-------
TMDL: Historical change in Focus
Old Approach:
WQS
WLA
NPDES
Repeat for each discharge
New Approach:
I"MDL->WLA/LA
NPDES
Consider all loads simultaneously
TMDL Workshop
L77, Umno-Tech, Inc.
-------
TMDL DEFINITIONS
Loading Capacity (LC):
Load Allocation (LA):
Waste Load Allocation
(WLA):
Total Maximum Daily
Load (TMDL):
Margin of Safety (MOS):
The greatest amount of
pollutant loading that a
water body can receive
without violating water
quality standards.
The portion of loading
capacity attributed to
nonpoint sources or
background conditions.
The portion of loading
capacity attributed to
point sources.
The sum of WLAs and
LAs.
The portion of the loading
capacity attributed to
uncertainty. The MOS
may be explicit or implicit
via conservative
assumptions.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Maximum Allowable
Nonpoint Load (LA)
Water Quality
Standard
Maximum Allowable
Point Load (WLA)
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
TMDL DEVELOPMENT
ACTIVITIES
Define quantitative objective
Relate load to water quality
Estimate pollution sources
Select/evaluate control alternatives
Allocate among sources
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Example Total Phosphorus TMDL
Water Quality Standard: Maintain total phosphorus concentration at 0.05 mg/L
on an annual average basis at watershed outlet.
Watershed is all agricultural.
Pilot studies have shown that
conservation tillage will reduce
loads by 75%.
WWTP Load (Observed)
= 22,000 kg/yr = 0.7 g/sec
Upstream Load = 9500 kg/yr = 0.3 g/sec
Total Flow = 10 nnP/sec
TMDL Workshop
LT1, Umno-Tech, Inc.
-------
TMDL CALCULATIONS
Define Objective
WQS = 0.05 mg/l = 0.05 gm/m3
Relate Load to Water Quality
C = W -r Q
Concentration = Load + Flow
W = C x Q
Allowable load = Target Concentration* Flow
= .05 g/m3 * 10m3/sec = 0.5 g/sec
Estimate Sources
Point Sources = 0.7 g/sec
Nonpoint Sources = 0.3 g/sec
Select & Evaluate Control Alternatives
BMP (Conservative tillage) reduces loads by 75%.
Allocate Among Sources
Policy is to achieve equal percent reduction from
all sources
Required reduction in total load = 50%
WLA = 0.35 g/sec
LA = 0.15 g/sec, achieved by BMP implementation
of 2/3 of the watershed
(V7f ~ -- Vj
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Determine Total Allowable Load
Water Quality
Model
Determine Present Load
Monitoring/
Nonpoint Source
Model
Define
Water Quality
Objectives
Present
Load
Allocate Load Reductions
Best Management
Practices
Allocation
Strategy
ym
Point Source
Controls
no
^ Monitoring/
Nonpoint Source
Model .
Load
Allocation
Wasteload
Allocation
Point Source
Load
Nonpoint Source
Load
Is Total
Load
(Nonpoint +
Point Sources)
Loss Than
Total
Allowable
Load?
TM L Workshop
LTI, Umno-Tech Inc.
-------
Real World TMDLs
Dillon Reservoir:
Parameters: Algae, Phosphorus
Objectives: Maintain present water quality
Define Objectives
Monitor Total Loads
Columbia Dioxin:
Parameters:
Model:
Allocation Strategy:
llocation
Strategy
Dioxin
C = W/Q
Pulp mills treat to level of detection
\llocatioi
Strategy
WQ Model
Loading
Capacity
Unallocated
Pulp Mill
WLA
Numeric Standard
Tualatin River:
Parameters:
Objectives:
Phosphorus, Algae
Lab and field studies relating
nutrient conc. to algal growth
Model: C = W / Q
Allocation Strategy: Tributaries at target levels
WLA = TMDL - LA
(^Define Objectives
llocatioi
Jtrategv
WQ Model
LA
Loading
Capacity
TMDL Workshop
L77, Limno-Tech, Inc.
-------
Real World TMDLs
Saginaw Bay:
Parameters:
Objective:
WQ Model:
Taste and Odor in drinking water
Correlate problems to observed
blue-green algae concentration
Complex multi-species
phytoplankton model
Present, Future Loads: Monitoring, Modeling
Allocation Strategy: Technology-based WLA followed
by cost-effectiveness analysis
Designated Use
-(^Ecological Effects^)
Numeric
Objective
ter Quality
Models
Load Response
Curve
T
Total Load
Allocation
Strategy
Point Source
Load
Non-point Source
Load
Best Management
Practices
TMDL Workshop
L77, Limno-Tech, Inc.
-------
More Complex TMDL Example
Water Quality Standard: 20 mg/L chlorophyll a
: Maintenance of spawning beds for fish/
no excess siltation
: 0.02 mg/L un-ionized ammonia (chronic toxicity)
Land Use: 50% Agricultural
20% Residential
30% Urban
Limited data available on tributary
quality, sufficient to estimate
annual loads
No policy on allocating loads
among sources
Flow = 10 m /sec
TMDL Workshop
L77, Umno-Tech, Inc.
-------
( Ecological Effects )
Analysis
Non-point
Source
Model
Non-point
Load
iter Qualify
Model
YES
scify Load
[eductions
Criteria Met?
NO
Point Source
Load
Done
Receiving
Water
Quality
Designated Use
Best Management
Practices
Numeric
Water Quality
Criteria
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
POTENTIAL PITFALLS
No numeric water quality standards
Insufficient data to support water quality model
Insufficient data to define present nonpoint loads
Lack of information on BMP efficiency
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
PHASED APPROACH TO
TMDL DEVELOPMENT
Develop preliminary TMDL
Replace rigorous models with simple
(yet reasonable) screening methods
Implement control strategies
Monitor to determine if water quality
objectives are achieved
Perform more rigorous modeling, if necessary
TMDL Workshop
LTI, LimnO'Tech, Inc.
-------
WATER QUALITY STANDARDS
(WQS)
Goal of TMDL is to achieve compliance with WQS
Numeric criteria
Designated uses
Narrative criteria
Need to define relationship between response
parameter and parameters of control
- Default
- Empirical
- Model
- Best Professional Judgment (BPJ)
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
RELATING RESPONSES
TO CONTROLS
Empirical Example
Silver Creek
• •
*» .V
«s* ¦
• ObMrv»d TurMity. NTU
Tufb . t.2738 • TSS
t —1
100 150
Total Smpandod Sotd*. mg/l
Water quality standard is for turbidity.
Parameter to control is solids.
San Luis Obispo
Water quality problem = nuisance algal growth
Conduct algal assays to determine algal growth
at several different concentrations of nitrogen
and phosphorus
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Relating Response to Controls: Model Example
Dissolved Oxygen
WWTP
Dissolved
Oxygen
Ammonia
Eutrophication:
Nutrient
Nitrogen
Phosphorus
TMDL Workshop
L77, Umno-Tech, Inc.
-------
RELATING RESPONSES
TO CONTROLS
BPJ EXAMPLE
...- — —... ' - ¦ ii ¦ ¦¦ — ¦ ™ '¦ ¦¦¦"'¦— ¦ i ¦ n ¦ in ¦ i i . i i,
San Luis Obispo Creek
Water quality problem = Siltation of spawning beds
Relationship between solids load and resulting
siltation is very complex, model is not feasible
Result: Interim target selected to reduce solids
loading by 50%.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Relate Load to Water Quality
Mode!
Criterion
Loading Capacity
Simple Model Can Be Run "In Reverse"
C = W/Q W = C x Q
More Complex Models Must Be Run Iteratively
Model
oncentration Comply
With Objectives?
Reduce
Load
Concentration
Load
DONE
TMDL Workshop L77, Limno-Tech, Inc.
-------
ESTIMATE LOADING
SOURCES
Point Sources: Relatively easy
Discharge monitoring reports
Nonpoint Sources:
Relatively difficult
Monitoring
Modeling
-------
ESTIMATION OF BMP
EFFICIENCY
Literature search
Simple model-based estimates
SCS-provided estimates
Pilot studies
Detailed nonpoint source modeling
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
Table 4
EFFECTIVENESS OF SELECTED BEST MANAGEMENT PRACTICES
% Reduction
BMP In Solids Load
URBAN
Retention Basins 45-93
56-95
81-91
Detention Basins
Extended Detention 60-90
Wet Extended Detention 60 +
Wet Detention 37-94
Dry Detention 0-77
Dry Extended Detention 60+
Detention/Infiltration 75-99
Infiltration Practices
Infiltration Basins 75-99
Infiltration Basins 60+
Infiltration Trenches 99
Infiltration Trenches 60 +
Porous Pavement 85-95
Porous Pavement 40-100
Vegetative Controls
Grass Filter Strips 60 +
Forest Filter Strips 60+
Vegetative Filter Strips 81-95
Grassed Swales 30-60
Grassed Swales 89
AGRICULTURAL
No-Till Planting 95 +
Conservation Tillage 60-98
30-90
up to 50
Stripcropping up to 75
Irrigation System
Improvements 80
Contour & Across
Slope Tillage 50-90
% Reduction in
Total Phosphorus Load Reference
40-80 Whalen and Cullum (1988)
Maas et al. (1987)
54-79 SAIC (1987)
40-50 SAIC (1987)
up to 40 EPA (1991b)
17-70 Whalen and Cullum (1988)
0-26 Whalen and Cullum (1988)
30-40 EPA (1991b)
50-75 SAIC (1987)
50-75 Schueler (1987)
up to 40 EPA (1991b)
65-75 Schueler (1987)
up to 40 EPA (1991b)
62-65 SAIC (1987)
60-80 Schueler (1987)
30-40 EPA (1991b)
up to 40 EPA (1991b)
30-98 EPA (1991b)
30-40 EPA (1991b)
0 SAIC (1987)
Novotny et al. (1981)
Maas et al. (1987)
35-90 EPA (1991b)
up to 45 EPA (1991b)
up to 50 EPA (1991b)
40 Maas (1987)
35-60 EPA (1991b)
-------
LOAD ALLOCATION ISSUES
Combine policy with technical issues
Technical Considerations
Relative contribution
Controllability
System response to different control efforts
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ALLOCATION OF
ALLOWABLE LOAD
Paradise Creek TMDL
Problems: Total phosphorus
Total nitrogen
Solids
Putaan
Water Quality Model: C=W/Q, applied at state line and mopth
NPS Loads: Estimated from simple model
BMP Effectiveness: Supplied by local SCS office
-------
PARADISE CREEK
ALLOCATION STRATEGY
Develop tool to predict loads associated with
various levels of BMP implementation
TMDL CALCULATION SPREADSHEET
Land Use/BMP
% of Area in BMP
Idaho Washington
Agriculture
Improved Practices
0
0
Divided Slopes
15
15
Strip Cropping
5
5
No Till
35
35
Filter Strips
35
35
Village/Urban
Retention
40
40
Street cleaning
0
0
WWTP TP Reduction
(%)
0
WWTP TN Reduction
(%)
0
WWTP SS Reduction
(*)
0
*** Press [ESC] for the menu **
State Mouth
Line
Summer TP Load 25.9 26.8
Summer Objective 6.2
Winter TP Load 4 5.7 51.7
Winter Objective 54.4
Summer TN Load 90.4 103.1
Summer Objective 37
Winter TN Load
Winter Objective
SS Load (MT/day) 16.5 36.0
SS Obj ective 3 6
211.0 302.1
327
TMDL CALCULATION SPREADSHEET
Land Use/BMP
% of Area in BMP
Idaho Washington
Agriculture
Improved Practices 0
Divided Slopes 0
Strip Cropping 0
No Till 0
Filter Strips 0
Village/Urban
Retention 0
Street cleaning 0
WWTP TP Reduction (%)
WWTP TN Reduction (%)
WWTP SS Reduction (%)
*** Press [ESC] for the menu
State Mouth
Line
itit
Summer TP Load
26.4
27.7
0
o
Summer Objective
6.2
w
0
Winter TP Load
48.8
58.3
0
o
Winter Objective
54.4
w
Summer TN Load
97.0
117.1
o
Summer Objective
37
V
0
Winter TN Load
258.5
402.8
o
Winter Objective
327
0
SS Load (MT/day)
32.6
71.9
0
SS Objective
36
-------
COMPLICATIONS ASSOCIATED
WITH TMDL ANALYSIS
TMDL Critical Conditions
TMDL Trading
Margin of Safety/Uncertainty
TMDL Worksh o p LTI, Limno-Tech, Inc.
-------
FREQUENCY/DURATION
CONSIDERATIONS OF
WATER QUALITY STANDARDS
Water quality criteria statements should consist of
three components:
Magnitude: How much is allowed
Duration: The period of time over which
concentrations are averaged
Frequency: How often criteria can be
exceeded
Model time scale will ideally be consistent with
duration of standard.
T M D L W o r It s h o p
LTI, Limno-Tech, Inc.
-------
CONTINUOUS SIMULATION
Direct accounting for magnitude, frequency,
& duration
Continuous Simulation
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it ni i unrn rr u i r i i i
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Rank and Compile
Downstroan Concflntration
-------
CONTINUOUS SIMULATION/
DESIGN CONDITIONS
Continuous Simulation Problems
Rarely enough data to drive model
Many nonpoint models give results on seasonal
or yearly basis
Resource-intensive to apply
Historical WLA Solution: Design Conditions
Pick single set of environmental conditions
"Critical conditions": Assume worst case
environmental condition
Design load to meet water quality standard for
critical conditions.
-------
Spatial Considerations
of TMDL Assessment
/
Need to consider water quality at multiple locations
in the watershed, not just at the outlet
o Major load entry points
o Subwatershed outlets
TMDL Workshop L77, Umno-Tech, Inc.
-------
Technical Considerations
Associated with TMDL Trading
Magnitude of TMDL may depend upon where the
load enters stream
Cone.
LA=50 kg/day
WQS
t
WLA= 100 kg/day WLA= 100 kg/day WLA= 100 kg/day
TMDL = 350 kg/day
We can redistribute this load, and violate WQS
Cone.
WQS
t
LA=50 kg/day
WLA=200 kg/day WLA=50 kg/day
WLA=50 kg/day
Need to consider all locations in the
receiving water, not just watershed outlet
TMDL Workshop
LTt, Umno-Tech, Inc.
-------
TMDL UNCERTAINTY/
MARGIN OF SAFETY
TMDLs must contain a margin of safety (MOS) to
account for uncertainty in predicted relationship
between pollutant loads and receiving water
quality.
Can be accounted for implicitly or explicitly
Explicit Example
Loading Capacity = WLA + LA + MOS
100 kg/day = 40 + 40 + 20
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
IMPLICIT CONSIDERATION OF
MARGIN OF SAFETY
Incorporate Conservative Assumptions in
Modeling Analysis
- Conservative design conditions
- Conservative pollutant decay rates
LC = WLA + LA
80 = 40 + 40
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
MARGIN OF SAFETY
Often included by assuming no instream
loss processes
Mater profile. Total Toxicant
Lower Colunbia Rluer
0.20
0 .16
Q.12
O .08
O.OO
260
3bO
250
l60
JLOO
SO
River Mile
Connerison of Results for Alternative Loss Assunotions
-------
MARGIN OF SAFETY
Need to estimate magnitude of loss processes,
to determine significance of neglecting them
Hater profile, Total Toxicant
Blackstana River Julu Run
10
4 -
River Mile
Predicted Lead Concentrations with Alternative Loss AsBunptions
-------
TECHNICAL RAMIFICATIONS OF
MARGIN OF SAFETY
As uncertainty increases, allocatable load shrinks
in comparison to MOS
LC = WLA + LA
100±90 =5 +5
Stakeholders reluctant to implement required
controls, as severity of reductions is directly
proportional to uncertainty of analysis.
TMPL Workshop
LTI, Limno-Tech, inc.
-------
PRACTICAL APPROACH
TO DEALING WITH PITFALLS
Example: Determining BMP Efficiency
Option
Estimated
Reliability
Resources
Required
Literature Review
± 60%
1 week
Simple Model Estimation
±40%
1 month
Pilot Study
±15%
1 year
Detailed Model Application
±10%
1 year
T M D L Wo r k s hop
LTI, Limno-Tech, Inc.
-------
PRACTICAL APPROACH
TO DEALING WITH PITFALLS
Define problem
In what way can problem be solved
What are the resources required.
What uncertainty does this option introduce
Which alternative provides best mix of costs
and benefits
The more simplifying assumption(s) required,
more likely that only a phased TMDL can
be conducted.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SUMMARY
Many technically oriented steps required.
Nothing that hasn't been done before.
Limited resources.
Significant ramifications.
BPJ often required.
Remainder of workshop will focus on main
component steps.
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
-------
NONPOINT SOURCE MODELS
OVERVIEW ~
Why Nonpoint Sources?
NPS Modeling Theory
Available Models
Model Selection
-------
Water Quality
Standard
« *> L Workshop
Maximum Allowable
Nonpoint Load (LA)
Maximum Allowable
Point Load (WLA)
LTI, Umno-Tec' 'nc.
-------
TYPES OF NONPOINT
SOURCES
Agricultural Runoff
Soils
Pesticides/Herbicides
Animal Wastes
Urban Runoff
Residential vs. Commercial/Industrial
Combined vs. Separate Sewers
Mining
Construction
Silviculture
Land Disposal
Atmospheric Deposition
Contaminated Sediments
-------
Are NPS Significant?
Estuaries Lakes Rivers
¦ Non-Point Sources S Industrial Point Sources
¦ Combined Sewer Overflows ¦ Other/Unknown
~ Natural Causes ¦ Municipal Point Sources
NPS Loadings to Great Lakes
(Sonzogni et al. 1978)
Pollutant
Total from Diffuse
Sources (%)
Diffuse Unit Area Load
(kg/ha-yr)
Total Phosphorus
71
0.40
Soluble Orthophosphate
60
0.10
Suspended Solids
98
310.00
Total Nitrogen
85
6.4
Chlorides
66
74.00
-------
ALLOCATION OF
ALLOWABLE LOAD
Areas to be targeted for reduction may be obvious.
San Luis Obispo
Source Category TP Load (kq/dav)
Agriculture 3.15
Residential 0.16
Urban 0.42
WWTP 109.0
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ALLOCATION OF
ALLOWABLE LOAD
Controllability: Need to consider more than just total
loading
Silver Creek Areas in
Solids Load Land Use
Source Category (MT/yr) (1,000 acres)
Range Land Erosion 11,900 350
Stream Bank Erosion 668 —
Forest 60 140
Urban 29 3
Feedlots 26 0.4
Also consider loading per unit arce.
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
WHAT MAKES NPS POLLUTION
DIFFICULT TO QUANTIFY
Event Related
Intermittent
Time variable
Event variable
Diffuse & Variable
No single location
Variable with location
Changes with travel
Changes with time
-------
NPS CONTROL MEASURES
Agriculture
Animal waste management
Conservation tillage
Contour farming
Contour strip cropping
Cover crops
Crop rotation
Fertilizer management
Integrated pest management
Livestock exclusion
Range and pasture management
Sod-based rotation
Terraces
Construction
Disturbed area limits
Nonvegetative soil
stabilization
Runoff detention/retention
Surface roughening
Urban
Flood storage
Porous pavements
Runoff detention/retention
Street cleaning
Silviculture
Ground cover maintenance
Limiting disturbed areas
Log removal techniques
Pesticide/herbicide
Proper handling of haul roads
Removal of debris
Riparian zone management
Road and skid trail management
Mining
Block-cut or haul-back
Underdrains
Water diversion
Multicategory
Buffer strips
Detention/sedimentation basins
Devices to encourage infiltration
Grassed waterway
Interception/diversion
Material ground cover
Sediment traps
Streamside management zones
Vegetative stabilization/mulching
Categories of Structural
Control
Storage
Treatment
Conveyance
-------
NPS NEEDS FOR TMDL
QUESTION:
If nonpoint sources are difficult to isolate,
characterize, and measure...
Then, how do we quantify the load allocation
necessary for TMDLs?
ANSWER:
Modeling
-------
NPS MODELING THEORY
INTRODUCTION
Simulation of water movement
Simulation of pollutants on suspended solids
Simulation of dissolved pollutant
Flow x Concentration
= Pollutant Load
-------
METHODS CATEGORIES FOR
NPS/LOAD ALLOCATION
1. Empirical (flow and concentration)
2. Unit Area Loading
3. Rating Curves
4. Computer Models
Nonpoint Source Assessment
Hydrology
Loading
Quality
T M O L Work » ho p
LTT, Umno-T»ch, Inc.
-------
EMPIRICAL NPS METHODS
Empirical = Direct Measurements
Event
~ Measure hydrograph & pollutograph (hourly)
Season/Annual
~ Monitor flow and concentrations long term;
compile and sum
Statistical
~ Monitor many hydrographs and pollutographs and
statistically characterize temporal variability
~ Monitor flow and concentrations long term and multiply
averages
-------
SilvertKorne
DENVER
CNLARGCD
ARCA
Dillon Reservoir TMDL
Water quality objectives were set equal to current water
quality:
TMDL = Current Loads
TMDL = WLA + LA
LA and WLA are determined by a complex trade-off
program to maintain or improve current water quality.
-------
LIMITATIONS OF EMPIRICAL
METHODS
Limited by Data
Quality of data
Site specific
Rainfall/hydrology specific
Pollutant specific
Only characterizes Existing Conditions
Changes in land use
Changes in agricultural practices
Changes in rainfall
-------
JNIT AREA LOADING
~ Pounds/acre/year
~ Potential Factors
Pollutant
Land Use
% Impervious
Slope
Soil type
Vegetative Cover
Tillage Practices
Rainfall
- BMPs
~ Pollutant Specific
~ Land Specific
~ Delivery Ratio?
-------
1.22
Chapter 1: Impacts of Urban Runoff
Table 1.5: Annual Storm Pollutant Export For Selected Values of
Impervious Cover (I) Developed from the Simple Method1
LAND2
SITE
TOTAL
TOTAL
BOD
EXTRACTABLE
USE
IMPERVIOUSNESS
PHOSPHORUS3
NITROGEN
5-day
ZINC
LEAD
j / /
--- pounas/acre/year
RURAL
0
0.11
0.8
2.1
0.02
0.01
RESIDENTIAL
5
0.20
1.6
4.0
0.03
0.01
10
0.30
2.3
5.8
0.04
0.02
LARGE LOT
10
0.30
2.3
5.8
0.04
0.02
SINGLE
15
0.39
3.0
7.7
0.06
0.03
FAMILY
20
0.49
3.8
9.6
0.07
0.04
MEDIUM
20
0.49
3.8
9.6
0.07
0.04
DENSITY
25
0.58
4.5
11.4
0.08
0.05
SINGLE
30
0.68
5.2
13.3
0. 10
0.05
FAMILY
35
0.77
6.0
15.2
0.11
0.06
TOWNHOUSE
35
0.77
6.0
15.2
0.11
0.06
40
0.87
6.7
17.1
0.12
0.07
45
0.97
7.4
18.9
0.14
0.07
50
1.06
8.2
20.8
0.15
0.08
GARDEN
50
1.06
8.2
20.8
0.15
0.08
APARTMENT
55
1. 16
8.4
22. 7
0.16
0.09
60
1.25
9.6
24.6
0.18
0.09
HIGH RISE,
60
1.25
9.6
24.6
0.18
0.09
LIGHT
65
1.35
10.4
26.4
0.19
0.10
COMMERICAL/
70
1.44
11.1
28.3
0.21
0.10
INDUSTRIAL
75
1.54
11.8
30.2
0.22
0.11
80
1.63
12.6
32.0
0.23
0.11
HEAVY
80
1.63
12.6
32.0
0.23
0.11
COMMERCIAL,
85
1.73
13.3
33.9
0.25
0.12
SHOPPING
90
1.82
14.0
35.8
0.26
0. 13
CENTER
95
1.92
14.8
37.7
0.27
0. 13
100
2.00
15.4
39.2
0.28
0. 14
1 P=40 inches, Pj=0.9, Rv=0.05+0.009(I), C=suburban values, A=1 acre.
2 Rural Residential: 0.25-0.50 Dwelling Units (DU)/acre
Large Lot Single Family: 1.0-1.5 DUs/acre
Medium Density Single Family: 2-10 DUs/acre
Townhouse and Garden Apartment: 10-20 DUs/acre
1 These values are for NEW DEVELOPMENT SITES ONLY. For older urban
areas, central business districts, sites with highways, or areas out-
side of the Middle Atlantic region, use a more appropriate "C" value
in Equation 1.1 (see Table 1.1).
-------
Comparative Pollutant Removal Of Urban BMP Designs
BMP/design
EXTENDED OETENTION POND
DESIGN 1
•
0
0
0
3
®
MODERATE
0ESIQN 2
•
3
0
3
9
®
MODERATE
DESIGN 3
•
•
3
3
9
®
HIGH
WET POND
DESIGN 4
3
0
0
0
®
MODERATE
0ESIGN S
*
3
0
0
9
®
MOOERATE
OESIGN 6
•
9
3
3
9
®
HIGH
INFILTRATION TRENCH
DESIGN 7
•
3
3
9
9
9
MOOERATE
DESIGN «
•
3
3
9
•
9
HIGH
OESIGN «
•
?
•
•
•
•
HIGH
INFILTRATION BASIN
OESIGN 7
3
3
9
3
9
MODERATE
DESIGN 8
•
3
3
9
•
9
HIGH
DESIGN S
•
9
9
•
•
•
HIGH
POROUS PAVEMENT
DESIGN 7
3
9
3
9
3
9
MODERATE
DESIGN a
•
9
9
9
•
•
HIGH
OESIGN 9
•
9
9
•
•
•
HIGH
WATER OUAUTY INLET
DESIGN 10
O
®
®
®
®
®
LOW
FILTER STRIP
OESIGN 11
0
O
O
O
0
®
LOW
DESIGN 12
•
3
3
9
•
®
MOOERATE
GRASSED SWALE
DESIGN 13
O
O
O
O
O
®
LOW
DESIGN 14
0
0
0
0
O
®
LOW
KEY:
O 0 TO 20* REMOVAL
(J 20 TO 40* REMOVAL
3 40 TO 60% REMOVAL
9 SO TO S0« REMOVAL
% SO TO 100% REMOVAL
(£) INSUFFICIENT
KNOWLEDGE
Design
1
Design
2
Design
3
Design
4
Design
5
Design
6
Design
7
Design
8
Design
9
Design
10
Design
11
Design
12
Design
13
Design
14
First-flush runoff volume detained for 6-12 hours.
Runoff volume produced by 1.0 inch, detained 24 hours.
As in Design 2. but with shallow marsh in bottom stage.
Permanent pool equal to 0.5 inch storage per impervious acre.
Permanent pool equal to 2.5 (Vr); where Vr*=niean storm runoff.
Permanent pool equal to 4.0 (Vr); approx. 2 weeks retention.
Facility exfiltrates first-flush; 0.5 inch runoff/imper. acre.
Facility exfiltrates one inch runoff volume per imper. acre.
Facility exfiltrates all runoff, up to the 2 year design storm.
400 cubic feet wet storage per impervious acre.
20 foot wide turf strip.
100 foot wide forested strip, with level spreader.
High slope swales, with no'check dams.
Low gradient swales with check dams.
(Controlling Urban Runoff; MWCOG 1987)
-------
Comparative Pollutant Removal Of Urban BMP Designs
BMP/design
EXTENDED DETENTION POND
MODERATE
OESIQN 1
MODERATE
DESIGN 2
KEY:
DESIGN 3
HIGH
0 TO 20* REMOVAL
WET POND
20 TO 40% REMOVAL
MODERATE
DESIGN 4
40 TO 00% REMOVAL
MOOERATE
DESIGN S
60 TO 80% REMOVAL
HIGH
DESIGN 6
80 TO 100% REMOVAL
INFILTRATION TRENCH
INSUFFICIENT
KNOWLEDGE
MODERATE
DESIGN 7
HIGH
DESIGN 8
HIGH
0ESIGN 9
INFILTRATION BASIN
MODERATE
DESIGN 7
HIGH
DESIGN 8
HIGH
DESIGN 8
-------
Measurement of Non-point Loads
Flow
X
Cone.
—
Pollutant
Load
Simulation of Non-point Loads
Hydrology
Pollutants
NPS Loading
TMDL Workshop
L77, Umno-Tech, Inc.
-------
SIMPLE METHOD FOR NPS
W = QC
W = load
Q = flow
C = concentration
FLOW
Measured (e.g., USGS, gauge)
Estimated
Rational Method
Q = RVA
Rv = Runoff coefficient
I = Rainfall
A = Area
CONCENTRATION
Measured
BPJ from similar sites
-------
Urban Areas
Runoff Coefficients.3
Runoff
Description of Area Coefficient
Flat, residential, with about 30% of area
impervious 0.40
Moderately steep, residential, with about 50%
of area impervious 0.65
Moderately steep, built-up, with about 70%
of area impervious 0.80
Table from Homer and Flynt-Transactions ASCE 1936.
Deductions from unity to obtain runoff coefficients for
agricultural areas
Type of Area Value of r1
Topography
Flat land, with average slopes of 0.02-0.06%
0.30
Rolling land, with average slopes of 0.3-0.4%
0.20
Hilly land, with average slopes of 3-5%
0.10
Soil
Tight, impervious clay
0.10
Medium combination of ciay and loam
0.20
Open, sandy loam
0.40
Cover
Cultivated lands
0.10
Woodland
0.20
Table from Bernard-Transactions ASCE 1935.
aReprinted from "Handbook on the Principles of Hydrology" by D.M
Gray18 by permission of the National Research Council of Canada.
bThe magnitude of the runoff coefficient, Rv, is obtained by adding values
of f for each of the three factors: topography, soil, and cover, and by
subtracting the sum from unity.
Novotny & Chesters, 1981
-------
Urban 'C' Values For Use With the Simple Method (mg/1)
NEW
OLDER
CENTRAL
NATIONAL
HARDWOOD
NATIONAL
SUBURBAN
URBAN
BUSINESS
NURP
FOREST
URBAN
NURP SITES
AREAS
DISTRICT
STUDY
(Northern
HIGHWAY
POLLUTANT
(Wash.,DC)
(Baltimore)
(Wash.,DC)
AVERAGE
Virginia)
RUNOFF
PHOSPHORUS
Total
0.26
1.08
-
0.46
0.15
-
Ortho
0. 12
0.26
1.01
-
0.02
-
Soluble
0.16
-
-
0.16
0.04
0.59
Organic
0. 10
0.82
-
0.13
0.11
NITROGEN
Total
2.00
13.6
2.17
3.31
0.78
-
Nitrate
0.48
8.9
0.84
0.96
0.17
-
Ammonia
0.26
1.1
-
-
0.07
-
Organic
1.25
-
-
-
0.54
-
TKN
1.51
7.2
1.49
2.35
0.61
2.72
COD
35.6
163.0
•
90.8
>40.0
124.0
BOD (5-day) 5.1
-
36.0
11.9
-
-
METALS
Zinc
0.037
0.397
0.250
0.176
-
0.380
Lead
0.018
0.389
0.370
0.180
-
0.550
Copper
•
0.105
•
0.047
•
•
(Controlling Urban Runoff; MWCOG 1987)
-------
SCS ESTIMATION OF FLOW
- CURVE NUMBER EQUATION -
(P-0.2S)
Q = -r V for P > 0.2S
(P + O.&S)
Q = runoff (cm)
P = precipitation, snow + rain (cm)
S = water retention parameter (cm)
S = f(soils, management, antecedent moisture)
-------
hydrology: solution OF RUNOFF EQUATION Q'
P« 0 lo 12 inches
0•O lo 8 Inchtt
Rainfall (P|
RUNOFF 10)
0. K-o «»|'
p«o.a
Initial
ttilKdlM I.
4 s • r
RAINFALL (P) IN INCHES
¦iiMia
Moctut. VIcIm; (t(l«ill«t 4htel maall aimali l«m »l«»m »»l»loll:
C*»li«l Ttchnlctl Unit, Oclobtf 193}
g • Mruma>f| or *ohouiwm
too. ooranvATioM auvicc
(S 1001
wn 1 o>
*•1 JJLH
SCS Curve Number Runoff Equation (Iiii = 2.54cm)
-------
Table B-1.
Descriptions of Soil Hydrologic Groups (Soil Conservation Service, 1986)
SoU
Hydrologic Group Descnption
A Low runoff potential and high infiltration rates even when thoroughly wetted. Chiefly
deep, well to excessively drained sands or gravels. High rate of water transmission
(> 0.75 cm/hr).
B Moderate infiltration rates when thoroughly wetted. Chiefly moderately deep to deep.
moderately well to well drained soils with moderately fine to moderately coarse
textures. Moderate rate of water transmission (0.40-0.75 cm/hr).
C Low infBtration rates when thoroughly wetted. Chiefly soils with a layer that impedes
downward movement of water, or soils with moderately fine to fine texture. Low rate
of water transmission (0.15-0.40 cm/hr).
0 High runoff potential. Very low infiltration rates when thoroughly wetted. Chiefly clay
soils with a high swelling potential, soils with a permanent high water table, soils
with a ciaypan or clay layer at or near the surface, or shallow soils over nearly
impervious material. Very low rate of water transmission (0-0.15 cm/hr).
Disturbed Soils (Major altering of soil profile by construction, development):
A Sand, loamy sand, sandy loam.
B SOt loam, loam
C Sandy day loam
D Clay loam, sflty day loam, sandy day, silty day, day.
-------
Table B-2. Runoff Curve Numbers (Antecedent Moisture Condition II) for Cultivated Agricultural
Land (Soil Conservation Service, 1986).
Hydrologic Sofl Hydrologic Group
Land Use/Cover Condition A B C ~
Fallow Bare Soil
77
86
91
94
Crop residue cover (CR)
Poor3/
76
85
90
93
Good
74
83
88
90
Row Crops Straight row (SR)
Poor
72
81
88
91
Good
67
78
85
89
SR + CR
Poor
71
80
87
90
Good
64
75
82
85
Contoured (C)
Poor
70
79
84
88
Good
65
75
82
86
C + CR
Poor
69
78
83
87
Good
64
74
81
85
Contoured & terraced (C&T)
Poor
66
74
80
82
Good
62
71
78
81
C&T + CR
Poor
65
73
79
81
Good
61
70
77
80
Small SR
Poor
65
76
84
88
Grains
Good
63
75
83
87
SR + CR
Poor
64
75
83
86
Good
60
72
80
84
C
Poor
63
74
82
85
Good
61
73
81
84
C + CR
Poor
62
73
81
84
Good
60
72
80
83
C&T
Poor
61
72
79
82
Good
59
70
78
81
C&T + CR
Poor
60
71
78
81
Good
58
69
77
80
Close- SR
Poor
66
77
85
89
seeded or
Good
58
72
81
85
broadcast C
Poor
64
75
83
85
legumes or
Good
55
69
78
83
rotation C&T
Poor
63
73
80
83
meadow
Good
51
67
76
80
Hydrologic condition is based on a combination of factors that affect inffltration and runoff,
including (a) density and canopy of vegetative areas, (b) amount of year-round cover, (c) amount
of close-seeded legumes in rotations, (d) percent of residue cover on the land surface (good £
20%), and (e) degree of surface roughness.
-------
Runoff Curve Numbers (Antecedent Moisture Condition II) for other Rural Land (Soil
Conservation Service, 1986).
Land Use/Cover
Hydrologic
Condition
Soil Hydrologic Group
ABC
D
Pasture, grassland or range
Poor3/
68
79
86
89
- continuous forage for grazing
Fair
49
69
79
84
Good
39
61
74
80
Meadow - continuous grass, protected
from grazing, generally mowed for hay
30
58
71
78
Brush - brush/weeds/grass mixture
Poor13/
48
67
77
83
with brush the major element
Fair
35
56
70
77
Good
30
48
65
73
Woods/grass combination
Poor
57
73
82
86
(orchard or tree farm)C/'
Fair
43
65
76
82
Good
32
58
72
79
Woods
Poor/d
45
66
77
83
Fair
36
60
73
79
Good
30
55
70
77
Farmsteads - buildings, lanes.
driveways and surrounding lots
59
74
82
86
a/ Poor < 50% ground cover or heavily grazed with no mulch; Fair 50 to 75% ground cover and
not heavily grazed; Good: > 75% ground cover and lightly or only occasionally grazed.
k/ Poor < 50% ground cover; Fair 50 to 75% ground cover; Good: > 75% ground cover.
c/ Estimated as 50% woods. 50% pasture.
Poor, forest litter, small trees and brush are destroyed by heavy grazing or regular burning; Fair
woods are grazed but not burned and some forest litter covers the soil; Good: Woods are protected
from grazing and litter and brush adequately cover the soil.
-------
Table B-4. Runoff Curve Numbers (Antecedent Moisture Condition II) for Arid and Semiarid
Rangelands (Soil Conservation Service, 1986).
Hydrologic
Soil Hydrologic Group
Land Use/Cover
Condition
A
B
C
D
Herbaceous - grass, weeds & low-
Poor2/
80
87
93
growing brush; brush the minor
Fair
-
71
81
89
component
Good
-
62
74
85
Oak/aspen - oak brush, aspen.
Poor
-
66
74
79
mountain mahogany, bitter brush.
Fair
-
18
57
63
maple and other brush
Good
-
30
41
48
Pinyon/juniper - pinyon, juniper or
Poor
-
75
35
89
both; grass understory
Fair
-
58
73
80
Good
-
41
61
71
Sagebrush with grass understory
Poor
-
67
80
85
Fair
-
51
63
70
Good
-
35
47
55
Desert scrub - saitbush. greasewood.
Poor
63
77
85
88
creosotebrush, blackbrush, bursage.
Fair
55
72
81
86
paJo verae, mesquite and cactus
Good
49
63
79
34
a/ Poor < 30% ground cover (litter, grass and brush overstory); Fair 30 to 70% ground cover
Good: > 70% ground cover.
Table 8-5. Runoff Curve Numbers (Antecedent Moisture Condition II) for Urban Areas (Soil
Conservation Service, 1986).
Soil Hydrologic Group
Land Use ABC
Open space (lawns, parks, golf
courses, cemeteries, etc.):
Poor condition (grass cover < 50%)
Fair condition (grass cover 50-75%)
Good condition (grass cover > 75%)
Impervious areas:
Paved parking lots, roofs,
driveways, etc.)
Streets and roads:
Paved with curbs & storm sewers
Paved with open ditches
Gravei
Dirt
Western desert urban areas:
Natural desert landscaping (pervious
areas, only)
Artificial desert landscaping
(impervious weed barrier, desert shrub
with 1 -2 in sand or gravel mulch
and basin borders)
68
79
86
89
49
69
79
84
39
61
74
80
98
98
98
98
98
98
98
98
83
89
92
93
76
85
89
91
72
82
87
89
63
77
85
88
96
96
96
96
-------
ANTECEDENT MOISTURE LIMITS FOR CURVE NUMBER SELECTION
(Ogrosky and Mockus, 1964)
5-Day Antecedent
Antecedent Precipitation
Moisture Condition (cm)
Dormant Growing
Season* Season
I <1.3 <3.6
II 1.3-2.8 3.6-5.3
III >2.8 >5.3
*During snowmelt, condition III is always assumed
regardless of antecedent precipitation.
-------
UNIT HYDROGRAPH
<
SX
-------
USING SCS CURVE NUMBER
METHOD FOR ANNUAL
CONDITIONS OR LARGE
AREAS
0r = I Qy
Qt = Total annual flow
Qy = Flow for subarea j, rainfall i
i = rainfall
j = subarea
-or-
Qr = Z Qi @ CN
CR = S ajCRj
aj = fraction of total watershed
for subarea j
CNj = Curve Number for subarea j
-------
3.0
.09
3.0
64
1.00
9.0
II.0
w o
(A)
(B)
(C)
(D)
Mean Annual Row Crop Runoff In Inches
For Selected Curve Numbers. A; CN2=67,
B: CN2=78; C: CN2=85; D: CN2=89. (1 in
= 2.54cm) (Stewart Ei Al, 1976)
-------
UNIVERSAL SOIL LOSS
EQUATION FOR
ESTIMATING LOAD
X = 1.29 (E) K(LS)C(P)
X = soil loss (metric tonne/ha)
E = rainfall/runoff erosivity index
(100 m tonne-cm/ha hr)
K = soil erodibility (tonne/ha/Unit E)
LS = topographic factor
C = crop and cover factor
P = management practices factor
Pollutant
Load = (X) (Solids concentration) SDR
SDR = Sediment Delivery Ratio
-------
Average Annual Erosivity Indices (English Units)
For Eastern U.S. (Wischmeier and Smith, 1978)
-------
Average Annual Erosivity Indices (English Units)
For Western U.S. (Wischmeier and Smith, 1978)
-------
Monthly distribution of erosive rainfall as a percentage of total rain-
fall. (Reprinted from references .)
-------
SOIL ERODIBILITY, K
(Stewart et al, 1975)
Texture
Organic Matter
0.5%
2%
4%
Sand
0.05
0.03
0.02
Fine sand
0.16
0.14
0.14
Very fine sand
0.42
0.36
0.28
Loamy sand
0.12
0.10
0.08
Loamy fine sand
0.24
0.20
0.16
Loamy very fine sand
0.44
0.38
0.30
Sandy loam
0.27
0.24
0.19
Fine sandy loam
0.35
0.30
0.24
Very fine sandy loam
0.47
0.41
0.33
Loam
0.38
0.34
0.29
Silt loam
0.48
0.42
0.33
Silt
0.60
0.52
0.42
Sandy clay loam
0.27
0.25
0.21
CI ay 1 oam
0.28
0.25
0.21
Silty clay loam
0.37
0.32
0.26
Sandy clay
0.14
0.13
0.12
Silty clay
0.25
0.23
0.19
CI ay
0.13-0.29
-------
SOIL STRUCTURE
-VERY FINE GRANULAR
2-F1NE GRANULAR
MATT
3-MED. OR COAR SE
GRANULAR
BLOCXY, PLATY,
OR MASSIVE
12 34
0.2 0.7
OJ 0.6
0 £0.5
RCENT SANO
i^O.4 ^
6 54 321
(0.1-2.0 mm)
PERMEABILITY
1-RAPID
2-MOD. RAPID
3-MODERATE
4-SLOW TO MOO
3-SLOW
6-VERY SLOW
-jO.I-4
Soil erodibility nomograph for determining (K) factor for U.S. main-
land soils. (Reprinted with permission from Wischmeier et al., J. Soil Water
Cons., 26:189-193.9 Copyright © 1971 by the Journal of Soil and Water
Conservation.)
-------
€0
°«
20 ~t
,°o
0 i
°|o
,°o
°o
0.6 J
0.3 H
600 1000
300
60 100
30
o
SLOPE LENGTH, meters
Length-slope factor (LS) for different slopes. (Taken from references )
LS =
L1/2 (0.00138 + 0.00974S + 0.00138S2)
-------
TABLE 5-3. Values of C for Cropland, Pasture,
and Woodland.*
Land Cover or Land Use C
Continuous fallow tilled up and down slope 1.0
Shortly after seeding or harvesting1* 0.3-0.8
For crops during main part of growing season
Corn® 0.1-0.3
Wheat® 0.05-0.15
Cotton 0.4
Soybeans® 0.2-0.3
Meadow® 0.01-0.02
For permanent pasture, idle land, unmanaged woodland
Ground cover 95-100%
As grass
As weeds
Ground cover 80%
As grass
As weeds
Ground cover 60%
As grass
As weeds
For managed woodland
Tree canopy of 75-100%
40-75%
20-40%
* Adapted from references 3, 7, and 10.
"Depending on root and residue density.
®Depending on yield.
TABLE 5-6. Values of P for Agricultural Land."
Strip Cropping and Terracing
Slope (%)
Contouring
Alternate Meadows
Close grown Crops
1.1-2.0
0.6
0.30
0.45
2.1-7.0
0.5
0.25
0.40
7.1-12.0
0.6
0.30
0.45
12.1-18.0
0.8
0.40
0.60
18.1-24.0
0.9
0.4S
0.70
>24.0 1.0
'Adapted from reference 7.
0.003
0.01
0.01
0.04
0.04
0.09
0.001
0.002-0.004
0.003-0.01
Novotny & Chesters, 1981)
-------
SEDIMENT DELIVERY RATIO
Not all eroded particles are delivered to receiving water
Ratio of sediment delivered:sediment eroded is
called delivery ratio
Factors affecting delivery ratio
Watershed size
Relief
Bifurcation
-------
g 0.40
£ 0.30
0.20
Ui
uj 0.08
0.06
h
S 0.04
en 0.02
10
100
1000
ORAINAGE AREA (km2)
Sediment Delivery Ratio As A Function Of
Watershed Drainage Area (Vanoni, 1975)
-------
MERITS AND LIABILITIES OF
SIMPLE/ANALYTICAL NPS
CALCULATION METHODS
Merits
~ Available for all types of watersheds
~ Applicable to event or annual use
~ Applicable to large and small areas
~ Suitable to any pollutant
~ User skill needs are small
q Easy to use for decision making
~ Good tool for relative comparison
Limitations
~ Not very accurate
~ Provides only reasonable estimates
~ Limited by site specific data
~ No information on variability
~ Best adapted for existing conditions
~ Limited accuracy for alternative scenarios
~ Not a good absolute predictor
-------
ESTIMATION OF NPS
POLLUTANT LOADS
DISSOLVED
Wd = Q Cd
Q = flow
Cd = concentration of pollutant dissolved
PARTICULATE
Wp = (SDR)(X)(CS)
SDR = sediment delivery ratio
X = soil/solids loading
CS = pollutant concentration/mass of
solids
-------
CRITICAL CONDmONS/
MODEL TIME SCALE ISSUES
Point source loads are generally independent
of stream flow
WLAs: low upstream flow = critical condition
Historical precedent for using 7Q10
Nonpoint source loads are typically positively
correlated to flows
For TMDL, what is the critical environmental
condition?
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TMDL Critical Conditions
WLA
River Cone. =
WWTP Load
River Flow
W
WWTP
Riv
Protect water quality at drought flow.
TMDL Workshop
L77, Umno-Tech, Inc.
-------
Long Criteria Durations Facilitate
Continuous Simulation Analysis
Rainfall
NPS
Load
Stream
Flow
n_n
Time
Instream
Cone.
fLn
Time
NPS
Model
Time
WWTP
Load
WQ
Model
Time
Time
TMDL Workshop
L77, Umno-Tech, Inc.
-------
TMDL CRITICAL CONDITIONS:
ROUGH APPROXIMATION
Set WLA to ensure compliance with WQS
during critical conditions
Set LA to ensure compliance with WQS
during high runoff conditions
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TMDL Critical Conditions
TMDL
(VWVTP Load + NPS Load)
River Cone. =
River Flow
NPS
VWVTP
Q
Riv
Riv
Riv
Riv
What is the design condition?
TMDL Workshop LT1, Umno-Tech, Inc.
-------
Continuous Simulation Analysis
Rainfall
NPS
Load
Stream
Flow
Time
Instream
Cone.
A
Time
NPS
Model
Time
WWTP
Load
WQ
Model
Time
Time
TMDL Workshop
L77, Umno-Tech, Inc.
-------
Nonpoint Source Assessment
Hydrology
Loading
Quality
Methods = Simple—~Complex
1. Load Estimates
2. Simulate Hydrology, Estimate Quality
3. Simulate Hydrology, Simulate Quality
-------
Rain (Snow Melt)
^Evaporation
Evaporation
Surface
Evaporation
Runoff
Infiltration
. Interflow
vapo-
anspiration
Stream Flow
and
Storage
Geological Water Loss.
Depression
Storage
Depression
Storage
Interception
Storage
Groundwater
Flow and
Storage
Upper Zone
Flow and
Storage
Pervious Areas
Impervious Areas
TMDL Workshop
L77, Umno-Tech. Inc.
-------
Rain (Snow Melt)
Pervious Areas
Impervious Areas
Evaporation
I
Interception
Storage
^Evaporation
I
I
Depression
Storage
Depression
Storage
Evaporation
Surface
Sewers
Runoff
Infiltration
i
^vapo-
Upper Zone
Flow and
Storage
i i i y t
- . Interflow - .* -
iranspiration
* i *
- : . - .* - '
! * 1 * » " .
Groundwater
Flow and
Storage
I Geological Water Loss
Stream Flow
and
Storage
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
AGRICULTURAL NPS MODELS
Rainfall — Driving Force
t
Loads
Rainfall Detachment
Flow Detachment
Deposition
Resuspension
Surface Detention
Surface Retention
i_
— Water Losses
Surface Erosion
Transport: Overland
Ditch
Tributary
-------
URBAN NPS MODELING
Hydrological
Runoff
Rainfall
Water Losses
LOADS
Sewer
Routing
Pollutant
Wash-Off
Storage
Treatment
Pollutant
Build Up
-------
HYDROLOGIC MODEL
COMPONENTS
Interception Storage
Plant types
Season
% Coverage
Depression Storage (Puddles)
Land use
Slope
Land use practices
Infiltration
Soil permeability
Soil moisture
Overland Flow
Slope
Roughness
Channel Routing
Open channels
Pipe conditions
Slope & size
Controls
-------
EROSION
Particles liberated by raindrop impact and/or overland flow
Factors affecting raindrop liberation
Rainfall intensity
Soil erodibility
Factors affecting overland flow liberation
Land slope
Depth of flow
Soil erodibility
Urban Area Factor
Pollutant build-up
land use
management practices (e.g., street cleaning)
atmospheric deposition
Pollutant washoff
intensity
flow
-------
Effectiveness of Agricultural Corjtrols
Over Further Point Source Qgntfols for
Reducing Phosphorus Uoaafs and
Improvmg Saginaw Bay Water Quality
LAKE
HURON
Wild-howl
&
Saginaw Bay
-------
IJC Goals for Saginaw Bay
Load
Source
Existing
Future
Target 1
(620 mta)
POTW
Q 1MGD
1.0 mgP/1
0.5 mgP/1
POTW
Q 1 MGD
—
Agricultural
Nonpoint
Level 1
Level 2
Urban
Nonpoint
Level 1
Level 2
-------
Agricultural
Nonpoint
59%
Other
Nonpoint
18%
fj'si'
Sources
J
a
Atmos.
3%
Phosphorus Contributions During
1980
-------
Special
Project
Area
Starkey's
Farm
Special Project Area
-------
MODELS VARY IN COMPLEXITY
Level of Detail
Data Requirements
Need for calibration and verification
-------
Watershed
Time
Spatial Detail
Pollutant Type
Level of Application
Data Needs
User Skill Needs
Decision/Management Needs
-------
MODEL APPLICATIONS
DICTATE MODEL SELECTION
Watershed Pollutant Type
Urban Solids
Rural Nutrients
Agriculture Organics
Mixed Metals
TDS
Time Scale
Hourly
Single Event
Continuous
Annual/Average
User Skills/Resources
NPS Knowledge
Model Experience
Computer Experience
Resources Available
Spatial Detail
Edge of Field
Subwatersheds
Watersheds
Basins
Data Available
Land Characteristics
Sewer Characteristic
Rainfall
Flow
Concentration
Level of Application
Screening
Intermediate
Detailed
Decision/Mgmt. Needs
Research
Planning
Regulation
Design
-------
DATA NEEDS - NPS MODELS
Input Variables
~ Rain
~ Evaporation
~ Atmospheric Fallout
Watershed - Common
~ Size
~ Homogeneous Subareas
~ Slope
~ % Impervious
~ Drainage Routing
~ Land Use
Urban
~ Curb Density
~ Street Sweeping
~ Pollutant Accumulation
~ Sewerage Characteristics
~ Storage
~ Treatment
Rural
~ Crop Type
~ Crop Stage
~ Soil Type
~ Tillage Practices
~ Fertilizers
~ Drains, Routing
-------
DRAFT
June 8, 1993
Table 3-1. Runoff Quantity Models
Model
Olher
Uses
M.in
Ref.
Reviews
Land Use
Simulation
Type
Time Step
Agency
EPA Statistical
NQ
1,2
a,b,c
U,R
Runoff Coeff.
Annual,
Event Ave.
El A
USGS Regression
NQ
3
a,b
U,R
Regression
Annual,
Event Ave.
uses
FHWA
NQ, RQ
2
a,b
Highway
Runoff Coeff.
Annual,
Event Ave.
FHWA
GWLF
NQ
4
a
U,R
Curve Number
Continuous
Monthly
Cornell Univ.
AGNPS
NQ,RF,
RQ
5
a,b
R(Ag)
Curve Number
Continuous
Hourly
USHA/
ARS
STORM-RWQM
NQ,RF,
RQ
6
a,b,c,d
U
Runoff Coeff./
Curve #
Continuous
Hourly
HEC
ANSWERS
NQ
7
a,b
R
Water Balance
Event
Univ.
DR3M
NQ
8
a,b,c
U
Kinematic
Wave
Continuous
Subhourly
USGS
SWRRBWQ
NQ,RF,
RQ
9
a,b
R
Curve #/ Water
Balance
Continuous
Daily
USDA/
ARS
SWMM
NQ
10,11
a,b,c,d
U
Kinematic &
Dynamic Wave
Continuous
Subhourly
EPA/ CEAM
EPA TGM Wet Weather TMDLs
-------
DRAFT June 8, 1993
Table 3-1 (continued)
i isrF
NQ,RF,
RQ
12
a,b,c,d
U,R
Water Balance,
1 lydrologic
Routing
Continuous
Subltourly
EPA / CEAM
Auto Q-'LLUDAS
NQ
13
a
U
Water Balance
Continuous
Event
Illinois State
Water Survey
CREAMS
NQ
14
a,d
R (field
scale)
Water Balance
Continuous
Daily
USDA/ARS
TR-20
RF
15
R
Curve Number
Event, Sub-
event
SCS
HEC-1
RF
16
R
Multiple (UH to
Kinematic)
Event, Sub-
Ev t
HEC
TR-55
17
U
Curve Number
Event
SCS
Key to References
1. Hydroscienee, 1979
2. Driscoll el al., 1990
3. Driver and Tasker, 1988
4. Haith el al., 1992
5. Young et al. 1986
6 NEC, 1977a
7. Beasley & Huggins, 1981
8. Alley and Smith, 1982a
9. Arnold et al., 1991
10. Huber & Dickinson, 1988
11. Roesner et al., 1988
12. Johanson et al., 1984
13. Terstriep et al., 1990
14. Knisel, 1980
15. SCS, 1973
16. HEC, 1985
17. SCS, 1986
Key to Reviews
a. U S. EPA, 1992
b. Donigian & Huber, 1991
c. WPCF, 1989
d. McKeon & Segna, 1987
EPA TGM Wet Weather TMDLs
-------
DRAFf
June 8, 1993
Table 3-2. Runoff Quality Models
Model
Other
Uses
Main
Ref.
Review
land
Use
Consti-
tuents
Load
Generation
Sediment
Erosion
Time Step
Routing -
Transfor-
mation
l
Agency
EPA Statistical
NR
1.2
a,b,c
U,R
General
Loading
Function
USLE/
MUSLE
Annual,
Event Ave.
no
EPA
USGS Regression
NR
3
a,b
U
N,0,M,C
Loading -
Regression
N/A
Annual,
Event Ave.
no
USGS
FHWA
NR, RQ
2
a,b
High-
way
N,C,M
Loading -
Median
Cone.
N/A
Annual,
Event Ave.
no
FHWA
Watershed
8
a
U,R
General
Loading
Function
USLE
Annual
no
USGS
GWLF
NR
4
a
U,R
N,S
Loading
Function
MUSLE
Continuous
Monthly
no
Univ.
AGNPS
NR
5
a,b
R(Ag)
N,S
Potency
Factors
MUSLE
Continuous
Hourly
no
USDA/
ARS
STORM RWQM
NR,RF,
RQ
6
a,b,c,d
U
N,0,M,S
Buildup-
Washoff
USLE
Continuous
Hourly
no
HEC
ANSWERS
NR
7,11
a,b
R(Ag)
N,S
Potency
Factors
Detach-
ment
Event
yes
Univ.
DR3M-QUAL
NR
13
a,b,c
U
N,S,C,M
Buildup-
Washoff
MUSLE
Continuous
Subhourly
no
USGS
SWRRBWQ
NR,RF,
HQ
9
a,b
R
S,N,CNC
Buildup-
Washoff
MUSLE
Continuous
Daily
yes
USDA/
ARS
EPA TGM Wet Weather TMDLs
-------
DRAFT
June 8, 1993
Table 3-2 (continued)
SWMM
NR
10
a,b,c,d
U
General
Buildup-
Washoff
MUSLE
Continuous
Subhourly
yes
EPA/
CEAM
MSPF
NR,RF,
RQ
12
a,b,c,d
U,R
General
Loading-
Washoff
Detach-
ment
Continuous
Subhourly
yes
EPA/
CEAM
CREAMS
NR
15
a,d
R
(field
scale)
S,N,C,NC
Potency
Factors
Continuous
Daily
yes
USDA/A
RS
Auto Q-
IL.LUDAS
NR
14
a
U
S>I,C,
NC,0
Buildup-
Washoff
Continuous
Event
no
Illinois
SWS
Watershed
Management
Model
RQ
16
a
U,R
N,M
Loading
Function
NA
Annual
no
Florida
DER
Key to References
1. Hydroscience, 1979
2. Driscoll et al., 1990
3. Driver and Tasker, 1988
4. Haith et al., 1992
5. Young et al. 1986
6. HEC, 1977a
7. Beasley & Huggins, 1981
8. Walker et al., 1989
9. Arnold et al., 1991
10. Huber & Dickinson, 1988
11. Dillaha et al., 1988
12. johanson et al., 1984
13. Alley & Smith, 1982b
14. Terstriep et al., 1990
15. Knisel, 1980
16. CDM, 1992
Key to Reviews
a. U.S. EPA, 1992
b. Donigian & Huber, 1991
c. WPCF, 1989
d. McKeon & Segna, 1987
EPA TGM Wet Weather TMDLs
-------
DRAFT
June 8, 1393
Models of Point and Nonpoint Wet-weather Runoff
Table 3-1 surveys models for Runoff Quantity (including models for rural NPS
and urban point sources, such as CSOs and storm water, which result from the collection
and point discharge of episodic, wet-weather flows.) The table provides the following
information:
CoL 1 lists the model name.
CoL 2 lists the other general categories of simulation addressed by the software
package, and is repeated throughout the following tables. NR stands for wet-weather
runoff (both nonpoint and point) quantity or flow simulations (this table); NQ stands for
wet-weather runoff quality simulation; RF stands for receiving water flow simulation;
and RQ stands for receiving water quality simulations.
Col. 3 provides a key to the main reference for the most recent release of a given
model (keyed to the bottom of the table).
CoL 4 provides a key to reviews of a given model in four selected sources, three
of them EPA guidance.
CoL 5 indexes the land use applications of a given model, with U standing for
urban and R for ruraL
CoL 6 indicates the method of simulation of flows, whether overland or collection
system. The major categories are (a) empirical runoff coefficient methods, (2) SCS curve
number ("Curve Number") methods, (3) water balance methods, based on the principle
of conservation of mass, without hydraulic simulation of momentum; (4) kinematic wave
methods, which include a simplified representation of the»jnergy equations; and (5)
dynamic wave methods, which address (more of less) the full momentum equations.
CoL 7 indicates the temporal resolution, or time step which can be achieved by
a given modeL There are two issues here: first, whether a model is applicable to
continuous simulation of flows or just response to individual events, and second the
minimum tin* s^p representation which can be reasonable achieved.
CoL 8 indicates the agency supporting a modeL Most EPA models have been
supported by the Center for Exposure Assessment Modeling (CEAM) at the
Environmental Research Laboratory, A than*. Other agencies cited include the Federal
Highway Administration (FHWA); U3. Geological Survey (USGS); Army Corps of
Engineers Hydraulic Engineering Center (KEC) in Davis, CA; Army Corps of Engineers
Waterways Experiment Station in Vicksburg, MS (WES); the Agricultural Research
Service of the USDA (USDA/ARS); and the Soil Conservation Service (SCS); as well as
several state agencies.
EPA TQM Wet Weather TMDLa
-------
DRAFT
June 8,1993
Models of Point and Nonpoint Wet-weather Loading
Tab1? 3-2 dir-cusses Runoff Quality ?vfodels, Le. the transport and loading of
pollutants from episodic, wet-weather sources. The first five columns are identical to
those in Tabic 3-1. The additional columns summarize the following characteristics:
Col. 6 summarizes the types of pollutants which can be addressed by a given
simulation model. Some models are rather general in their potential application. Others
are characterized as applicable to nutrients (N), oxygen and oxygen demand (O), metals
(M), conservative organic pollutants (C), and nonconservative, reactive organic pollutants
(NO.
Col. 7 addresses the manner in which pollutant loads are simulated. Most models
use one of two methods: Loading Functions, in which event mean concentrations are
empirically related to land use; and buildup-washoff formulations, in which the time-
dependent availability of pollutants is attempted to be simulated. Several models rely
on sediment potency factors; Le. pollutant load is based on a fixed fraction of sediment
scoured. This method is generally available in any model which simulates sediment
erosion.
Col. 8 summarizes sediment transport in overland flow. Many models use die
Universal Soil Loss Equation (USLE) or the Modified USLE (MUSLE). Other models
attempt to simulate sediment detachment and transport by physical processes.
Col. 9 indicates the time step achievable by the runoff quality routine, analogous
to the time step presented in Table 3»1.
Col. 10 indicates whether the model is capable of addressing pollutant routing
(timing within the runoff), transformation and degradation during transport
Col. 11 states the supporting Agency, as in Table 3-1.
EPA TQM Wet Weather TMDLs
-------
Final Model Design
Project
Constraints
Decision Needs
System/Chemical
Characteristics
Simplicity/Complexity Issues
Judgement
Experience
Coordination
Complexity
Relation Between Model Complexity
and Reliability
-------
EVALUATION OF PROPER
MODEL COMPLEXITY
MATCHED TO DATA
Uncertain
Reliable
Data
-------
AGRICULTURAL NONPOINT
SOURCE POLLUTION
MODELS (AGNPS)
Purpose: Simulate pollutant loads in agricultural watersheds
Evaluate loads - Evaluate effectiveness of BMPs
Identify critical sources
Time Scale: Event, single storms
Watershed size: Small to large
Components: 1. Rainfall/Runoff
2. Pollutant loading
Hydrology: SCS curve number analysis
Unit hydrograph
Load Generation: USLE
Point source subroutines (e.g., feedlots, gully erosion,etc.)
Pollutants: Nutrients
Solids
Others under development
Detail: Grid system (acres)
Distributed parameters
Square elements
Special Features;
Economic analyses
Adaptable to GIS
Modular for point source inputs
Widely used
-------
AGNPS INPUTS
Topographic data
Soil data
Land use
Land practices (crops, fertilizers, tillage,...)
Rainfall
AGNPS OUTPUT
Hydrology:
volume
peak flow
Sediment:
yield
concentration
size distribution
location of erosion & deposition
Chemicals:
concentration
load
-------
AGNPS LIMITATIONS
Single event
Limited use for pesticides/herbicides
No instream processes of transformation
Limited applications for transport
No subsurface components
Simple characterization of runoff & pollutant
load generation
Simplistic analyses of BMP
AGNPS STRENGTHS
Widely used, large experience base
Large data base for inputs
Simple & reliable
-------
GENERALIZED WATERSHED
LOADING FUNCTIONS (GWLF)
Purpose: Simulate urban and agricultrual nonpoint
loadings
Evaluate loads
Evaluate land use changes
Determine relative importance
Time Scale: Continuous
Daily time steps
Long term periods
Watershed Size: Large basins
Components: 1. Rainfall/Runoff
2. Groundwater (shallow saturated,
and unsaturated)
3. Pollutants
Hydrology: SCS curve number analysis
Groundwater mass balance
Load Generation: Agricultural using USLE
Urban using exponential washoff
Pollutants: Total & dissolved nitrogen
Total & dissolved phosphorus
Sediments
-------
GWLF LIMITATIONS
No urban stormwater storage or treatment
Very limited routing
No analysis of peak conditions
Urban washoff functions have
limited accuracy
GWLF STRENGTHS
- Estimates of total & dissolved pollutants
- Includes groundwater interflow sources
- Agricultural parameters are commonly available
- Adaptable for multiple land uses
- Provides seasonal, annual, or long term loads
- Good for watershed planning
-------
GWLF INPUTS
• Daily precipitation
• Daily temperature
¦ Transport parameters: runoff curve number, soil
loss factor, evapotranspiration/cover coeff,
erosion factors, groundwater coefficients,
sediment delivery ratio
• Chemical parameters: urban pollutant buildup
rates, dissolved nutrient concentration,
exponential washoff, solid phase concentrations
¦ Point sources
GWLF OUTPUT
• Annual/seasonal streamflow
• Annual/seasonal sediment yield
• Annual/seasonal nutrient loads (dissolved
& solids)
• Annual/seasonal groundwater source
• Loading by land use, source, area, etc.
-------
COMPLEX NPS COMPUTER
MODELS
Urban: SWMM
Purpose: Simulate stormwater & CSO loads
Evaluate loads - Compare sources
Compare controls - Design controls
Components: 1. Runoff
2. Sewer Routing
3. Pollutant buildup & washoff
Time Scale: Event or continuous
Detailed Inputs: Rainfall, land topography & use,
sewer system characteristic,
land use characteristics & practices
Calibration & Verification Required
Non Urban Watersheds: HSPF
Purpose: Simulate loadings from complex watersheds
Evaluate loads - Guide planning
Compare sources
Components: 1. Runoff
2. Watershed routing
3. Pollutant washoff & transformation
Time Scale: Event or continuous
Detailed Inputs: Rainfall, soils, topography, land use,
land practices
Calibration & Verification Required
-------
NPS SOURCE MODELING
SELECTED REFERENCES
Handbook of Nonpoint Pollution. Novotny & Chesters, Van
Nostrand Reinhold Co., New York, NY, 1981.
Modeling of Nonpoint Source Water Quality in Urban and
Nonurban Area, Donigian & Heber, U.S. Environmental
Research Laboratory, Athens, GA, 1991.
Control and Treatment of Combined Sewer Overflows. Moffa
etal., Van Nostrand Reinhold Co., New York, NY, 1990.
Combined Sewer Overflows Pollution Abatement. Manual of
Practice FD-17, WPCF, Alexandria, Va, 1989.
-------
GENERAL REFERENCES
Novotny, V., and Chesters, G., 1981, Handbook of Nonpoint
Pollution Sources and Management, Van Nostrand
Reinhold Company, New York, N.Y.
MWCOG, July 1987, "Controlling Urban Runoff: A Practical
Manual for Planning and Designing Urban BMPs",
Metropolitan Washington Council of Governments Water
Resources Planning Board, Washington, D.C.
Local U.S.D.A. Soil Conservation Service offices can provide
the coefficients necessary for the USLE, models that are
based on the USLE (e.g. AGNPS), wind erosion
calculations, and SCS engineering equations.
UNIT AREA LOADING GUIDANCE
Omernik, J.M., 1977, "Non-Point Source - Stream Nutrient
Level Relationships: A Nationwide Study", EPA-600/3-
77-105, U.S. Environmental Protection Agency, Corvallis,
Oregon.
Reckhow, K.H., Beaulac, M.N., and Simpson, J.T., Jun. 1980,
"Modeling Phosphorus Loading and Lake Response
Under Uncertainty: A Manual and Compilation of Export
Coefficients", EPA-440/5-80-011, U.S. Environmental
Protection Agency Office of Water Regulations and
Standards, Washington, D.C.
TMDL Workshop
L77, Limno-Tech, Inc.
-------
SCS, 1983, "Section 3, Sedimentation", 2nd ed., National
Engineering Handbook, U.S. Department of Agriculture
Soil Conservation Service, Washington, D.C.
Stewart, B.A., Woolhiser, D.A., Wischmeier, W.H., Caro, J.H.,
and Frere, M.H., 1975, "Control of Water Pollution from
Croplands, Vol.1", EPA-600/2-75-026a, U.S.
Environmental Protection Agnecy, Washington, D.C.
Vanoni, V.A., ed., 1975, Sedimentation Engineering,
American Society of Civil Engineers, New York, N.Y.
Wischmeier, W.H., and Smith, D.D., 1978, "Predicting Rainfall
Erosion Losses - A Guide to Conservation Planning",
Agriculture Handbook No. 537, U.S. Department of
Agriculture, Washington, D.C.
COMPUTER MODELS GUIDANCE
U.S. EPA, Jun. 1992, "Compendium of Watershed-Scale
Models forTMDL Development", EPA841 -R-92-002, U.S.
Environmental Protection Agency Office of Water,
Washington, D.C.
TMDL Workshop
L77, Limno-Tech, Inc.
-------
Rast, W., and Lee, G.F., Apr. 1983, "Nutrient Loading
Estimates for Lakes", Journal of Environmental
Engineering, Vol. 109, No. 2, pp. 502-517.
Sonzogni, W.C., Chesters, G., Coote, D.R., Jeffs, D.N.,
Konrad, J.C., Ostry, R.C., and Robinson, J.B., Feb.
1980, "Pollution from Land Runoff', Environmental
Science & Technology, Vol. 14, No. 2, pp. 148-153.
Uttormark, P.D., Chapin, J.D., and Green, K.M., Aug. 1974,
"Estimating Nutrient Loadings of Lakes from Non-Point
Sources", EPA-660/3-74-020, U.S. Environmental
Protection Agency Office of Research and Monitoring,
Washington, D.C.
RATING CURVES METHOD GUIDANCE
Mills, W.B., Porcella, D.B., Ungs, M.J., Gherini, S.A.,
Summers, K.V., Lingfung Mok, Rup, G.L., Bowie, G.L.,
and Haith, D.A., Sept. 1985, "Water Quality Assessment:
A Screening Procedure for Toxic and Conventional
Pollutants in Surface and Ground Water - Part 1", 1985
revision, EPA/600/6-85/002a, U.S. Environmental
Protection Agency Office of Research and Development,
Athens, Georgia, pp. 142-277.
Ogrosky, H.O., and Mockus, V., 1964, "Hydrology of
Agricultural Lands", Handbook of Applied Hydrology, V.T.
Chow, ed., Mcgraw-Hill, New York, N.Y.
T M PL Workshop
LT7, Limno-Tech, Inc.
-------
H
-------
HANDS-ON NONPOINT
SOURCE MODELING
EXPORT COEFFICIENTS/SPREADSHEETS
GWLF
AGNPS
SWRRB
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
EXPORT COEFFICIENTS
(i.e., UNIT AREA LOADS)
Load by Land Use
Loadj = Unit Area Load * Area in Land Use
Load by Watershed
Number of
land uses
Watershed Load = Z Loadj
» = l
Example Spreadsheet
Run OBS.EXE
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
DATA OUTPUTS FROM SWRRBWQ INCLUDE:
~ input data for inspection
~ soil properties generated by model
~ precipitation, surface runoff, subsurface flow, water yield, percolation,
transmission losses, ET, sediment yield, and soil water content estimated
by model
~ organic nitrogen and phosphorus yield due to surface, subsurface,
percolation flow, and reservoir storage
~ final sediment and water contents for ponds and reservoirs
~ irrigation application information
~ sub-basin average annual values for rainfall, surface runoff, subsurface
flow, sediment yield and crop biomass
~ average monthly basin values for rainfall, snow fall, surface runoff,
subsurface flow, water yield, ET, and sediment yield
~ annual average basin values for water, sediment, reservoirs, ponds,
nutrients, and pesticides
a lake data including secchi disk values and annual averages of pesticides
in lake
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SWRRBWQ IS CAPABLE OF GENERATING WEATHER AND SOIL
PARAMETERS. THE SWRRBWQ USER'S MANUAL PROVIDES TABLES
FOR MOST REQUIRED HYDROLOGICAL PROPERTIES. HOWEVER,
THERE ARE SOME DATA THAT THE USER MUST PROVIDE:
~ basin area
~ rainfall correction factor - used when rainfall data is taken from rain gage
a considerable distance from basin to correct difference between annual
average rainfall of rain gage and basin
~ basin lag time - estimates number of days subsurface flow from a
precipitation event is expected to contribute to stream flow
~ x and y centroid coordinates of each sub-basin are required if rainfall is
simulated for multiple basins
~ physical and hydrological properties of basin
~ pesticides applied
~ physical and hydrological properties of each sub-basin
~ physical and hydrological properties of main channel
~ physical and hydrological properties of ponds and reservoirs
~ estimate of seepage through dams
~ reservoir spillway release rates
~ estimates of initial pesticide on foliage and on ground
~ enrichment ratio for pesticides
~ estimates of organic N, phosphorus, and soluble phosphorus in upper
soil layer
~ soil series names for sub-basins
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
a sub-basin crop data including planting and harvest dates, tillage
operations, etc.
~ dates and amounts of nitrogen and phosphorus applied
~ dates and amounts of pesticides applied
~ if irrigation is automatic by water stress or applied by schedule
~ lake water quality data for lake simulations
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
WHAT HAPPENS TO OUR CORNFIELD DURING A BIG STORM?
1
2
3
4
Land slope = 2% Slope is uniform Slope length = 300 ft
Design storm is 4 inches, with energy intensity factor is 65
No well defined channels (assume 1.0 ton/acre residue)
-------
What is SWRRBWQ?
Simulator for Water Resources in Rural Basins - Water Quality
MODEL OBJECTIVES:
~ to predict the effect of management decisions on water, sediment,
nutrient (nitrogen and phosphorus), and pesticide yields with reasonable
accuracy for ungaged rural basins
~ to predict the effects of management decisions on lakes
SWRRBWQ PREDICTS:
~ water yields
~ sediment yields
~ nutrient yields
~ pesticide yields
Predictions performed on a continuous simulation basis.
TMDL Workshop
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-------
To see where water bodies are located, select Variables, Add
Variables, AGNPS Parameters, Soil Texture, Water, Grid Patterns,
choose the lower right pattern, Light Cyan, Light Cyan, and then from
the main menu Plot, Plot Settings to the Screen.
To examine which model cells have the worst erosion problems, from
the main menu select Details, Graduation, Create a Graduation,
Color, set ranges to 8, select O) Runoff and Soil Loss Output, Cell
Erosion.
To easier identify cells, add cell numbers to the plot. Select Variables,
Add Variables, Whole Watershed, Numbers, start with 5, skip 4,
White, and then from the main menu Plot, Plot Settings to the Screen.
To save the above variable settings for future sessions and other data
files, select File, Variable File, Save Variables, type EROSION, hit
< return >.
The main sources of erosion are now displayed and identified, and BMP
efforts can be focussed in those areas.
Quit Grafix.
In an actual TMDL evaluation the problems areas would be identified and various
BMPs would be proposed according to site-specific factors and feasibility. The
proposed BMP sediment reduction would be entered into a model, and the results
examined to determine if the goal was met. If the goal was not met additional BMPs
would be proposed and possibly additional areas examined for BMPs. These additional
reductions would be entered into the model, and the cycle followed until the goal is
met. For purposes of this workshop we will assume that these intermediate cycles have
been completed. The proposed BMP sediment reductions below represent the final
iteration in this process.
To model the effects of BMPs we will need to change specific data inputs as
appropriate. For the sake of simplicity, for this example we will represent the BMP
cell erosion reductions by changing the USLE practice factor for the identified problem
areas. We will assume that a combination of BMPs, such as conservation tillage and
stripcropping, will result in a 70% reduction in cell erosion from selected areas.
Critical areas (primarily those observed in the graphical display) require reductions as
high as 90%, and these will be accomplished with combinations of the above BMPs and
grassed waterways and filter strips.
Select Edit Input from the main AGNPS menu. Hit to clear the Initial
Data menu. In the data input spreadsheet, change the following P-Factors of
the listed cells:
P-Factor = 0.3 for cells 30.100, 30.200, 30.300, 32.200;
P-Factor = 0.1 for cells 32.400, 34.300, 42.300, 42.400, 44, 45, 49, and 60.
Hit to quit. Answer Y to save the changed input file, and change the
name to BEAVRBMP.DAT. You must save the file or AGNPS will not
recognize the changes.
-------
Select Run Model, Run AGNPS to run the model.
From the main menu select File, Load, hit < return > at the Load: *.DAT
prompt, select BEAVRBMP.DAT. An apparent quirk in the program requires
this step after running the model if you only look at the tabular output, or else
the tabular output module reads in the previous model run.
To see if the BMP reductions achieved in sediment yield reduction goal, from
the main menu select View Output, Tabular Output, View, C. Sediment.
The practice factor changes listed above should result in a 50% reduction in
sediment yield and sediment concentration, to 4.3 tons and 111 ppm.
After reviewing the tabled output examine the graphical display. To quit the
tabled display hit , Quit, Quit.
From the main menu select Grafix Display, File, Variable File, Load
Variable, EROSION. This loads the variable settings that you saved
previously, but with the model output from the current file. Notice the
reduction in erosion rates (the graduation scale has changed). The previous
highest rate was 3.5 tons/acre, and the current high rate is 0.35 tons/acre.
-------
3rd Level
2nd Level
AGNPS Cell Division
TMDL Workshop
LTJ, Limno-Tech, Inc.
-------
AGNPS EXAMPLE
The following is a brief example of how AGNPS can be used to evaluate the effects of
management scenarios on water quality flowing out of a watershed.
The example used is of the Beaver Creek Reservoir watershed, one of several data files
that are distributed with AGNPS version 3.65. This watershed covers approximately
2500 acres. Land uses include forest, residential, pasture, and cropland.
For this example, we will assume that a goal has been established to reduce by 50%
sediment concentration and mass in water flowing out of the watershed from a specified
design storm. To determine the best way to accomplish this goal we will model
sediment yield reductions through BMP implementation with AGNPS.
To begin this example: At the C:\AGNPS prompt, type AGNPS. Hit until
the main menu appears.
The first step is to examine current conditions to determine present water quality and
help identify possible problem areas.
Select File, Load, hit < return > at the Load: ~.DAT prompt, and select
BEAVER.DAT.
Select Run Model.
Run Check Data first to make sure there are no inconsistencies in the
watershed configuration entered into the input file. Hit < return > to
display error messages to the screen, and hit < return > when the menu
states that there are no errors. Select Cell #'s to the screen to see a
representation of the watershed. Press , the select Quit-No
printing or displaying to exit Check Data.
Select Run AGNPS, type N for no GIS output (or Y if you want one).
To see the model results, from the main menu select View Output, Tabular
Output, View. For this example we are only concerned with item C.
Sediment. This output contains the sediment and erosion results flowing from
the outlet of the watershed. The total sediment yield is 8.6 tons and the average
outflow concentration is 222 ppm. Our goal is to reduce this sediment yield by
50%. To leave the Tabular display, hit , I (for Quit), and select Quit.
AGNPS also provides a graphical display of input and output data. The
graphical display of data allows for easier location of problem erosion areas in
the watershed.
Select Grafix Display from the View Output menu. We will plot
selected variables to the screen.
To add flow paths in the watershed, select Variables, Add Variables,
Whole Watershed, Vectors, White. To see the changes, select Plot
from the main menu, then Plot Settings to the Screen.
-------
Sources and Sinks:
Feedlots are treated as a point source, and are routed directly into the channel.
The model calculates N, P, and COD concentrations and mass at the edge of the
feedlot and at the entry to the channel (accounts for a buffer strip). Feedlot
pollutants are treated as soluble.
Other point source inputs can also be entered.
Sediment and runoff routing through impoundment systems is dependent on
surface and depth of the pond, particle size, outlet pipe diameter, and infiltration
rate.
Transport:
Sediment and nutrient routing are done on a per cell and per particle class size
basis. The model starts at the headwaters of watershed and routes to the outlet.
Routing is based upon the steady state continuity equation. The model takes into
account sediment discharge into the cell, lateral sediment inflow rate, reach
length, sediment deposition rate, and channel width.
TMPL Workshop
L77, Umno-Tech, Inc.
-------
SENSITIVITY OF MODEL TO INPUTS
AGNPS calculations are most sensitive to the following input parameters:
For sediment and sediment-associated nutrients:
land slope
soil erodibility factor
SCS curve number
cropping factor
practice factor
For soluble nutrients:
SCS curve numbers (management practices and crop cover)
TMPL Workshop
LT7, Umno-Tech, Inc.
-------
COD factor
impoundment factor
channel indicator
OUTPUT
Available in both graphical or tabular formats
Can be examined for a single cell, specific subwatersheds, or the entire
watershed.
Hydrology Output
runoff volume
peak runoff rate
fraction of runoff generated within cell
Sediment Output
sediment yield
sediment concentration
sediment particle size distribution
upland erosion
amount of deposition
sediment generated within the cell
enrichment ratios by particle size
delivery ratios by particle size
Chemical Output
nitrogen and phosphorus
sediment associated mass
concentration of soluble material
mass of soluble material
chemical oxygen demand
concentration of soluble material
mass of soluble material
TMDL Workshop
L77, Limno-Tech, Inc.
-------
MODEL CALCULATIONS
Erosion:
Upland erosion in each cell is predicted with a modified USLE.
The fractional distribution of eroded sediment is determined by the soil texture of
the original soil.
Streambank, stream bed, and gully erosion are input as point sources and added
to upland sediment transport.
Runoff:
Cell runoff volume is determined by SCS curve number method. This is
dependent upon land use, soil type, and hydrologic soil conditions.
Overland runoff duration is calculated using the SCS runoff velocity.
The peak runoff rate for each cell is determined with an empirical relationship
between drainage area, channel slope, runoff volume, and watershed length to
width ratio.
Nutrients:
Soluble N and P are based upon rainfall concentration, fertilization, and leaching.
No losses of soluble nutrients are allowed in channel flow. Cell N and P yields
use the same methodology as in CREAMS.
N and P yield associated with sediment is calculated using total sediment yield
form each cell, the N and P concentration in the soil, and an enrichment ratio.
COD concentrations are assumed to be soluble, and are based upon runoff
volume and empirically determined averages from different land uses. COD
accumulates only after flow is channelized, and has no allowable losses.
TMPL Workshop
L77, Limno-Tech, Inc.
-------
HOW DOES AGNPS WORK?
AGNPS works on a square grid cell basis, with up to 1900 cells.
It uses a combination of distributed and sequential modeling. Pollutants are
routed through the watershed in a stepwise fashion.
Basic Model Components:
hydrology
runoff volume
peak concentrated flow
erosion
total upland
total channel
5 particle size classes
clay, silt, small aggregates, large aggregates, & sand
sediment transport
nitrogen and phosphorus transport
sediment attached
soluble
concentration and mass
COD transport (soluble only)
concentration and mass units
TMDL Workshop
L77, Limno-Tech, Inc.
-------
DATA REQUIREMENTS
Two categories of data are needed, watershed data and individual cell data.
Data can be obtained from visual inspection, maps, or easily obtained technical
publications, tables, and graphs. The User's Manual contains tables with the
majority of necessary data coefficients referenced to soil type, land use, etc. On-
line help screens also contain some of these coefficients.
Collecting the necessary data is estimated to require approximately 3 person-
days for a watershed < 500 acres to 1 person-month for a watershed up to 23,000
acres.
Watershed data:
watershed I.D..
cell area (same for each cell)
number of cells
storm duration or intensity
Cell data:
cell I D..
cell into which it drains and direction
SCS curve number
average land slope and shape factor
field slope length
average channel slope and side slope
Manning's roughness coefficient for the channel
USLE soil erodibility factor, cropping factor, & practice factor
surface condition factor (based on land use)
soil texture
fertilization level (zero, low, medium, high)
incorporation factor
point source information
gully source level
TMDL Workshop
LT7, Limno-Tech, Inc.
-------
Cover Dormant Season Growing Season
Annual crocs (foliage only
in growing season) 0.3 1.0
Perennial crops (year-round foliage:
grass, pasture, meadow, etc.) 1.0 1.0
Saturated crops (rice) 1.0 1.0
Hardwood (deciduous) forests 4 orchards 0.3 1.0
Softwood (conifer) forests 4 orchards 1.0 1.0
Disturbed areas & bare soil (barn yards,
fallow, logging trails, construction
and mining) 0.3 0.3
Urban areas (I = impervious fraction) 1-1 1 • I
Table B-a. Approximate Values for Evapotranspiration Cover Coefficients.
Latitude North (°)
46
46
44
42
40
38
36
t
hp /Hau
*
\
iir/vjay
1
Jan
8.7
8.9
92
9.3
9.5
9.7
9.9
Feb
10.0
10.2
10.3
10.4
10.5
10.6
10.7
Mar
11.7
11.7
11.7
11.7
11.8
11.8
11.8
Apr
13.4
13.3
132
13.1
13.0
13.0
12.9
May
14.9
14.7
14.5
14.3
14.1
14.0
13.8
Jun
15.7
15.4
152
15.0
14.7
14.5
14.3
Jul
15.3
15.0
14.8
14.6
14.4
14.3
14.1
Aug
14.0
13.8
13.7
13.6
13.6
13.4
132
Sep
12.3
12.3
12.3
12.3
122
122
122
Oct
10.6
10.7
10.8
10.9
11.0
11.0
11.1
Nov
9.1
9.3
9.5
9.7
9.8
10.0
10.1
Dec
8.3
8.5
8.8
9.0
9.2
9.4
9.6
34
32
30
28
26
24
Jan
10.0
10.2
102
10.5
10.5
10.7
Feb
10.8
10 3
11.0
11.1
11.1
112
Mar
11.8
11.8
11.8
11.8
11.8
11.9
Apr
12.8
12.8
12.7
12.7
12.5
12.6
May
13.7
13.6
13.5
13.4
132
13.1
Jun
14.2
14.0
13.9
13.7
13.6
13.4
Jut
14.0
13.8
13.7
13.5
13.4
13.3
Aug
13.2
13.3
13.0
13.0
12.9
12.8
Sep
122
122
122
12.1
12.1
12.1
Oct
112
112
11.3
11.3
11.4
11.4
Nov
10.2
10.4
10.5
10.6
10.7
10.9
Dec
9.8
10.0
10.1
10.3
10.4
10.6
Table B-9. Mean Daylight Hours fMSls et aL 1985).
29
-------
WHAT IS AGNPS?
AGNPS is a tool for comparing surface effects of management scenarios and
BMPs in agricultural watersheds.
Developed by: U.S.D.A. Agricultural Research Service
Minnesota Pollution Control Agency
U.S.D.A. Soil Conservation Service
Comparative model, not a predictive model
Analyzes and provides estimates of runoff water quality from agricultural
watersheds as large as 50,000 acres.
Single storm event model.
Calculates upland erosion and upland transport of sediments and nutrients from
all points in the watershed.
Analyzes pollutant loads from feedlots and other point sources.
Addresses overland flow and ephemeral concentrated flow. It does not model
processes in perennial streams, lakes, or groundwater.
TMDL Workshop
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-------
Sus- Total Total
Land Use pended BOO Nitrogen Phosphorus
Solids
( kg/ha-day )
Impervious Surfaces
Single family residential
Low density (units/ha < 0.5) 2.5
Medium density (units/ha £ 0.5) 6.2
Townhouses & apartments 6.2
High rise residential 3.9
Institutional 2.8
Industrial 2.3
Suburban shopping center 2.8
Central business district 2.8
Pervious Surfaces
Single family residential
Low density (units/ha < 0.5) 1.3
Medium density tunits/ha > 0.5) 1.1
Townhouses & apartments 2.2
High rise residential 0.8
Institutional 0.8
Industrial 0.8
Suburban shopping center 0.8
Central business district 0.8
0.15 0.045 0.0045
0.22 0.090 0.0112
0.22 0.090 0.0112
0.71 0.055 0.0067
0.39 0.056 0.0067
0.71 0.101 0.0112
0.71 0.056 0.0067
0.85 0.101 0.0112
0.08 0.012 0.0016
0.15 0.022 0.0039
0.29 0.045 0.0078
0.08 0.012 0.0019
0.08 0.012 0.0019
0.08 0.012 0.0019
0.08 0.012 0.0019
0.08 0.012 0.0019
Table B-17. Contaminant Accumulation Rates for Northern Virginia Urban Areas (Kuo, et al..
1988).
Parameter Value
e, per capita daily nutrient load
in septic tank effluent (g/day)
Nitrogen 12.0
Phosphorus
Phosphate detergents use 2.5
Non-phosphate detergents use 1.5
um, per capita dafly nutrient uptake
by plants during month m (g/day)
Nitrogen: Growing season 1.6
Non-growing season 0.0
Phosphorus: Growing season 0.4
Non-growing season 0.0
Table B-18. Default Parameter Values for Septic Systems.
41
-------
Land Use Nitrogen Phosphorus
( 75% Agriculture
1.82
0.80
1.70
i 90% Agriculture
5.04
0.77
0.71
PhosDhorus*3/:
^ 90% Forest
0.006
0.009
0.012
^ 75% Forest
0.007
0.012
0.015
2: 50% Forest
0.013
0.015
0.015
^ 50% Agriculture 0.029 0.055 0.083
£ 75% Agriculture 0.052 0.067 0.069
i 90% Agriculture 0.067 0.085 0.104
^Measured as total inorganic nitrogen.
^Measured as totaJ orthophosphorus
Table 6-16. Mean Dissolved Nutrients Measured in Streamfiow by the National Eutrophication
Survey (Omemik, 1977).
40
-------
2onea// Location
Season^/
Cool Warm
1
Fargo ND
0.08
0.30
2
Sioux City IA
0.13
0.35
3
Goodland KS
0.07
0.15
4
Wichita KS
0.20
0.30
5
Tulsa OK
0.21
0.27
6
Amanllo TX
0.30
0.34
7
Abilene TX
0.26
0.34
8
~alias TX
0.28
0.37
9
Shreveport LA
0.22
0.32
10
Austin TX
0.27
0.41
11
Houston TX
0.29
0.42
12
SL Paid MN
0.10
0.26
13
Lincoln NE
0.26
0.24
14
Dubuque IA
0.14
0.26
15
Grand Rapids Ml
0.08
0J23
16
Indianapolis IN
0.12
0.30
17
ParKersburg WV
0.08
0.26
18
Springfield MO
0.17
0.23
19
Evansville IN
0.14
0.27
20
Lexington KY
0.11
0.28
21
Knoxville TN
0.10
0.2B
22
Memphis TN
0.11
0.20
23
Mobile AL
0.15
0.19
24
Atlanta GA
0.15
0.34
25
Apalachacoia FL
0.22
0.31
26
Macon GA
0.15
0.4O
27
Columbia SC
0.08
0.25
28
Charlotte NC
0.12
0.33
29
Wilmington NC
0.16
0.28
30
Baltimore MO
0.12
0.30
31
Albany NY
0.06
0.25
32
Caribou ME
0.07
0.13
33
Hartford CN
0.11
0.22
a/ Zones given in Figure B-1.
^ Cod season: Oct - Mar Warm season: Apr - Sept.
Table B-14. Rainfall Erosivity Coefficients (a) for Erosivity Zones in Eastern U.S. (Selker et al..
1990).
-------
Cover
Value
Permanent pasture, idle land, unmanaged woodland
95-100% ground cover
as grass 0.003
as weeds 0.01
80% ground cover
as grass 0.01
as weeds 0.04
60% ground cover
as grass 0.04
as weeds 0.09
Managed woodland
75-100% tree canopy 0.001
40-75% tree canopy 0.002-0.004
20-40% tree canopy 0.003-0.01
Table B-12. Values of Cover and Management Factor (C) for Pasture and Woodland (Novotny
& Chesters, 1981).
Practice Slope(%): 1.1-2
2.1-7
7.1-12
12.1-18
18.1-24
No support practice
1.00
1.00
1.00
1.00
1.00
Contouring
0.60
0.50
0.60
0.80
0.90
Contour strip cropping
0.30
0.25
0.30
0.40
0.45
R-W-M-M
0.30
0.25
0.30
0.40
0.45
R-R-W-M
0.45
0.38
0.45
0.60
0.68
R-W
0.52
0.44
0.52
0.70
0.90
R-O
0.60
0.50
0.60
0.80
0.90
Contour listing or
ridge planting
0.30
0.25
0.30
0.40
0.45
Contour terracing*3/
0.6/\/n
0.5/Vn
0.6/Vn
o.ayn
0.9/Vn
a/ R = row crop, W = fall-seeded grain, M = meadow. The crops are grown in rotation and so
arranged on the field that row crop strips are always separated by a meadow or winter-grain stnp.
k/ These factors estimate the amount of soil eroded to the terrace channels. To obtain off-field
values, multiply by 0.2. n = number of approximately equal length intervals into which the field slope
is divided by the terraces. Tillage operations must be parallel to the terraces.
Table B-13. Values of Supporting Practice Factor (P) (Stewart et a!.. 1975).
35
-------
Hydrologic Soil Hydrologic Group
Land Use/Cover Condition A B C D
Fallow Bare Soil
77
36
91
94
Crop residue cover (CR)
Poor3/
76
35
90
93
Good
74
33
38
90
Row Crops Straight row (SR)
Poor
72
31
38
91
Good
67
78
85
89
SR + ca
Poor
71
30
87
90
Good
64
75
32
85
Contoured (C)
Poor
70
79
84
88
Good
65
75
82
86
c + ca
Poor
69
78
83
37
Good
64
74
81
85
Contoured & terraced (C&T)
Poor
66
74
80
82
Good
62
71
78
81
c&t '+ ca
Poor
65
73
79
81
Good
61
70
77
80
Small SR
Poor
65
76
84
88
Grains
Good
63
75
83
87
sr + ca
Poor
64
75
83
86
Good
60
72
80
84
C
Poor
63
74
82
85
Good
61
73
81
84
C + CR
Poor
62
73
81
84
Good
60
72
80
83
G&T
Poor
61
72
79
82
Good
59
70
78
81
C&T + CR
Poor
60
71
78
81
Good
£8
69
77
80
CJose- SR
Poor
66
77
85
89
seeded or
Good
58
72
81
85
broadcast C
Poor
64
75
83
85
legumes or
Good
55
69
78
83
rotation C&T
Poor
63
73
80
83
meadow
Good
51
67
76
80
a/ Hydrologic condition is based on a combination of factors that affect inffltration and runoff,
induding (a) density and canopy of vegetative areas, (b) amount of year-round cover, (c) amount
of dose-seeded legumes in rotations, (d) percent of residue cover on the land surface (good >
20%), and (e) degree of surface roughness.
Table B-2. Runoff Curve Numbers (Antecedent Moisture Condition II) for Cultivated Agricultural
Land (Sofl Conservation Service, 1986).
25
-------
Watershed Sediment Delivery Ratio
(Annuai Sediment Yield/Annual Erosion)
o
ac.
13
a
0.1
0.01
I I I I 11
I I I I I 11
I I ' ' 1111
' I'll
I III 11
10 100 1000
Watershed Area (sq. km)
10000
Figure 8-2. Waiersned Sediment Delivery Ratio (Vanoni, 1975).
Texture
Organic Matter Content (%)
<0.5 2 4.
Sand
0.05
0.03
0.02
Fine sand
0.16
0.14
0.10
Very fine sand
0.42
0.36
0.28
Loamy sand
0.12
0.10
0.08
Loamy fine sand
0.24
0.20
0.16
Loamy very fine sand
0.44
0.38
0.30
Sandy loam
0.27
0.24
0.19
Fine sandy loam
0.35
0.30
0.24
Very fine sandy loam
0.47
0.41
0.33
Loam
0.38
0.34
0.29
SQt loam
0.48
0.42
0.3G
sot-
0.60
0.52
0.42
Sandy day loam
0.27
0.25
0.21
Gay loam
0-28
0.25
0.21
SQty day loam
0.37
0.32
0.26
Sandy day
0.14
0.13
0.12
SDty day
0.25
0.23
0.19
Cay
-
0.13-0.29
-
Table B-10. Values of Sofl Erodibflity Factor (K) (Stewart et aL 1975).
32
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MODEL YOUR VERY OWN CORNFIELD IN IOWA
Using the following information, and attached tables, set up a GWLF simulation
Watershed site = 10 km2 = 1000 hectares
75% of the land is used to grow corn in straight rows (May-October)
Soil is loamy sand with moderate infiltration rates, 0.4% organic matter in
good condition. No agricultural erosion control support practices are in place.
25% of the land is urbanized into low density residential (95% lawns), with
1000 residents serviced by normal septic systems
Additional Information
LS factor for USLE = 2.0
Cover factor for USLE = 0.5
Groundwater recession constant = 0.1
Seepage constant = 0
Unsaturated zone soil moisture capacity = 10 cm
Groundwater nitrogen concentration = 0.06 mg/l, phosphorus = 0.005 mg/l
Soil nitrogen concentration = 3000 mg/kg, phosphorus = 1300 mg/kg
Growing season nitrogen uptake = 1.6 g/day
Growing season phosphorus uptake = 0.4 g/day
Evapotranspiration Day Length
Month
Cover Coefficient
(hrs.)
May
1.0
14.2
June
1.0
14.8
July
1.0
14.5
August
1.0
13.6
September
1.0
12.2
October
1.0
10.9
November
0.3
9.7
December
0.3
9.1
January
0.3
9.4
February
0.3
10.4
March
0.3
11.7
April
0.3
13.1
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Hydrologic Group
Description
A Low runoff potential and high infiltration rates even when thoroughly wetted. Chiefly
deep, weii to excessively drained sands or gravels. High rate of water transmission
(> 0.75 cm/hr).
B Moderate infiltration rates when thoroughly wetted. Chiefly moderately deep to deep,
moderately well to well drained soils with mooerately fine to moderately coarse
textures. Moderate rate of water transmission (0.40-0.75 cm/hr).
C Low infiltration rates when thoroughly wetted. Chiefly soils with a layer that imoedes
downward movement of water, or soiis with moderately fine to fine texture. Low rate
of water transmission (0.15-0.40 cm/hr).
D High runoff potential. Very low infiltration rates when thoroughly wetted. Chiefly day
soils with a high swelling potential, soils with a permanent high water table, soils
with a daypan or day layer at or near the surface, or shallow soils over neariy
impervious material. Very low rate of water transmission (0-0.15 cm/hr).
Disturbed Soiis (Major altering of soil profile by construction, development):
A Sand, loamy sand, sandy loam.
B Sat loam, loam
C Sandy day loam
D Clay loam, silty day loam, sandy day, silty day, day.
Table B-1. Descriptions of Sofl Hydrologic Groups (Soil Conservation Service, 1986)
Soil Hydrologic Group
Land Use A B C D
Open space (lawns, parks, golf
courses, cemetenes. etc.):
Poor condition (grass cover < 50%)
68
79
86
89
Fair condition (grass cover 50-75%)
49
69
79
84
Good condition (grass cover > 75%)
39
61
74
80
Impervious areas:
Paved parking lots, roofs.
driveways, etc.)
98
98
98
98
Streets and roads:
Paved with curbs 4 storm sewers
98
98
98
98
Paved with open ditches
83
89
92
93
Gravel
76
85
89
91
Dirt
72
82
87
89
Western desert urban areas:
Natural desert landscaping (pervious
areas, only)
63
77
85
88
Artificial desert landscaping
(impervious weed barrier, desert shrub
with 1-2 in sand or gravel mulch
and basin borders)
96
96
96
96
Table B-5. Runoff Curve Numbers (Antecedent Moisture Condition II) for Urban Areas (Soil
Conservation Service, 1986).
27
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What do I need to do to run GWLF?
1. Obtain necessary data and parameter estimates
2. Set up "WEATHER.DAT' file
3. Type GWLF20
(GWLF will lead you through the program)
WEATHER.DAT
TRANS.DAT
GWLF
NUTRIENT.DAT
TMDL Workshop
LTI, Limno-Tech, Inc.
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GWLF EXAMPLE
The following example demonstrates use of the GWLF program.
It uses data files provided with the GWLF program used in the GWLF
validation for the West Branch Delaware River Watershed in New York.
Two weather data files are provided, WALT478.382 (covering the four-year
period 4/78 - 3/82 for Walton, NY) and WALT462.392 (covering the thirty-
year period 4/62 - 3/92, also for Walton, NY). The original transport and
nutrient files are named TRANSPRT.EX1 and NUTRIENT.EX1.
The Watershed is in a dairy farming area in southeast New York and covers
850 km. It consists of 30% agricultural, 67% forested, and 2% urban land
uses. The Watershed was divided into 16 source areas, as follows:
Rural
Corn Hay Pasture
Forest Logging Barnyards
Urban
Residential-Impervious
Commercial-Impervious
Industrial-Impervious
Other Sources
Groundwater Point Sources Septic Systems
In this example, we will run a four-year simulation using the file
WALT478.382. We will use the existing data files which include septic
systems and manure spreading on one land use. We will run the model,
view the results, and then modify the data files to compare the effects of
changes in model inputs.
Inactive Agr.
Residential-Pervious
Commercial-Pervious
Industrial-Pervious
TMDL Workshop
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What is GWLF?
Generalized Watershed Loading Functions
• A set of loading functions to estimate sediment
and nutrient loads in complex watersheds
• A compromise between empirical export
coefficients, which provide gross estimates
of loads but have very limited applicability,
and chemical simulation models, which provide
the most complete, mechanistic description
of loads but are too data-intensive for wide-
spread use.
What does GWLF do?
• Estimates dissolved and total monthly nitrogen
and phosphorus loads in streamflow from
complex watersheds
• Includes surface runoff and groundwater sources
• Includes nutrient loads from point sources and
on-site wastewater disposal (septic) systems
• Provides monthly streamflow, soil erosion and
sediment yield values
GWLF models dissolved and solid-phase nitrogen and phosphorus in
stream flow from the following source areas:
Dissolved nutrients Solid-phase nutrients
Point Sources
Groundwater
Rural Runoff
Urban Runoff
Rural Runoff
TMDL Workshop
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How does GWLF work?
Runoff computed by Soil Conservation Service Curve Number
Equation.
Solid-phase rural nutrient loads given by product of monthly
sediment yield and average sediment nutrient concentrations.
Erosion computed using Universal Soil Loss Equation.
Sediment yield is the product of erosion and sediment delivery ratio
Urban nutrient loads (assumed entirely solid-phase) modeled by
exponential accumulation and washoff functions.
Streamflow consists of runoff and discharge from groundwater.
Groundwater obtained from a lumped parameter watershed water
balance.
TMDL Workshop
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5
-------
MONITORING IN SUPPORT OF
TMDLs
Components of this Session
• Overview of the process involved in developing a
monitoring program
• Available technical guidance to support monitoring
program development
• Defining monitoring program goals and objectives
• Defining monitoring needs
• Designing the monitoring program
• Case study example
TMPL Workshop
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MONITORING FOR TMDL
DEVELOPMENT
The Development of a Monitoring
Program is Often an Iterative Process
f
DATA \
COLLECTION I
MODELING
MONITORING
DESIGN
TMDL Workshop
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Ten Principles for Designing Successful Environmental Studies (Green 1979).
1. State concisely to someone what question you are asking. Your results will be as
coherent and as comprehensible as your initial conception of the problem.
2. Take replicate samples within each combination of time, location, and any other
controlled variable. Differences among can only be demonstrated by comparison to
differences within.
3. Take an equal number of randomly allocated replicate samples for each combination of
controlled variables. Putting samples in "representative" or "typical" places is not
random sampling.
4. To test whether a condition has an effect, collect samples both where the condition is
present and where the condition is absent (reference site) but all else is the same. An
effect can only be demonstrated by comparison with a control.
5. Carry out some preliminary sampling to provide a basis for evaluation of sampling
design and statistical analysis options. Deleting this step to save time usually results in
losing time.
6. Verify that the sampling device or method is sampling the population it should be
sampling, and with equal and adequate efficiency over the entire range of sampling
conditions to be encountered. Variation in efficiency of sampling from area to area
biases among-area comparisons.
7. If the area to be sampled has a large-scale environmental pattern, break the area up
into relatively homogeneous subareas and allocate samples to each in proportion to the
size of the subarea. If it is an estimate of total abundance over the entire area that is
desired, make the allocation proportional to the number of organisms in the subarea.
8. Verify that the sample unit size is appropriate to the size, densities, and spatial
distributions of the organisms being sampled. The estimate the number of replicate
samples required to obtain the needed precision.
9. Test the data to determine whether the error variation is homogeneous, normally
distributed, and independent of the mean. IF it is not, as will be the case for most field
data, the (a) appropriately transform the data, (b) use a distribution-free
(nonparametric procedure), (c) use an appropriate sequential sampling design, or (d)
test against simulated Hq data.
10. Having chosen the best statistical method to test the hypothesis, stick with the result
An unexpected or undesired result is not a valid reason for rejecting the method and
searching for a "better" one.
TMPL Workshop
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MONITORING FORTMDL
DEVELOPMENT
Steps In The Development Of a
Monitoring Program
1. Identify the purpose for collecting data
Why are data required3
2. Define the monitoring objectives
What data need to be collected?
3. Design the sampling program
How will the data be collected?
4. Implement the design
TMDL Workshop
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MONITORING FOR TMDL
DEVELOPMENT
IStep 1: Defining Monitoring Purposefs)
• Compliance assessment
• Use attainment
• Source identification
• Load estimation
TMDL Workshop
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MONITORING FOR TMDL
DEVELOPMENT
Step 1: Defining Monitoring Purpose(s)
• Compliance assessment
• Use attainability
• Source identification
• Load estimation
TMDL Workshop L77, Umno-Tech, Inc.
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MONITORING FOR TMDL
DEVELOPMENT
Step 2: Defining Monitoring Objectives
Monitoring objectives are defined through a series of
steps that address specific questions, as follows:
A. Describe data requirements - What data do we
need?
B. Evaluate existing data - What data are already
available?
C. Define what, where, and how often variables are
to be measured, and any constraints on
monitoring - What will be measured, where, and
when?
D. Define Data Quality Objectives - What is
expected/required from the monitoring effort?
TMDL Workshop
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DEFINING OBJECTIVES
A. Identify Data Needs
Data are typically required to support:
• Pollutant load estimates
• Characterization of the system
• Model specification, calibration, and
validation
Data required for these three types of analyses
ultimately define monitoring program objectives.
TMPL Workshop
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DEFINING OBJECTIVES
Data Needs: Pollutant Load Estimation
or
Load = Y.(Concentratior{ x FloWj)
, X Mass,
Load =
X Flowt
Tributaries:
Point source discharges:
Non-Point Sources:
Atmospheric:
Flows
Water quality
Flows
Water quality
Flows
Mass export rates
Precipitation volume
Rainfall concentrations
Dry deposition rates
TMDL Workshop
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DEFINING OBJECTIVES
Data Needs: System Characterization (a)
Typically, we are trying to:
1. Characterize system variables and/or
processes
2. Assess and track impacts to resident
biological populations and communities
3. Assess and track benefits from treatment,
control, or management efforts
4. Determine if waters are in compliance with
requirements for designated uses
TMDL Workshop
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DEFINING OBJECTIVES
Data Needs: System Characterization (b)
Common Parameters of Interest
Physical: Depth
Surface area
Velocity
Flow
Volume
Detention time
Temperature
T ransparency
Chemical: Nutrients
Dissolved oxygen
Conductivity
Toxics
Biological: Biomass
Ecological health
Habitat quality
T MP L Workshop
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DEFINING OBJECTIVES
Data Needs: Selected Watershed Models
EPA Screening
WMM
AGNPS
SWMM
HSPF
Watershed and land use
Loading factors
Land use and soils
Annual precipitation and evaporation
EMC for runoff
Hydraulic characteristics
BMP efficiencies
Land use and soils
Watershed practices
Rainfall
Topography
BMP efficiency
Meteorologic and hydrologic data
Land use distribution and characteristics
Accumulation and washoff parameters
Decay coefficients
Meteorologic and hydrologic data
Land use distribution and characteristics
Loading factors and washoff parameters
Receiving water characteristics
Decay coefficients
TMDL Workshop
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DEFINING OBJECTIVES
B. Compile and Evaluate Existing Data
Objective: Determine what is already known and
where data inadequacies exist.
• Point sources
• Known and suspected nonpoint sources
• Locations with existing monitoring data
• Ambient flow and water quality
• Potentially important or sensitive habitats
• Indicator, listed, and special concern species
found in the area
This effort also ensures that important known
characteristics of the system are recognized and
considered in the monitoring design - for example,
the presence of a threatened or endangered species
in the watershed.
TMDL Workshop
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EXISTING DATA
Common Sources of Existing Data
Type of Data
Source
Format
Point sources
EPA, State permitting
Discharge permits,
agencies
DMRs
Nonpoint
Soil Conservation
Soil maps, Soil
sources
Service
erosion info.
Soil & Water
Soil erosion info.
Conservation Districts
EPA/State agencies
Stormwater permits
NURP Studies
Urban runoff
monitoring
Local governments
Land use, zoning,
septic permits
Flow and water
State and local agencies
STORET, misc.
quality data
EPA
STORET
USGS
Water Resources
Data
Local colleges &
Misc.
universities
Sensitive
State agencies
Narratives, maps
species
USF&WS
NWI maps
TMDL Workshop
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EXISTING DATA
Evaluation of Existing Data
• Purpose of data collection
• Location and frequency of monitoring/sampling
• Sampling and analytical methods used
- Parameters
- Detection limits
- Precision and accuracy
- Year of analysis
• QA/QC protocols employed during collection &
reporting
• Data checks:
- Range checks
- Internal consistency
Dissolved < Total
£ (cations) = E (anions)
Solubility
TDS oc conductivity
- Estimates of precision
- Consistency with other studies
TMDL Workshop
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DEFINING OBJECTIVES
C. Define Basic Monitoring Requirements
and Constraints
The parameters to be monitored reflect data
requirements and deficiencies in existing data.
Fundamental constraints must be recognized to
ensure realistic objectives:
• Funding
• Staff resources and skills
• Time
• Technology
TMDL Workshop
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DEFINING OBJECTIVES
D. Define Data Quality Objectives
DQOs consist of qualitative and quantitative
statements that specify the data quality required to
support decisions or actions.
The basic elements of data quality are:
Precision
Representativeness
Accuracy
Completeness
Comparability
• Define minimum acceptable data requirements
to support application of the selected analytical
method(s).
References:
Guidance for State Water Monitoring and WLA Programs.
EPA 440/4-85-031
Rapid Bioassessment Protocols for Use in Streams and
Rivers. EPA 440/4-89-001.
TMDL Workshop
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MONITORING FORTMDL
DEVELOPMENT
Step 3: Design the Monitoring Program
Once realistic objectives have been defined, the
specifics of the monitoring program may be
developed.
A. Select sampling locations
B. Determine sampling frequencies
C. Identify and select field and laboratory
methodologies
TMPL Workshop
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DESIGNING THE PROGRAM
A. Select Sampling Locations
Sampling locations are identified and selected on the
basis of both macro- and micro-scale considerations.
Macro-Scale Considerations
• Boundaries of the system
• Nodes in drainage network
• Locations of significant point source discharges,
land uses, etc.
• Locations of significant geomorphological or
habitat features
Micro-scale Considerations
• Homogeneity of site
- Flow and depth
- Water quality
- Habitat
• Accessability
TMDL Workshop
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SAMPLING LOCATION
SELECTION
Macro-Scale
Pig
Farms
Confluence of
Upper Drainage
Basins
Wetlands
Industrial
Discharge
TMDL Workshop
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SAMPLING LOCATION
SELECTION
Micro-Scale
Sewer m
Manhole
TMPL Workshop
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DESIGNING THE PROGRAM
C. Define Sampling Intervals and
Frequencies
The basic question is:
How many samples do we have to collect, and
how often do we have to collect them?
Frequency is determined by:
• Parameter Variability - High variability requires
more observations.
• Parameter Significance - Large uncertainty may
be acceptable for a parameter with low
significance.
• Monitoring Objectives - The smaller the
differences that are to be detected, the larger the
number of observations that will be required.
• Data Analysis Techniques - Equally spaced
observations, or a minimum number of
observations may be required.
TMPL Workshop
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DESIGNING THE PROGRAM
Sample Size Determination
Estimated sample size requirements may be
calculated in a variety of ways, depending principally
on the statistical techniques that are to be used in
analyzing the resulting data.
Parametric - Assume
normally distributed data.
Sample size is estimated
using exact solutions
derived from the
computational form of the
statistical model.
Non-parametric - Data
distribution is not
specified. Sample size is
estimated using empirical
methods.
TMDL Workshop
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SAMPLE SIZE
DETERMINATION
Calculation of Sample Size Required to
Detect a Difference in the Mean
Objective: Detect a difference between p.-) and |a2
Hypothesis: H0: M-i - M-2 ~ ^
Assumption: Normally distributed parameter
Basic formula:
V{5=»+2z«}2
where: nd = Number of samples required
a = Standard deviation of the parameter
z = Critical values of the z distribution
a = Probability of a false positive
(i.e., incorrectly conclude that Hi = H2)
p = Probability of a false negative
(i.e., incorrectly conclude that m * H2)
TMPL Workshop
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SAMPLE SIZE
DETERMINATION
Example: Assessing Compliance with a
Water Quality Standard
Problem Definition:
• Water quality standard = 0.5 mg/l
• Compliance defined as mean concentration of
0.3 mg/l, power of 80% (i.e., p = 0.8)
• a = 0.43, a=0.05
• 5% of samples collected are unusable
nd = (0.43)2 {Z'-0.8+*1-0.05} 2
" v ' 1 0.5-0.3 }
/n io\ rO-842 + 1 645\2
«J=(o.i8){ 02 }
nd =28.6
To allow for only 95% of the samples being usable:
nj
N = = 30.
0.95
TMPL Workshop
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SAMPLE SIZE
DETERMINATION
Example: Long-Term Trend Detection (1)
The nonparametric Seasonal Kendall Test:
Season (g)
Year
1
2
P
1
Xn
Xt2
X-|p
2
*21'
x22
x2p
• •
n
• •
Xfi1
• • • • • *
Xn2
• • •
^np
For each season: Sg = ~£sgn(Xjg -
>y
= 0 if x=y
= -1 if x
-------
SAMPLE SIZE
DETERMINATION
Example: Long-Term Trend Detection (2)
—l.l—T^WIB I ¦!¦'¦ Tf—1-»-»^—im-m-l-rw-f ¦ » ¦ W-rW1T»—¦ I ¦¦ ! ¦ I. ¦ ,¦¦¦¦ ¦ I ¦ * -
Objective: Determine how many monitoring sites
within a basin are required to detect a trend of a
given magnitude over a number of years.
The variables that determine the sample size
required are as follows:
• Length of the monitoring record
• Frequency of monitoring (monthly vs. quarterly)
• Variance of the measurements
• Magnitude of the trend that is to be detected
Although it may be possible to derive an exact
solution, Monte Carlo techniques provide a means
for empirically estimating sample size.
TMPL Workshop
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SAMPLE SIZE
DETERMINATION
Example: Long-Term Trend Detection (3)
Sample size is determined as follows:
1. Generate a large number of synthetic time-series
records of a fixed duration with known variance
and trend. The trend corresponds to the
smallest trend that is to be detected by
monitoring over the duration of the records.
2. Test each record for trend using the Seasonal
Kendall test at an acceptable a level. Each test
is categorized as either correct or incorrect.
3. Select K random subsets of n records and
calculate K estimates of the power of the test for
that sample size as: Power = (Correct tests In)
4. Calculate the 10th percentile value for the K
estimates of power - this is the detection power
at 90% confidence level and sample size of n.
The process is repeated over a range of n and trend
values to develop a series of power curves.
TMDL Workshop
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SAMPLE SIZE
DETERMINATION
Example: Long-Term Trend Detection (4)
A flow-chart of the Monte Carlo process is as
follows:
Repeat for
. _ Range of
Variance;^Duration,
and Trend Values'"
^"Repeat foF"^
n=10,20 500
Repeat
K Times
Select n
Time Series
Set Variance
Duration
Trend'
Test Each Time
Series for Trend
@ a=0.S
Generate 10,000
Time Series
Plot-
Power vs. n
Power = #Correct/n
Calculate'10th
Percentile Power
Value
TMDL Workshop
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SAMPLE SIZE
DETERMINATION
Example: Long-Term Trend Detection (5)
The following example was developed for a 10-year
monitoring program with quarterly sampling and a
relative standard deviation of 0.5 (i.e., CV = 50%).
Annual trends representing decreases of 2 -10% in
a hypothetical water quality parameter are
represented by the different power curves:
In this example, sample sizes greater than 50-60 do
not provide a significant increase in the detection
power.
TMDL Workshop
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DESIGNING THE PROGRAM
Considering Sampling Bias
Bias is systematic difference between estimates of a
parameter value and the "true" value of that
parameter.
B = |x - m
where: B = Bias
|i = True parameter value
m - Mean measured value
The presence of bias limits the value of the data in
extrapolating results and making inferences.
Common potential sources of bias include:
• Site location
• Timing of sampling
• Sequence of sampling
TMDL Workshop
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DESIGNING THE PROGRAM
Introducing Randomness
Ideally, sampling should be perfectly random to
eliminate bias and allow statistically valid inference.
In the real world, sampling is constrained by
numerous factors. Opportunistic introduction of
aspects of randomness in the sampling design will
strengthen the value of the resulting data.
Spatial Opportunities
• Sampling grids
Compass headings
Temporal Opportunities
• Ordering of site visits
• Time of day of visits
TMDL Workshop
177, LJmno-Tech,lnc.
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DESIGNING THE PROGRAM
D. Select Field and Lab Techniques
The selection of specific techniques for data
collection is determined by:
• Data requirements
• Site conditions
• Available personnel - Numbers, skill levels, etc.
• Logistical considerations
• Schedule
• Budget
Successful monitoring programs are a balanced
compromise between requirements and limitations.
TMDL Workshop
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DESIGNING THE PROGRAM
|Type$ of Field and Lab Techniques
• Physical
• Hydrologic
• Water Quality
• Sediment Quality
• Atmospheric deposition
• Rates
• Ecological health and habitat quality
TMDL Workshop
LT7, Umno-Tech, Inc.
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FIELD AND LAB TECHNIQUES
The measurement of flow is essential for most
quantitative water quality assessments, and is the
basis for virtually all loading estimates.
A wide range of flow estimation and measurement
technologies are available. Selection of an
appropriate technique is largely a function of data
requirements and site conditions. The alternatives
include:
• Runoff curves
• Current meters
• Gaging stations
• Primary flow measurement devices
• Dilution method
• Acoustic velocity meters
Flow Measurement
TMPL Workshop
L77, Umno-Toch, Inc.
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FLOW MEASUREMENT
TECHNIQUES
Current Meters
This technique involves manual measurement of
velocity and depth at a number of intervals along a
stream cross-section.
The flow within each interval is calculated as the
average velocity times the cross-sectional area of
the interval.
Qn = Vnwndn
Total flow is the sum of the interval flows.
Q = I Qn
TMDL Workshop
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FLOW MEASUREMENT
TECHNIQUES
Current Meters - Pros and Cons
Advantages
• Accurate
• Minimal equipment requirements
• Suitable for unstable and irregular cross-sections
Disadvantages
• Relatively time and labor intensive
• Not suited to automated monitoring
TMPL Workshop
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FLOW MEASUREMENT
TECHNIQUES
Gaging Stations
Where the cross-section of a stream or channel is
well-defined and stable, it is often possible to
establish a predictive relationship between the water
surface elevation and discharge flow. Once
established, flow may be estimated as a function of
staff gage elevation.
u_
a)
ca
o
V)
m mmm
a
Staff Gage Reading
TMPL Workshop
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FLOW MEASUREMENT
TECHNIQUES
Gaging Stations - Pros and Cons
Advantages
• Often possible to use existing USGS stations
• Simple structural requirements - staff gage
• May be established on large streams
• Readily adaptable to automated monitoring
• Relatively inexpensive
Disadvantages
• Require a large number of manual
measurements to establish an accurate rating-
curve
• Subject to error from sedimentation, scour, and
aquatic vegetation
• May be inaccurate where backwater effects are
significant
TMDL Workshop
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FLOW MEASUREMENT
TECHNIQUES
Primary Flow Devices
Characterized by predictable relationships between
depth of flow and discharge rate.
Weir: "Notch of a regular form through which water
flows"
Flume: "Specially shaped, open-channel flow
section which may be installed in a canal, lateral, or
ditch..."
-- Headwater TaBwater
\
FLOW^
4 "-5-
5 Crest—-n
~ \
is —. _ — •
IE—
O
*
Q = (3.6875^+2.5) Hal6
TMPL Workshop
LTT, Umno-Tech, Inc.
-------
FLOW MEASUREMENT
TECHNIQUES
Primary Flow Devices - Pros and Cons
Advantages
• Accurate
• Reliable
• Well-suited to automated instrumentation
Disadvantages
• Installation may be difficult
• Weirs may be subject to fouling from sediment
and debris
• Backwater effects may be undesirable
• Cost
TMPL Workshop
LTI, Umno-Tech, Inc.
-------
FLOW MEASUREMENT
TECHNIQUES
Dilution Method
Principle: The dilution of a tracer introduced into a
stream at a given rate will be a function of the
discharge rate of the stream.
CA
OA
a = Qlc~f
l2 ~M)
The most commonly used tracers are dyes, the
concentrations of which can be measured using a
portable fiourometer.
TMDL Workshop
L77, Umno-Tech, Inc.
-------
FLOW MEASUREMENT
TECHNIQUES
Dilution Method - Pros and Cons
Advantages
• Very accurate flow measurement
• Allows rapid collection of a large number of flow
measurements
• Readily adaptable to continuous monitoring
• Suitable for a wide range of flows
• Flows may be measured at several locations
downstream of the point of tracer injection.
Disadvantages
• Tracer may be susceptible to background
interference'
• Tracer concentrations and injection rates are
critical
• Equipment maintenance
TMDL Workshop
L77, Limno-Tech, Inc.
-------
FLOW MEASUREMENT
TECHNIQUES
Acoustic Velocity Meters
Principle: The difference in the travel speed of
sound upstream and downstream is a direct function
of the stream velocity.
, - Instrument control station
ator.
Vs m
X
* 'Transmitter - receiver
Instr
>^> X Acoustic ^ | ) \
\
Sound pulse.
'~C = Tr
i
V^T = Measured time difference.
\
• > -t*
Transmitter - receiver
To estimate flow, stage is measured coincident with
stream velocity, and used to calculate cross-
sectional area. Flow (Q) is then calculated as:
Q = VA
TMDL Workshop
L77, Umno-Tech, Inc.
-------
FLOW MEASUREMENT
TECHNIQUES
Acoustic Velocity Meters - Pros and Cons
Advantages
• Very accurate measurements of average velocity
• Relatively immune from floating debris, boat
traffic, etc.
• Well-suited to continuous monitoring
Disadvantages
• Equipment may be costly
• Accurate measurement of distance between
transducers and cross-sectional area is essential
• Best suited to larger streams and rivers
TMPL Workshop
L77, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
There are two options for obtaining precipitation (and
evaporation) data for watershed investigations:
1. Existing monitoring stations operated by USWS,
agricultural research stations, universities, etc.
2. On-site precipitation gages. Components of a
typical station are shown below.
Precipitation Monitoring
Data
Port
Datalogger
Tipping Bucket
Gage
TMDL Workshop
L77, Limno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
[Deposition Monitoring
Atmospheric deposition information is most often
obtained from existing monitoring stations.
Where local conditions or objectives warrant
deposition monitoring, specialized equipment and
expertise is normally required. The two basic
approaches are:
• Water traps and bulk samplers
. Filter traps
References:
Guidelines for Design, Installation, Operation and Quality
Assurance for Dry Deposition Monitoring Networks. NTIS No. PB
89-127 492/AS
Wet Deposition and Snowpack Monitoring. NTIS No. PB 88-165
717/AS.
TMDL Workshop
LT7, LJmno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
ln-situ Water Quality Monitoring
ln-situ devices (i.e., probes) are available for the
measurement of a variety of water quality
parameters.
Advantages
• Quick and inexpensive
• Portable
• Eliminates sample storage and handling issues
• Well-suited to automated monitoring.
Disadvantages
• Accuracy and reliability may be questionable
• Field calibration may be tricky
• Costs spread over total measurements collected
Physical
Chemical
Depth
Temperature
Velocity
Turbidity
Conductivity
PH
Salinity
Dissolved oxygen
ORP
Ammonia
Other ions
TMPL Workshop
L77, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
>r Quality Sampling Strategic
Water quality monitoring can be conducted either as
grab or continuous sampling.
Grab Sampling
• Cheap
• Simple
• Only represents a single point in time
Continuous Sampling
• Labor and/or resource intensive
• Technically complicated
• Highly programmable
• Allows accurate characterization of water quality
integrated over time
TMDL Workshop
LT7, Limno-Tech, Inc.
-------
WATER QUALITY SAMPLING
STRATEGIES
| Continuous Monitoring Alternatives (1)
Continuous water quality sampling is usually
conducted by compositing samples that have been
collected at equal intervals of either time or flow.
Equal Time Intervals
tl=t2
CO
t2
tl
Tim©
Equal Flow Intervals
VI = V2
-------
WATER QUALITY SAMPLING
STRATEGIES
Continuous Monitoring Alternatives (2)
• Compositing of samples of equal volume
collected at equal intervals through time will
generally bias the results towards the water
quality under lower flows.
• Samples of equal volume collected at equal flow
intervals may be composited to provide an
accurate estimate of average water quality
throughout the monitoring period (i.e., mean
event concentration).
• The bias introduced by sampling at equal time
intervals may be eliminated by compositing the
samples using volumes that are proportional to
the flow during the time interval that each
sample represents.
TMDL Workshop
L77, Llmno-Tech, Inc.
-------
WATER QUALITY SAMPLING
STRATEGIES
Automated Water Quality Monitoring
Station Components
r
i ¦ ¦¦ ¦ ¦ i
Antlywt
Pr«elpiUtlon
I
Station Houalng
•
}
A-O
Converter
Controller
!
Oeteioooe'
O0tto«l
LEGEND
¦¦i Electrical Conniption
t • ¦ ¦ 8uotton Line
Kerdoopy^De Transfer
OurniiiTylaf
In ftltu
Probes)
This schematic illustrates the components that would
be found in a comprehensive, fully automated water
quality monitoring station.
Reference:
Automatic Stormwater Sampling Made Easy. C. Thrush and D.
DeLeon. Water Environment Federation. 1993
TMPL Workshop
L77, Limno-Tech, Inc.
-------
WATER QUALITY SAMPLING
STRATEGIES
Monitoring Station Considerations
Issues that must be considered in locating and
installing a monitoring station include:
• Accessibility
- Proximity to roads, paths, etc.
- Required rights-of-way or clearance
- Permits
• Utilities
- Power and telephone
• Structures
- Instrument housing and foundation
- Mountings for probes
- Cable and tubing runs
- Flood elevation
• Security
- Visibility
- On-site personnel
TMPL Workshop
L77, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
The most common sediment quality parameters of
interest are:
• Organic carbon content
• Active sediment depth
• Solids concentration
• Sediment oxygen demand (SOD)
• Sediment velocity
Sediment Quality
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
SEDIMENT QUALITY
TECHNIQUES
Active Sediment Depth
Definition: The depth of sediments that are subject
to active exchange with the overlying water column.
Active Depth
Concentration
Active sediment depth is estimated from data on the
concentration of critical constituents at various
depths in a sediment core.
TMPL Workshop
L71, Umno-Tech, Inc.
-------
SEDIMENT QUALITY
TECHNIQUES
[Solids Concentration
Solids concentration is most easily obtained if
sediment porosity and density measurements are
available:
[S] = (1 - +)pxx 106
If porosity is not available, but moisture content or
percent dry weight (PDW) are:
PDW = 100 - % moisture
and
pP22--!
Hs\pdw
-------
SEDIMENT QUALITY
TECHNIQUES
Sediment Oxygen Demand
ln-situ measurements of SOD are collected using a
chamber to isolate a volume of water over an area of
stream or lake bottom. Dissolved oxygen is
monitored over time to estimate depletion rate.
Corrections must be made for algal respiration and
water column BOD.
DO Meter
Stirrer
DO Probe
sod,
At
where: Co = Initial DO concentration
C = Final DO concentration
V = Volume of dome chamber
A = Surface area of bottom covered
t = Duration of the test
TMDL Workshop
L77, Umno-Tech, Inc.
-------
SEDIMENT Q1
TECHNIQUES
UALITY
Sedimentation
Sedimentation Rates
Historical sedimentation rates may be estimated by
collecting sediment cores and using radio-isotope
dating techniques to relate sediment age and
thickness.
The two most commonly used isotopes are lead-210
(<150 years) and cesium-137 (since 1954).
Reference
Wetzel, R.G. Limnology. CBS College Publishing. 1983.
Surfac* (1993)
G = 0.26 in/yr
O s 0.17 Intyr
TMPL Workshop
L77, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
1
Other Rates and Constants
There are a variety of rates and constants that may
be directly or indirectly measured in the field,
including:
• Pollutant decay rates
• Nitrification rates
• Reaeration rates
• Specific growth rates
• Nutrient half-saturation coefficients
For most investigations, the rational selection of
appropriate literature values for these terms will be
adequate.
TMDL Workshop
L77, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
Ecological Health and Habitat Quality (a)
Quantitative measures of the health of aquatic
systems are based on integrated assessments of
various system elements. Some examples of
commonly applied indices are shown below.
Index
System
Elements
Characteristics
Index of Biotic
Integrity (IBI)
Fish
Species richness and
composition
Trophic composition
Abundance and condition
Hilsenhoff Biotic
Index
Macro-
invertebrates
Taxa richness
EPTtaxa
Pollution tolerance
Functional feeding groups
Rapid
Bioassessment
Protocols I - V
Water quality,
habitat, macro-
invertebrates
fish
Taxa composition and
abundance
Functional feeding groups
Habitat quality
Sources of impact
TMPL Workshop
LT7, Umno-Tech, Inc.
-------
FIELD AND LAB TECHNIQUES
Ecological Health and Habitat Quality (b) |
References
Biological Criteria - Guide to the Technical Literature.
EPA 440/5-91/004.
Macroinvertebrate Field and Laboratory Techniques
for Evaluating the Biological Integrity of Surface
Waters. EPA 600/4-90/030.
Rapid Bioassessment Protocols for Use in Streams
and Rivers. EPA 444/4-89/001.
TMPL Workshop
LT7, Limno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
|Background
Sponsored by the Southwest Florida Water
Management District in Tampa, Florida.
Lake
• Surface area: 820 acres
• Depth: 11.5 ft. (mean), 14.1 ft (max)
• Retention time: 0.18 - 0.36 years
Watershed
• 55-square miles (16% in Plant City)
• Predominently agricultural
• Two point source discharges
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Nature of the Problem
2.5
2.0 • -
• 1.5
5
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1.0
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0.5 - - •¦¦ i
0.0
08/28/76 05/25/79 02/18/82 11/14/84 08/11/87 05/07/90 01/31/93 1028/95
Target TP concentration is 0.07 mg/l
TMPL Workshop
L77, Limno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Project Objectives
• Quantify water quality conditions in Lake
Thonotosassa
• Quantify pollutant loading to the lake
• Identify principal pollutant sources
• Develop recommendations for further
management actions
TMPL Workshop
LTI, Umno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Data Collection Strategy
• Historical and existing data sources
- Water quality at lake and tributary stations
- Lake elevation and discharge
- Precipitation and evaporation
- Point source discharges
- Nonpoint sources (i.e., land use, export rates)
• In-lake monitoring
- Depth
- Water quality
- Sediment thickness and chemistry
- Groundwater
• Tributary monitoring
- Discharge
- Water quality
• Quality Assurance/Quality Control Samples
- Field blanks
- Field duplicates
TMPL Workshop
L77, Umno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Monitoring Stations
WWTP
LSno-Man
MILES
Monitoring Constraints & Concerns
• Flow reversals at tributary stations
• Limited personnel and laboratory resources
• Extreme heat and humidity
• Aquatic vegetation
• Pests
TMDL Workshop
LT7, Umno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Summary of Monitoring Data
LT-6
LT-5
LT-4
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jl-Aug-ai 0<-No«-t1 _ 02-Jbi-«2 04-Mar-M 03-Mgr-M 0«-JU-#2
• More than 190 water quality samples collected at
10 stations over a 10-month period.
• Products:
- Lake bathymetry and sediment maps
- Development of water and nutrient budgets
- Identification and ranking of point and nonpoint
sources of nutrients
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TMPL Workshop
LT7, Umno-Tech, Inc.
-------
CASE STUDY:
LAKE THONOTOSASSA
Hazards of Existing Data
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08/28/76 05/25/79 02/18/82 11/14/84 08/11/87 05/07/90 01/31/93 10ft 8/95
TMPL Workshop
1.77, Umno-Tech, Inc.
-------
MONITORING IN SUPPORT OF
TMDLS
Summary
Monitoring design is driven by several key factors:
• Purpose of the monitoring
• Availability and quality of existing data
• Constraints on the collection of new data
• Objectives of the monitoring
• Type of system to be monitored
• Site specific conditions
Nonpoint source monitoring is significantly more
complicated (and uncertain) than monitoring of point
sources.
• Diffuse nature of the sources
• Intermittent loading driven by weather
Key to Success: Proper planning and a realistic
approach, followed by assessment of progress
throughout the implementation phase
TMDL Workshop
LT7, Umno-Tech, Inc.
-------
MONITORING FOR TMDL
DEVELOPMENT
Available EPA Technical Guidance
• Guidance for Water Quality-based Decisions: The
TMDL Process
• Technical Support Manual: Waterbody Surveys
and Assessments for Conducting Use Attainability
Analyses - Volumes I, II, and III
• Technical Guidance for Performing Waste Load
Allocations - Books I, II, III, and IV
• Handbook: Stream Sampling for Waste Load
Allocations Applications
• Guidance Specifying Management Measures for
Sources of Nonpoint Pollution in Coastal Waters
• Rapid Bioassessment Protocols for Use in
Streams and Rivers
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
MONITORING FORTMDL
DEVELOPMENT
• Design of Water Quality Monitoring Systems -
Ward, etal., 1990
• Sampling Design and Statistical Methods for
Environmental Biologists - Green, 1979
• Methods for Evaluating Stream, Riparian, and
Biotic Conditions - Platts, et al., 1983
• Design of Networks for Monitoring Water Quality -
Sanders, etal., 1983
Additional Technical Guidance
TMDL Workshop
L77, Umno-Tech, Inc.
-------
(o
-------
RECEIVING WATER
MODELING
WATER QUALITY MODELING:
INTRODUCTION
Objective:
Define relationship between pollutant
loads and receiving water quality.
Focus of
Talk:
Model Theory
Model Application
Available Models
Model Selection
NPS Linkage
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
WATER QUALITY MODEL OBJECTIVE
External Loadings
Environmental
Conditions
<27
Water Quality Model
Water Quality
What level of loading will result in
acceptable water quality?
T'^OL Workshop LTI, Umno-Tech. inc.
-------
TYPES OF MODELS
Empirical
Predicts based on observed data
tendencies
Median total phosphorus (Tp) concentration, ug/l
Mechanistic (aka Deterministic)
Mass balance models
Accumulation = Inputs - Outputs ± Reactions
Future Amount = Present Amount + Accumulation
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
MASS BALANCE MODEL
ecumulation = Inputs - Outputs
SAVINGS ACCOUNT ANALOGY
With-
Month
Balance
Deposits
drawals
Interest
Accumulation
Jan
$1000
S10
$10
$5
5
Feb
$1005
$10
$10.05
$5.03
4.98
March
$1009.98
$10
$10.10
$5.05
4.95
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
GENERAL MASS BALANCE MODEL
Loading
Accumulation
Mass Transfer
Kinetic Terms
Terms
Terms
V —— Qin Cjn QoutC
dt
V K C + W
dC
dt
= 0
Advection
Dispersion
Steady State
Conditions
Decomposition
Bio-Chemical
Transformations
Recycle
Settling
Municipal
Industrial
Non-point
TMDL Workshop
L77, Umno-Tech, Inc.
-------
MODEL APPROACH
Determine Temporal Resolution
Determine Spatial Resolution
Define Water Movement
Define Kinetic Processes
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TEMPORAL RESOLUTION
Steady-State: Predicts concentration in response
to steady loads and environmental
conditions. ("How much?")
Easiest to apply
Limited information
Time Variable: Predicts change in concentration
over time. (How much and when?)
Most difficult to apply
Detailed information
TMDL Workshop
LTl, Limno-Tech, Inc.
-------
STEADY STATE VS.
TIME VARIABLE
SAVINGS ACCOUNT ANALOGY
With-
Month
Balance
Deposits
drawals
Interest
Accumulation
Jan
$1000
$10
$10
$5
5
Feb
$1005
$10
$10.05
$5.03
4.98
March
$1009.98
$10
$10.10
$5.05
4.95
Sept
$2000
$10
$20
$10
0
Accumulation = 10 - .01 (Balance) + .005 (Balance)
(change in $/mo)
Steady State Model (Accumulation =0)
0 = 10 - .01 (Balance) + .005 (Balance)
= 10 - .005 (Balance)
10
Balance = —— = 2000
.005
TMPL Workshop
LTI, Limno-Tech, Inc.
-------
STEADY STATE VS.
TIME VARIABLE
WHEN TO USE STEADY STATE
Water quality responds immediately to changing
conditions
Point of mix in a river
Only interested in long term response
Standard expressed as annual average
Current water quality is acceptable,
how much can load be increased?
WHEN TO USE TIME VARIABLE
Water quality responds slowly to change in
load
Interested in concentration during time of
response
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Varying Time Response to
Change in Model Inputs
Load
Time
Cone.
System 1
Time
Cone.
System 2
TMDL Workshop
Time
LTI, Limno-Tech, Inc.
-------
SPATIAL RESOLUTION
Choice of model depends upon:
• Degree of mixing
• Spatial variability in concentrations
• Spatial variability of concern
0-Dimensional
Slow acting pollutant or rapid mixing
Point of mix in a river, eutrophication in a lake
1-Dimensional
Laterally well mixed river, downstream
decay important
Concentrations along the length of a river
2-Dimensional
Concentrations vary over length and width
Concentrations vary over length and depth
3-Dimensional
Concentration vary with length, width, and depth
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Examples of Different Spatial Resolution
0 - Dimensional
OR
1 - Dimensional
2 - Dimensional
T ^ L Workshop LTI, Umno-Tecb inc.
-------
HYDRODYNAMIC
CONSIDERATION
(WATER MOVEMENT)
EITHER THE EASIEST STEP OF THE
MODELING EFFORT, OR THE HARDEST.
EASY
Free flowing streams with stable flow
Qdown = Qup ¦*" Qin
Well mixed lakes
HARD
Storm surges
Embayments
Large lakes
NOTE: IF YOUR MODEL DOESN'T MOVE WATER
PROPERLY IT WILL NEVER SIMULATE QUALITY
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ONE-DIMENSIONAL
HYDRODYNAMIC
SIMULATION
Real-time hydrodynamic simulations
SA 5Q . .
— + —Continuity
St Sx
su TTSU
+ U = a + af + Momentum
St Sx 8 f w
A = Area
Q = Flow
U = Velocity
a = Acceleration forces
INPUTS
Geometry
Inflows
Boundary Water Elevation
Bottom Roughness
OUTPUTS
Velocity
Depths
Calibration typically consists of selecting a value for the
bottom roughness coefficient that allows adequate
simulation of observed state and velocity data.
TMPL Workshop
LTI, Limno-Tech, Inc.
-------
KINETIC CONSIDERATIONS
None
Mixing zone assessment
Conservative pollutant
Some
First-Order Loss
Dissolved oxygen
Eutrophication
Toxics
TMPL Workshop
LTI, Limno-Tech, Inc.
-------
MIXING ZONE ASSESSMENT
Determine pollutant concentration at the
edge of the regulatory mixing zone.
Time of travel from discharge to edge of
mixing zone is fast enough that kinetic
processes can be ignored.
~ Simple Equations
Discharge-induced vs. ambient induced
xQUp Cup + Qw Cw
Cmix =
xQud + Qw
where
x = fraction of cross-sectional area
allowed for mixing
~ EPA-supported Models
CORMIX
UM/PLUMES
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
FIRST-ORDER LOSS
PROCESSES
dC! dt = -k
*
Change in
concentration = Rate Concentration
over time Constant
Loss rate is proportional to concentration. 90+% of
model kinetics are based on first-order kinetics.
In & = -kt
¦ ^0
. c
In *
¦
¦
¦
¦
Time, t
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
"Mechanistic" Examination
of Dissolved Oxygen
Reaeration
i
CBOD
Degredation
Dissolved
Oxygen
Photosynthesis
w
Respiration
W
Ammonia
Nitrification
Sediment
Oxygen
Demand
T"DL Workshop
LTI, Umno-Tech Inc.
-------
DISSOLVED OXYGEN
MODEL APPROACH
¦ ¦ ¦ ¦¦ ¦¦ l ——' -¦ i ¦ ¦ i ¦ ——————^— i i —¦ - i. —
1. Define spatial, temporal scales
2. Define water movement
3. Calibrate BOD, NH3, decay rates to
observed data
4. Define remaining parameters
(reaeration, P/R, SOD)
i) Measurement
ii) Empirical relationship/calibration
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TOXICS MODELING
What makes toxics so special?
Toxicity
Many different chemicals
Many different loss processes
Affinity for solids
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Conceptual Framework
Toxic Chemical Model
Atmosphere
S.
(0
Kl
Photolysi:
Decay
Chemical on
Particulate Organic
Carbon
(ug/kg O.C)
Water Column
O)
c
S
a>
CO
Koc
Chemical in
Dissolved Phase
(ug/i)
Photolysis
Decay
Decay
^9
(0
c
0)
Q.
c/>
3
(0
0)
a:
c
o
'*¦» y-vx-x • ¦¦¦* ^
Koc
Chemical Dissolved
in Pore Water
(ug/l)
Decay
>v•••:«rxyw"-^ x-.-:•.:«x
x-. ¦ .wow • nAwr-' ¦'•y-y •:
•¦v.v x<-xw:X'. y» •> w1-: >•» •
Deep Sediment
Net Sedimentation
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
LOSS PROCESSES
Volatilization
Photolysis
Hydrolysis
Biodegradation
Settling
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
VOLATILIZATION
Exchange of chemical across the air-water
interface.
Volatilization is to toxics what reaeration is to
dissolved oxygen.
Information required:
Henry's law constant
Reaeration rate
Toxicant molecular weight
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
PHOTOLYSIS
Chemical breakdown of pollutant via solar energy.
Depends upon:
Quantum yield: Amount of light required to
cause a given reaction
Incoming solar radiation.
Light attenuation in water column.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
HYDROLYSIS
Chemical reaction with water
Often accelerated by high or low pH
Information required:
pH of receiving water
neutral hydrolysis rate
acid-catalyzed hydrolysis rate
base catalyzed hydrolysis rate
K,=K,+K[h'}+k1[oh]
PH
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
BIODEGRADATION
Bacterial degradation of pollutant
Estimated in one of two ways:
1. loss of toxic/bacterial cell
2. site specific loss rate
Kb - Kb2[Bacteria\
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TOXIC LOSS
PROCESS SUMMARY
Biodegradation Guesswork
Photolysis
Hydrolysis
Volatilization Less Guesswork
Fortunately, loss rates are additive and our
ultimate goal is total loss rate.
Kioss = Kbiodegradation
+ Kphotolysis
+ Khydrolysis
+ Kyolatilization
Can calibrate loss rate from observed data.
|n
uo
In -9- = -kt
Time, t
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
PARTITION COEFFICIENT
Ratio of sorbed to dissolved pollutant
Concentration on solids (mg/kg)
Concentration in solution (mg/l)
= Cp/[SS]
Cd
Kp can be measured or estimated by
chemical (Kow) and particulate (f0c)
characteristics.
Units for Kp are inverse of the units used for
solids (l/kg or l/mg)
TMDL Workshop
LTI, Limno-Tech, Inc,
-------
Suspended Solids
Dissolved Pollutant
[SS] =
mg SS
liter
O
O
O
[CJ =
mg Pollutant
liter
[CJ =
mg bound pollutant
liter
mg bound pollutant
kg solid
[CP]
[SS]
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Repeat experiment with different levels of solids and polllutant, plot results
mg Pollutant
[Cd]
At low [C d ], relationship between and [C d] is linear
[C n 1/[SS]
Partition coefficient (K , p )
[Cd] [Cd]
T 'HL Workshop LTI, Limno-Tecb inc.
-------
AFFECT OF PARTITION
COEFFICIENT OF PERCENT
PARTICULATE DISSOLVED
Percent
1
Percent
Kp[SS]
Dissolved
1+Kp [SS]
Particulate
1+Kp[SS]
Percent
Percent
SS (mg/l)
Kp
Particulate
Dissolved
50
103
4.8
95.2
50
104
33.3
66.7
50
105
83.3
16.7
1
104
1.0
99.0
10
104
9.1
90.9
100
104
50.0
50.0
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ESTIMATION OF PARTITION
COEFFICIENTS FOR ORGANIC
CHEMICALS
Assumes: Chemical sorbs solely to the organics
carbon content of solids
Organic carbon partition coefficient is
equal to the octanol water partition
coefficient, Kqw
Kp
Partition
Coefficient
Kow
K
ow
Octanol water
Partition Coefficient
* f
oc
Fraction organic
content of solids
Tabulate in scientific literature
for most organic chemicals
foe
= 0.05-0.25,
0.001 - 0.05,
Water column
Bed sediments
T M D L Workshop
LTV, Limno-Tech, Inc.
-------
ESTIMATION OF
"CONDITIONAL"
PARTITION COEFFICIENTS
The partitioning of most metals and some
organics are driven by complex chemical
interactions, and are not easily defined a priori.
Site specific, conditional, partition coefficient can
be estimated from observed data on dissolved
pollutant, total pollutant, and solids.
(Cr-C^/fSS]
N Ca
where
Cj = Total pollutant conc. (M/L3)
Cd = Dissolved pollutant conc. (M/L3)
[SS] = Solids conc. mg/l or kg/I
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SOLIDS MASS BALANCE
Settling
Depends on particle size and density
Sand >100m/day
Silt 0.3-10m/day
Phytoplankton 0.1-0.3/day
Can be calibrated during periods of no resuspension
Resuspension
Scour of bottom sediments
Typically event driven
Can be calibrated on an event specific basis
Burial
Can be determined from radioactive tracer
measurements in sediment cores
Can be estimated from settling and resuspension fluxes
Water
Column
Surface
Sediments
Deep
Sediments
T MDL Workshop
LTI, Limno-Tech, Inc.
-------
TOXICS MODEL APPROACH
1. Time Variable or Steady State?
2. Define Spatial Resolution
3. Define water movement
4. Define solids mass balance
5. Describe chemical behavior
a) Partitioning
b) Kinetic reactions
Steps 1 and 2 require 90% of the effort.
TMPL Workshop
LTI, Limno-Tech, Inc.
-------
MODEL APPLICATION
ANALYTICAL SOLUTIONS
Dilution equation
First-order loss models
CANNED MODELS
Conventional pollutants
Multi-SMP
QUAL-2E
WASP
Toxic Pollutants
SMPTOX3
WASP
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
STEADY-STATE ANALYTICAL
SOLUTIONS
Predicting River Water Quality:
Cup Qup + 2-Cw Qw Total Load
Criver ~ =
Qup + Qw Total Flow
where
Cup = Upstream concentration
Qup = Upstream flow
Cw = Wastewater concentration
Qw = Wastewater flow
River Loading Capacity:
Total Load
Wiver ~
Total Flow
Total Load = Criver * Total Flow
Total Allowable = Criver * Total Flow
Load
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
FIRST ORDER LOSSS MODEL
Required when pollutant decays significantly
between point of entry to stream and
downstream point of interest.
C(x) =
C(0) * e"kx/u
C(X) =
Concentration at distance
x downstream
C(0) =
Concentration of upstream
location
k
Pollutant decay rate
u =
Stream velocity
x/u =
Time of travel
Example:
U =
2 mi/day
k
0.5/day
X (miles)
X/U (days)
e-kx/u
(mg/l)
0
0
1
100
0.5
0.25
0.88
88
1.0
0.50
0.78
78
1.5
0.75
0.69
69
2.0
1.00
0.61
61
TMDL Workshop
LTt, Limno-Tech, Inc.
-------
PRINCIPLE OF SUPERPOSITION
WHEN A LINEAR RELATIONSHIP EXISTS
BETWEEN LOAD AND RECEIVING WATER
QUALITY, EACH LOADING SOURCE CAN BE
CONSIDERED INDEPENDENTLY AND THEN
SUMMED.
River
Mile
Upstream
Loading
Source 1
Loading
Source 2
Loading
Source 3
Total
Cone.
10
1.0
—
—
—
1.0
9
0.9
—
—
—
0.9
8
0.8
—
—
—
0.82
7
0.74
4.0
—
—
4.74
6
0.67
3.3
—
—
4.27
5
0.60
3.2
7.0
—
10.8
4
0.54
2.9
6.3
2.1
11.8
3
0.48
2.6
5.6
1.9
10.6
WORKS FOR EVERYTHING EXCEPT ALGAE
AMENABLE TO SPREADSHEET APPLICATION
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
MODEL SELECTION
Objective: Select the simplest model that includes
the relevant physical and chemical
phenomena.
Considerations
Site-specific Characteristics
Physical system
Chemistry
Management Objectives
Required accuracy
Project Resources
Data
Staff
Time
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Relationship Between
Complexity and Reliability
c ^
<5 .<9
O
-------
AVAILABLE MECHANISTIC
MODELS
Receiving Water Quantity
NR: Wet weather runoff quantity
RQ: Receiving water quantity
NQ: Wet weather runoff quality
Receiving Water Quality
Constituents
N: Nutrients
M: Metals
C: Conservative organic pollutants
NC: Nonconservative pollutants
S: Sediment
T: Temperature
Sediment Transport
NA: Not addressed
Transp: Transport of sediments
Eros: Erosion
Dep: Deposition
TMPL Workshop
LTI, Limno-Tech, Inc.
-------
Ihe Cmlinus Onnift, Inc.
DIIAF1
June 8, 1993
Table 3-3. Receiving Water Quantity (I;loiv) Models
Model
Oilier
Uses
Main
Ref
Rev.
Walerbody
Type
Dimension-
ality
Rouling
Tyi>e
Time Step
Agency
QUAI.2I.
RQ
1
River
Esluary
ID
Water
Da la nee
Steady
EPA/
CEAM
DYNIIYD (WASP4)
RQ
2
a,d
River
lake
Esluary
1-D» (lo
3D link
node)
Dynamic
Wave
Transient
Sublioorly
EPA/
CEAM
AGNPS
NQ,NR,
RQ
3
a,h
River
ID
IJnil
1 lydmgraph
Transient
1 lourly
IJSDA/ARS
SWRRBWQ
NF.NQ,
RQ
4
a,b
Lake
River
1-D, 0 D
Waler
Balance
Transient
Daily
USDA/ARS
IISPF
fJI:,NQ,
HQ
5
a,b,c,d
River
Lake
ID
1 lydroiogic
Routing
Transient
1 lourly.
EPA/
CEAM
1IEC-2
6
d
River
ID
Bernoulli
Equation
Steady
IIEC
NEC 6
RQ
,»
7
d
River
ID
Bernoulli
Equation
Series of
steady evts.
NEC
DWOPER
H
d
River
1-D
Dynamic
Wave
Transient
Subhourly
NWS
TR-21)
NR
9
River
1-D
Storage i
Kinematic
Transient
Subhourly
SCS
1IOC-3
11
Reservoir
1-D
Water
Balance
Monthly
IIEC
3-25
-------
lite Catlmus Group, Inc. DRAFT June 8, 1993
Table 3-3 (continued)
1IEC-5
12
Reservoir
River
ID
network
1 lydrologlc
Routing
Transient
Hourly
IIEC
IIEC 1
NF
14
River
Reservoir
ID
Kinematic 4c
others
Event
NEC
CE-QUAL-RI
RQ
15
Reservoir
1-D
thermal 4c
density
Transient
WES
CE-QUAI.-W2
RQ
16
lake
£stuary
2-D
thermal 4c
density
Transient
WES
RWQM (STORM)
RQ,NF,
NQ
17
Niver
ID
Kinematic
Wave
(to support
longer term
quality)
IIEC
WQRSS
RQ
18
River
Reservoir
1-D
Multiple
methods
Transient
Subhonrly
IIEC
TADS-2
RQ
19
River
Estuary
2D
Transient
Subhourly
WES
WIFM-SA1.
RQ
20
River
Estuary
2D
Transient
Subhourly
WES
Key lo References
1 Drown & Dam well, 1987
2. Ambrose el al., 1988
3. Young el al., 1987
4. Arnold e al., 1991
5. Johanson el al., 1984
6 NEC, 197J
7. NEC, 19771)
8. Fread, 1978
9. SCS, 1979
11. NEC, 1976
12. IIEC, 1979b
14. NEC, 1985
15. WES, 1988
16. draft available
17. NEC, 1979a
18. IIEC, 1978
19. Thomas and McAnally,
1985
20. Schmalz, 1985
Key to Reviews
a. U.S. El'A, 1992
b. Donlgian it lluber, 1991
c. WPCF, 1989
d. McKeon ic Segna, 1987
3-26
-------
'I'lte Cadmus Group, Inc.
DRAFI
June 8, 1993
Table 3-4. Receiving Water Qualily Models
Model
Oilier
Uses
Main
Ref.
Rev.
Wale
body lype
Consti-
tuents
Dimensi-
onality
Sedi
ment
Sim.
Time
Step
Transf.
Degrd.
Special
Charar-
tcristics
Agncy
QUAL2F
RF
1
River
Estuary
0,N,A,C,
NC,T
ID
NA
Steady
Degr
Uncert.
analysis
EPA/
CEAM
WASP4
RF
2
d
River
Estuary
Lake
General
1-D» (to
3-D link
node)
transp
Cont.
Subluly
full
EUTRO 6c
TOXI
versions
EPA/
CEAM
AGNPS
NQ,NF,
RQ
3
a,b
River
N,S,C
ID
transp
eros/
dep
Q>nt.
Hourly
no
USDA/
ARS
SWRRBWQ
NQ,NF,
RF
4
a(b
River
(sed)
Lake (N)
S,N,C,NC
2-D
Compart
ment
transp
eros.
dep.
Cont.
Daily
Degr
Uncer-
tainty
Analysis
USDA/
\RS
I1SPF
NQ,NF,
RF
5
a,b,
c,d
River
Lake
General
ID
transp
Cont
Subhrly
full
Freq-Dur
module
EPA/
CEAM
EXAMS II
6
d
General
C,NC
1-D to 3-
D
NA
Steady-
Monthly
full
Flows
spec
external
EPA/
CEAM
MEXAMS
7
d
General
M
1-D to 3-
D
NA
Steady-
Montlily
full
Flows
spec
exterital
EPA/
CEAM
1IEC-6
RF
8
d
River
Reservoir
S
ID
transp
eros
dep
Contin
Dally
NA
stream
bed
profiles
IIEC
3-28
-------
77le Cadmus Croup, Inc.
DRAFT
June 8, 1993
Table 3-4 (continued)
WQKSS
RF
22
River
Reservoir
Ceneral
ID
transp
Contin.
Subhrly
full
lemp,
ecologlc
sim.
JIEC
CORMIX
23
Estuary
Lake
Dilution
2-D, 3 D
NA
Steady
no
near field
EPA/
CEAM
SEM
24
Estuary
0,C,NC
ID
NA
Steady
degr
flows
Input
EPA
AUTOQUAL
25, 26
River
Estuary
Q,N
ID
NA
Steady
no
flows
Input
EPA
FETRA
27
d
Esliidiy
I.ake
S.CNC
2D
transp
eros
dcp
Contin.
degr
finite
element
NUREG
TABS-2
RF
28
River
Estuary
S,C,NC
2-D
transp
dep
Contin.
degr
WES
WIFM SAL
RF
29
River
Estuary
bacteria
2-D
NA
Contin.
Subluly
degr
WES
PLUMES
30
Estuary
Lake
Dilution
2-D, 3 D
NA
Steady
no
near field
EPA/
CEAM
Key lo Keierences
III. zander 6t uove,
yyu
"2V.
Schmalz, 19
to
1. Drown & Barnwell, 1987
2. Ambrose et al, 1988
3. Young el al., 1986
4. Arnold el al., 1991
5. Jolianson e( al , 1984
6. Diims & Cline, 1985
7. Dunis cl al , 1982
8. IIEC, 1977b
11. Saunders el al., 1993
12. I.linnoTech, 1993
13. Drlscoll el al., 1990
14. CDM, 1992
17. I.linnoTech, 1985
18. Bodeen el al., 1989
19. WES, 1986
20. draft available
21. NEC, 1979a
22. IIEC, 1978
23. Jirka, 1992
24. Hydroscience, 1971
25. L-ovelace, 1975
26. Crim ic Lovelace, 1973
27. Onlshl, 1981
28. Thomas & McAnally, 1985
30. CEAM BBS
Key to Reviews
a. US. EPA, 1992
b. Donlglan & l lubcr, 1991
c. WPCF, 1989
d. McKeon Sc Segna, 1987
3-30
-------
Hie Cadmus Group, Inc.
DRAFT
June 8, 1993
Table 3-4 (continued)
WQRSS
RE
22
River
Reservoir
General
ID
transp
Contin.
Sublirly
hill
temp.,
ecologic
sim.
i IEC
CORMIX
23
Estuary
Lake
Dilution
2-D, 3-D
NA
Steady
no
near field
EPA/
CEAM
SEM
24
Estuary
0,C,NC
ID
NA
Steady
degr
flows
input
EPA
AUTOQUAL
25, 26
River
Estuary
0,N
ID
NA
Steady
no
flows
Input
EPA
FETRA
27
d
Esluary
l.ake
S,C,NC
2D
transp
eros
dep
Contin.
degr
finite
element
NUREG
TABS 2
RF
28
River
Estuary
S,C,NC
2D
transp
dep
Contin.
degr
WES
W1FM SAL
RF
29
River
Esluary
bacteria
2D
NA
Contin.
Subhrly
degr
WES
PLUMES
30
Esluary
Lake
Dilution
2 D, 3 D
NA
Steady
no
near field
EPA/
CEAM
Key lo Keierences
iu. fancier & Love,
99U
29
Schmalz, iy
lb
1. Brown & Barnwell, 1987
2. Ambrose el al., 1988
3. Young el al., 1986
4. Arnold el al , 1991
5. Jolianson el al., 1984
6. Bums & Cline, 1985
7. Dums el al., 1982
8 IIEC, 1977b
11. Saunders el al., 1993
12. LiinnoTech, 1993
13. Driscoll el al., 1990
14. CDM, 1992
17. LimnoTedt, 1985
18. Bodeen el al., 1989
19. WES, 1986
20. draft available
21. IIEC, 1979a
22. NEC, 1978
23. Jirka, 1992
24. I lydrosdence, 1971
25. l-ovelace, 1975
26. Ciim ic Lovelace, 1973
27. Onishi, 1981
28. Thomas & McAnally, 1985
30. CEAM BBS
Key lo Reviews
a. U S. EPA, 1992
b. Donigian & f luber, 1991
c. WPCF, 1989
d. McKeon Si Segna, 1987
3-30
-------
iH»a»lummu»«mig>tf«mM.aoc: 1
NEW TECHNOLOGY:
Geo-WAMS
Geo-WAMS (Geographically-based
Watershed Analysis and Modeling
System) is a modeling support system
being developed with U.S. EPA funding.
Key features include scenario
management, aids for input deck
development, linkage of disparate
models, and close coupling with
Geographic Information Systems (GISs).
The prototype application of Geo-WAMS
links a simple loading model and WASP
to evaluate dissolved oxygen problems in
the Buffalo River. Scenario management,
database functions, and data visualization
are handled by the ARC/INFO GIS and by
custom programs written in C++.
TMDL Work shop
L77, Umno-Tech, Inc.
-------
'if if tifm ri>¦<>¦ P>jwiw» oac:2
Geo-WAMS Framework
The GIS acts as Geo-WAMS' data
repository, with all model inputs and
results stored in a "normalized" form.
GaographKatty-basoti
Data Management
System (C8MS)
GtS
with Support
Data Bases
Oata input
Display output
Data
Management
Interface
(DM1)
Ecosystem I
Medea
fyVmoiSg
:ai>LSOffacn^ittta
^Contaminant'?:
,»r.Expoaute-^rr
feTrw»p«t£S
;i°J3*a.Tw..
Ecosystem
Food Wee
Model
T M D L Workshop
L77, Umno-Tech, Inc.
-------
LINKAGE BETWEEN NPS
AND RECEIVING WATER
MODELS
Need Consistency in:
Parameters
Temporal Resolution
Spatial Domain
Level of Complexity
Nonpoint
Source
Model
0
u
T
P
u
T
Receiving Water
Model
TMPL Workshop
LTI, LimnO'Tech, Inc.
-------
NPS/WATER QUALITY MODEL LINKAGES
NPS Model Output
Water Quality Model Inputs
Flow, concentration,
and/or load
Parameters:
Must match
Flow, concentration, or load
Annual average, single storm,
continuous
Time Scale:
Must be consistent
Steady state or dynamic
Watershed outlet
Location:
Potentially many
NPS runs
Each input
Single number or complex
format
Format:
Complex formats
don't match
Single number or complex
format
T M D L Workshop
LTI, Umno-Tech, Inc.
-------
NPS/WATER QUALITY
MODEL LINKAGE
SILVER CREEK, ARIZONA
W.Q. Objective: TN Annual Average Basis
NPS Model: USLE Model for Entire Watershed
W.Q. Model: C = W/Q
Linkage:
Spreadsheet
SAN LUIS OBISPO CREEK, CALIFORNIA
W.Q. Objective: TP Annual Average Basis
NPS Model: USLE by Watershed
W.Q. Model: C = W/Q eikt
Linkage: Spreadsheet
PARADISE CREEK, IDAHO - WASHINGTON
W.Q. Objective: TN on Seasonal Basis
NPS Model: Modified USLE for Watershed
W.Q. Model: C = W/Q
Linkage:
Spreadsheet
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
NPS/WATER QUALITY
MODEL LINKAGE
WILLAMETTE RIVER, OREGON
W.Q. Objective:
NPS Model:
W.Q. Model:
Linkage:
Bacteria, metals
SWMM
C = W/Q e'kl
Process program
SAGINAW BAY, MICHIGAN
W.Q. Objective: Blue Green Algae
NPS Model: ANSWER, Empirical Model by Basin
W.Q. Model: WASP
Linkage: Manual conversion of NPS outputs into
WASP format.
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
REFORMATTING NPS
OUTPUTS
SWMM OUTPUT
Junction 7 Junction 8
Time Flow Cone. Load Flow Cone. Load
0.0 — — 0.0 — — 0.0
1.0 — — 63.4 — — 12.9
2.0 — — 58.6 — — 15.6
3.0 — — 46.1 — — 16.8
4.0 — — 29.2 — — 14.3
5.0 — — 18.6 — ~ 12.1
6.0 — — 12.1 — — 3.0
7.0 — — 0.0 — — 0.0
WASP INPUT
5 (Number of segments receiving loads)
1.0 1.0 (Scale and unit converse factors)
5 (Model segment)
8 (Number of points in series)
58.6 3.0 46.1 4.0 29.2
0.0
O.O(Time) O.O(Load) 1.0(Time) 63.4(Load) 2.0
5.0 18.6 6.0 12.1 7.0
7 (Model segment)
8 (Number of points in series)
O.O(Time) O.O(Load) 1.0(Time) 12.9(Load) 2.0 15.6 3.0 16.8 4.0 14.3
5.0 12.1 6.0 3.0 7.0 0.0
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
HANDS-ON RECEIVING
WATER MODELING
MULTI-SMP/SMPT0X3
QUAL-2E
WASP
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
MULTI-SMP
What is MULTI-SMP?
Computerized version of EPA's Simplified
Analytical Method (Simplified Method
Program) for multiple discharge situations
Steady-state model of dissolved oxygen and
ammonia toxicity for rivers
Processes Contained in SMP
Reaeration
Deoxygenation of CBOD
Nitrification of Ammonia
Net photosynthesis
Sediment Oxygen Demand
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
MULTI-SMP INPUT
REQUIREMENTS
~ General Information
~ Upstream Parameters
Flow
Temperature
D.O.
BOD5
~ Effluent Parameters
Flow
Temperature
D.O.
BOD5
~ Reach Parameters
Number of Reaches
Velocity
Depth
Temperature
~ Observed River Data
Location
BOD
CBODU.BOD5
PH
Ammonia
Alkalinity
CBODu.BOD5
PH
Ammonia
Alkalinity
River Mile
BOD Decay Rate
SOD
Reaeration Rate
(or formulation)
D.O.
NH3
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Big River Example
Parameters for Discharge 1
Comments
Flow
(tIGD)
20.08
Observed
Temperature
(*C)
15.00
Observed
Dissolved Oxygen
(mg/1)
4.00
Observed
5-Day BOD
(mg/1)
40.00
Observed
Ult. CBOD ~ 5-Day BOD
1.50
Observed
PH
(su)
7.00
Observed
Ammonia
Cng^l)
10. B0
Observed
Alkalinity
Cmg/l)
100.00
Observed
Beginning of Reach Number
1
Observed
Name of Discharger
UUTP A
Use PgUp and PgDn to get other discharges
—Fl-Upstream F2-Ef f1uent F3-Reach F4-0bserwed F5-1nfo F10-Qu it Inpu t-
Big River Example
Parameters for Reach 1
Comments
Length
(mile)
3.00
Observed
Uelocity
(fps)
0.30
Observed
S lope
(ft/mile)
0.08
Not used
Average Depth
(ft)
9.00
Observed
Temperature
CO
21.11
Calculated
BOD Removal Rate
(1/day)
0.00
Cal ibrated
HH3 Decay Rate
(1/day)
0.00
Cal ibrated
Sed intent Oxygen Demand (g/m'/day)
0.00
Calibrated
Photosynthes is/resp irat ion (ng/L/day)
0.00
Use PgUp and PgDn to get other reaches
—Fl-Upstream F2—Eff luent F3-Heach F4—Observed F5-Info F 10-Quit Input—
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
NULTISNP 2.0 Main Menu
(11
Read inputs from file
[21
Saue inputs to f ile
[3]
Enter or modify model inputs
C4 J
Run model simulation
[51
View tabular output
[61
Uieu graphics output
[71
Print results
[81
Quit
Hake selection and press [Return]
Big Riuer Example
Run information screen
Name of receiving stream
Number of discharges (max = 10)
Number of reaches (max = 10)
Reaeration type (0, T, (1)
Run title for screen display
Graphics printer type (HP, FX, LQ, None)
Printed graph resolution (Lowj Ned, High)
BIG RIUER
2
4
0
Big Riuer Example
HP LaserJet
Nedium resolution
-Fl-Upstream F2-Eff luent F3-Reach F4-0bserved FS- Info F10-Quit Input-
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Big
Riuer Example
OBSERVED RIUER DATA
River Mile
DO (ng/L)
BOD (mg/L)
NH3 (mg/L)
1
43.50
5.70
15.20
2.40
2
38.00
4.20
13.20
2.20
3
34.00
4.00
11.78
2.10
4
30.00
3.80
17.60
3.20
5
25.00
3.60
15.60
3.00
6
20.00
3.60
13.90
2.80
7
15.00
3.40
12.10
2.60
8
9.00
3.35
10.10
2.40
9
5.00
3.50
8.90
2.30
10
11
12
13
14
15
—Fl-Upstream F2-Ef f luent F3-Reach F4-0bserwed F5- Info F10-Quit Input—
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
MULTI-SMP ILLUSTRATIVE
EXAMPLE (BIGRIVER.SMP)
Sit* Schematic of the Big River:
WWTPa WWTPb
I . L
River Mile 45
Effluent Characteristics:
Upstream WWTPa WWTPb
MGD
Flow
100 0 cfs
20 0 MGD
15 0
Tem p. C
23 0
15 0
15 0
D 0 . mg/L
7 0
4 0
4 0
BODS. mg/L
1 0
40 0
40 0
BODu. mg/L
2 0
60 0
60 0
pH
8 0
8 0
8 0
NH3-N. mg/L
0 1
10 0
10 0
Alk. mg/L
100 0
100 0
100 0
Field Survey Data:
River Mile Depth,
. ft.
Velocity, fps D.O.,
mg/L CBOO,
mg/L
NH1N, mg/L
43 5
9 0
0 30
5 70
15 2
2 4
38 0
5 0
0.20
4 20
13 2
2 2
34 0
5 0
0 20
4 00
11 7
2 1
30 0
5 0
0.25
3 80
17 6
3 2
25 0
5 0
0 25
3 60
15 6
3 0
20 0
5 0
0 25
3 60
13 9
2.8
15 0
6 0
0 20
3 40
12 1
2 6
9 0
6 0
0 20
3 35
10 1
2 4
5 0
6.0
0 20
3 50
8 9
2 3
Modeling Tasks:
1) Calibrate CBOD decay rate by
a) Specify CBOD decay rate for every river reach.
b) Compare simulation to observed data.
c). Adjust CBOD decay rate.
d) Go to step b) until satisfactory agreement is reached
2) Calibrate NH3-N in similar way
3) Calibrate 0 O
a) Select Reaeration calculation option (O is currently selected).
b) Specify SOD values for each reach.
c) Compare simulation to observed D O data.
4) Waste Load Allocation by Cutting Outflow CBOD and NH3-N. Assuming
a) Standard for D O is 6 0 mg/L. 'or un-ionized ammonia is 0 02 mg/L.
b) Both dischargers treat to the same concentration
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SMPTOX3
WHAT IS SMPT0X3?
Simplified Method Program for Toxics
Three different levels of complexity
1) Total pollutant in water column
2) Dissolved and sorbed pollutant in water column
3) Dissolved and sorbed pollutant in water column
and bed sediments
Steady-State River model for toxics
PROCESSES CONTAINED IN SMPTOX3
Solids settling and resuspension
Toxicant partitioning to solids
Toxicant fate processes
Volatilization
Photolysis
Other
T M D I Workshop
LTI, Limno-Tech, Inc.
-------
SMPTOX3 MODEL
FRAMEWORK
Conceptual Framework
Toxic Chemical Model
Atmosphere |i k
Photolysi:
Photolysis
Koc
Decay
Decay
Water Column
a>
Decay
Decay
Koc
Active Sediment
Deep Sediment
Net Sedimentation
Chemical Dissolvec
in Pore Water
(ug/l)
Chemical on
Particulate Organic
Carbon
(ug/kg O.C)
Chemical in
Dissolved Phase
(ug/l)
Chemical on
Particulate Organic
Carbon
(ug/kg O.C)
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SMPTOX3 INPUT
REQUIREMENTS
ALL LEVELS
Number of loading sources
Number of stream reaches
Effluent flow
Effluent concentration
Observed pollutant concentration
LEVEL 2
Upstream solids Effluent solids
Partition coefficient
Observed suspended solids
Observed dissolved pollutant concentration
Observed total pollutant concentration
LEVEL 3
Bed solids concentration Active sediment depth
Solids resuspension velocity Diffusive exchange rate
Reach length, velocity
Upstream flow
Upstream concentration
Decay Rate
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
SMPTOX3 ILLUSTRATIVE
EXAMPLE (phase2 .st3)
Site Schematic of Two Faced Brook:
Outfall 1 outfall 2
I I
River Mile: M 6 39 9 27„ 170
Effluent Characteristics:
Upstreani Outfall 1 Outfall 2
Flow 111.0 cfs 23.2 MGD 19.1 MGD
SS 13.5 mg/L 24.0 mg/L 39 5 mg/L
Total Cone. 10.0 ug/L 38.0 ug/L 80.0 ug/L
Field Survey Data:
River Mile Depth, ft.
44 7 1.48
43 8 1.48
41.2 1.48
38.0 2.1
32.7 2.1
27.0
22.0 1.54
Velocity, fps SS, mg/L
0.59 13 5
0 59 13
0.59 13
0.69 14
0 69 14
19
0.82 17
Total C,ug/L Dissolved C,ug/L
10 9
10 9
8 7.5
12 11
14 13
15 14
22 20
Modeling Tasks:
1) At complexity Level 1
a). Specify total loss rate and calibrate total concentration to observed data.
b). Perform waste load allocation assuming.
-Instream standard is 10.5 ug/L;
-Both dischargers will treat to the same concentration.
2). At complexity Level 2
a). Increase complexity to level 2
b). Specify SS settling rate and calibrate SS to observed data;
c). Specify decay rate and calibrate total concentration to observed data;
d) Select partition coefficient and calibrate both dissolved and particulate
concentrations to observed data;
e) Perform waste load allocation assuming.
-Instream standard for total is 10 5 ug/L, and
-Both dischargers will treat to the same outflow concentration.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
QUAL2E CAPABILITIES
SIMULATES 15 WATER QUALITY CONSTITUENTS
Dissolved oxygen
Temperature
Nitrogen (4 compartments)
Organic, Ammonia, Nitrite, and Nitrate
Phosphorus (2 compartments)
Organic and Dissolved
- Algae as Chlorophyll a
- Arbitrary Non-conservative
- CBOD (ULT or 5-DAY)
- Conservative Minerals (3)
- Coliform Bacteria
MODELS DENDRITIC STREAM SYSTEMS
- Tributary Streams
- Junctions
ACCEPTS MULTIPLE
LOADS
- Point Discharges
- Distributed Loads/Losses
- Unsimulated Tributary loads
- Withdrawals
TRIBUTARIES
MAIN STREAM
COMPUTES FLOW AUGMENTATION FOR DO
CONTROL
SIMULATES STEADY STATE OR
DIURNAL WATER QUALITY RESPONSES
-------
TYPICAL QUAL2E USES
• STREAM ASSIMILATIVE CAPACITY
• WASTELOAD ALLOCATION STUDIES
- Point Source
- Non-Point Source
• DIURNAL RESPONSE TO CLIMATOLOGY
- Temperature
- Algae
-DO
QUAL2E LIMITATIONS/CONSTRAINTS
• INPUT FLOWS AND LOADS ARE CONSTANT
OVER TIME
• FLOWS ARE STEADY OVER TIME
• STREAM WELL MIXED VERTICALLY AND
LATERALLY (One-Dimensional System)
-------
QUAL2EU SCHEMATIC
NH
ORG-P
DIS-P
ORG-N
NO
NO
CBOD
SOD
ALGAE
Chla
Atmospheric
Reaeratlon
-------
QUAL2E INPUT DATA REQUIREMENTS
WATER QUALITY CONSTITUENTS FOR SIMULATION
DEFINITION OF STREAM NETWORK
- Reaches
- Headwaters
- Junctions
- Downstream Boundary
HYDRAULIC CHARACTERIZATION
- Flow
- Velocity
- Depth
FORCING FUNCTIONS (FLOW AND QUALITY)
- Wasteloads, Withdrawals
- Unsimulated Tributaries
- Incremental Inputs/Withdrawals
- Headwaters
OXYGEN DEMAND COEFFICIENTS
- Bioxidation, Settling/Scour
- Reaeration, SOD
NUTRIENT COEFFICIENTS
- Nitrification rate coefficients
- Stoichiometric and hydrolysis coefficients
- Sediment interactions
ALGAL COEFFICIENTS
- Maximum specific growth rate
- Oxygen production and respiration rates
- Saturation coefficients for light and nutrients
- Coefficients for algal composition
- Light extinction
TEMPERATURE SIMULATION
- Geographical data
- Evaporation coefficients
- Local climatology 1.7
-------
Most Upstream
Point
Junction
Junct ion
Reach
Number
Computational—/[
Element Number
CONCEPTUAL REPRESENTATION
STREAM NETWORK OF COMPUTATIONAL ELEMENTS AND REACHES
11-5
-------
SALT2.02I: QUAL-2E input deck for Salt Creek - 3 August 1988 survey
T1TLE01
TITLE02
T1TLE03 HO
TITLE04 NO
TITLE05 NO
TITLE06 MO
TITLE07 YES
TITLE08 NO
TITLE09 YES
TITLE10 NO
TITLE11 YES
TI TLE12 MO
TITLE13 YES
TITLE14 HO
TITIE15 HO
ENDT1TLE
NO LIST DATA INPUT
WRITE OPTIONAL SUMMARY
NO FLOW AUGMENTATION
STEADY STATE
NO TRAPEZOIDAL X-SECT IONS
NO PRINT SOIAR/LCO DATA
PLOT DO AND 300
FIXED DOWNSTREAM CONC
INPUT METRIC
NUMBER OF REACHES =
MUM OF HEADWATERS =
TIME STEP (HOURS)
MAXIMUM ITERATIONS =
LATITUDE OF BASIN (DEG) =
STANDARD MERIDIAN (DEG) =
EVAP. COEFF. (AE)
ELEV. OF 8ASIN (ELEV) =
ENDATA1
0 UPTAKE BY NH3 0X1D(MG O/MG N) =
0 PROD BY ALGAE (MG O/MG A) =
N CONTENT OF ALGAE CMG N/MG A) =
ALG MAX SPEC GROWTH RATE(1/0AT)=
N HALF SATURATION CONST (MG/L) =
LIN ALG SHADE CO (1/H-UGCHA/L) =
LIGHT FUNCTION OPTION (LFNOPT) =
DAILY AVERAGING OPTION (LAVOPT)s
NUMBER OF DAYLIGHT HOURS (DIN) =
ALGY GROWTH CALC OPTION(LGROPT)=
ALG/TEMP SOLR RAD FACTOR(TFACT)»
ENDATA1A
EN0ATA1B
SALT CREEK
THERESA ST VIUTP ANO NORTH EAST UWTP
CONSERVATIVE MINERAL I
CONSERVATIVE MINERAL II
CONSERVATIVE MINERAL MI
TEMPERATURE
BIOCHEMICAL OXYGEN DEMAND (ULTIMATE)
ALGAE AS CHL-A IN UG/L
PHOSPHORUS CYCLE AS P IN MG/L
(0RGAN1C-P; DISSOLVED-?)
NITROGEN CYCLE AS N IN MG/L
(ORGANIC-N; AMMONIA-N; MITRITE-N; NITRATE-N);
DISSOLVED OXYGEN IN MG/L
FECAL COL I FORMS IN NO./TOO ML
ARBITRARY NON-CONSERVATIVE
0.0
0.0
11.0
1.0
0.0
30.
40.90
97.3660
0.000680
1200.
3.430
0.180
0.020
0.0
0.30
0.0
2.0
2.0
16.0
2.0
1.0
5-0 ULT 300 CONV K COEF = 0.0
OUTPUT METRIC 0.0
NUMBER OF JUNCTIONS = 0.0
NUMBER OF POINT LOADS = 2.0
LNTH COUP ELEMENT (DX) = 0.10
TIME INC. FOR RPT2 (HRS)= 1.0
LONGITUDE OF SASIN (DEC)' 96.550
DAY OF YEAR START TIME = 200.
EVAP. COEFF. (BE) = 0.000270
" DUST ATTENUATION COEF. = 0.10
0 UPTAKE BY N02 OXIDCMG O/MG N)= 1. 140
0 UPTAKE BY ALGAE (MG O/MG A} = 0.160
P CONTENT OF ALGAE (MG P/MG A) = 0.0020
ALGAE RESPIRATION RATE (1/DAY) = 0.20
P HALF SATURATION CONST (MG/L) = 0.010
NL1N SHADE (1-H-(UGCHA/L)**2/3)= 0.0
LIGHT SATURATION COEF (INT/MIN)= 0.170
LIGHT AVERAGING FACTOR (AFACT) = 1.0
TOTAL DAILY SOLAR RAOTN (INT) = 2500.
ALGAL PREF FOR NH3-M (PREFN) = 0.80
NITRIFICATION INHIBITION COEF = 10.
STREAM
REACH
1.0RCH=SALT
CREEK
FROM
15.90
TO
15.30
STREAM
REACH
2.0RCH=SALT
CREEK
FROM
15.30
TO
14.0
STREAM
REACH
3.0RCH=SALT
CREEK
FROM
14.0
TO
12.60
STREAM
REACH
4.0RCH=SALT
CREEK
FROM
12.60
TO
11.0
STREAM
REACH
5.0RCH=SAlT
CREEK
FROM
11.0
TO
10.
STREAM
REACH
6.0RCH=SALT
CREEK
FROM
10.
TO
9.20
STREAM
REACH
7.0RCH=SALT
CREEK
FROM
9.20
TO
8.60
STREAM
REACH
fl.ORCH=SALT
CREEK
FROM
8.60
TO
7.20
STREAM
REACH
9.0RCH=SALT
CREEK
FROM
7.20
TO
6.10
STREAM
REACH
10.0RCH=SALT
CREEK
FROM
6.10
TO
5.0
STREAM
REACH
11.0RCH=SALT
CREEK
FROM
5.0
TO
3.40
EKDATA2
-------
SALT2.Q2I: QUAL-2E input deck for Salt Creek - 3 August 1988 survey (continued)
FLOW AUGMT
SOURCES
RCHs i.o
0. 0.0
FLOW AUGMT
SOURCES
RCH» 2.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 3.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= <».0
0. 0.0
FLOU AUGMT
SOURCES
RCH= 5.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 6.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 7.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 8.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 9.0
0. 0.0
FLOW AUGMT
SOURCES
RCHs 10.0
0. 0.0
FLOW AUGMT
SOURCES
RCH= 11.0
0. 0.0
ENDATA3
FLAG FIELD
RCH=
1.0
6.
1.6.2.2.2.2.
FLAG FIELD
RCH=
2.0
13.
2.2.2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
3.0
14.
2.2.2.2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
4.0
16.
2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
5.0
10.
2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
6.0
8.
2.2.6.2.2.2.2.2.
FLAG FIELD
RCH=
7.0
6.
2.2.2.2.2.2.
FLAG FIELD
RCH=
S.O
14.
2.2.2.2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
9.0
11.
2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
10.0
11.
2.2.2.2.2.2.2.2.2.2.2.
FLAG FIELD
RCH=
11.0
'S.
2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.5.
ENDATA4
HYDRAULICS
RCHs
1.0
383.
0.180 0.40 0.120 0.410
0.0250
HYDRAULICS
RCH=
2.0
295.0
0.190 0.40 0.160 0.410
0.0250
HYDRAULICS
RCHs
3.0
295.0
0.180 0.40 0.1570 0.410
0.0250
HYDRAULICS
RCHs
4.0
360.
0.190 0.380 0.160 0.410
0.0250
HYDRAULICS
RCH=
5.0
142.0
0.190 0.380 0.1230 0.410
0.0250
HYDRAULICS
RCHs
6.0
295.0
0.180 0.40 0.120 0.410
0.0250
HYORAULICS
RCHs
7.0
295.0
0.170 0.40 0.110 0.410
0.0250
HYDRAULICS
RCH=
8.0
295.0
0.180 0.390 0.110 0.410
0.0250
HYDRAULICS
RCH»
9.0
295.0
0.190 0.380 0.160 0.410
0.0250
HYDRAULICS
RCH=
10.0
295.0
0.190 0.380 0.160 0.410
0.0250
HYORAULICS
RCH«
11.0
295.0
0.160 0.350 0.220 0.450
0.0250
ENOATAS
REACT COEF RCH
s 1.0
0.020
0.0
0.0 3.0
0.0
0.0
0.0
REACT COEF RCH
= 2.0
0.020
0.0
0.0 3.0
0.0
0.0
0.0
REACT COEF RCH
= 3.0
0.020
0.0
-1.0 3.0
0.0
0.0
0.0
REACT COEF RCH
= 4.0
0.020
0.0
-1.0 3.0
0.0
0.0
0.0
REACT COEF RCHs 5.0
0.020
0.0
-1.50 3.0
0.0
0.0
0.0
REACT COEF RCHs 6.0
0.020
0.0
1.0 3.0
0.0
0.0
0.0
REACT COEF RCH
M
O
0.020
0.0
0.0 3.0
0.0
0.0
0.0
REACT COEF RCH
s 8.0
0.020
0.0
-0.50 3.0
0.0
0.0
0.0
REACT COEF RCH'
= 9.0
0.020
0.0
-0.50 3.0
0.0
0.0
0.0
REACT COEF RCH» 10.0
0.020
0.0
-0.50 3.0
0.0
0.0
0.0
REACT COEF RCHs n.o
0.020
0.0
-0.50 3.0
0.0
0.0
0.0
ENDATA6
N AND P COEF
RCH*
1.0 1.0
5.0
0.30 •
•60.
0.0
0.0
0.10
0.0
N AND P COEF
RCHs
2.0 0.80
0.0
0.30 •
-60.
0.0
0.0
0.10
0.0
N AND P COEF
RCHs
3.0 0.60
0.0
0.30
¦60.
0.0
0.0
0.10
0.0
N ANO P COEF
RCHs
4.0 0.40
0.0
0.30 -
¦60.
0.0
0.0
0.10
0.0
N ANO P COEF
RCHs
5.0 0.40
0.0
0.30 ¦
•60.
0.0
0.0
0.10
0.0
N AND P COEF
RCHs
6.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
N AND P COEF
RCHs
7.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
N ANO P COEF
RCHs
8.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
M AND P COEF
RCHs
9.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
N AND P COEF
RCHs
10.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
N AND P COEF
RCHs '
11.0 0.0
0.0
0.30
0.0
0.0
0.0
0.10
0.0
EN0ATA6A
-------
SALT2.Q2I: QUAL-2E input deck for Salt Creek - 3 August 1988 survey (continued)
ALG/OTHER COEF
RCH=
1.0
ALG/OTHER COEF
RCH=
2.0
ALG/OTHER COEF
RCH=
3.0
ALG/OTHER COEF
RCH»
4.0
ALG/OTHER COEF
RCH=
5.0
ALG/OTHER COEF
RCH=
6.0
ALG/OTHER COEF
RCH=
7.0
ALG/OTHER COEF
RCH=
8.0
ALG/OTHER COEF
RCH=
9.0
ALG/OTHER COEF
RCH=
10.0
ALG/OTHER COEF
RCH=
11.0
ENDATA6B
INITIAL COND-1
RCH=
1.0
INITIAL CONO-1
RCH=
2.0
INITIAL CONO-1
RCH=
3.0
INITIAL CONO-1
RCH=
4.0
INITIAL CONO-1
RCH=
5.0
INITIAL CONO-1
RCH=
6.0
INITIAL CONO-1
RCH=
7.0
INITIAL CONO-1
RCH=
S.O
INITIAL COND-1
RCH=
9.0
INITIAL CONO-1
RCH=
10.0
INITIAL CONO-1
RCH=
11.0
ENDATA7
INITIAL COND-2
RCH=
1.0
INITIAL COND-2
RCH=
2.0
INITIAL COND-2
RCH=
3.0
INITIAL COND-2
RCH=
4.0
INITIAL COND-2
RCH»
5.0
INITIAL COND-2
RCH=
6.0
INITIAL COND-2
RCH=
7.0
INITIAL COND-2
RCH«
8.0
INITIAL COND-2
RCH=
9.0
INITIAL COND-2
RCH=
10.0
INITIAL COND-2
RCH=
11.0
ENDATA7A
I NCR INFLOU-1
RCH»
1.0
INCR INFLOU-1
RCH=
2.0
INCH INFLOW-1
RCH=
3.0
I NCR INFLOW-1
RCH=
4.0
I NCR INFLOW-1
RCH»
5.0
I NCR INFLOW-1
RCH»
6.0
I NCR INFLOW-1
RCH*
7.0
I NCR INFLOU-1
RCH*
8.0
I NCR INFLOU-1
RCH=
9.0
INCR INFLOW-1
RCHs
10.0
INCR INFLOW-1
RCH»
11.0
ENDATA8
I NCR INFLOW-2
RCH»
1.0
INCR INFLOU-2
RCH®
2.0
I NCR INFLOU-2
RCH»
3.0
I NCR INFLOW-2
RCH»
4.0
I NCR INFLOW-2
RCH=
5.0
I NCR INFLOW-2
RCH=
6.0
I NCR INFLOW-2
RCH»
7.0
I NCR INFLOW-2
RCH-
8.0
I NCR INFLOW-2
RCH=
9.0
INCR INFLOW-2
RCH=
10.0
I NCR INFLOU-2
RCHs
11.0
ENDATA8A
ENDATA9
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
1.0
0.0
0.050
0.0
0.0
0.0
0.0
o
CO
7.50
8.0
0.0
0.0
0.0
0.0
0.0
82.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
87.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
91.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
91.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
83.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
81.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
84.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
84.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
84.0
7.50
8.0
0.0
0.0
0.0
0.0
0.0
90.
7.50
8.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
1.0
1.0
0.0
1.40
0.10
3.0
0.0
0.0
0.0
0.0
0.
.0 0.
,0 0.
.0
0.0
0.0
11.0
0.0
0.0
0.0
0.
.0 0.
o
o
.0
0.0
0.0
2.0
0.0
0.0
0.0
0.
.0 0.
o
o
.0
0.0
0.0
4.0
0.0
0.0
0.0
0.
.0 0.
o
o
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.
.0 0.
o
o
,0
0.0
0.0
3.0
0.0
0.0
0.0
0.
o
o
o
o
.0
0.0
0.0
3.0
0.0
0.0
0.0
0.
.0 0.
o
o
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.
o
o
,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
o
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.
,0 0.
o
o
.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
0.0
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
0.0
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
0.0
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
-------
SALT2.Q2I: QUAL-2E input deck for Salt Creek - 3 August 1988 survey (continued)
HEADVTR-1 HDU= 1.0SALT CREEC 41.0 82.0 7.50 8.0 0.0 0.0 0.0
ENDATA10
HEADWTR-2 HDW= 1.0 0.0 0.0 0.0 1.20 0.30 0.0 0.40 0.0 0.20
ENDATA10A
POINTLD-1 PTL= 1.0THERESA :T 0.0 37.0 77.0 7.50 24.0 0.0 0.0 0.0
POINTLD-1 PTL= 2.0NORTH EAST 0.0 8.0 78.0 7.70 15.0 0.0 0.0 0.0
EN0ATA11
POINTLD-2 PTL= 1.0 0.0 0.0 0.0 3.0 8.0 0.0 4.0 0.70 7.0
POINTLD-2 PTl= 2.0 0.0 0.0 0.0 0.20 14.0 0.0 1.50 0.20 9.80
EN0ATA11A
ENDATA12
DOWNSTREAM BOUNDARY-1 88.0 11.50 9.0 0.0 0.0 0.0 0.0 0.0
ENDATA13
DOWNSTREAM BOUNOARY-2 0.0 1.40 1.10 0.0 1.20 0.10 1.80
EN0ATA13
LOCAL CLIMATOLOGYA 2500.00 0.010 98.0 97.0 30. 3.0
BEGIN RCH 1
PLOT RCH 1 2 3 4 5 6 7 8 9 10 11
DATA
SALT2.Q2P: QUAL-2E PLOT DATA DECK FOR SALT CREEK
DATA
Observed 8/3/88 data for SALT CREEK
11
8
0 0
0 0
1 0
1 1
1 1
1 1
1 0
0
BOO CHLA
ORGP D1SP
ORGN AMMN
NUN NIAN
DO
MG/L uG/L
MG/L MG/L
MG/L MG/L
MG/L MG/L
MG/L
15.900
8.000
0.001
0.200
1.200
0.300
0.001
0.400
7.500
15.300
15.000
0.001
3.900
3.500
3.900
0.001
1.900
8.300
14.000
14.000
0.300
3.200
1.200
3.300
0.001
1.700
8.300
12.600
15.000
0.600
3.100
1.200
2.600
0.001
1.600
9.900
11.000
12.000
0.100
2.700
1.000
1.800
0.001
1.400
13.700
10.000
20.000
0.100
2.300
1.000
1.400
0.001
1.400
12.600
10.000
9.000
0.700
2.600
1.000
3.000
0.001
0.900
6.900
9.200
12.000
0.031
4.800
2.100
4.300
0.001
1.200
6.700
8.600
12.000
0.200
3.700
1.100
3.700
0.001
1.200
7.700
7.200
12.000
0.330
3.200
1.100
3.400
0.001
1.200
8.800
5.000
12.000
0.430
2.900
1.200
2.800
0.001
1.400
9.700
3.500
13.000
0.0-31
3.000
1.800
2.200
0.001
1.500
11.200
-------
RUNNING QUAL2E
AQIJAI ?
Edits input file
Use F2 key to proceed to next screen
Use F4 key to back-up to previous screen
Choose "EDIT on existing QUAL2E or QUAL2E
data file"
Choose "WRITE current QUAL2E or QUAL
data to a file"
Choose "RETURN to operating system
command line" to exit
QUAL2E
Executes model
Q2PLOT
Saves results in plottable form
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
QUAL2E ILLUSTRATIVE
EXAMPLE (SMRIVER.DAT)
Site Schematic of Small River:
STP
River Mile: 30 25 20 15 10 5 0
Water Quality Standards:
D.O. > 5 mg/L; Un-ionized ammonia < 0.02 mg/L
Effluent Characteristics:
Upstream STP
Flow
100.0
cfs
11.6
cfs
CBODu
2.0
mg/L
80.0
mg/L
CBOD5
1.0
mg/L
40.0
mg/L
NH3-N
o.-
mg/L
15.0
mg/L
D.O.
8.2
mg/L
8.2
mg/L
Temperature
77
F
77
F
River Characteristics:
Area (ftA2) = 19.5 Q ;cfs)A0.6
Depth (ft) = 0.312 C (cfs)*0.S
Velocity (fps) = 0.0513 3 (cfs)A0.4
7Q10 = 30 Cfs
The steady state input data file includes: D.O., BOO, and NH3-N. It is calibrated to the field survey data.
Modeling Tasks:
1). Run the calibrated input data deck, check if water quality standards are violated.
2). Use the 7Q10 flow, assuming other conditions remain the same, and
check water quality violations again.
3). Conduct waste load allocation by imposing a percentage reduction
in STP effluent concentrations.
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
WASP4
WHAT IS WASP?
Water Quality Analysis Simulation Program
General purpose surface water quality modeling
system
FEATURES OF WASP
1,2 or 3-dimensional systems
Time-variable or steady-state solutions
Numerous state variables
Hydrodynamics
Dissolved oxygen
Eutrophication
Toxics
COMPONENTS OF WASP MODELING SYSTEM
Hydrodynamics model
DYNHYD
Water Quality Model(s)
EUTRO: Conventional Pollutants
TOXI: Toxic Pollutants
TMDL Workshop LTI, Limno-Tech, Inc.
-------
EXAMPLE WASP
SEGMENTATION
MODEL NETWORK
T M D L W o r k s h o p
LTI, Limno-Tech, Inc.
-------
Two Options for Specifying
Water Movement
Predict Flows with DYNHYD
fater Quality Input File
(without flows) .
Flow File
TOXI/EUTRO
Model
DYNHYD
Model
Predicted Water Quality
Specify Flows Manually
'ater Quality Input
(without flows)
TOXI/EUTRO
Model
Predicted Water Quality
L77, Umno-Tech, Inc.
-------
DYNHYD-HYDRODYNAM1C
MODEL
Predicts:
In Response To:
Water velocity and depth
Inflows, tides, bottom friction
initial conditions, wind, bed
slope
T M PL Workshop
LTI, Limno-Tech, Inc.
-------
DYNHYD DATA GROUPS
A: Simulation Control
Number of channels, number of junctions,
time step, beginning and end time
B: Printout Control
Printout internval, junctions to be printed
C: Hydraulic Summary
Parameters for setting up water quality model
input file
D: Junction Data
Initial head, surface area, bottom elevation,
channels entering junction
E: Channel Data
Length, width, depth, orientation, rougness,
mean velocity, connecting junctions
F: Inflow Data
Number of constant flows, flow values,
number of time variable flows, flow values
G: Seaward Boundary Data
Tide heights, frequency
H: Wind Data
Speed and direction
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Site Schematic of Branched Estuary:
City STP
Town WWTP
23
Village WPCP
Legends
21 ~ Boundary Junction for DYNHYD5 Only
4 Junction for DYNHYD5 and Segment for EUTR04
21
Schematic for Illustrative Branching Estuary
BRANCH2XLS.M2/B3-11.39AM 2^2 Tl, LiRlflO'TQCh, IflC.
-------
WS-DH4.INP DYNHYD4 Hypothetical Estuary
Dynamic Tidal Boundary Condition
****** Program Control Data**************************************************
23 22 0000 60. 0 1 0000 5 1200
****** Printout Control Data************************************************
24.0 4.0 3
1 14 19
****** summary Control Data*************************************************
1 4.0 1200 24.0 30 10
****** junction Data********************************************************
1 0.03 19.0E06 -07.0 1 20 0 0 0 0
2 0.04 17.0E06 -06.3 12 0 0 0 0
3 0.05 15.5E06 -05.5 2 3 0 0 0 0
4 0.06 14.5E06 -04.7 3 4 0 0 0 0
5 0.07 14.0E06 -04.7 4 5 0 0 0 0
6 0.08 13.5E06 -05.0 5 6 0 0 0 0
7 0.10 13.5E06 -05.7 6 7 0 0 0 0
8 0.12 11.0E06 -04.5 7 8 0 0 0 0
9 0.13 10.0E06 -03.5 8 9 15 0 0 0
10 0.15 9.0E06 -02.8 9 10 0 0 0 0
11 0.16 9.0E06 -02.2 10 11 0 0 0 0
12 0.17 8.5E06 -02.0 11 12 0 0 0 0
13 0.18 8.0E06 -02.0 12 13 0 0 0 0
14 0.19 8.0E06 -01.8 13 14 0 0 0 0
15 0.20 8.0e06 -01.5 14 21 0 0 0 0
16 0.13 8.0E06 -03.5 15 16 0 0 0 0
17 0.15 7.0E06 -02.7 16 17 0 0 0 0
18 0.15 6.5E06 -02.0 17 18 0 0 0 0
19 0.15 5.0E06 -01.2 18 19 0 0 0 0
20 0.15 4.0E06 -01.0 19 22 0 0 0 0
21 0.01 1.0E06 -07.0 20 0 0 0 0 0
22 0.22 1.0e06 -01.5 21 0 0 0 0 0
23 0.17 1.0E06 -01.0 22 0 0 0 0 0
******Channel Data *********************************************************
1 20000.0 1759. 6.7 180.0 0.040 0.0 1 2
2 20000.0 1627. 5.9 180.0 0.040 0.0 2 3
3 20000.0 1429. 5.3 180.0 0.040 0.0 3 4
4 20000.0 1429. 4.9 180.0 0.040 0.0 4 5
5 20000.0 1286. 4.9 180.0 0.040 0.0 5 6
6 20000.0 1388. 5.6 180.0 0.040 0.0 6 7
7 20000.0 1224. 4.9 180.0 0.040 0.0 7 8
8 20000.0 1000. 4.0 180.0 0.040 0.0 8 9
9 20000.0 952. 3.1 180.0 0.040 0.0 9 10
10 20000.0 900. 2.5 180.0 0.040 0.0 10 11
11 20000.0 837. 2.1 180.0 0.040 0.0 11 12
12 20000.0 800. 2.0 180.0 0.040 0.0 12 13
13 20000.0 842. 1.9 180.0 0.040 0.0 13 14
14 20000.0 727. 1.6 180.0 0.040 0.0 14 15
15 12500.0 520. 3.6 270.0 0.040 0.0 9 16
16 20000.0 362. 3.3 270.0 0.040 0.0 16 17
17 20000.0 538. 3.2 270.0 0.040 0.0 17 18
18 20000.0 462. 1.9 270.0 0.040 0.0 18 19
19 20000.0 381. 1.0 270.0 0.040 0.0 19 20
20 1000.0 1759. 6.7 180.0 0.040 0.0 1 21
21 1000.0 727. 1.6 180.0 0.040 0.0 15 22
22 1000.0 381. 1.0 270.0 0.040 0.0 20 23
* * * * * *constant Inflows************************************** ****************
5
5 -0.25 (Village WPCP)
12 -1.20 (City STP)
18 -0.50 (Town WWTP)
22 -20.00 (Upstream of Main Channel)
23 -5.00 (Upstream of Branch)
******Variable Inflows******************************************************
0
******Seaward Boundary Data************************************************«
-------
1
3 21 17 100 0 0. 0. 1.
1.0 0000 0.5 1.0 1200 -0.5 2.0 0000 0.5 2.0 1200 -0.5
3.0 0000 0.5 3.0 1200 -0.5 4.0 0000 0.5 4.0 1200 -0.5
5.0 0000 0.5 5.0 1200 -0.5 6.0 0000 0.5 6.0 1200 -0.5
7.0 0000 0.5 7.0 1200 -0.5 8.0 0000 0.5 8.0 1200 -0.5
9.0 0000 0.5
••••••Wind Data •*••*•*••••»****•*«**•••*•••••«*•*••*•**•**•*•*****•*•*•**•*••
0
•••••Junction Geometry Data •*••••••••••••*•••••*•«••••••••••«*•••••*•••*•
0
•••••Channel Geometry Data ***********************************************
0
•••••Evap/Precip. Data •*•**•*«•*••*••••*••••**•••*••••••*••••••••**••***•
0
•••••MAP TO WASP4 *******************************************************
1 23
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 0
22 0
23 0
-------
EUTRO STATE VARIABLE
INTERACTIONS
PHYT
CacP
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
TOXI4 STATE VARIABLE
INTERACTIONS
Up to 3 Classes of Solids
Different partitioning, settling for solids class
Up to 3 Separate Chemicals at One Time
Can simulate reactions between chemicals
Full Range of Kinetic Processes
Volatilization
Hydrolysis
Photolysis
Biodegradation
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
WASP4 DATA GROUPS
A: Model Options
Titles, Dynamic vs. Steady-State, number of State
Variables Model Time Step, printout intervals
B: Exchange Data
Dispersion across interfaces X-section area, lengths, sediment
numbers, dispersion coefficients
C: Volumes
Segment orientation, velocity and depth, relationship to flow
D: Flows
Advection of water or solids between segments, flow pattern,
magnitude
E: Boundary Concentrations
Concentration segment number for each system boundary
F: Loads - Loading rate, segment number
G: Parameters
Spatially variable environmental parameters, e.g. salinity,
light extinction, SOD
H: Constants e.g., rate coefficients
Note: TOXI4 requires specification of molecular weight,
whether used or not
I: Kinetic Time Functions
Specification of time variable environmental conditions
J: Initial Concentrations - Each segment, each parameter
-------
WS-EST.INP: 1-D Estuary for T0XI4
Salinity Tracer Simulation for T0XI4 with User Specified Flwo Field
NSEG NSYS ICRD MFLG IDHP NSLN INTY ADFC DD HHMM A: MODEL OPTIONS **«
.0
02
0 0
1
0
0 0.0
01 0000
1
1
2
3 4
7
10
1
0.200
100.0
1
10.00
200.0
0
0
System By-Passes (0=model, l=Bypass)
1
(Water Column Only) ***
B: DISP
COEFS **«
3
1
.000 1
.000
Number of Dispersion/Exchange Coefficients
2
Number
of interfaces/cross-sections
3000.
3000.
0
1
Seaward
Boundary
o
2500.
3000.
1
2
Mainstem Estuary
4
A
20.00
0.0
20.00
365.0
4
2000.
3000.
2
3
1500.
3000.
3
4
1300.
3000.
4
5
1000.
3000.
5
6
Z,
A
15.00
0.0
15.00
365.0
900.
3000.
6
7
700.
3000.
7
8
500.
3000.
8
9
o
300.
3000.
9
10
z
7.00
0.0
7.00
365.0
0
0
System
bypasses
for dispersive
exchanges
i
0
365.0
+
*
~ *
+ *
+ * +
*** c:
VOLUMES '
1.000
1.000
Volume(cu m) a
b
c
d
1
0
1
8.250E+06
0.00
0.00
2.50
0.00
2
0
1
6.750E+06
0.00
0.00
3.00
0.00
3
0
1
5.250E+06
0.00
0.00
5.50
0.00
4
0
1
4.200E+06
0.00
0.00
5.00
0.00
5
0
1
3.450E+06
0.00
0.00
5.50
0.00
6
0
1
2.350E+06
0.00
0.00
6.00
0.00
7
0
1
2.400E+06
0.00
0.00
5. 50
0.00
8
0
1
1.300E+06
0.00
0.00
3.00
0.00
9
0
1
1.200E+06
0.00
0.00
2.80
0.00
10
0
1
0.900E+06
0.00
0.00
2.50
0.00
i
3
* +
* +
* + *
*** D;
FLOWS «**
i
1
.0
1
.0
(water
column field)
. 1
1.0
0 10
1.0
10 9
1.0
9 8
1.0
8 7
1.0
7 6
1.0
6 5
1.0
5 4
1.0
4 3
1.0
3 2
1.0
2 1
1.0
1 0
L
20.8
0.0
20.8
365.0
0
1
.0
1
.0
(pore water field)
0
1
.0
1
.0
(solid field 1)
0
0
Bypass options for
flow transport
2
* * * *
System 1
- Salinity ***
*** E:
BCs ***
1.0
1.0
Scale and conversion factor
1
2
25.0
0.0
25.0
365.0
L0
2
0.1
0.0
0.1
365.0
2
* * * *
System 2
- Solidsl ***
1.0
1.0
Scale and
conversion factor
1
2
5.0
0.0
5.0
365.0
L0
2
1.0
0.0
1.0
365.0
-------
0
0
0
1
16
1
16
2
16
3
16
4
16
5
16
6
16
7
16
8
16
9
16
10
16
*
1
XC
0
+ *
1.0e-19
**** System 1 - Salinity *** F: LOADS ***
*•** system 2 - Solidsl ***
(«** NO NPS LOADS ***)
•Number of parameters ** G: PARAMETERS ***
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
~
0
1
2
111
0
20.0
8.0
4.0
1.0
5.0
3.0
2.0
1.0
0.0 MOLWT
Time Functions *
1.0
1.0
1.0
1.0
0.0
0.0
0.0
0.0
2
5
8
15.0
7.0
3.0
4.0
3.0
2.0
* * * *
H: CONSTANTS ***
81
0.0
1.0
1.0
1.0
2.0
0.0
0.0
0.0
59.0
+*** I:
1.0E03
TIME FUNCTIONS ***
*** Js INIT CONC «*«
3
6
9
1.0E03
10.0
6.0
2.0
3.0
2.0
1.0
1.0
1.0
1.0
0.0
0.0
0.0
-------
DYNHYD5/EUTR04
ILLUSTRATIVE EXAMPLE
(WS-DH4. IN P/WS-E4. IN P)
Description of the Problem:
A tidal branching estuary can be treated as one-dimensional. Three wastewater treatment
plants discharge into this system as shown in the schematic plot. DYNHYD5 and EUTR04
are set up to simulate this tidal system. There are 23 junctions for DYNHYD5,
and 21
segments for EUTR04. The three shaded junctions are pseudo junctions used
only by
DYNHYD5 to accommodate boundary conditions so that a proper hydraulic summary data
file can be generated for latter use by EUTR04.
DYNHYD5 simulation time step is 60 seconds, and EUTR04 time step is 30 time
the
DYNHYD5 time step, or 30 minutes.
Point Source Loads:
cms
mg/L
kg/day
Flow
NH3-N
CBOD
NH3-N
CBOD
Main Upstream
20
0.1
0.3
-
-
Branch Upstream
5
0.1
0.3
-
--
Village WPCP
0.25
40
80.0
864
1728
City STP
1.2
25
100.0
2592
10368
Town WWTP
0.5
55
80.0
2376
3456
Other Conditions:
High Tide: 0.5 meter Low Tide: -0.5 meter
Tidal Period: 24 hours
Constituents Simulated: BOD, NH3-N, DO, Salinity (OP in the model)
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
T0XI4 ILLUSTRATIVE EXAMPLE
WS-EST.INP
Site Schematic of an Estuary:
River km: 30
25 20 15 10
Description of Problem:
A straight section of estuary with a length of 30 km is evenly divided into 10 segments.
Net river flow of 20.8 mA3/sec is specified by the user in the input data file WS-EST.INP.
Salinity (chemical 1) and suspended solid are the two state variables being simulated.
No point or non-point loading exist in the system. The large dispersion coefficients include
the tidal mixing effects.
Modeling Tasks:
1). Run the WISP shell;
2). Select input data file WS-EST.INP, and review it;
3). Execute TOXI4, view simulation results via the post-processor option.
TOXU XLS a/22/93.1 SS PM
1011
LTI, Limno-Tech, Inc.
-------
8
-------
STATEWIDE SCREENING AND
TARGETING
-------
9
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BIO ACCUMULATION
INTRODUCTION
Why Bioaccumulation is a Concern
Low concentrations in water can lead to
dangerous concentrations in fish
Great Lakes Water Quality Initiative criteria
driven by bioaccumulation
Focus of this Workshop Segment
Describe principles of bioaccumulation
Present methods for calculating
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Bioconcentration
Exchange between organism and surrounding water
Bioaccumulation
Exchange between organism and all sources (food, water)
T M D L Workshop LTI, Umno-Tecl; "tc.
-------
Pollutants in a Simple Aquatic Food Chain
Pollutants
Bioconcentration
t
Water Column
Sorbed
Chemical
Koc
Dissolved
Chemical
Small Fish
Zooplankton
Phytoplankton
Large Fish
c
.O
(0
.6
8
<0
.o
CO
Bed Sediments
Sorbed
Chemical
Koc
Dissolved
Chemical
Benthic Organisms
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
BIOCONCENTRATION
FACTOR (BCF)
BCF = Concentration in Tissue (ug chemical/ g wet weight)
Concentration in Water (ug chemical/1)
BCF= Uptake Rate (l/day .g net weight)
Elimination Rate (1/day)
Conditions:
Steady State
Uptake only from water
Equations can be used to calculate BCF
from field or laboratory data
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
BIOACCUMULATION
FACTOR (BAF)
BAF = Concentration in Tissue (ug chemical/g wet weight)
Concentration in Water (ug chemical/1)
BAF = Uptake Rate
Elimination Rate
Conditions:
Steady State
Uptake from food and water
Equations can be used to calculate BAF
from field or laboratory data
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ESTIMATION OF BCF
For 7.6% Lipid Content in Fish:
log BCF = 0.79 log P - 0.4
Derived from 122 BCF values for 13
freshwater and saltwater fishes, r2 = 0.86.
95% confidence limit for predicting individual
BCF is one order of magnitude
95% confidence limit for predicting mean BCF
is 5%
Overestimate BCF for Log P>6.5
TMDL Workshop
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-------
Food Chain Model Framework
Trophic Level 4
Trophic Level 3
Trophic Level 1
Trophic Level 2
Large Fish
Small Fish
Zooplankton
Phytoplankton
Dissolved Water Concentration
TMDL Workshop
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-------
Food Chain Multiplier (FM)
Concentration in tissue due to all sources
Concentration in tissue due only to bioaccumulation
FM = BAF/BCF
FM« 1
FM^>1
L Workshop
L77, Umno-T ' inc.
-------
ESTIMATION OF FM*
Based upon modeling work of Thomann
Trophic Level
Log P
2
3
4
3.5
1.0
1.0
1.0
4.0
1.1
1.0
1.0
4.5
1.2
1.2
1.2
5.0
1.6
2.1
2.6
5.5
2.8
5.9
11
6.0
6.8
21
67
6.5*
19*
45*
100*
* For Log P>6.5, some uncertainty caused by
low bioavailability
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Measured BAFs at Trophic Level 4
10
9
8
Id
7
>
<1)
—i
6
LL
<
5
CD
O)
O
4
3
2
8
10
2 4 6
Log P
* Thomann, R.V., "Bioaccumulation Model of Organic Chemical Distribution
In Organic Food Chains", Environ. Sci. Technol., 1989, 23, 699-707.
TMDL Workshop LTI, Umno-Tech, Inc.
-------
LIPID NORMALIZATION
Although BCFs and BCFs are calculated using a
chemical per wet weight basis, scientific theory
assumes that chemicals are stored in lipid tissue.
Lipid content varies across fish, and within fish.
Bioconcentration calculations must consider fish lipid
content:
ug chemical/ g lipid
Lipid-normalized BCF =
ug chemical/1
Factoring lipid content into BCF calculations:
log BCF = 0.79 log P - 0.40 - log (7.6/% lipid)
TMDL Workshop
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-------
EXAMPLE CALCULATION OF
FISH TISSUE CONCENTRATION
Given:
Water column concentration = 10 ug/l
Log P = 5.0
Calculate:
Fish tissue concentration (assuming 3% lipid)
at the top of the food chain
| Fish Tissue Concentration = Water Concentration * BAF
log BCF = log P - 0.4 - log (7.6/3.0) = 3.146
BCF = 103.146 = 1400
FCM = 2.6
BAF = BCF * FCM = 1400 * 2.6 = 3640
Tissue Concentration = BAF * Water concentration
= 3640* 10
= 36,400 ug/kg (ppb)
= 36.4 mg/kg (ppm)
TMDL Workshop LTl, Limno-Tech, Inc.
-------
EXAMPLE CALCULATION OF
WATER QUALITY CRITERION
Given:
Fish lipid content = 7.6%
Desired tissue level = 3000 ug/kg
Log P = 5.5
Calculate:
Allowable water column concentration
1 Water Concentration = Fish Tissue Concentration/BAF
log BCF = 0.79 log P - 0.40 = 3.945
BCF = 103-945 = 8800
FCM = 11.4
BAF = BCF* FCM = 8800x11,4 = 10,000
Water concentration = Fish tissue/BAF
3000/10,000
= 0.3 ug/l
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
GREAT LAKES WATER
QUALITY INITIATIVE:
DIFFERENCES FROM
EXISTING GUIDANCE
Field measured BAF's take precedence
over calculations
Assumed lipid content of fish flesh:
5%: Human consumption
7.9%: Wildlife consumption
Food chain multiplier for super-lipophilic
compounds (log P>6.5) = 1.0
TMDL Workshop
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-------
METALS BIOAVAILABILITY
WHY DO WE CARE?
Metals exist in many forms in the environment.
Certain forms are more toxic than others.
Free ionic form is most toxic.
Site-specific toxicity varies greatly depending upon
phase distribution.
But what about sediments?
TMDL Workshop
LT7, Umno-Tech, Inc.
-------
HOW DO WE ASSESS IT
MONITORING
Go out and measure fraction dissolved
Describes bioavailability during monitoring conditions
May not help for future conditions
MODELING
Varying levels of complexity
Partition coefficients
Metals speciation
Accuracy of results proportional to quantity of data
T MPL Workshop
LTI, Limno-Tech, Inc.
-------
SPECIATION OF METALS IN
THE AQUATIC ENVIRONMENT
FREE ION
TOTAL
METAL
PRECIPITATES
SOLUBLE COMPLEXES
WITH ORGANIC LJGANDS
SOLUBLE COMPLEXES
WITH INORGANIC
L1GANDS
• ION EXCHANGE
ADSORPTION TO CLAYS. SILICATES.
OTHER MINERALS
ADSORPTION/COPRECIPITATION ON
HYDROUS IRON/MANGANESE OXIDES
ADSORPTION TO ORGANIC SOLIDS
ADSORBED SPECIES
TMDL Workshop
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-------
METHODS TO TRANSLATE
TOTAL RECOVERABLE
METALS TO DISSOLVED
METALS
1. Assume all metals are dissolved
Protective, but typically overly stringent
2. Use partition coefficient to estimate fraction
dissolved
Requires estimate of partition coefficient, TSS
3. Use metals speciation model (e.g., MINTEQ)
Requires detailed description of aquatic
chemistry
T M D L Workshop
LT7, Limno-Tech, Inc.
-------
USE OF PARTITION
COEFFICIENTS TO ESTIMATE
FRACTION DISSOLVED
Remember from earlier
c = ^
d 1+K •TSS
where
Cd = concentration of constituent in dissolved form
Cj = total concentration
Kp = partition coefficient (l/kg or l/mg)
TSS = total suspended solids (kg/I or mg/l)
Note that units for Kp and TSS must be consistent
(if Kp is l/kg, TSS must be kg/I), otherwise a unit
conversion factor must be used.
EPA(1993) memo mixes these units and adds a
conversion factor, i.e.
c = ^
4 1 + K TSS • 10'
p
assuming Kp in units of l/kg
TSS in units of mg/l
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
HISTORICAL EXAMINATION
OF METALS PARTITION
COEFFICIENTS
EPA(1984) Technical Guidance Manual conducted
statistical analysis to determine relationship between
percent dissolved and other environmental parameters.
Suspended solids was found to be the only statistically
significant parameter. Partition coefficient was found to
vary as a function of suspended solids.
where
TSS
a
K,
po
partition coefficient (l/kg)
partition coefficient, prior to solids correction
total suspended solids (mg/l)
metal specific regression coefficient
T M D L Workshop
L77, Limno-Tech, Inc.
-------
HISTORICAL EXAMINATION
OF METALS PARTITION
COEFFICIENTS
REGRESSION RESULTS FOR RIVERS
Metal
Kpo(l/kg)
a
r
n
Arsenic
0.48 x 10°
-0.7286
-0.993
1635
Cadmium
4.00 x 10s
-1.1307
-0.998
254
Chromium
3.36 x10b
-0.9304
-0.914
345
Copper
1.04 x 10B
-0.7436
-0.994
2722
Lead
0.31 x 106
-0.1856
-0.350
1545
Mercury
2.91 x 10B
-0.5719
-0.974
1394
Zinc
1.25 x10e
-0.7038
-0.995
2253
These results likely overpredict percent dissolved, due to
the sampling/laboratory practices in effect when
measurements were made.
TMDL Workshop
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-------
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
NICKEL IN STREAMS
T M D L W o r k shop
LT7, Umno-Tech, Inc.
-------
-L-(793)|J
1000
10 100
SUSPENOED SOLIOS (mg/l)
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
MERCURY IN STREAMS
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
I04
icr
io4
NO TE -
» POINT OMITTED OUE TO VERY HIGH
COEFFICIENT OF VARIATION.
J I ' ''''1
J 1 1 ' • ' '
' 1 ' 1 '
10 100
SUSPCNOED SOLIDS (mg/l)
1000
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
LEAD IN STREAMS
TMDL Work shop
L77, Limno-Tech, Inc.
-------
io4r
iq3 1 i i i *¦ i i ¦ I ! 1 ¦¦¦¦¦! ¦ i ¦ i ¦ t . i
i 10 100 1000
SUSPENOCD SOLIOS (mg/l)
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
COPPER IN STREAMS
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
10*
.5
10'
10*
10
I 00
lOOO
SUSPENDED SOLIDS (mq/l)
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
ARSENIC IN STREAMS
T M D L W o r k s h o p
L77, Umno-Tech, Inc.
-------
10
s
10'
«
10
I03
(870)
iOO
1000
SUSPENOED SOLIDS (mq/l)
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
CADMIUM IN STREAMS
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
10
5
4
0
,3
0"
10
100
1000
SUSPENOED SOL IOS (mg/l)
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
CHROMIUM IN STREAMS
T M D L W o r k s h o p
L77, Llmno-Tech, Inc.
-------
,o V
10 —
Q.
10
10
' 11 i ¦ > i i
' 1 1 ' ' ¦
J I I I I I 1
10 100
SUSPENOED SOLIDS (mq/l)
1000
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
ZINC IN STREAMS
TMDL Workshop
L77, Limno-Tech, Inc.
-------
SAMPLE CALCULATION OF
PERCENT DISSOLVED USING
HISTORICAL PARTITION
COEFFICIENT
MISSION
Calculate percent of cadmium in dissolved form at a solids
concentration of 50 mg/l.
CALCULATION
For cadmium: ^=4.00 x 10, a= -1.1307
K =K x TSS° = 4.00 x 106 x 50'11307
P po
= 4.8 X 104 l/kg
f =2,= 1
CT 1+K TSS
T p
1
" 1+4.8x10^ x 50 x1(T
1
3.4
= 29%
TMDL Workshop
L77, LimnO'Tech, Inc.
-------
MINTEQ
METALS EQUILIBRIUM SPECIATION MODEL
Can predict entire phase distribution of metals, if:
1) The ambient chemical environment can be
described completely (enough)
2) The system is at equilibrium
3) The relevant chemical coefficients can be
defined
Basic Requirements
1. Good chemist: to narrow down the range of data
required.
2. Some data: could be as simple as pH,
temperature, alkalinity
could be more complex then you
could imagine
T M P L Wo r k s h o p
L77, Limno-Tech, Inc.
-------
HOW MINTEQ WORKS
"Equilibrium chemistry meets algebra"
Solution Chemistry
[H,0]o[H-] + [OH]
= {h-}{oh} = 10„,
• {H 2 0}
Metal + Ligand <-> Complex
[c.-]+[co,-]«[c.co,]
„ {CaCO,}
{Ca!*}{C) log K. = 9.75
{AgCI} / {Ag+ }{CI"} = 10975
Adsorption Reactions
Linear partitioning, plus six other choices
T M D L Workshop
LTI, Limno-Tech, Inc.
-------
HOW MINTEQ WORKS
1. Write solubility equation for each ligand
V W
- {M}{L}
2. Impose mass balance constraints
TM (Total Metal) = {M} + {ML}
TL (Total Ligand) = {L} + {ML}
3. Algebraically solve for unknowns
Given K, TM, TL
Determine {M} {L} {ML}
4. For complex system, could have 100+
equations and unknowns
TMDL Workshop
LTI, Umno-Tech, Inc.
-------
REDOX REACTIONS
Electrochemical redox potential (availability of
electrons) also drives equilibrium chemistry.
Oxidation reduction status, pe
pe = - log {e"}
Electrochemical redox potential
Eh (volts) = 0.059 pe @25°C
Redox potential can be estimated from
observed D.O. & pH
p. =1/4 log P„,+20.75 - pH
TMDL Work shop
Z.77, Limno-Tech, Inc.
-------
16.9
13.5
10.1 —
6.8-
3.4
Cu
3.4
6.8 -
10.1
13.S
CdOH
CdNH]
-log MOLAR
CONSTITUENT CONCENTRATION
-------
ADSORPTION SUBMODELS
IN MINTEQ
Partition model
Longmuir isotherm
Ion-Exchange
Freundlich
Donnan
Surface Complexation
Helmholtz
Goug-Chapman
Triple Layer
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
BUT WHAT ABOUT
SEDIMENTS
A common concern about using dissolved metals for
water quality criteria comparison is the potential for
sediment contamination.
Proposed sediment quality criteria for metals will
restrict pore water concentration to levels protective
of chronic water quality standards.
All things being equal, however, dissolved
concentrations in the sediment should be identical to
dissolved concentrations in the sediments.
Present criteria only considers chronic aquatic
exposure, doesn't consider ingestion of
contaminated sediment.
TMDL Workshop
L77, Umno-Tech, Inc.
-------
Pollutants in a Simple Aquatic Food Chain
Pollutants Bioconcentration
t
Water Column
Sorbed
Chemical
Koc
Dissolved
Chemical
Small Fish |
Zooplankton
Large Fish
Phytoplankton
c
.O
co
6
o
o
<0
.O
CQ
Bed Sediments
Sorbed
Chemical
Koc
Dissolved
Chemical
Benthic Organisms
T ** D L Workshop
LTI, Limno-Tech, Inc.
-------
SEDIMENT METALS WILL BE
A CONCERN IF THERE IS A
GREATER TENDENCY TO
TOXIC/DISSOLVED FORMS IN
THE BOTTOM SEDIMENTS
WHAT WE KNOW
WHAT IT MEANS
Acid volatile sulfides (AVS)
exist in reducing environments
(e.g., anoxic sediments) and
precipitate freely with available
metals
Less bioavailability
in sediments
Deeper, stagnant
Lower particulate organic carbon in Higher bioavailability
sediments, therefore less partitioning in sediments
Different pH, redox potential, ligand ???
concentrations in sediment
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
Conceptual Framework
Toxic Chemical Model
Atmosphere
Koc
Water Column
oi
*=
Free
Sulfide
Ksp
Koc
Active Sediment
Deep Sediment y
Net Sedimentation
Chemical in
Dissolved Phase
(ug/i)
Chemical on
^articulate Organic
Carbon
(ug/kg O.C)
Chemical Dissolved
in Pore Water
(ug/i)
Precipitated
Chemical
Chemical on
articulate Organic
Carbon
(ug/kg O.C)
M
D L
Workshop
LTI, Limno-Tech. Inc.
-------
SMPTOX4 SEDIMENT METALS
MODEL
Revised standard toxics modeling framework to include reaction
of metals-AVS
Ramifications
Mini-MINTEQ considering sorption & precipitation
Free [AVS] dependent upon total metals
Must simulate all metals simultaneously
TMDL Workshop
LTI, Limno-Tech, Inc.
-------
ii—iHinwii
a
Sediment Metal-AVS Interactions
Sorbed
Cu
Sorbed
Cd
KocJ
Jl 1
t <
' r
Sorbed
Dissolved
. Kso .
« Ksp »
Dissolved
,*06.
Sorbed
Ni
Ni
Free Sulfide
Zn
Zn
Precipitated
X-P
' i
'
Dissolved
Pb
-
Precipitated
Pb
Precipitated
Koc
Sorbed
Pb
T*4DL Workshop
LTI, Limno-Tech. Inc.
-------
|C
-------