
      WRITE (6,477) TWBLMT
      WRITE (6,1208)
      WRITE (6,1210) ERSTOT.ERSNTT
      WRITE (6,1211) SRERMT.SRRTMT
      WRITE (6,1212) EIMTT
         IF (HYCAL  .NE. 0) GO TO 1064
      WRITE (6,1207)
      WRITE (6,1221) STSMET.STSTMT
      WRITE (6,1222) SASMET.SASTMT
      WRITE (6,1223) SCSMET.SCSTMT
      WRITE (6,1227) SDSMET,SDSTMT
      WRITE (6,1224  UTSMET.UTSTMT
      WRITE (6,1222  UASMET.UASTMT
      WRITE (6,1223  UCSMET.UCSTMT
      WRITE (6,1227) UDSMET.UDSTMT
      WRITE (6,1228) LSTRMT
      WRITE (6,1229) LASMET
      WRITE (6,1230) LCSMET
      WRITE (6,1231) LDSMET
      WRITE (6,1231) GSTRMT
      WRITE (6,1229) GASMET
      WRITE (6,1230) GCSMET
      WRITE (6,1231) GDSMET
  1064 WRITE (6,1239) PRTTOT.PRTT
      WRITE (6,1241) PROTOT.SPROTT
      WRITE (6,1242) PRSTOT.SPRSTT
      WRITE (6,1243) UPITOT.UPRITT
         IF (HYCAL  .EQ. 1) GO TO 1065
      WRITE (6,1238)
      WRITE (6,1245) VLTMMT
      WRITE (6,1246) VOLSOT
      WRITE (6,1247) VOLUOT
      WRITE (6,1249)
                                    - 167 -

-------
Appendix  C     (continued)

      WRITE (6,1245) DEGTMT
      WRITE (6,1246) DEGSMT
      WRITE (6,1247) DEGUMT
      WRITE (6,1252) DEGLMT
      WRITE (6,1266) TPBALM
C
C
C
                   ZEROING OF VARIABLES
 1065    PRTOT =0.0
      RUTOT =0.0
      NEPTOT =0.0
      ROSTOT =0.0
      RITOT = 0.0
      RINTOT = 0.0
      BASTOT =0.0
      RCHTOT =0.0
         EPTOT =0.0
         ERSNTT =0.0
         EIMTT =0.0
         PRTT = 0.0
         SPROTT =0.0
         SPRSTT =0.0
         UPRITT = 0.0
         VOLSOT =0.0
         VOLUOT =0.0
         DEGSOT =0.0
         DEGUOT =0.0
         DEGLOT =0.0

         DO 1068  1=1,5
            ERSTOT(I) = 0.0
            ROBTOT(I) =0.0
            INFTOT(I) = 0.0
            PRTTOT(I) = 0.0
            PRSTOT(I) = 0.0
            PROTOT(I) =0.0
            UPITOT(I) = 0.0
 1068       ROITOT(I) = 0.0

 1070    CONTINUE
C
C
C
 1080 CONTINUE
      WRITE (6,1260)
                   FORMAT STATEMENTS

1090 FORMAT ('I1,'*****ERROR*****  INCORRECT INPUT DATAJg   DESIRED  '
    *   'CARD  ',11,'  FOR ',I2,'/1,I2,1/M4,1; READ CARD  ',11,' FOR
                                    - 168 -

-------
Appendix   C   (continued)

1091
1092
1093
1094
1095
1101
1102
1103
* 12,'
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
Of2'7

('0'
('!'
(IX,
(IX,
( ' 1 '
( '+'
Co1
C 5X,' TOTAL
1104
1105
1106
1107
1108
1109
1200
1201
1208
1207
1209
1210
1211
1212
1220
1221
1222
1223
1224
1227
1228
1229
1230
1231
1232
1239
1238
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
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FORMAT
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FORMAT
FORMAT
FORMAT
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FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
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FORMAT
('0'
('0'
('0'
('0'
('0'
Co1
C r
( ' + '
Co1
('0'
Co1
( ' •
( ' '
( ' '
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/ 1 i
( ' '
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('0'
I ' '
( ' '
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('0'
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( ' '
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('!'
,25X
)
,25X
312,
312,
,25X
,25X
,35X
i \
,32X
,32X
,32X
,32X
,32X
,32X
,25X
,25X
,8X,
,5X,
, 8X
,11X
,11X
,11X
,5X,
, 8X
,11X
,11X
, 8X
,11X
, 8X
,11X
,11X
,11X
, 8X
,8X,
,8X,
, 8X
,11X
,11X
,11X
,8X,
,11X
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,8X,
,8X,
,25X
',14)
,'THIS IS A PRODUCTION RUN1)

,'THIS IS A CALIBRATION RUN FOR LANDS')
11,1216)
11,3612)
,I2,':',I2,1 ON ',I2,1X,A4,A4,1X,I4)
' )
,'ZONE 1 ZONE 2 ZONE 3 ZONE 4 ZONE 5',

, 'APPLICATION: SURFACE-APPLIED')
, 'APPLICATION: SOIL-INCORPORATED1)
, 'PESTICIDE: ' ,A10)
, 'WATERSHED: ' ,A10)
,' INPUT UNITS: ENGLISH')
,' INPUT UNITS: METRIC')
,' SUMMARY FOR MONTH OF ' ,A4,A4,1X,I4)
i i \
'SEDIMENT, TONNES')
'PESTICIDE, KILOGRAMS')
,' SEDIMENT, TONS')
, 'TOTAL SEDIMENT LOSS ' ,5(3X,F7.3) ,4X,F7.3)
, 'FINES DEPOSIT' ,6X,5(3X,F7.3) ,4X,F7.3)
,' IMPERVIOUS EROSION', 55X,F7. 3)
"PESTICIDE, POUNDS')
, -SURFACE LAYER' ,10X,5(3X,F7.3),3X,F8.3)
,' ADSORBED ' ,12X,5(3X,F7. 3), 3X,F8. 3)
,' CRYSTALLINE ' ,9X,5(3X,F7. 3), 3X,F8. 3)
, 'UPPER ZONE LAYER',7X,5(3X,F7.3),3X,F8.3)
,' DISSOLVED ',11X,5(3X,F7. 3), 3X,F8. 3)
,' LOWER ZONE LAYER' ,60X,F8.3)
, "ADSORBED1 ,65X,F8.3)
, 'CRYSTALLINE', 62X,F8. 3)
, 'DISSOLVED', 64X,F8. 3)
,'GROUNDWATER LAYER' ,59X,F8.3)
'PESTICIDE REMOVAL, KGS. ' ,2X,5(F7.3,3X) ,F8.3)
'PESTICIDE VOLATILIZATION LOSS, KGS.')
, 'PESTICIDE REMOVAL, LBS. ' ,2X,5(F7.3,3X) ,F8.3)
, 'OVERLAND FLOW REMOVAL ' ,1X,5(F7.3,3X) ,F8.3)
, 'SEDIMENT REMOVAL' ,6X,5(F7.3,3X),F8.3)
,' INTERFLOW REMOVAL' ,5X,5(F7.3,3X) ,F8.3)
'PESTICIDE VOLATILIZATION LOSS, LBS.')
,'TOTAL',68X,F7.3)
,'FROM SURFACE', 61X,F7. 3)
,'FROM UPPER ZONE',58X,F7.3)
'PESTICIDE DEGRADATION LOSS, LBS.1)
'PESTICIDE DEGRADATION LOSS, KGS.1)
,' SUMMARY FOR ',14)
                                  - 169 -

-------
Appendix
(continued)
1251
FORMAT
1252 FORMAT
1260 FORMAT
1262 FORMAT
1263 FORMAT
X
1265 FORMAT
1264 FORMAT
1266 FORMAT
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
380
381
382
460
461
463
464
465
466
480
481
468
469
471
472
473
474
475
476
477
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
FORMAT
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                                   -  170  -

-------
Appendix  C    (continued)
      STOP
      END
C
C
      BLOCK DATA
C
C
C                   BLOCK DATA TO INITIALIZE VARIABLES
C
C
      IMPLICIT  REAL(L)
C
      DIMENSION  RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5), ROSB(5)
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
      DIMENSION  ROBTOM(5), ROBTOT(5),  INFTOM(5), INFTOT(5), INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5),  ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5),  PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5),  RESB1(5), SSTR(5), USTR(5)
C
      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
      COMMON  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
      COMMON  MNAM, IX,  IZ, DAY, I FLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
      COMMON  STS, STST, SAST, SCST, SDST, UTS, UTST, UAST, UCST, UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS,  SDS, AREA, M, K, FP, CMAX, SSTR, Ml, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS,  UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
                                    -  171  -

-------
Appendix  C     (continued)

C
      INTEGER  PRNTKE, UNIT
C
      REAL  M, K, NI, MUZ, MLZ
      REAL  LZSN, IRC, NN, L, LZS, KV, K24L, KK24, INFIL, INTER
      REAL  IPS, K24EL, K3, NEPTOM, NEPTOT
      REAL  INFTOM, INFTOT, INTF
      REAL  MMPIN, METOPT, KGPLB
C
      DATA  PRTOT, ERSNTT, EIMTT, PRTT/4*0.0/
      DATA  PRTOM, ERSNTM, EIMTM, PRTM/4*0.0/
      DATA  RUTOM, ROSTOM, RITOM, RINTOM, NEPTOM/5*0.0/
      DATA  RUTOT, ROSTOT, RITOT, RINTOT, NEPTOT/5*0.0/
      DATA  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITQM, ROITOT/30*0.0/
      DATA  PROTOM, PROTOT, PRSTOM, PRSTOT, UPITOM, UPITOT/30*0.0/
      DATA  TWBAL, RESB, SRGX, INTF, ERSTOM, ERSTOT, SDST/27*0.0/
      DATA  RESB1, BASTOM, RCHTOM, BASTOT, RCHTOT/9*0.0/
      DATA  SPROTM, SPRSTM, EPTOM, EPTOT/4*0.0/, PRNTKE/0/
      DATA  STS, STST, SAST, SCST, UTS, UTST, UAST, UCST,UDST/17*0.0/
      DATA  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, DPST/28*0.0/
      DATA  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS/9*0.0/
      DATA  A, UZS,- LZS, SGW, GWS, KV, K24L, KK24/8*0.0/
      DATA  IFS, K24EL, K3, EPXM, COVER, COVMAX/6*0.0/
      DATA  ERSN/5*0.0/, SRER/5*0.0/, SRERT/0.0/
      DATA  SAS/5*0.0/, SCS/5*0.0/, SDS/5*0.0/, AREA, M, K/3*0.0/
      DATA  NI, FP, CMAX, SSTR/8*0.0/
      DATA  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT/9*0.0/
      DATA  UAS/5*0.0/, UCS/5*0.0/, UDS/5*0.0/, USTR, MUZ, FPUZ/7*0.0/
      DATA  UPRP/5*0.0/, UPRITT, UPRITM/2*0.0/
      DATA  LSTR, LAS, LCS, LDS, MLZ, LPRP/6*0.0/
      DATA  GSTR, GAS, GCS, GDS, FPLZ/5*0.0/
      DATA  MNAM/' JAN','UARY','FEBR','UARY','   MA'.'RCH ','   AP1,
     *      'RIL V   MA'.'Y   ','  JUVNE  ','  JU'.'LY  ',' AUG' ,
     *      'UST ' ,'SEPT1,'MBERV OCT','OBER1,'NOVE','MBER','DECE',
     *      'MBER1/
      DATA  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS/6*0.0/
      DATA  MMPIN/25.4/, METOPT/0.9072/, KGPLB/0.4536/
      DATA  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS/6*0.0/
      DATA  DEGLOM, DEGLOT/2*0.0/
C
      END
C
C
      SUBROUTINE LANDS
C
C
C                             HSP LANDS
C
                                    - 172 -

-------
Appendix C     (continued)
      IMPLICIT  REAL(L,K)

      DIMENSION  RXB(5), RGB(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5)5 PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
      DIMENSION  SHRD(5), RXX(5), ROSB(5), DEEPL(5)
      DIMENSION  UZRA(5), R6X(5), RESBMT(5), SRGXMT(5), UZSBMT(5)
      DIMENSION  PRE(5), INFL(5), UZI(5), DPERCB(5)
      DIMENSION  EVDIST(24), ROSINT(5), MNAM(24)
      DIMENSION  ARXB(5), ARGX(5), ADPRCB(5), ARIB(5)
      DIMENSION  AROSB(5), AINTF(5), AROSIT(5)

      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
      COMMON  ROBTOM, ROBTOT,  INFTOM,  INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT,  PROTOM,  PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSB, TWBAL, SRGX, RU, HYMIN, INTF
      COMMON  MNAM, IX,  IZ, DAY, I FLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM,  ERSTOM,  ERSTOT, EPTOM, EPTOT, PRNTKE
      COMMON  STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
      COMMON  PR, P3, RXB, RGB, RUZB,  UZSB, PERCB, HYCAL, DPST, RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
      COMMON  IFS, K3,  EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER,  KSER, ERSN, SRERT
      COMMON  SAS, SCS,  SDS, AREA, M,  K, FP, CMAX, SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS,  UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
      COMMON  GSTR, GAS, GCS,  GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT,  VOLUOM,  VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT,  DEGUOM,  DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT

      INTEGER  TF, PRNTKE, HYCAL, DAY, UNIT

      REAL   INFIL, INTER, NN,  INFLT, IRC, INTF, INFL
      REAL   IRC4, ICS,  IFS, NEPTOM, NEPTOT
                                     -  173  -

-------
Appendix   C    (continued)

      REAL  INFTOM, INFTOT, QMETRC
      REAL  MMPIN, METOPT, K6PLB
      REAL  UZSMET, LZSMET, SGWMET, SCEPMT, RESSMT
      REAL  TWBLMT, SRGXTM, RESBMT, SRGXMT
C
      DATA  SIMP/0.0/, IHRR/0/
      DATA  PERC, INFLT/0.0,0.0/
      DATA  SBAS/0.0/
      DATA  RG, RIBT/2*0.0/, DPERCB, DPERC/6*0.0/
      DATA  SNET1, SNET, SRCH/3*0.0/, NUMI/0/
      DATA  ROSINT/5*0.0/, REPIN, EPIN1, AETR, KF/4*0.0/
      DATA  EVDIST/6*0.0,0.019,0.041,0.067,0.088,0.102,3*0.11,0.105,
     C      0.095,0.081,0.055,0.017,5*0.O/
      DATA  ARXB, ARGX, ADPRCB, ARIB, ADPST, APR, AEPIN/23*0.0/
      DATA  AROSE, AINTF, AROSIT/15*0.0/
      DATA  ARU, ARUI, AROS, ARGXT, ASNET, ASBAS, ASRCH/7*0.0/
C
C                   ZEROING OF VARIABLES
C
      LZS1 = LZS
      UZS1 = UZS
      IHRR = 1
      NUMI = 0
      DPST = 0.0
C
        PA=1.0-A
        IRC4=IRC**(1.0/96.0)
        LIRC4=1.0-IRC4
       KK4=KK24**(1.0/96.0)
        LKK4= 1.0 - KK4
C
      IF ((24.*60./TIMFAC) .61. 100.)  GO TO 185
      GO TO 187
 185  LIRC4 = LIRC4/3.0
      LKKR = LKK4/3.0
C
 187  DEC= 0.00982*((NN*L/SQRT(SS))**0.6)
      SRC= 1020.*SQRT(SS)/(NN*L)
C
      RESS =0.0
      LNRAT=LZS/LZSN
      D3FV=(2.0*INFIL)/(LNRAT*LNRAT)
      D4F= (TIMFAC/60.)*D3FV
      RATIO= INTER*EXP(0.693147*LNRAT)
      IF ((RATIO).LT.(1.0)) RATIO=1.0
      D4RA= D4F*RATIO
      DO 155  111=1,TF
                                     - 174 -

-------
Appendix  C    (continued)
      LNRAT = LZS/LZSN
      IF (TF .LT. 2)  GO TO 4
      H = TF/24
      NUMI =NUMI + 1
      IF (NUMI  .EQ. H)  GO TO 2
      GO TO 4
  2   NUMI = 0
C
  4   RX = 0.0
      SBAS = 0.0
      SRCH = 0.0
      ROS = 0.0
      RU = 0.0
      GWF = 0.0
      RGXT = 0.0
      PERC = 0.0
      INFLT = 0.0
C
C   TIMFAC - TIME INTERVAL IN MINUTES
C   L      - LENGTH OF OVERLAND SLOPE
C   NN     - MANNING'S N FOR OVERLAND SLOPE
C   A      - IMPERVIOUS AREA
C   PA    - PERVIOUS AREA
C
C
C
C
C PR IS INCOMING RAINFALL
C P3 IS RAIN REACHING SURFACE(.OO'S INCHES)
C P4 IS TOTAL MOISTURE AVAILABLE( IN.
C RESS  IS OVERLAND FLOW STORAGE( IN.
C D4F IS 'B' IN OP. MANUAL
C RATIO IS 'C1   IN OP. MANUAL
C EP -  DAILY EVAP (IN.)
C EPHR - HOURLY EVAP
C EPIN - INTERVAL EVAP
C
C
       IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0))  GO TO 202
       GO TO 197
  202 IF (TF .LT. 2)  GO TO 200
       IHRR = IHRR + 1
      GO TO 201
  200 IHRR = IHR + 1
  201 IF (IHRR  .61. 24)  IHRR = 24
       EPHR • EVDIS?(!H!lR)*EP
       IF (EPHR.LE.(0.0001))   EpHR=0-0
                                    -  175  -

-------
Appendix  C     (continued)

       EPIN= EPHR
       EPIN1=EPIN
C
C
C   * * *    INTERCEPTION  FUNC.     * * *
C
C
C EPXM - MAX. INTERCEPTION STORAGE
C SCEP - EXISTING INTER. STORAGE
C EPX  - AVAILABLE INTER. STORAGE
C SIMP - SUM OF IMPERVIOUS RUNOFF
C RUI  - IMPERVIOUS RUNOFF DURING INTERVAL
C
C
C
 197   IF (COVER - 0.0001)  198,198,204
 198   SNET = SNET + SCEP
       SCEP = 0.0
       EPX = 0.0
       GO TO 203
C
C
 204    EPX=EPXM*(COVER/COVMAX)-SCEP
        IF(EPX.LT.(0.0001))  EPX=0.0
        IF (PR-EPX)   205,203,203
  203   P3= PR-EPX
        RU= P3*A
         RUI=RU
        SIMP=SIMP+RU
        SCEP = SCEP+EPX
        GO TO 206
  205   SCEP = SCEP+PR
        P3=0.0
        RU=0.0
        RUI=0.0
C
C
C
C   * * *      INTERCEPTION EVAP      * * *
C
C
 206   IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0))  GO TO 207
       GO TO 221
C
  207  IF  (SCEP)  221,221,208
  208  IF (SCEP-EPIN)  209,210,210
  209  EPIN = EPIN - SCEP
       SNET = SNET + SCEP
                                    -  176  -

-------
Appendix  C     (continued)

       SCEP - 0.0
       GO TO 221
  210  SCEP=SCEP-EPIN
  220  SNET=SNET+EPIN
       EPIN - 0.0
C
  221  REPIN=0.0
C
C     ***  INFILTRATION FUNC.  ***
C  P4 IS TOTAL MOISTURE IN STORAGE BLOCK
C SHRD(I) = SURFACE DETENTION AND INTERFLOW FROM BLOCK I
C RXX(I) = SURFACE DETENTION FROM BLOCK I
C RGXX(I) = INTERFLOW  COMPONENT FROM BLOCK I
C  RGX(I) = VOLUME TO INTER. DETEN STOR. FROM BLOCK I
C
C
C      BEGINNING OF BLOCK LOOP
C
C
      DO 100 1=1,5
      P4 = P3 + RESB(I)
      RESBI(I) = RESB(I)
      IF ((10.*P4)-(((2.*I)-1)*D4F)) 10,10,15
   10 SHRD(I)=0.0
      GO TO 25
   15 SHRD(I)= (P4-(((2.*!)-!.0)*D4F/10.))
   16 IF ((10.*P4)-(((2.*!)-!.0)*D4RA)) 25,25,30
   25 RXX(I)= 0.0
      GO TO 31
   30 RXX(I)= (P4-(((2.*!)-!.0)*D4RA/10.))
   31 RGXX  = SHRD(I)-RXX(I)
C
C
C   ***  UPPER ZONE FUNCTION ***
C
C PRE(I)  - % SURFACE DETENTION TO OVERLAND FLOW
C UZSB(I) -  UPPER ZONE STORAGE IN EACH BLOCK
C UZS  - TOTAL UPPER ZONE STORAGE
C RUZB(I) - ADDITION TO U.Z. STORAGE DURING INTERVAL
C
      UZRA(I)= UZSB(I)/UZSN
      IF (UZRA(I)-2.0) 7,7,8
   7  UZI(I)= 2.0*ABS((UZRA(I)/2.0)-1.0) +1.0
      PRE(I)= (UZRA(I)/2.0)*((1.0/(1.0+UZI(I)))**UZI(I))
      GO TO 9
   8  UZI(I)= (2.0*ABS(UZRA(I)-2.0))+1.0
      PRE(I)= 1.0-((1.0/(1
   9  RXB(I)= RXX(I)* PRE(I)
                                    -  177  -

-------
Appendix  C    (continued)

        RGX(I)=RGXX*PRE(I)
        RGXX=0.0
        RUZB(I)=SHRD(I)-RGX(I)-RXB(I)
        UZSB(I)=UZSB(I)+RUZB(I)
C
        RIB(I) - P4 - RXB(I)
C
C
C
C   * * *      UPPER ZONE EVAP    * * *
C
C
C REPIN - EVAP POT. FOR I.I. AND GRDWATER,  I.E
C         PORTION NOT SATISFIED FROM U.Z.
C
C
        IF  ((NUMI  .EQ. 0).AND.(IMIN .EQ. 0))  GO TO  235
        GO  TO 290
C
 235    IF  (EPIN.LE.(O.O))  GO TO 290
          EFFECT-1.0
          IF(UZRA(I)-2.0)  230,230,240
  240     IF  (UZSB(I)-EPIN)  270,270,260
  260     UZSB(I)=UZSB(I)-EPIN
          RUZB(I)= RUZB(I)-EPIN
          SNET=SNET+PA*EPIN*0.20
          GO TO 290
  230     EFFECT= 0.5*UZRA(I)
          IF  (EFFECT.LT.(0.02))  EFFECTED.02
          IF  (UZSB(I)-EPIN*EFFECT)  270,270,280
  280     UZSB(I)=UZSB(I) - (EPIN*EFFECT)
          RUZB(I)= RUZB(I)-(EPIN*EFFECT)
          EDIFF= (1.0-EFFECT)*EPIN
          REPIN=REPIN + EDIFF*0.20
          EDIFF=0.0
          SNET= SNET + (PA*EPIN*EFFECT)*0.20
          GO TO 290
  270     EDIFF= EPIN - UZSB(I)
          REPIN= REPIN +  EDIFF*0.20
          EDIFF=0.0
          SNET= SNET + PA*UZSB(I)*0.2rO
          UZSB(I)=0.0
          RUZB(I)=0.0
C
C
c    * * *  *    INTERFLOW FUNCTION * * *
C
                                    - 178 -

-------
Appendix  C     (continued)

C     SRGX(I) - INTERFLOW DETENTION STORAGE FROM BLOCK I
C     INTF(I) - INTERFLOW LEAVING STORAGE FROM BLOCK I
C     SRGXT - TOTAL INTERFLOW STORAGE
C     RGXT  - TOTAL INTERFLOW LEAVING STORAGE DURING INTERVAL
C
  290   INTF(I) = LIRC4*SRGX(I)
        SRGX(I)=SRGX(I)+(RGX(I)*PA)-INTF(I)
         RU-RU + INTF(I)*0.20
        SRGXT= SRGXT + (RGX(I)*PA-INTF(I))*0.20
        RGXT=RGXT + INTF(I)*0.20
C
C ***    OVERLAND FLOW ROUTING ***
C
C
C RXB(I) = VOLUME TO OVERLAND SURFACE DETENTION FROM BLOCK I
C  ROSB(I) = VOLUME OF OVERLAND FLOW TO STREAM FROM BLOCK I
C  RESB(I) = VOLUME OF OVERLAND Q REMAINING ON SURFACE
C              FROM BLOCK I
C
      Fl= RXB(I)-(RESB(I))
      F3= (RESB(I))+ RXB(I)
      IF  (RXB(I)-(RESB(I))) 34,34,32
   32 DE= DEC*((F1)**0.6)
      GO TO 35
   34 DE= (F3)/2.0
   35 IF  (F3-(2.0*DE)) 38,38,36
   36 DE=(F3)/2.0
   38 IF  ((F3)-(.005)) 40,40,42
   40 ROSB(I)= 0.0
      GO TO 43
   42    DUMV=(1.0+0.6*(F3/(2.0*DE))**3.)**1.67
      ROSB(I)=(TIMFAC/60.)*SRC*((F3/2.)**1.67)*DUMV
      IF  ((ROSB(I)).GT.(.95*RXB(I))) GO TO 122
      GO TO 43
  122 IF  ((RXB(I)).GT.(0.0)) GO TO  121
      GO TO 43
  121  ROSB(I)=(.95)*RXB(I)
   43 RESB(I)= RXB(I)-ROSB(I)
         ROSB(I) = ROSB(I)*PA
        ROSINT(I) = ROSB(I) + INTF(I)
C
C
C
C         * * *  UPPER ZONE DEPLETION * *  *
C
C  DEEPL(I)  - DIFFERENCE  IN UPPER AND LOWER ZONE  RATIOS
C  PERCB(I)  - UPPER ZONE  DEPLETION  FROM EACH  BLOCK
C  PERC   - TOTAL U.Z. DEPLETION
                                   - 179 -

-------
Appendix  C     (continued)

C  INFLT  - TOTAL INFILTRATION
C  ROS  - TOTAL OVERLAND FLOW TO THE STREAM FROM ALL BLOCKS
C
          IF ((NUMI .EQ. 0).AND.(IMIN .EQ. 0))  GO TO 44
          PERCB(I) = 0.0
          GO TO 47
C
  44      DEEPL(I)= ((UZSB(I)/UZSN)-(LZS/LZSN))
          IF (DEEPL(I)-.Ol)   47,47,45
   45     PERCB(I)=0.1*INFIL*UZSN*(DEEPL(I)**3)
          UZSB(I)=UZSB(I)-PERCB(I)
          PERC=PERC+PERCB(I)*0.2
          RUZB(I) = RUZB(I) - PERCB(I)
  47      INFL(I)= P4-SHRD(I)
           INFLT=INFLT + INFL(I)*0.20
      RESS = RESS + RESB(I)*0.2
       UZS= UZS + RUZB(I)*0.20
      ROS = ROS + ROSB(I)*0.2
  100 RX  = RX + RXB(I)*0.2
      IF  (UZS .LE. 0.0001) UZS=0.0
C
C END OF  BLOCK LOOP
C
          RU=RU + ROS
        IF ((RESS).LT.(0.0001))  GO TO 301
        GO TO 302
  301   LZS = LZS + RESS
        RESS = 0.0
        DO 306  IK= 1,5
  306   RESB(IK)= 0.0
  302   IF (SRGXT.LT.(0.0001))  GO TO 303
        GO TO 305
  303   LZS = LZS + SRGXT/PA
        SRGXT = 0.0
          DO 304  IK= 1,5
  304     SRGX(IK)= 0.0
C
C
C    * *  * LOWER ZONE AND GROUNDWATER  * * *
C
C  SBAS   - BASE STREAMFLOW
C  SRCH   - SUM OF GRDWATER RECHARGE
C PREL -  % OF INFILTRATION AND U.Z. DEPLETION ENTERING L.Z
C  F1A  - GROUNDWATER RECHARGE - IE. PORTION OF INFIL.
C         AND U.Z. DEPLETION ENTERING GRDWATER
C  K24L   - FRACTION OF F1A LOST TO DEEP GRDWATER
C
  305      LZI=1.5*ABS((LZS/LZSN)-1.0)+1.0
                                    -  180  -

-------
Appendix  C    (continued)

           PREL=(1.0/(1.0+LZI))**LZI
           IF (LZS.LT.LZSN)  PREL=1.0-PREL*LNRAT
           F3= PREL*(INFLT)
           F1A =  (1.0-PREL)*INFLT
           IF ((NUMI  .EQ. 0).AND.(IMIN  .EQ. 0))  GO TO 308
           GO TO  309
  308      F3 = F3 +  PREL*PERC
           F1A =  F1A  +  (1.0-PREL)*PERC
  309      LZS= LZS+F3
        Fl= F1A*(1.0  -  K24L)*PA
        6WF=SGW*LKK4*(1.0 + KV*GWS)
        SBAS= GWF
        SRCH= F1A*K24L*PA
          SGW-SGW  - GWF  + Fl
          GWS=GWS  + Fl
C
C   * * *      GROUNDWATER EVAP   * *  *
C
C
C LOS  -  EVAP LOST FROM GROUNDWATER
C
C
        IF (IHR  .EQ.  24)  GO TO 307
        GO TO 101
  307   IF (GWS  .61.  0.0001)  GWS = 0.97*GWS
        LOS= SGW*K24EL*REPIN*PA
        SGW=SGW - LOS
        GWS=GWS - LOS
        SNET= SNET +  LOS
        REPIN= REPIN  -  LOS
        IF (GWS.LT.(0.0))  GWS=0.0
C
C  * * *       LOWER  ZONE EVAP  * * *
C
C AETR -  EVAP LOST FROM L.Z.
C
C
       LNRAT = LZS/LZSN
       IF (REPIN.LT.(0.0001))  GO TO 101
        IF (K3-1.0)   300,310,310
  310   KF=50.0
        GO TO 320
  300   KF=0.25/(1.0-K3)
  320   IF (REPIN -  (KF*LNRAT))  330,330,340
  330   AETR= REPIN*(1.0-(REPIN/(2.0*KF*LNRAT)))
        GO TO 350
  340   AETR- 0.5*(KF*LNRAT)
                                     - 181 -

-------
Appendix  C     (continued)

  350   IF (K3.LT.(0.50))  AETR=AETR*(2.0*K3)
        LZS=LZS - AETR
        SNET= SNET + PA*AETR
  101   SNETI = SNET - SNET1
C
C
C
C WBAL - WATER BALANCE IN THE INTERVAL
C TWBAL - ACCUMULATED WATER BALANCE
C
C
  352   WBAL = (LZS-LZS1+UZS-UZS1+RESS-RESS1)*PA+(SNET-SNET1+SGW-SGW1+
     X          SCEP-SCEP1+SRCH+SRGXT-SRGXT1+GWF+RU-PR)
        IF ((WBAL .LE.  0.0001).AND.(WBAL .GE. -0.0001))  WBAL = 0.0
       TWBAL=TWBAL+WBAL
C
      DPS = F1A*PA
      DPST = DPST + DPS
C
C
C                   RESETTING VARIABLES
C
      LZS1=LZS
      UZS1=UZS
      RESS1=RESS
      SCEP1=SCEP
      SRGXT1=SRGXT
      S6W1=SGW
      SNET1=SNET
C
C
C                   ZEROING OF VARIABLES
C
C
C                   PREPARATION  OF OUTPUT
C
      ADPST = ADPST + DPST
       ASBAS = ASBAS + SBAS
       ASRCH = ASRCH + SRCH
      APR = APR + PR
         ARU = ARU + RU
         ARUI = ARUI + RUI
         AROS = AROS + ROS
         ARGXT = ARGXT + RGXT
         IF ((NUMI.EQ.O).AND.(IMIN.EQ.O))  GO TO 146
         GO TO 148
  146    AEPIN = AEPIN  + EPIN1
         ASNET = ASNET + SNETI
                                   - 182 -

-------
Appendix  C    (continued)
C

C
C
C
C
148 DO 150  1=1,5
       DPERCB(I) = RIB(I) - RGX(I)  - RUZB(I)  + 2*PERCB(I)
       ARXB(I) = ARXB(I) + RXB(I)
       ARGX(I) = ARGX(I) + RGX(I)
       ADPRCB(I) = ADPRCB(I) + DPERCB(I)
       ARIB(I) = ARIB(I) + RIB(I)
       AROSB(I) = AROSB(I) + ROSB(I)
       AINTF(I) = AINTF(I) + INTF(I)
       AROSIT(I) = AROSIT(I) + ROSINT(I)
150    CONTINUE

155 CONTINUE

    IF (PRNTKE .EQ. 0)  GO TO 180

       RX = 0.0
       RG = 0.0
       RIBT = 0.0
       DPERC = 0.0

       DO 158  1=1,5
          DPERC = DPERC + ADPRCB(I)*0.2
          RG = RG + ARGX(I)*0.2
          RX = RX + ARXB(I)*0.2
          RIBT = RIBT + ARIB(I)*0.2
158       CONTINUE

                   CUMULATIVE RECORDS

    PRTOM = PRTOM + APR
    EPTOM = EPTOM + AEPIN
    RUTOM = RUTOM + ARU
    ROSTOM = ROSTOM + AROS
    RITOM = RITOM + ARUI
    RINTOM = RINTOM + ARGXT
    NEPTOM = NEPTOM + ASNET
    BASTOM = BASTOM + ASBAS
    RCHTOM = RCHTOM + ASRCH
  157
      DO 157  1=1,5
         ROBTOM(I)  =
         ROBTOT(I)  =
         INFTOM(I)  =
         INFTOT(I)  =
         ROITOM(I)  =
         ROITOT(I)  =
ROBTOM(I)
ROBTOT(I)
INFTOM(I)
INFTOT(I)
ROITOM(I)
ROITOT(I)
AROSB(I)
AROSB(I)
AINTF(I)
AINTF(I)
AROSIT(I)
AROSIT(I)
                                   - 183 -

-------
Appendix  C    (continued)
C
C
C
C
 159
C
C
C
PRTOT = PRTOT + APR
EPTOT = EPTOT + AEPIN
RUTOT = RUTOT + ARU
ROSTOT = ROSTOT + AROS
RITOT = RITOT + ARUI
RINTOT =RINTOT + ARGXT
NEPTOT = NEPTOT + ASNET
BASTOT = BASTOT + ASBAS
RCHTOT = RCHTOT + ASRCH

IF (HYCAL)  159, 160, 159

              OUTPUT FOR HSP LANDS CALIBRATION RUN
IF (TF .61. 2)  GO TO 170
RU = (RU*AREA*43560.)/(TIMFAC*720.)
IF (RU .LT. HYMIN)  GO TO 170
QMETRC=RU*.0283
WRITE (6,379) MNAM(IZ),MNAM(IX),DAY,IHR.IMIN
WRITE (6,378) RU,QMETRC
GO TO 170

              OUTPUT FOR HSP LANDS PRODUCTION RUN  AND SUMMARIES
  160
IF (UNIT .EQ. 1) GO TO 161
WRITE (6,360)
WRITE (6,361) APR, APR, APR, APR, APR, APR
WRITE (6,362)
WRITE (6,363) AROSE, AROS
WRITE (6,364) AINTF, ARGXT
WRITE (6,365) ARUI
WRITE (6,366) AROSIT,ARU
WRITE (6,380) ASBAS
WRITE (6,381) ASRCH
WRITE (6,367)
WRITE (6,368) AEPIN, AEPIN5AEPIN, AEPIN, AEPIN
WRITE (6,369) ASNET, ASNET, ASNET, ASNET, ASNET
WRITE (6,370)
WRITE (6,371) UZSB,UZS
WRITE (6,372) LZS,LZS,LZS,LZS,LZS,LZS
WRITE (6,373) SGW,SGW,SGW,SGW,SGW,SGW
WRITE (6,374) SCEP,SCEP,SCEP,SCEP,SCEP,SCEP
WRITE (6,375) RESB,RESS
WRITE (6,376) SRGX,SRGXT
WRITE (6,377) TWBAL











, AEPIN
, ASNET








                                   - 184 -

-------
Appendix  C    (continued)
  161 IF (UNIT .EQ. -1) GO TO 170

   METRIC CONVERSIONS FOR OUTPUT
      APR   =APR*MMPIN
      AROS  =AROS*MMPIN
      ARGXT =ARGXT*MMPIN
      ARUI  =ARUI*MMPIN
      ARU   =ARU*MMPIN
      ASBAS =ASBAS*MMPIN
      ASRCH =ASRCH*MMPIN
      AEPIN =AEPIN*MMPIN
      ASNET =ASNET*MMPIN
      UZSMET=UZS*MMPIN
      LZSMET=LZS*MMPIN
      SGWMET=SGW*MMPIN
      SCEPMT=SCEP*MMPIN
      RESSMT=RESS*MMPIN
      TWBLMT=TWBAL*MMPIN
      SRGXTM=SRGXT*MMPIN
      DO 162 1=1,5
        AROSB(I) =AROSB(I)*MMPIN
        AINTF(I) =AINTF(I)*MMPIN
        AROSIT(I)=AROSIT(I)*MMPIN
        UZSBMT(I)=UZSB(I)*MMPIN
        RESBMT(I)=RESB(1)*MMPIN
        SRGXMT(I)=SRGX(I)*MMPIN
  162 CONTINUE
      WRITE (6,460)
      WRITE (6,461) APR,APR,APR,APR,APR,APR
      WRITE (6,362)
      WRITE (6,463) AROSB,AROS
      WRITE (6,464) AINTF,ARGXT
      WRITE (6,465) ARUI
      WRITE (6,466) AROSIT.ARU
      WRITE (6,480) ASBAS
      WRITE (6,481) ASRCH
      WRITE (6,367)
      WRITE (6,468) AEPIN,AEPIN,AEPIN,AEPIN,AEPIN,AEPIN
      WRITE (6,469) ASNET,ASNET,ASNET,ASNET,ASNET,ASNET
      WRITE (6,370)
      WRITE (6,471) UZSBMT, UZSMET
      WRITE (6,472) LZSMET, LZSMET, LZSMET, LZSMET,
      WRITE (6,473) SGWMET, SGWMET, SGWMET, SGWMET,
      WRITE (6,474) SCEPMT, SCEPMT, SCEPMT, SCEPHT!
      WRITE (6,475) RESBMT, RESSMT
      WRITE (6,476) SRGXMT, SRGXT
      WRITE (6,477) TWBLMT
      GO TO 170
LZSMET,  LZSMET
SGWMET,  SGWMET
SCEPMT,  SCEPMT
                                    -  185  -

-------
Appendix  C     (continued)
C
C
C
C
360 FORMAT ('0
361 FORMAT ('0
362 FORMAT ('0
363 FORMAT ('
364 FORMAT ('
365 FORMAT ('
366 FORMAT ('
367 FORMAT ('0
368 FORMAT ('
369 FORMAT ('
370 FORMAT ('0
371 FORMAT ('
372 FORMAT ('
373 FORMAT ('
374 FORMAT ('
375 FORMAT ('
376 FORMAT ('
377 FORMAT ('0
378 FORMAT ('+
379 FORMAT (2X
380 FORMAT ('0
381 FORMAT ('
460 FORMAT ('0
461 FORMAT ('0
463 FORMAT ('
464 FORMAT ('
465 FORMAT ('
466 FORMAT ('
480 FORMAT ('0
481 FORMAT ('
468 FORMAT ('
469 FORMAT ('
471 FORMAT ('
472 FORMAT ('
473 FORMAT ('
474 FORMAT ('
475 FORMAT ('
476 FORMAT ('
477 FORMAT ('0
C
170 APR = 0.0
ADPST = 0.(
AEPIN = 0.(
ARU = 0.0


F

,8X,
,11X
,11X
,14X
,14X
,14X
,14X
,11X
,14X
,14X
,11X
,14X
,14X
,14X
,14X
,14X
,14X
,11X
,25X
A4,A
,11X
,11X
,8X,
,11X
,14X
,14X
,14X
,14X
,11X
,11X
,14X
,14X
,14X
,14X
,14X
,14X
, 14
,14X
,11X


)
)

                    FORMAT STATEMENTS

                  ,8X,'WATER, INCHES')
                       PRECIPITATION',6X,5(3X,F7.3),4X,F7.3)
                       RUNOFF1)
                       OVERLAND_FLOW',3X,5(3X,F7.3),4X,F7.3)
                       INTERFLOW',7X,5(3X,F7.3),4X,F7.3)
                       IMPERVIOUS1,55X,5X,F7.3)
                       TOTAL',11X,5(3X,F7.3),4X,F7.3)
                       EVAPORATION1)
                       POTENTIAL',7X,5(3X,F7.3),4X,F7.3)
                       NET',13X,5(3X,F7.3),4X,F7.3)
                       STORAGES')
                       UPPER_ZONE',6X,5(3X,F7.3),4X,F7.3)
                       LOWER_ZONE',6X,5(3X,F7.3),4X,F7.3)
                       GROUNDWATER',5X,5(3X,F7.3),4X,F7.3)
                       INTERCEPTION',4X,5(3X,F7.3),4X,F7.3)
                       OVERLAND_FLOW',3X,5(3X,F7.3),4X,F7.3)
                       INTERFLOW',7X,5(3X,F7.3),4X,F7.3)
                       WATER_BALANCE=',F8.4)
                  ,25X,F5.1,3X,F5.3)
                 A4,A4,2X,I2,2X,I2,':',I2)
                       'BASE_FLOW',64X,F7.3)
                       'GRDWATER_RECHARGE',56X,F7.3)
                  ,8X,'WATER, MILLIMETERS')
                       1 PRECIPITATION',6X,5(3X,F7.2),4X,F7.2)
                       'OVERLAND_FLOW',3X,5(3X,F7.2),4X,F7.2)
                       1 INTERFLOW',7X,5(3X,F7.2),4X,F7.2)
                       1 IMPERVIOUS',60X,F7.2)
                       'TOTAL',11X,5(3X,F7.2),4X,F7.2)
                       'BASEFLOW1,65X,F7.2)
                       1GRDWATER_RECHARGE',56X,F7.2)
                       1 POTENTIAL',7X,5(3X,F7.2),4X,F7.2)
                       'NET',13X,5(3X,F7.2),4X,F7.2)
                       'UPPER_ZONE',6X,5(3X,F7.2),4X,F7.2)
                       'LOWER_ZONE',6X,5(3X,F7.2),4X,F7.2)
                       'GROUNDWATER',5X,5(3X,F7.2),4X,F7.2)
                       1 INTERCEPTION',4X,5(3X,F7.2),4X,F7.2)
                       ,'OVERLAND_FLOW',3X,5(3X,F7.2),4X,F7.2)
                  ,14X,'INTERFLOW' ,7X,5(3X,F7.2),4X,F7.2)
                       WATER BALANCE=',F8.3)
                                     - 186 -

-------
Appendix  C     (continued)
      ARUI = 0.0
      AROS =0.0
      ARGXT =0.0
      ASNET = 0.0
      ASBAS =0.0
      ASRCH = 0.0
      DO 172  1=1,5
         ARXB(I) = 0.0
         ARGX(I) = 0.0
         ADPRCB(I) = 0.0
         ARIB(I) = 0.0
         AROSB(I) = 0.0
         AINTF(I) = 0.0
         AROSIT(I) = 0.0
  172    CONTINUE
C
  180 CONTINUE
      RETURN
      END
C
C
      SUBROUTINE SEDT
C
C
C                            SEDIMENT EROSION MODEL
C
C
      DIMENSION  RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5),  RIB(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5),  MNAM(24)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5),  INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5),  USTR(5)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5),  UTS(5)
      DIMENSION  AERSN(5), ROSB(5), AERSNM(5), SRERMT(5)
C
      COMMON  PRTOT,  ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM,  ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM,  NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM,  RCHTOM
      COMMON  RUTOT,  NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT,  RCHTOT
      COMMON  ROBTOM,  ROBTOT,  INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM,  PRSTOT,  PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB,  RESB1, ROSE, TWBAL,  SRGX, RU, HYMIN,  INTF
      COMMON  MNAM,  IX,  IZ, DAY, I FLAG,  COVER, COVMAX
      COMMON  SPROTM,  SPRSTM,  ERSTOM, ERSTOT, EPTOM,  EPTOT,  PRNTKE
      COMMON  STS,  STST, SAST,  SCST, SDST, UTS, UTST, UAST,  UCST,  UDST
      COMMON  PR, P3,  RXB, RGX,  RUZB, UZSB, PERCB, HYCAL,  DPST,  RIB,UNIT
                                    - 187 -

-------
Appendix  C     (continued)

      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24,'K24EL, TF, EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS, UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS                            !
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEG.U, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
C
      INTEGER  PRNTKE, HYCAL, UNIT
C
      REAL JRER, KRER, JSER, KSER, A
      REAL  ERSNTT, SRRTMT, EIMMET
      REAL  MMPIN, METOPT, KGPLB
C
      DATA  ERSNT/0.0/, AERSN, AEIM/6*0.0/
C
C                   ZEROING OF VARIABLES
C
      SRERT =0.0
      ERSNT =0.0
      AR = 0.2*AREA
C
C                   SOIL EROSION LOOP
C
      DO 4452 1=1,5
         RER = (1.0 - COVER)*KRER*PR**JRER
            SRER(I) = SRER(I) + RER*AR
         EIM = A*RER*AREA
         IF (ROSB(I)+RESB(I)) 4442, 4442, 4444
C
 4442       ERSN(I) = 0.0
            SER = 0.
            GO TO 4446
C
 4444       SER = KSER*SRER(I)*(ROSB(I)+RESB(I))**JSER
            IF ((SER*AR) .GT. SRER(I)) SER = SRER(I)/AR
            ERSN(I) = (SER)*AR*(ROSB(I)/(ROSB(I)+RESB(I)))
            SRER(I) = SRER(I) - ERSN(I)
            IF (SRER(I) .LT. 0.) SRER(I) = 0.
                                    - 188 -

-------
Appendix  C    (continued)

C
 4446    AERSN(I) = AERSN(I) + ERSN(I)  + EIM*0.2
 4452    CONTINUE
C
      AEIM = AEIM + EIM
C
      IF (PRNTKE .EQ. 0)  GO TO 4490
C
         DO 4456  1=1,5
            ERSNT = ERSNT + AERSN(I)
            SRERT = SRERT + SRER(I)
            ERSTOM(I) = ERSTOM(I) + AERSN(I)
            ERSTOT(I) = ERSTOT(I) + AERSN(I)
 4456       CONTINUE
C
C                   CUMULATIVE RECORDS
C
      ERSNTM = ERSNTM + ERSNT
      EIMTM = EIMTM + AEIM
C
      ERSNTT = ERSNTT + ERSNT
      EIMTT = EIMTT + AEIM
C
        ERSNTP =0.0
        ERSNTK = 0.0
        ERSNCE = 0.0
        ERSNCM = 0.0
C
       IF (HYCAL .EQ. 0)  GO TO 4460
       IF (RU .LT. HYMIN)  GO TO 4487
C
C   CONVERSION OF SEDIMENT LOSS TO IBS., KGS., AND GM/L FOR OUTPUT
C
       ERSNTP = ERSNT*2000.
       ERSNTK = ERSNTP*.454
       ERSNCM = ERSNTP*454./(RU*TIMFAC*60.*28.32)
       WRITE (6,4484) ERSNTP, ERSNTK, ERSNCM
       GO TO 4487
C
C
C                     PRINTING OF OUTPUT
C
 4460 IF (UNIT .EQ. 1) GO TO 4462
      WRITE (6,4480)
      WRITE (6,4481)  (AERSN(I), 1=1,5), ERSNT
      WRITE (6,4482)  (SRER(I), 1=1,5), SRERT
      WRITE (6,4483)  EIM
                                    -  189  -

-------
Appendix  C    (continued)

 4462 IF (UNIT .EQ. -1) GO TO 4487
      ERSNTT=ERSNT*METOPT
      SRRTMT=SRERT*METOPT
      EIMMET=EIM*METOPT
      DO 4461 1=1,5
        AERSNM(I)=AERSN(I)*METOPT
        SRERMT(I)=SRER(I)*METOPT
 4461 CONTINUE
      WRITE (6,4485)
      WRITE (6,4481) AERSNM, ERSNTT
      WRITE (6,4482) SRERMT, SRRTMT
      WRITE (6,4483) EIMMET

                      FORMAT STATEMENTS

 4480 FORMAT ('0',8X,'SEDIMENT, TONS')
 4481 FORMAT ('   ',11X,'ERODED SEDIMENT1,4X,5(3X,F7.3),4X,F7.3)
 4482 FORMAT ('   ',11X,'FINES DEPOSIT',6X,5(3X,F7.3),4X,F7.3)
 4483 FORMAT ('   ' ,11X,'IMPERVIOUS EROSION',55X,F7.3)
 4484 FORMAT ('+',40X,3(3X,F7.2))
 4485 FORMAT ('0',8X,'SEDIMENT, TONNES')

 '4487 AEIM = 0.0
      DO 4489  1=1,5
         AERSN(I) = 0.0
 4489    CONTINUE-
C
C
C
 4490 CONTINUE

      RETURN
      END
C
C

C
C
C
C
C
      SUBROUTINE ADSRB1
                             SURFACE SOLUTION ADSORPTION MODEL
      DIMENSION  RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
      DIMENSION  ERSN(5), SRER(5), SR6X(5), RESB(5), MNAM(24)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5),, STSJ5), ROSB(5
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
      DIMENSION  SAPS(5), SCPS(5), SPRS(5), SPRO(5)
                                   -  190  -

-------
Appendix  C    (continued)

      DIMENSION  SPR(5), SPS(5), SCSC(5)S SASC(5),  SDSC(5),SPOFS(5)
      DIMENSION  ASPR(5), ASPRS(5), ASPRO(5), ASPRP(5)
      DIMENSION  STSMET(5), SASMET(5), SCSMET(5),  SDSMET(5)
C
      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM,  RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT,  RCHTOT
      COMMON  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSE, TWBAL, SRGX, RU,  HYMIN, INTF
      COMMON  MNAM, IX, IZ, DAY, IFLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT,  PRNTKE
      COMMON  STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST,  UCST,  UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB,  HYCAL, DPST,  RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L,  SS,  SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL,  TF,  EP
      COMMON  IKS, K3, EPXM, RESS1, RESS, SCEP, SCEP1,  SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN,  SRERT
      COMMON  SAS, SCS, SDS, AREA, M, K, FP, CMAX,  SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS, UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT,  KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, LDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
C
      INTEGER  PRNTKE, HYCAL, UNIT
C
      REAL  M, NI, K, KK, INFW
      REAL  STSTMT, SASTMT, SCSTMT, SDSTMT
      REAL  STSMET, SASMET, SCSMET, SDSMET
      REAL  MMPIN, METOPT, KGPLB
C
      DATA  SPST, SASCT, SCSCT, SPRT, SPRST/5*0.0/
      DATA  SPROT, SPRPT/2*0.0/, INFW/0.0/
      DATA  ASPR, ASPRS, ASPRO, ASPRP/20*0.0/
      DATA  SDSC, SPOFS, SDSCT/11*0.0/
C
C                   ZEROING VARIABLES
C
      STST = 0.0
      SAST = 0.0
      SCST = 0.0
                                   -  191 -

-------
Appendix   C    (continued)

      SDST = 0.0
      ERSNT =0.0
C
C                   ADSORPTION-SOLUTION LOOP
C
      Z =  1000000.**(NI-1)
      KK = M*K*Z
      DO 5320  1=1,5
         INFW = 0.2*AREA*(P3+RESB1(I))*226512.
         PTOT = SAS(I) + SCS(I) + SDS(I) + SSTR(I)-0.2*(VOLS+DEGS)
         IF (PTOT .LE. 0.0)  PTOT = 0.0
         IF (PTOT-FP)  5310, 5310, 5315
 5310       SAS(I) = PTOT
            SCS(I) = 0.0
            SDS(I) = 0.0
            GO TO 5320
 5315    X = KK*CMAX**NI + FP
         PSLD = PTOT - X - INFW*CMAX
         IF (PSLD .LT. 0.0)  GO TO 5316
            SAS(I) = X
            SCS(I) = PSLD
            SDS(I) = CMAX*INFW
            GO TO 5320
C
 5316       SCS(I) = 0.0
C
            C = CMAX*PTOT/(X + INFW*CMAX)
 5317       X = KK*C**NI + FP
            Q = (PTOT/(X+INFW*C)) - 1.
            IF (ABS(Q) - 0.01)  5319, 5319, 5318
 5318          C = C*PTOT/(X + INFW*C)
               GO TO 5317
C
 5319          IF (INFW  .LE. 0.001)   X = PTOT
C
               SDS(I) =  (C*INFW)*(PTOT/(X+C*INFW))
               SAS(I) =  X*(PTOT/(X+C*INFW))
C
 5320    CONTINUE
C
C                   PESTICIDE REMOVAL LOOP
C
 5325 DO 5330  1=1,5
C
         QS = 2000.*ERSN(I)/M
         IF (QS .GT. 1.0)  QS = 1.0
         SAPS(I) = SAS(I)*QS
         SCPS(I) = SCS(I)*QS
                                     -  192 -

-------
Appendix C     (continued)

         SPRS(I) = SAPS(I) + SCPS(I)
         SAS(I) = SAS(I) - SAPS(I)
         SCS(I) = SCS(I) - SCPS(I)
C
            SPRO(I) = 0.0
            SPOFS(I) = 0.0
            SPRP(I) = 0.0
            SPR(I) = 0.0
         IF (P3 +RESB1(I))  5327, 5327, 5328
 5327       GO TO 5329
C
 5328       SPRO(I) = SDS(I)*(ROSB(I)/(RESB1(I)+P3))
C
         SPOFS(I) = SDS(I)*(RESB(I)/(RESB1(I)+P3))
         SPRP(I) = SDS(I) - SPRO(I) - SPOFS(I)
         IF (SPRP(I) .IT. 0.0) SPRP(I) = 0.0
         SPR(I) = SPRO(I) + SPRS(I) + SPRP(I)
 5329    SDS(I) = SPOFS(I)
C
C
C
 5330
C
      IF
C
C
C
ASPR(I) =
ASPRS(I)
ASPRO(I)
ASPRP(I)
RESBl(I)
CONTINUE
(PRNTKE .

DO 5335
SPRT =
SPROT
SPRST
SPRPT
SAST =
SCST =
SDST =
ASPR(I) + SPR(I)
= ASPRS(I) + SPRS(I)
= ASPRO(I) + SPRO(I)
= ASPRP(I) + SPRP(I)
= 0.0

EQ. 0) GO TO 5390
PREPARATION OF OUTPUT
1=1,5
SPRT +ASPR(I)
= SPROT +ASPRO(I)
= SPRST +ASPRS(I)
= SPRPT +ASPRP(I)
SAST + SAS(I)
SCST + SCS(I)
SDST + SDS(I)
            SASC(I) = (SAS(I)/M)*1000000.
            SASCT = SASCT + SASC(I)*0.2
            SCSC(I) = (SCS(I)/M)*1000000.
            SCSCT = SCSCT + SCSC(I)*0.2
            SDSC(I) = (SDS(I)/M)*1000000.
                                   - 193 -

-------
Appendix C     (continued)
C

C
 c
C
C
 5335
 5337
C


C
C
C
   SDSCT = SDSCT + SDSC(I)*0.2

   STS(I) = SAS(I) + SCS(I) + SDS(I)
   SPS(I) = SASC(I) + SCSC(I) + SDSC(I)
   STST = STST + STS(I)
   SPST = SPST + SPS(I)*0.2

   ERSNT - ERSNT + ERSN(I)

   CONTINUE

           CUMULATIVE RESULTS
DO 5337  1= 1,5
   PRSTOM(I) = PRSTOM(I) + ASPRS(I)
   PROTOM(I) = PROTOM(I) + ASPRO(I)
   PROTOT(I) = PROTOT(I) + ASPRO(I)
   PRSTOT(I) = PRSTOT(I) + ASPRS(I)
         SPROTM  -  SPROTM +  SPROT
         SPRSTM  =  SPRSTM +  SPRST

         SPRTT = SPRTT  + SPRT
         SPROTT  =  SPROTT +  SPROT
         SPRPTT  =  SPRPTT +  SPRPT
         SPRSTT  =  SPRSTT +  SPRST
•\

         IF  (HYCAL .EQ. 0)   GO  TO  5340
         IF  (HYCAL .EQ. 1)   GO  TO  5370
         IF  (RU  ,LT.  HYMIN)  GO TO 5370
            SPRTGW =  SPROT*454.
            SPRTCW =  (SPROT/(RU*TIMFAC*60.*62.43))*1000000
            SPRTGS =  SPRST*454.
         IF  (ERSNT .LE. 0.0)  GO TO 5338
            SPRTCS =  (SPRST/(ERSNT*2000.))*1000000.
         GO  TO 5339
5338     SPRTCS  -  0.0
5339        WRITE  (6,5360)  SPRTGW,  SPRTCW,  SPRTGS,  SPRTCS
            GO TO  5370
                   PRINTING OF OUTPUT

5340  IF  (UNIT  .EQ. 1)  GO TO 5341
      WRITE  (6,5350)
      WRITE  (6,5351)  STS,  STST
      WRITE  (6,5352)  SAS,  SAST
      WRITE  (6,5353)  SCS,  SCST
      WRITE  (6,5361)  SDS, SDST
      WRITE  (6,5354)  SPS,  SPST
      WRITE  (6,5352)  SASC,  SASCT
                                    -  194 -

-------
Appendix C     (continued)
WRITE (6,5353)
WRITE (6,5361)
WRITE (6,5355)
WRITE (6,5356)
WRITE (6,5357)
WRITE (6,5359)
SCSC, SCSCT
SDSC, SDSCT
ASPR, SPRT
ASPRS, SPRST
ASPRO, SPROT
ASPRP, SPRPT
 5341 IF (UNIT .EQ. -1) GO TO 5370

   METRIC CONVERSIONS FOR OUTPUT
      STSTMT=STST*KGPLB
      SASTMT=SAST*KGPLB
      SCSTMT=SCST*KGPLB
      SDSTMT=SDST*KGPLB
      SPRT  =SPRT*KGPLB
      SPRST =SPRST*KGPLB
      SPROT =SPROT*KGPLB
      SPRPT =SPRPT*KGPLB
      DO 5343 1=1,5
        STSMET(I)=STS(I)*KGPLB
        SASMET(I)=SAS(I)*KGPLB
        SCSMET(I)=SCS(I)*KGPLB
        SDSMET(I)=SDS(I)*KGPLB
        ASPR(I)  =ASPR(I)*KGPLB
 5343
  ASPRS(I)
  ASPRO(I)
  ASPRP(I)
CONTINUE
WRITE (6,5350)
      (6
      (6
      (6
      (6
                 =ASPRS(I)*KGPLB
                 =ASPRO(I)*KGPLB
                 =ASPRP(I)*KGPLB
 5345
WRITE
WRITE
WRITE
WRITE
IF (UNIT
WRITE (6
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
WRITE
               5363) STSMET, STSTMT
               5352) SASMET, SASTMT
               5353) SCSMET, SCSTMT
               5361) SDSMET, SDSTMT
               .EQ. 0) GO TO 5345
C
C
C
         5354)  SPS, SPST
         5352)  SASC, SASCT
         5353)  SCSC, SCSCT
         5361)  SDSC, SDSCT
         5374) ASPR,SPRT
         5356)  ASPRS, SPRST
      (6,5357)  ASPRO, SPROT
      (6,5359)  ASPRP, SPRPT

              FORMAT STATEMENTS
(6
(6
(6
(6
(6
 5350
 5351
 5352
 5353
FORMAT  ('0', 5X,1SURFACE LAYER PESTICIDE1)
FORMAT  ('0',8X,'PESTICIDE, LBS',9X,5(3X,F7.3),3X,F8.3)
FORMAT  ('  ',11X,'ADSORBED1,12X,5(3X,F7.3),3X,F8.3)
FORMAT  ('  ',11X,'CRYSTALLINE1,9X,5(3X,F7.3),3X,F8.3)
                                    - 195 -

-------
Appendix  C     (continued)
 5354
 5355
 5356
 5357
 5359
 5360
 5361
 5363
 5374
C
C
C
 5370
 5380
 s

 '5390
 5391
FORMAT  ('0',8X,'PESTICIDE, PPM1,9X,5(3X,F7.3),3X,F8.3)
FORMAT  ('0',8X,'REMOVAL, IBS',11X,5(3X,F7.3),3X,F8.3)
FORMAT  ('  ',11X,'SEDIMENT',12X,5(3X,F7.3),3X,F8.3)
FORMAT  ('  MIX,'OVERLAND FLOW ,7X,5(3X,F7.3),3X,F8.3)
FORMAT  ('  MIX, 'PERCOLATION1,9X,5(3X,F7.3),3X,F8.3)
FORMAT ('+',70X,2(2X,F8.3,3X,F7.3))
FORMAT ('  ',11X,'DISSOLVED',11X,5(3X,F7.3),3X,F8.3)
FORMAT ('0',8X,'PESTICIDE, KGS',9X,5(3X,F7.3),3X,F8.3)
FORMAT ('0',8X,'REMOVAL, KGS M1X,5(3X,F7.3) ,3X,F8. 3)

              ZEROING VARIABLES

DO 5380  1=1,5
   ASPR(I)  = 0.0
   ASPRO(I)  = 0.0
   ASPRS(I)  = 0.0
   ASPRP(I)  = 0.0
   CONTINUE

SPST = 0.0
SASCT =0.0
SCSCT =0.0
SDSCT =0.0
SPRT = 0.0
SPRST =0.0
SPROT =0.0
SPRPT = 0.0

DO 5391  1= 1,5
SSTR(I) = 0.0

RETURN
END
C
C

C
C
C
C
C
SUBROUTINE ADSRB2
                       UPPER ZONE SOLUTION ADSORPTION MODEL
      DIMENSION  RXB(5), R6X(5), RUZB(5), UZSB(5), PERCB(5), RIB(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5), MNAM(24)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5), INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5), USTR(5)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5), ROSB(5)
                                   - 196 -

-------
Appendix  C     (continued)

      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5),  UTS(5)
      DIMENSION  JNFW(5), UDSCJ5), UPRIS(5)
      DIMENSION  UPR(5), UPS(5), UCSC(5), UASC(5)
      DIMENSION  AUPR(5), AUPRI(5), AUPRP(5)
      DIMENSION  UTSMET(5), UASMET(5), UCSMET(5), UDSMET(5)
C
      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM, RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT, RCHTOT
      COMMON  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSB, TWBAL, SRGX, RU, HYMIN, INTF
      COMMON  MNAM, IX,  II, DAY, IFLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
      COMMON  STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST,  UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN,  INFIL, INTER, IRC, NN, L, SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS,  SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS,  UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
C
      INTEGER  PRNTKE, HYCAL, UNIT
C
      REAL  UTSTMT, UASTMT, UCSTMT, UDSTMT
      REAL  UTSMET, UASMET, UCSMET, UDSMET
      REAL  MUZ, NI, K,  KK, JNFW,  INTF, JJ
      REAL  MMPIN, METOPT, KGPLB
C
      DATA  UPST, UASCT, UCSCT, UPRT, UPRIS/9*0.0/
      DATA  UDSCT, UPRPT/2*0.0/, JNFW/5*0.0/, UPRIT/0.0/
      DATA  AUPR, AUPRI, AUPRP/15*0.0/
C
C                   ZEROING VARIABLES
C
      UTST = 0.0
      UAST = 0.0
                                     -  197 -

-------
Appendix  C    (continued)

      UCST = 0.0
      UDST = 0.0
C
C                   SOLUTION ADSORPTION LOOP
C
      DO 6305  I = 1,5
         JNFW(I) = AREA*(RIB(I)+UZSB(I)-RUZB(I)+(2*PERCB(I)))*45302.4
 6305    CONTINUE
C
      Z =  1000000.**(NI-1)
      KK = MUZ*K*Z
C
      DO 6320  1=1,5
         JJ = JNFW(I)
         PTOT =UAS(I)+UCS(I)+ UDS(I) + SPRP(I) + USTR(I)-0.2*(DEGU+VOLU)
         IF (PTOT .LE. 0.0)  PTOT = 0.0
         C5 = 0.0
         IF (JJ  .61. 0.0)  C5 = -UDS(I)/JJ
C
         IF (PTOT-FPUZ)  6310, 6310, 6315
 6310       UAS(I) = PTOT
            UCS(I) = 0.0
            UDS(I) = 0.0
            GO TO 6320
C
 6315    X = KK*CMAX**NI + FPUZ
         PSLD = PTOT - X - JJ*CMAX
         IF (PSLD .LT. 0.0)  GO TO 6316
            UAS(I) = X
            UCS(I) = PSLD
            UDS(I) = CMAX*JJ
            GO TO 6320
C
 6316       UCS(I) = 0.0
C
            C = C5
            IF (C .LE. 0.0)  C = 0.001
 6317       X = KK*C**NI + FPUZ
            Q = (PTOT/(X+JJ*C)) - 1.
            IF (ABS(Q)-O.Ol)  6319, 6319, 6318
 6318          C = C*PTOT/(X+JJ*C)
               GO TO 6317
C
 6319          IF (JJ .LE. 0.001)  X = PTOT
C
               UDS(I) = (C*JJ)*(PTOT/(X+C*JJ))
               UAS(I) = X*(PTOT/(X+C*JJ))
                                   - 198 -

-------
Appendix  C     (continued)

C
 6320    CONTINUE
C
c                   PESTICIDE REMOVAL LOOP
C
 6325 DO 6330   1=1,5
C
         IF  (JNFW(I)  .LE. 0.0001)  GO TO 6327
         QSP =  (RIB(I)-RGX(I)-RUZB(I)+(2*PERCB(I)))/JNFW(I)
         IF  (QSP  .61.  1.0)  QSP = 1.0
         UPRP(I) = UDS(I)*QSP
         QSI =  R6X(I)/JNFW(I)
         IF  (QSI  .61.  1.0)  QSI = 1.0
         IF  (SRGX(I)  .LT. 0.0001)  GO TO 6329
         UPRII  = UDS(I)*QSI
         UPRIS(I) = UPRIS(I) + UPRII
         UPRI(I)  = (UPRIS(I))*(INTF(I)/SR6X(I))
         UPRIS(I) = UPRIS(I) - UPRI(I)
         GO TO  6328
 6327    UPRP(I) = 0.0
 6329    UPRI(I) = 0.0
 6328    UDS(I) = UDS(I) - UPRP(I) - UPRI(I)
         IF  (UDS(I) .LT. 0.0)  UDS(I) = 0.0
         UPR(I) = UPRP(I) + UPRI(I)
C
         AUPR(I) = AUPR(I) + UPR(I)
         AUPRI(I) = AUPRI(I) + UPRI(I)
         AUPRP(I) = AUPRP(I) + UPRP(I)
C
 6330    CONTINUE
C
      IF (PRNTKE .EQ.  0)  GO TO 6380
C
C                    PREPARATION OF OUTPUT
C
         DO 6335  1=1,5-
UPRT = UPRT +
UPRIT = UPRIT
UPRPT = UPRPT
UAST = UAST +
UCST = UCST +
UDST = UDST +
AUPR(I)
+ AUPRI(I)
+ AUPRP(I)
UAS(I
UCS(I
UDS(I
            UASC(I) = (UAS(I)/MUZ)*1000000.
            UCSC(I) = (UCS(I)/MUZ)*1000000.
            IF (UZSB(I)  .LE. 0.0001)  GO TO 6333
            UDSC(I) = (UDS(I)/(UZSB(I)*AREA*45302.4))*1000000.
                                   - 199 -

-------
Appendix  c    (continued)
 6333
 6334
C
C
C
C
C

C
C
C
      GO TO 6334
      UDSC(I)  = 0.0
      UPS(I) = UASC(I)
UCSC(I) + UDSC(I)
            UASCT = UASCT
            UCSCT = UCSCT
            UDSCT = UDSCT
            UPST = UPST +
                    + UASC(I)*0.
                    + UCSC(I)*0,
                    + UDSC(I)*0,
                    UPS(I)*0.2
            UTS(I) = UAS(I) + UCS(I) + UDS(I)
            UTST = UTST + UTS(I)
 6335
      CONTINUE
              CUMULATIVE RESULTS
 6340
   DO 6340  1= 1,5
      UPITOM(I)  = UP-ITOM(I)
      UPITOT(I)  = UPITOT(I)
UPRITM = UPRITM  + UPRIT
UPRITT = UPRITT  + UPRIT
     AUPRI(I)
     AUPRI(I)
    IF (HYCAL .NE.  0)  60 TO 6365

              PRINTING OF OUTPUT
IF (UNIT .EQ.
WRITE (6,6350)
WRITE (6,6351)
WRITE (6,6352)
WRITE (6,6353)
WRITE (6,6354)
WRITE (6,6355)
WRITE (6,6352)
WRITE (6,6353)
WRITE (6,6354)
WRITE (6,6357)
WRITE (6,6358)
WRITE (6,6359)
1) GO TO 6342

UTS, UTST
UAS, UAST
UCS, UCST
UDS, UDST
UPS, UPST
UASC, UASCT
UCSC, UCSCT
UDSC, UDSCT
AUPR, UPRT
AUPRI, UPRIT
AUPRP, UPRPT
 6342 IF (UNIT .EQ. -1) GO TO 6365
C
C  METRIC CONVERSIONS FOR OUTPUT
      UTSTMT=UTST*KGPLB
      UASTMT=UAST*KGPLB
      UCSTMT=UCST*KGPLB
      UDSTMT=UDST*KGPLB
      UPRT  =UPRT*KGPLB
                                    - 200 -

-------
Appendix  C     (continued)
     UPRIT =UPRIT*KGPLB
     UPRPT =UPRPT*KGPLB
     DO 6344 1=1,5
       UTSMET(I)=UTS(I)*KGPLB
       UASMET(I)=UAS(I)*KGPLB
       UCSMET(I)=UCS(I)*KGPLB
       UDSMET(I)=UDS(I)*KGPLB
       AUPR(I)  =AUPR(I)*KGPLB
       AUPRI(I) =AUPRI(I)*KGPLB
       AUPRP(I) =AUPRP(I)*KGPLB
6344 CONTINUE
     WRITE (6,6350)
     WRITE (6,6360) UTSMET, UTSTMT
     WRITE (6,6352) UASMET, UASTMT
     WRITE (6,6353) UCSMET, UCSTMT
     WRITE (6,6354) UDSMET, UDSTMT
     IF (UNIT .EQ. 0) GO TO 6345
     WRITE (6,6355)  UPS, UPST
           (6,6352)  UASC, UASCT
           (6,6353)  UCSC, UCSCT
           (6,6354)  UDSC, UDSCT
           (6,6361) AUPR, UPRT
           (6,6358)  AUPRI, UPRIT
6345
      WRITE
      WRITE
      WRITE
      WRITE
      WRITE
      WRITE
C
C
C
           (6,6359)  AUPRP, UPRPT
 6350
 6351
 6352
 6353
 6354
 6355
 6357
 6358
 6359
 6360
 6361
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
     FORMAT
              '0'
              '0'
              '0'
              i   i
              i   i
              '0'
              '0'
C
C
C
  FORMAT STATEMENTS

,  5X,'UPPER ZONE LAYER PESTICIDE')
,8X,'PESTICIDE,  LBS1,9X,5(3X,F7.3),3X,F8.3)
     1 ADSORBED',12X,5(3X,F7.3),3X,F8.3)
     'CRYSTALLINE' ,9X,5(3X,F7.3),3X,F8.3)
     1 DISSOLVED',11X,5(2X,F8.3),3X,F8.3)
     PESTICIDE,  PPM',9X,5(3X,F7.3),3X,F8.3)
     REMOVAL,  LBS',11X,5(3X,F7.3),3X,F8.3)
     1 INTERFLOW',11X,5(3X,F7.3),3X,F8.3)
     'PERCOLATION',9X,5(3X,F7.3),3X,F8.3)
,8X,'PESTICIDE,  KGS',9X,5(3X,F7.3),3X,F8.3)
,8X,'REMOVAL,  KGS1,11X,5(3X,F7.3),3X,F8.3)

  ZEROING VARIABLES
,8X,
,8X,
 6365 DO 6370  1=1,5
         AUPR(I) = 0.0
         AUPRI(I) = 0.0
         AUPRP(I) = 0.0
 6370    CONTINUE
                                  - 201 -

-------
Appendix  C     (continued)

C
 6380 UPST =0.0
      UASCT =0.0
      UCSCT = 0.0
      UDSCT = 0.0
      UPRT = 0.0
      UPRPT = 0.0
      UPRIT = 0.0
C
      DO  6381  1= 1,5
 6381 USTR(I) = 0.0
C
      RETURN
      END
C
C
      SUBROUTINE ADSRB3
C
C
C                             LOWER ZONE AND GROUNDWATER
C                             SOLUTION ADSORPTION MODEL
C
C
      IMPLICIT  REAL(L)
C
      DIMENSION  RXB(5), R6X(5), RUZB(5), UZSB(5), PERCB(5),  RIB(5)
      DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5),  MNAM(24)
      DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5),  INTF(5)
      DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
      DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
      DIMENSION  UPITOM(5), UPITOT(5), RESB1(5),  SSTR(5),  USTR(5)
      DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5),  ROSB(5)
      DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
C
      COMMON  PRTOT, ERSNTT, EIMTT, PRTT, IHR
      COMMON  PRTOM, ERSNTM, EIMTM, PRTM, IMIN
      COMMON  RUTOM, NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM,  RCHTOM
      COMMON  RUTOT, NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT,  RCHTOT
      COMMON  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
      COMMON  PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
      COMMON  RESB, RESB1, ROSE, TWBAL, SRGX, RU, HYMIN, INTF
      COMMON  MNAM, IX, IZ, DAY, I FLAG, COVER, COVMAX
      COMMON  SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM,  EPTOT,  PRNTKE
      COMMON  STS, STST, SAST, SCST, SDST, UTS, UTST, UAST,  UCST, UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL,  DPST, RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC,  NN, L,  SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24,  K24EL,  TF, EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
                                  -  202  -

-------
Appendix C     (continued)

      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI,  BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS, UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
C
      INTEGER  PRNTKE, HYCAL, UNIT
C
      REAL  KNFW, MLZ, K, KK, NI
      REAL  LSTRMT, LASMET, LCSMET, LDSMET
      REAL  GSTRMT, GASMET, GCSMET, GDSMET
      REAL  MMPIN, METOPT,  KGPLB
C
      DATA  ALPRP/0.0/
C
C                   SOLUTION ADSORPTION LOOP
C
      LCS - 0.0
      LAS = 0.0
      LDS = 0.0
C
      KNFW = AREA*(LZS+DPST)*226512.
      Z  = 1000000.**(NI-1)
      KK = MLZ*K*Z
C
      DO 7305   1=1,5
         LSTR =  LSTR  + UPRP(l)
 7305    CONTINUE
C
         IF  (LSTR  .LE. 0.0001)   GO TO  7330
C
      PTOT = LSTR
      C5 = 0.0
      IF (KNFW  .61. 0.0)   C5  =  LAS/KNFW
      IF (PTOT-FPLZ)   7310, 7310,  7315
 7310    LAS =  PTOT
         LCS =  0
         LDS =  0
         GO TO  7320
                                   - 203 -

-------
Appendix  C     (continued)

C
 7315 X = KK*CMAX**NI + FPLZ
      PSLD = PTOT - X - KNFW*CMAX
      IF  (PSLD .LT. 0.0)  GO TO 7316
          LAS = X
          LCS = PSLD
          LDS = CMAX*KNFW
          GO TO 7320
C
 7316     LCS = 0.0
C
          C = C5
          IF (C .LE. 0.0)  C - 0.001
 7317     X = KK*C**NI + FPLZ
          Q = (PTOT/(X+KNFW*C)) - 1.
          IF (ABS(Q)-O.Ol)  7319, 7319, 7318
 7318       C = C*PTOT/(X+KNFW*C)
            GO TO 7317
C
 7319 IF  (KNFW .LE. 0.001)  X = PTOT
C
        LDS = (C*KNFW)*(PTOT/(X+C*KNFW))
            LAS = X*(PTOT/(X+C*KNFW))
C
 7320     CONTINUE
C
C                   PESTICIDE REMOVAL LOOP
C
      LPRP = LDS*DPST/(DPST+LZS)
      LDS = LDS - LPRP
C
      LSTR = LAS + LCS + LDS
C
      ALPRP = ALPRP + LPRP
C
 7330 IF  ((PRNTKE .EQ. 0).OR.(HYCAL .NE. 0))  GO TO 7380
C
C                   PREPARATION OF OUTPUT
C
C
      LASC = (LAS/MLZ)*1000000.
      LCSC = (LCS/MLZ)*1000000.
      LDSC = (LDS/(LZS*AREA*226512.))*1000000.
C
C                    PRINTING OF OUTPUT
C
      IF  (UNIT .EQ. 1) GO TO 7340
      WRITE (6,7350)
                                   - 204 -

-------
Appendix C     (continued)
      WRITE (6,7351)  LSTR
      WRITE (6,7352)  LAS
      WRITE (6,7353)  LCS
      WRITE (6,7354)  IDS
      WRITE (6,7355)
      WRITE (6,7352)  LASC
      WRITE (6,7353)  LCSC
      WRITE (6,7354)  LDSC
      WRITE (6,7357)  ALPRP
      WRITE (6,7359)  ALPRP
^7340 IF (UNIT .EQ. -1) GO TO 7379

3  METRIC CONVERSIONS FOR OUTPUT
      LSTRMT=LSTR*KGPLB
      LASMET=LAS*KGPLB
      LCSMET=LCS*KGPLB
      LDSMET=LDS*KGPLB
      ALPRP =ALPRP*KGPLB
      WRITE (6,7350)
      WRITE (6,7360) LSTRMT
      WRITE (6,7352) LASMET
      WRITE (6,7353) LCSMET
      WRITE (6,7354) LDSMET
      IF (UNIT .EQ. 0) GO TO 7345
      WRITE (6,7355)
      WRITE (6,7352)  LASC
      WRITE (6,7353)  LCSC
      WRITE (6,7354)  LDSC
 7345 WRITE (6,7361) ALPRP
      WRITE (6,7359) ALPRP
C
C
C
 7350 FORMAT
 7351 FORMAT
 7352 FORMAT
 7353 FORMAT
 7354 FORMAT
 7355 FORMAT
 7357 FORMAT
 7359 FORMAT
 7360 FORMAT
 7361 FORMAT
C
C
C
      FORMAT STATEMENTS

('0',  5X,'LOWER ZONE LAYER PESTICIDE1)
('0',8X,'PESTICIDE,  LBS',62X,F8.3)
('  ',11X,'ADSORBED1,65X,F8.3)
('  ',11X,'CRYSTALLINE',62X,F8.3)
('  ',HX,'DISSOLVED1,64X.F8.3)
('O'.SX,1PESTICIDE,  PPM',62X,F8.3)
('0',8X,'REMOVAL,  LBS' ,64X,F8.3)
('  MIX,'PERCOLATION1,62X.F8.3)
('0',8X,'PESTICIDE,  KGS',62X,F8.3)
('0',8X,'REMOVAL,  KGS',64X,F8.3)
      ZEROING OF VARIABLES
 7379 ALPRP = 0.0
                                 - 205 -

-------
Appendix  C     (continued)

C
 7380 CONTINUE
C
C
C                             GROUNDWATER ADSORPTION MODEL
L
c
      GSTR = GSTR + LPRP
      IF  (FPLZ  .GT. 0.0)  GO TO 7520
          GAS =  0.0
          GDS =  GSTR
          GCS =  0.0
C
 7520     GAS =  GSTR
          GCS =  0.0
          GDS =  0.0
C
      IF  ((PRNTKE .EQ. 0).OR.(HYCAL .NE. 0))  GO TO 7580
C
C                   PRINTING OF OUTPUT
C
      IF  (UNIT  .EQ. 1) GO TO 7530
      WRITE (6,7550)
      WRITE (6,7551)  GSTR
      WRITE (6,7552)  GAS
      WRITE (6,7553)  GCS
      WRITE (6,7554)  GDS
 7530 IF  (UNIT  .EQ. -1) GO TO 7580
C
C  METRIC CONVERSIONS FOR OUTPUT
      GSTRMT=GSTR*KGPLB
      GASMET=GAS*KGPLB
      GCSMET=GCS*KGPLB
      GDSMET=GDS*KGPLB
      WRITE (6,7550)
      WRITE (6,7555) GSTRMT
      WRITE (6,7552) GASMET
      WRITE (6,7553) GCSMET
      WRITE (6,7554) GDSMET
C
C                   FORMAT STATEMENTS
C
 7550 FORMAT  ('0',5X,'GROUNDWATER LAYER PESTICIDE1)
 7551 FORMAT  ('0',8X,'PESTICIDE,  LBS',62X,F8.3)
 7552 FORMAT  (' ',11X,'ADSORBED1,65X,F8.3)
 7553 FORMAT  (' ',11X,'CRYSTALLINE',62X,F8.3)
 7554 FORMAT  (' ',11X,'DISSOLVED1,64X,F8.3)
 7555 FORMAT ('0',8X,'PESTICIDE, KGS1,62X,F8.3)
                                   -  206  -

-------
Appendix  C     (continued)
 7580 CONTINUE

      RETURN
      END
C
C

C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
               SUBROUTINE VOLDEG
                   VOLATILIZATION-DEGRADATION MODEL
           PESTICIDE VOLATILIZATION

DIFC - DIFFUSION COEFFICIENT (MM2/WK) AT TEMPERATURE
        TDIFC (C DEGREES)
CONCIU - INITIAL PEST. CONC. IN U.Z. IN G/CC
BULKD - BULK DENSITY OF SOIL IN G/CC
AREA - WATERSHED AREA IN ACRES
EVC - EQUIL. VAPOR PRES.  IN MICROGRAMS/CC
MOLEWT - MOLECULAR WEIGHT
WIND - WIND IN MI/DAY
WCFAC - WIND CALIBRATION CONSTANT
APMODE - APPLICATION MODE - 0=SURFACE, 1=SOIL
PRES - PRESSURE IN MM OF HG
APFAC - CONSTANT FOR PRES-TEMP ADJUSTMENT
BPFAC - CONSTANT FOR PRES-TEMP ADJUSTMENT

   DIMENSION  RXB(5), RGX(5), RUZB(5), UZSB(5), PERCB(5),  RIB(5)
   DIMENSION  ERSN(5), SRER(5), SRGX(5), RESB(5),  MNAM(24)
   DIMENSION  ROBTOM(5), ROBTOT(5), INFTOM(5), INFTOT(5),  INTF(5)
   DIMENSION  ROITOM(5), ROITOT(5), ERSTOM(5), ERSTOT(5)
   DIMENSION  PRSTOM(5), PRSTOT(5), PROTOM(5), PROTOT(5)
   DIMENSION  UPITOM(5), UPITOT(5), RESB1(5), SSTR(5),  USTR(5)
   DIMENSION  SAS(5), SCS(5), SDS(5), SPRP(5), STS(5)
   DIMENSION  UAS(5), UCS(5), UDS(5), UPRP(5), UPRI(5), UTS(5)
   DIMENSION  AERSN(5), ROSB(5)

   COMMON  PRTOT,  ERSNTT, EIMTT, PRTT, IHR
   COMMON  PRTOM,  ERSNTM, EIMTM, PRTM, IMIN
   COMMON  RUTOM,  NEPTOM, ROSTOM, RITOM, RINTOM, BASTOM,  RCHTOM
   COMMON  RUTOT,  NEPTOT, ROSTOT, RITOT, RINTOT, BASTOT,  RCHTOT
   COMMON  ROBTOM, ROBTOT, INFTOM, INFTOT, ROITOM, ROITOT
   COMMON  PRSTOM, PRSTOT, PROTOM, PROTOT, UPITOM, UPITOT
   COMMON  RES5; B£SB1, ROSE, TWBAL, SRGX, RU, HYMIN,  INTF
   COMMON  MNAM, IX, IZ, DAY, IFLAG, COVER.
                                   - 207 -

-------
Appendix C     (continued)

      COMMON  SPROTM, SPRSTM, ERSTOM, ERSTOT, EPTOM, EPTOT, PRNTKE
      COMMON  STS, STST, SAST, SCSI, SDST, UTS, UTST, UAST, UCST, UDST
      COMMON  PR, P3, RXB, RGX, RUZB, UZSB, PERCB, HYCAL, DPST, RIB,UNIT
      COMMON  TIMFAC, UZSN, LZSN, INFIL, INTER, IRC, NN, L, SS, SGW1
      COMMON  A, UZS, LZS, SGW, GWS, KV, K24L, KK24, K24EL, TF, EP
      COMMON  IFS, K3, EPXM, RESS1, RESS, SCEP, SCEP1, SRGXT, SRGXT1
      COMMON  SRER, JRER, KRER, JSER, KSER, ERSN, SRERT
      COMMON  SAS, SCS, SDS, AREA, M, K, FP, CMAX, SSTR, NI, BULKD
      COMMON  SPRP, SPROTT, SPRPTT, SPRTT, SPRSTT
      COMMON  UAS, UCS, UDS, USTR, MUZ, FPUZ
      COMMON  UPRP, UPRITM, UPRITT, MMPIN, METOPT, KGPLB
      COMMON  FPLZ, MLZ, LSTR, LAS, LCS, IDS, LPRP
      COMMON  GSTR, GAS, GCS, GDS
      COMMON  APMODE, CADIF, CBDIF, TEMP, WIND, CONCIU
      COMMON  MOLEWT, APFAC, BPFAC, WCFAC
      COMMON  VOLSOM, VOLSOT, VOLUOM, VOLUOT, VOLU, VOLS
      COMMON  DEGSOM, DEGSOT, DEGUOM, DEGUOT, DEGU, DEGS, DEGCON
      COMMON  DEGLOM, DEGLOT
C
C
       REAL KFAC, MOLEWT, LSTR
       REAL  TVOLMT, VOLSMT, VOLUMT
       REAL  MMPIN, METOPT, KGPLB
       INTEGER APMODE, PRNTKE, HYCAL , UNIT
C
C
       DATA TVOL, TFLUX/2*0.0/, DEGL/0.0/
C
C
C
C              SOIL INCORPORATED PESTICIDE VOLATILIZATION
C                   AND DEGRADATION
C
       IF (UTST  .LE. 0.0)  GO TO 8040
       DEGU = DEGCON*UTST
             IF  (APMODE  .EQ. 0) GO TO 8022
       IF (CADIF  .EQ. 0.0)  GO TO 8022
C
             DIFCI = CADIF*(EXP(CBDIF*TEMP))
             KFAC = (SQRT(DIFCI/2198.))*CONCIU
             IF  (IFLAG  .EQ. 1)  GO TO 8010
             GO TO 8020
 8010        FLUXI = KFAC*(2.0)
             IFLAG = 0
             GO TO 8025
 8020        FLUXI =  (2*((KFAC)**2))/TFLUX
 8025        VOLU = FLUXI*AREA*(40.469)/1000.
              TFLUX = TFLUX +  FLUXI
             VOLU = VOLU/.454
                                   - 208 -

-------
appendix  C    (continued)

C
 8022        UTST = UTST - VOLU - DEGU
             IF  (UTST)  8026,8027,8029
 8026          VOLU =0.0
               DEGU =0.0
 8027          UTST =0.0
 8029        DO 8030  I =1,5
                UTS(I) = UTS(I) - 0.2*(VOLU+DEGU)
 8030        IF (UTS(I) .LE. 0.0)  UTS(I) = 0.0
C
C
C
C            SURFACE PESTICIDE VOLATILIZATION AND DEGRADATION
C
 8040 IF (STST .LE. 0.0)  GO TO 8070
      DEGS =DEGCON*STST
       IF ((APFAC  .EQ. 0.0).AND.(BPFAC  .EQ. 0.0))  GO TO 8042
C
              PRES = 10.**(APFAC-BPFAC/(TEMP+273.))
              EVC =  PRES*MOLEWT*10E6/((TEMP+273.)*62365.6)
              FLUXI = WCFAC*WIND*EVC/SQRT(MOLEWT)
              VOLS = FLUXI*AREA*(40.469/1000.)
              VOLS = VOLS/.454
C
 8042         STST = STST - VOLS - DEGS
              IF   (STST)  8050,8055,8057
 8050             VOLS =0.0
                  DEGS = 0.0
 8055             STST =0.0
 8057         DO  8060  1= 1,5
                  STS(I) = STS(I) - 0.2*(VOLS+DEGS)
 8060         IF (STS(I)  .LE. 0.0)  STS(I) = 0.0
C
C
C    LOWER ZONE PESTICIDE DEGRADATION
C
 8070 IF (LSTR .LE. 0.0) GO TO 8090
      DEGL = DEGCON*LSTR
      LSTR = LSTR  - DEGL
      IF (LSTR) 8076, 8077, 8090
 8076     DEGL =0.0
 8077     LSTR = 0.0
C
C
 8090        CONTINUE
C
C
                                   - 209 -

-------
Appendix C     (continued)
C
C
C
                CUMULATIVE RESULTS
      VOLSOM
      VOLSOT
      VOLUOM
      VOLUOT
      DEGSOM
      DEGSOT
      DEGUOM
      DEGUOT
      DEGLOM
      DEGLOT
              VOLSOM
              VOLSOT
              VOLUOM
              VOLUOT
              DEGSOM
              DEGSOT
              DEGUOM
              DEGUOT
              DEGLOM
              DEGLOT
  VOLS
  VOLS
  VOLU
  VOLU
  DEGS
+ DEGS
+ DEGU
+ DEGU
+ DEGL
+ DEGL
      TVOL = VOLU + VOLS
      TDEG = DEGS + DEGU
                        + DEGL
C
C
       IF ((PRNTKE .EQ.  0).OR.(HYCAL .NE.  0))  GO TO 8600

     IF (UNIT .EQ. 1) GO TO 8200
        WRITE (6,8500)
        WRITE (6,8501) TVOL
        WRITE (6,8502) VOLS
        WRITE (6,8503) VOLU
        WRITE (6,8505)
        WRITE (6,8501) TDEG
        WRITE (6,8502) DEGS
        WRITE (6,8503) DEGU
        WRITE (6,8507) DEGL
8200 IF (UNIT .EQ. -1) GO TO 8600

  METRIC CONVERSIONS FOR OUTPUT
     TVOLMT=TVOL*KGPLB
     VOLSMT=VOLS*KGPLB
     VOLUMT=VOLU*KGPLB
     TDEGMT=TDEG*KGPLB
     DEGSMT=DEGS*KGPLB
     DEGUMT=DEGU*KGPLB
     DEGLMT=DEGL*KGPLB
     WRITE (6,8504)
     WRITE (6,8501) TVOLMT
     WRITE (6,8502) VOLSMT
     WRITE (6,8503) VOLUMT
     WRITE (6,8506)
     WRITE (6,8501) TDEGMT
     WRITE (6,8502) DEGSMT
     WRITE (6,8503) DEGUMT
     WRITE (6,8507) DEGLMT
                                  - 210 -

-------
Appendix  C     (continued)
C
C
 8500 FORMAT  ('0'
 8501 FORMAT  ('  '
 8502 FORMAT  ('  '
 8503 FORMAT  ('  '
 8504 FORMAT  ('0'
 8505 FORMAT  ('0'
 8506 FORMAT  ('0'
 8507 FORMAT  ('  '
,5X,'PESTICIDE VOLATILIZATION LOSS, LBS.
,8X,'TOTAL',72X,F7.3)
,8X,'FROM SURFACE',65X,F7.3)
,8X,'FROM UPPER ZONE1,62X,F7.3)
,5X,'PESTICIDE VOLATILIZATION LOSS, KGS.
,5X,'PESTICIDE DEGRADATION LOSS, LBS.1)
,5X,'PESTICIDE DEGRADATION LOSS, KGS.')
,8X,'FROM LOWER ZONE',62X,F7.3)
 8600 RETURN
      END
/*
//LKED.SYSLMOD DD DSNAME=C510.TONY.PEST10,DISP=(NEW,KEEP),
//              SPACE=(TRK,(15,1,1),RLSE),UNIT=2314,
//              VOL=SER=SYS13
//LKED.SYSIN  DD *
    NAME   PEST
/*
                                     - 211 -
                                                 MJ.S. GOVERNMENT PRINTING OFFICE: 1974  546-318/383 1-3

-------
 SELECTED WATER
 RESOURCES ABSTRACTS

 INPUT TRANSACTION FORM
 4.  Title
         PESTICIDE TRANSPORT AND RUNOFF MODEL FOR
               AGRICULTURAL  LANDS
 7.  Author(s)

 N.H. Crawford and A.s.  Doniaian.  Jr
 9.  Organization
   Hydrocomp,  Inc.
   1502 Page Mill  Road
   Palo Alto,  California   94304
                         11.  Contract/Grant No.

                             68-01-0887
 75,  Supplementary Notes

     Environmental Protection Agency report number,  EPA-660/2-74-013,  December 1973.
 16.  Abstract
The development  and  testing of a mathematical model to simulate the loss of pesticides
from agricultural  lands  is  presented.   The Pesticide Transport and Runoff (PTR) Model
is composed of submodels concerned with hydrology, sediment loss, pesticide-soil
interaction, and pesticide  attenuation functions.  The Model 'piggybacks' the applied
pesticide  onto the movement of water through the soil profile and the loss of water and
sediment from the land surface.  The pesticide-soil interaction is based on the
Freundlich adsorption-desorption isotherm.  Attenuation functions of volatilization and
degradation are  provided but were not tested due to lack of data.
Comparison of simulated  and recorded runoff and sediment loss showed considerable agree-
ment.  Simulated pesticide  loss agreed reasonably well with recorded values for those
pesticides completely adsorbed on sediment particles.  The Freundlich adsorption model
did not accurately predict  the division between the adsorbed and dissolved'states for
those pesticides which are  transported by runoff and sediment loss.  Recommendations
for future work  include  further calibration and testing of the PTR Model, and additional
development on the pesticide adsorption and attenuation functions.  The regulation of
pesticide  releases to the environment are explored as possible eventual uses of the
PTR Model.
  17a. Descriptors
Pesticide pollution,  Regulation, *Pesticide transport, Simulation, *Hydrologic
modeling, *Sediment transport, *Pesticide-soil adsorption, Volatilization.
  17b. Identifiers

*Pesticide  transport,  Agricultural runoff, Sediment transport.
  17c. CO WRR Field & Group
  IS.  Availability
  Abstractor A.S.  Donigian,Jr
                                                      WATER RESOURCES SCIENTIFIC INFORMATION CENTER
                                                      U.S. DEPARTMENTOFTHE INTERIOR
                                                      WASHINGTON, D. C. 2O24O
nstitution  Hvdrocomp.  Inc.
••--'C102IREV JUNE 1971)

-------