United States
Environmental Protection
Agency
Great Lakes National
Piogram Office
536 South Clark Street
Chicago, Illinois 60605
EPA-905/4-79-029-E
Volume 5
The IJC Menomonee
River Watershed Study
Simulation of
Pollutant Loadings
And Runoff Quality
Menomonee River
-------
FOREWORD
The Environmental Protection Agency was established to coordinate adminis-
tration of the major Federal programs designed to protect the quality of our
environment.
An important part of the Agency's effort involves the search for information
about environmental problems, management techniques, and new technologies
through which optimum use of the nation's land and water resources can be
assured and the threat pollution poses to the welfare of the American people
can be minimized.
The Great Lakes National Program Office (GLNPO) of the U.S. EPA, was
established in Region V, Chicago to provide a specific focus on the water
quality concerns of the Great Lakes. GLNPO also provides funding and
personnel support to the International Joint Commission activities under
the U.S.- Canada Great Lakes Water Quality Agreement.
Several land use water quality studies have been funded to support the
pollution from Land Use Activities Reference Group (PLUARG) under the
Agreement to address specific objectives related to land use pollution to
the Great Lakes. This report describes some of the work supported by this
Office to carry out PLUARG study objectives.
We hope that the information and data contained herein will help planners
and managers of pollution control agencies make better decisions for
carrying forward their pollution control responsibilities.
Madonna F. McGrath
Director
Great Lakes National Program Office
-------
EPA-905/4-79-029E
December 1979
Simulation or Pollutant Loadings
and Runoff Quality
by
V. Novotny
Marquette University, Milwaukee, Wisconsin
D. Balsiger
R. Bannerman
J.G. Konrad
Wisconsin Department of Natural Resources
D.S. Cherkauer
University of Wisconsin-Milwaukee
G.V. Simsiman
G. Chesters
Wisconsin Water Resources Center
for
U.S. Environmental Protection Agency
Chicago, Illinois
Grant Number R005142
Grants Officer
Ralph G. Christensen
Great Lakes National Program Office
Chicago, Illinois 60605
This study, funded by a Great Lakes Program grant from the U.S. EPA, was
conducted as part of the TASK C-Pilot Watershed Program for the International
Joint Commission's Reference Group on Pollution from Land Use Activities.
GREAT LAKES NATIONAL PROGRAM OFFICE
ENVIRONMENTAL PROTECTION AGENCY, REGION V
536 SOUTH CLARK STREET, ROOM 932
CHIGAGO, ILLINOIS 60605
U.S. Cnvlronmentat Protection Agency
5, Library (PL.12J)
i .
CWcazo.ll 60604-3590
-------
DISCLAIMER
This report has been reviewed by the Great Lakes National Program Office
of the U.S. Environmental Protection Agency, Region V Chicago, and approved
for publication. Mention of trade names of commercial products does not
constitute endorsement or recommendation for use.
as i -.HI \UH,««-- fw>j»
-------
PREFACE
Prediction of pollutant loadings from non-point sources is an important
aspect of water quality management. A well-calibrated mathematical model
verified with extensive monitoring data may be applied to other watersheds for
predictive purposes. This volume contains two reports on the application of
the LANDRUN model and a discussion of a simple, empirical model for predicting
runoff quality. The LANDRUN model is utilized to 1. assess sediment loadings
from 48 subwatersheds in the Menomonee River Watershed in an attempt to
identify critical areas that are most cost-effective in terms of pollution
control and 2. obtain unit pollutant loadings for typical land uses to better
understand the processes involved in pollution generation and transport from
urban and non-urban areas.
iii
-------
CONTENTS
Title Page i
Disclaimer ii
Preface iii
Content s ••• iv
*Part I Assessing Pollutant Loadings from Subwatersheds with
Mixed Land Uses 1-i
*Part II Model Enhanced Unit Loading (MEUL) - A Method of
Assessing Pollutant Loadings from a Single Land Use Il-i
*Part III A Simple, Empirical Model for Predicting Runoff Quality
from Small Watersheds Ill-i
*Detailed contents are presented at the beginning of each part.
iv
-------
PART I
ASSESSING POLLUTANT LOADINGS FROM
SUBWATERSHEDS WITH MIXED LAND USES
D, BALSIGER
R, BANNERMAN
G, V, SIMSIMAN
J, G, KONRAD
G, CHESTERS
I-i
-------
ABSTRACT
Simulations of sediment loadings for various land uses in 48
subwatersheds of the Menomonee River Watershed are performed using the LANDRUN
model. In order to determine critical source areas, simulated loadings are
adjusted based on delivery ratios estimated for pervious areas in each
subwatershed. Nine subwatersheds, consisting of 16% of the total area of the
Watershed, are identified as critical source areas with developing lands being
the primary contributors of sediments. The criticality of a subwatershed in
terms of nonpoint source pollution appears to be enhanced by the extent of
connected imperviousness and proximity to the stream of that area.
I-ii
-------
CONTENTS - PART I
Title Page I-i
Abstract I-ii
Contents I-iii
Figures I-iv
Tables I-v
1-1. Introduction 1-1
1-2. Conclusions 1-2
1-3 Methodology 1-3
Source and Form of Data for LANDRUN Simulation 1-3
Manipulation of Land DMS Data Prior to Calibration 1-3
Calibration, Verification and Determination of Degree
of Connected Imperviousness 1-6
Simulations for 48 Subwatersheds and Determination of
Sediment Delivery Ratios 1-7
1-4. Results and Discussion 1-10
References 1-15
Appendix
I-A. Simulated Loadings for 48 Subwatersheds 1-16
I-iii
-------
FIGURES
Number Page
1-1 The 48 subwatersheds in the Menomonee River Watershed ... 1-4
1-2 Simulated (S) and monitored (M) sediment loadings
(kg/ha) from area adjacent to mainstem monitoring
stations—summer, 1977 1-11
1-3 Distribution of simulated sediment loadings in the
Menomonee River Watershed—summer, 1977 1-12
I-iv
-------
TABLES
Number Page
1-1 Land use categories (1975) in the 48 subwatersheds of
the Menomonee River Watershed I~5
1-2 Estimated sediment delivery ratios for various land
uses (LU) in the 48 subwatersheds of the Menomonee
River Watershed I~9
1-3 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in the Menomonee River
Watershed—summer, 1977 1-14
I-A-1 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 12A—summer,
1977 1-16
I-A-2 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 12B—summer,
1977 1-16
I-A-3 Water (m3) and sesiment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 12C—summer,
1977 1-17
I-A-4 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 12D—summer
1977 1-17
I-A-5 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 12E—summer
1977 1-18
I-A-6 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 10A—summer
1977 1-18
I-A-7 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 10B—summer
1977 1-19
I-A-8 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed IOC—summer
1977 1-19
I-v
-------
I-A-9 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 10D—summer
1977 1-20
I-A-10 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 10E—summer
1977 1-20
I-A-11 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7A—summer
1977 1-21
I-A-12 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7B—summer
1977 1-21
I-A-13 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7C—summer
1977 1-22
I-A-14 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7D—summer
1977 1-22
I-A-15 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7E—summer
1977 1-23
I-A-16 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7F—summer
1977 1-23
I-A-17 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7G—summer
1977 1-24
I-A-18 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 7H—summer
1977 1-24
I-A-19 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 11A—summer
1977 1-25
I-A-20 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 11B—summer
1977 1-25
I-A-21 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 11C—summer
1977 1-26
I-vi
-------
I-A-22 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 8A—summer
1977 1-26
I-A-23 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 8B—summer
1977 1-27
I-A-24 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 8C—summer
1977 1-27
I-A-25 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 9—summer
1977 1-28
I-A-26 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6A—summer
1977 1-28
I-A-27 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6B—summer
1977 1-29
I-A-28 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6C—summer
1977 1-29
I-A-29 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6D—summer
1977 1-30
I-A-30 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6E—summer
1977 1-30
I-A-31 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 6F—summer
1977 1-31
I-A-32 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 4A—summer
1977 1-31
I-A-33 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 4B—summer
1977 1-32
I-A-34 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 4C—summer
1977 1-32
I-vii
-------
I-A-35 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 4D—summer
1977 1-33
I-A-36 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3A—summer
1977 1-33
I-A-37 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3B—summer
1977 1-34
I-A-38 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3C—summer
1977 1-34
I-A-39 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3D—summer
1977 1-35
l-A-40 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3E—summer
1977 1-35
I-A-41 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3F—summer
1977 1-36
I-A-42 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3G—summer
1977 1-36
I-A-43 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 3H—summer
1977 1-37
I-A-44 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 5—summer
1977 1-37
I-A-45 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 2—summer
1977 1-38
I-A-46 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 1A—summer
1977 1-38
I-A-47 Water (m ) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed IB—summer
1977 1-39
I-viii
-------
I-A-48 Water (m3) and sediment (kg) loadings estimated by
LANDRUN for each land use in Subwatershed 19—summer
1977 1-39
I-ix
-------
1-1. INTRODUCTION
Identifying critical source areas of nonpoint pollution in a watershed is
imperative if economical means of remedial control measures are to be
adopted. Because monitoring of all potential source areas in relatively large
watersheds, like the Menomonee River Watershed (35,000 ha), incurs extremely
large expense and time, a model capable of predicting pollutant loads from
smaller components of the total watershed is very useful.
LANDRUN, a dynamic runoff-sediment overland transport model, after
initial calibration and verification, has demonstrated its capability of
simulating field data for such parameters as runoff, sediment and adsorbed
phosphorus (1). One application of LANDRUN is the prediction of pollutant
loadings from subwatersheds of diverse land uses and physical
characteristics. An attempt was made to use LANDRUN in simulating runoff and
sediment loadings from 48 subwatersheds in the Menomonee River Watershed.
Such application of the model is described in this report and results obtained
should aid in demonstrating what land features, land uses or land activities
contribute to high pollutant loadings. Water and sediment loadings were
simulated during the summer of 1977.
1-1
-------
1-2. CONCLUSIONS
The LANDRUN model was capable of simulating water and sediment loadings
for various land uses in 48 subwatersheds. Delivery ratio for each land use
was necessary to adjust sediment loadings from pervious areas. Simulated
sediment loadings were found to compare reasonably well with monitored data
from the mainstem stations.
Nine critical nonpoint source subwatersheds, constituting 16% of the
total area of the Watershed, were identified and contributed about 50% of the
total sediment loadings. Developing areas were the primary contributor of
sediments. Although developing lands occupy a small portion of the
subwatershed (1 to 5%), they contributed high amounts (50 to 85%) of sediment
loadings. The criticality of a source area can be enhanced by the extent of
connected imperviousness and proximity to the stream of that subwatershed. It
appears that developing areas in urbanizing subwatersheds are the most cost-
effective in terms of management.
1-2
-------
1-3. METHODOLOGY
Source and Form of Data for LANDRUN Simulation
LANDRUN is a mathematical model developed as a method of analysis for
estimating the quantity and quality of runoff and eroded particulates
emanating from watersheds having mixed land uses. The description of this
model and the discussion of its initial calibration and verification are given
in (1).
To perform LANDRUN simulations for the 48 subwatersheds in the Menomonee
River Watershed (Fig. 1-1.) two types of data are needed, namely, 1. land use
and associated characteristics in each subwatershed and 2. meteorological
information! obtained within and near the Watershed. Data on land use,
soils, slope and degree of imperviousness on the 48 subwatersheds were
provided by the Land Data Management System (Land DMS) described in (2). The
79 land use descriptions were consolidated into 14 land use categories (Table
1-1). The consolidation grouped similar land uses and land uses that have
similar potential for non-point pollution (3). Data obtained from the Land
DMS were in the form of area of each slope category for each soil type found
for each of the 14 land uses in each subwatershed. The Land DMS also provided
the degree of imperviousness for each land use for each of the subwatersheds.
Meteorological data were obtained from two sources. Precipitation data,
in the form of hourly precipitation totals, were furnished by the U.S.
Geological Survey (USGS) from eight precipitation gauges located throughout
the Watershed. Maximum and minimum daily temperatures, as well as daily
evaporation values, were obtained from the National Weather Service at
Mitchell Field.
Dust and dirt data which include dust and dirt fallout, washout
coefficient and sweeping efficiency were obtained from the Chicago study on
pollution from urban areas (4). Information on sweeping frequency was
provided by the Engineering Office of the cities in the Watershed.
Manipulation of Land DMS Data Prior to Calibration
LANDRUN, like other similar overland flow models, is sensitive to the
degree of imperviousness connected directly to storm sewers and streams, and
for pervious areas, to soil permeability, interception and depression
storage. The model requires dividing the Watershed into uniform areas based
on land use and soil characteristics. A land use with two different soil
groups was considered as two sub-areas. For a single land use in a
subwatershed, the many soil types were grouped into hydrologic soil groups B,
C and D (soils under group A are insignificant in the Watershed). An area-
1-3
-------
Menomonee River
and tributaries
Fig. 1-1. The 48 subwatersheds in the Menomonee River Watershed.
1-4
-------
Table l-l. Land use categories (1975) In the 48 subwatersheds of Che Menomonee River Watershed
No.
12A
12C
12D
IDA
10B
IOC
10D
7A
7C
7D
7E
7F
7G
7H
11A
113
11C
9
8A
BC
6A
6B
6C
6D
6E
6F
4A
4B
4C
4D
3A
38
3C
3D
3E
3F
3G
3H
5
*
1A
IB
19
Total
and
Area, ha
429
571
981
1, 592
599
459
502
1,610
981
718
1,406
301
832
1,343
251
527
852
765
555
599
1,011
970
744
669
974
294
545
752
707
7<»9
527
940
225
605
230
496
151
642
175
132
1,143
389
305
34,397
1234
0.4 3.3 0
0.6 1.8 0 0
0 2.4 0 0
0.2 4.0 0 2.0
I.I 4.6 5.9 1.8
0.1 3.1 2.7 0
1.8 1.1 2.2 0.1
3.1 5.7 8.0 0
0 3.1 0 0,2
1.1 3.2 0 0.1
2.7 8.2 3.5 0
1.1 6.6 0 0
2.7 4.9 2.4 0.2
0.2 8.3 0 4,5
0 1.2 0 0
0 0.2 0 0.2
0 1,3 0 0
3.4 15 1.6 6.8
0.1 2.0 7.7 2.9
2.2 9.8 0 1.9
2.0 8 9 4.1 2.9
0.5 6.9 0 1.8
5.1 1.0 0 1.3
0 8.3 0 0
0.3 4.3 0 0
4.0 11 7.0 5.4
2.5 9.1 0.4 2.3
0,1 5.5 0 11
0 6.4 2.7 5.1
0.2 5.8 0 4.1
1.3 4.5 0.2 5.0
0 20 4.7 8.4
15 24 2.1 0.5
7.9 34 7.9 3.7
3.0 17 1.9 9.1
0.2 85 0 4.4
8.1 15 1.2 4.4
0 5.6 0 0.4
0 7.2 0 33
17 25 38 23
8.3 18 5.9 5 5
5.0 12 0 25
2.C 7.3 1 8 1.9
5
9.6
3 5
37
36
9.6
'2
18
48
56
28
24
8.3
65
2.4
7.6
12
41
36
4.3
47
58
53
45
25
47
69
67
49
42
69
23
26
11
32
8.6
32
87
77
33
40
65
29
6
1.9
1 5
0.2
2.5
1.4
1.4
0.6
0.7
0.4
1.0
0.9
1,5
0
2.5
1.4
1.5
0.2
0.4
1.0
0
0.1
0.4
0.3
0.3
0
0
0
0.1
0
0
0
0
0
0.1
0
0
0
0
0 1
0
0
0,7
7
1.4
0.3
4.4
1.9
1 8
5.9
5.0
6.7
6.5
3.0
4.9
6 5
4.6
0.2
2.7
2.1
3.9
4.5
6.2
1.0
4.7
4.9
5.8
0.1
0.2
0.4
2.5
5.2
0.1
0.3
0.8
1.4
0.4
2.4
0
1.8
2 0
0.2
0
o 2
1.1
3 1
8 9
3 1 38
45 32
1 3 25
15 25
31 32
19 38
8.9 40
9.0 28
5.1 19
5.5 41
21 37
28 32
0.1 16
68 18
33 38
39 31
0.2 24
26 40
26 28
0 30
0 26
0 20
0 24
3.0 26
0 50
0 26
0 16
0 13
1.9 25
1.5 43
0 19
0 39
0 30
0 33
0.6 33
0 1.5
0 37
0 5.3
0 13
0 18
0 18
0 13
14 29
11
9,
12
1
4
13
13
7,
2,
3
5,
3.
8,
0,
6,
10
12
0,
3
13
0,
0.
6,
4,
7,
10
0
0
0
1.
2,
0
2,
1,
0.
0
0
0
0
0
0
0
0
5,
D
4
3
,5
.0
,6
,6
,9
,9
,9
,4
,2
,4
.1
.4
.3
,4
4
.7
,1
, 5
,1
.3
,7
11 12 13 14
•
4.8 0.5 0 0.4
3.2 0.1 0 0
24 0 0.2 0.6
030 0 1.0
5.3 0 0 0
'..4 0.2 0 05
220 0 1.7
0.7 0 0 0
5.0 0 0 0.3
0 0 0 0.8
0.4 0 0 0.2
41 0.2 0 0.1
1.3 0.1 0 0
1.5 0 0 0.2
4.0 0.8 0 1,5
0.8 0.7 0 0
01 0 2.6 04
0.6 0 0 0.5
6.9 0 0 04
0 0 3.8 0.1
10 0 0.3 0.6
1.0 0 0 01
6.3 0 0 0.1
4.5 0 0 0.2
10 0 0 0
0000
0 0 0 03
0 0 0 0.1
1.1 0 1.6 0.1
0 0 0 1.7
0 0 0 0.7
0 0 0 1.8
0 0 0.1 0.6
0 0 0.6 1.3
0 0 0 0.6
0000
0 0 0 07
0000
0000
o o o o a
0 0 3.2 0.8
0 0 0.4 0 8
3.1 0.1 0.3 0.4
S described in Table UI-5 found
per
Total
1 7
8
2
2
23
28
7
8
6
23
11
17
18
24
12
11
34
5
5
3
42
26
12
41
23
14
21
10
51
48
52
32
46
51
47
51
46
46
21
47
55
5f>
70
59
56
24
in (3)
viousnest %
Connect c-d
1
1
12
2
1
2
2
11
1
1
1
7
1
1
17
1
2
1
27
10
1
17
1
1
1
33
30
32
20
32
30
33
18
36
30
11
30
33
34
50
41
36
11
nsltv
1-5
-------
weighted mean slope was calculated for each land use-soil group sub-area
(e.g., the row crop land use in a subwatershed was computed as Row Crop B, Row
Crop C and Row Crop D; and having an associated area and mean slope).
Saturation permeability and other soil characteristics could be inputted for
each of the 3 soil groups within a particular land use.
Land DMS-land use data segregated all streets, freeways and off-street
parking areas from other land uses into a transportation land use. In order
to represent accurately the nature of urban land uses, it was necessary to
integrate these impervious areas back into the various land uses. Total area
and degree of imperviousness data were adjusted to account for this additional
area. Freeways were retained as a separate land use.
Calibration, Verification and Determination of
Degree of Connected Imperviousness
Starting with values used in the initial calibration and verification of
the model (5), individual events, sequences of events and eventually the
entire 1977 summer season were simulated for subwatersheds in which good
monitored data were available for comparison.
The hydrology portion of the model was first calibrated on subwatersheds
5 and 9 (Schooninaker Creek-413010 and Noyes Creek-413011), each of which had
water quality data and flow information from a sampling site which monitored
only that subwatershed. Both subwatersheds are predominantly medium density
residential although the Noyes Creek area is a newer development. Additional
calibration was performed on the 3 subwatersheds (HA, 11B and 11C) which
comprise the area monitored by the Donges Bay Road station (463001) and the 4
subwatersheds (4A, 4B, 4C and 4D) monitored by the Honey Creek sampling site
(413006). The Donges Bay Road subwatersheds are predominantly rural while the
Honey Creek subwatersheds are mostly residential, but with significant
pervious areas on the southernmost subwatershed (4D). Simulation of these
urban, rural and mixed land use areas and comparisons of simulated flows with
monitored flows led to the determination of connected imperviousness values
for the calibration subwatersheds. Calibration of the sediment portion of the
model was done on the Noyes and Schoonmaker Creeks subwatersheds as these
small urban areas were expected to have a delivery ratio much closer to unity
than the larger subwatersheds or rural areas.
Simulations on other subwatersheds for verification showed that the
degree of connected imperviousness could be described as a function of the
extent of storm sewering in a subwatershed. The degree of directly connected
imperviousness is the single most important factor influencing simulated
runoff from urban areas. For this reason, it was necessary to obtain detailed
information from maps, conversation with city engineers, etc. concerning the
extent of storm sewering, the precise location of new residential
developments, the usage of grass ditches for drainage, etc. The result of
this exercise was a set of connected imperviousness values for the land uses
modified according to individual differences in each subwatershed. The area
of directly connected impervious surfaces was calculated for each land use in
each subwatershed in the following manner. The model was used to determine
percentages of directly connected imperviousness for completely sewered and
1-6
-------
unsewered subwatersheds. Values for partially sewered subwatersheds were
derived by prorating on the basis of the land use which was in the sewered
area of that subwatershed. Examples of percentages of directly connected
imperviousness are shown below.
Land use
Industrial
Medium density
Completely
sewered
80
60
Partially
sewered 0>60%)
45
35
Unsewered
8
3
residential
Low density 20 5 1
residential
Parks/recreation 30 15 1
Simulations for 48 Subwatersheds and Determination
of Sediment Delivery Ratios
After calibration and verification was completed, simulations were run on
all subwatersheds. Simulated flow values from the individual subwatersheds
were summed accordingly and were found to compare favorably with measured
flows at the several mainstem river sampling stations. Simulated sediment
values corresponded reasonably well with loading estimates calculated from
monitored values for urban areas where a large part of the sediment originates
from impervious surfaces and the degree of connected imperviousness is high.
Calibration in these areas is accomplished by manipulation of the cropping
management factor for developing areas and doubling the literature values for
dust and dirt accumulation values. In more pervious areas and rural areas,
simulated sediment values were much higher than monitored loading estimates
(e.g., as much as 20 to 30 times higher in Donges Bay Road). Thus, there was
a need to develop a series of sediment delivery ratios for the land uses in
each subwatershed.
Proceeding as in the runoff calibration process, it was determined that
sediment delivery ratios were dependent on the extent of storm sewering
(connected imperviousness) in the subwatersheds. Other important factors are
proximity to runoff channels and characteristics of the land use (e.g., parks
vs. small grains, airport vs. shopping center). Again, it was necessary to
collect detailed information to characterize the land uses in each watershed.
Land uses were grouped into three categories and each category was
assigned a sediment delivery ratio for each subwatershed. "Urban" land uses
included industrial, commercial, medium and high density residential. "Rural"
land uses included agricultural areas, parks, low density residential and
landfills. Developing lands (construction), the third category, had such a
high sediment yield compared to the other land uses that it was assigned its
own delivery ratio. Resulting delivery ratios ranged from 1.0 for "urban"
land uses in completely storm sewered urban areas to 0.01 for developing lands
1-7
-------
in non-sewered areas. Table 1-2 shows the sediment delivery ratios for the
subwatersheds for the 1977 summer simulations.
1-8
-------
Table 1-2. Estimated sediment delivery ratios for various land uses (LU) in the 48 subwatersheds
of the Menomonee River Watershed
STORE! No.
673001
683002
683001
463001
413011
413008
413007
413006
413005
413010
413009
413004
Monitoring station
Location
MR at River Lane Rd.
(Hwy.F)
MR at Pilgrim Rd.
(Hwy. YY)
MR at 124th St.
(Hwy. M)
Donges Bay Rd. , (tequon
Noyes Creek at 91st St.
Little MR at Appleton Ave.
(Hwy. 175)
Underwood Creek above
Hwy. 45 off North Ave.
Honey Creek 140 m above
confluence with MR
MR at 70th St. Bridge
Schoonmaker Creek at Vliet St.
MR at Hawley Rd.
MR above 27th St. at Falk Corp.
Adjacent
subwatershed
12A.12E
12B,12C,12D
10A
10B,10C,10D
IDE
7A
7B,7D,7F,7G
7C
7E
7H
HA.llB.HC
9
8A
8B
8C
6A
6B
6C,6D,6F
6E
4A , 4B , 4C
4D
3A , 3B , 3C , 3E , 3F , 3H
3D
3C
5
2
1A,1B,19
Delivery ratios *
LU 1-5
0.34
0.03
0.80
0.03
0.60
1.0
0.03
0.15
0.53
0.81
0.03
1.0
1.0
1.0
0.03
0.35
0.25
0.03
0.03
1.0
1.0
1.0
0.52
0.60
1.0
1.0
1.0
LU 7
0.02
0.015
0.02
0.015
0.02
0.30
0.03
0.04
0.15
0.30
0.03
0.70
0.40
0.10
0.07
0.10
0.04
0.015
0.01
0.70
0.50
0.70
0.30
0.70
0.70
0.70
1.0
LU 6,8-13
0.03
0.03
0.03
0.03
0.03
0.06
0.03
0.03
0.03
0.06
0.03
0.06
0.06
0.03
0.03
0.10
0.05
0.03
0.03
0.30
0.30
0.30
0.10
0.15
0.30
0.30
1.0
*For pervious areas only; delivery of dust and dirt from impervious areas is assumed to be 1.0.
1-9
-------
1-4. RESULTS AND DISCUSSION
Extensive monitoring at the mainstem of the Menomonee River reveals that
the more urbanized areas in the lower portion of the Watershed contributed
greater sediment loadings than the rural upper portion (Fig. 1-2). Mainstem
monitoring could show general areas of nonpoint sources of pollutants,
however, identification of critical areas is quite difficult because adjacent
areas monitored by the major stations are too large (3,000 to 7,000 ha).
Estimation of pollutant loadings on smaller units should provide reasonable
precision for identifying critical source areas where best management
practices can be applied.
Water and sediment loadings simulated by LANDRUN during the summer of
1977 for the 48 subwatersheds (200 to 1,600 ha) of the Menomonee River
Watershed are shown in Tables I-A-1 to I-A-48. Loadings are given for all
land uses identified in a particular subwatershed. The sediment data were
adjusted accordingly taking into account delivery ratios (Table 1-2) for
pervious areas in the various land uses. Dust and dirt accumulations on
impervious surfaces were assumed to have 100% delivery. Delivery ratio for a
land use was estimated based on its physical characteristics, extent of
connected imperviousness and proximity to the stream.
Simulated sediment loadings were found to compare reasonably well with
those monitored at all but one of the mainstem stations (Fig. 1-2). At
station 673001, the simulated data was almost 3 times as high as the monitored
data. The extremely low sediment loading measured at this station could be
due to the trapping effect of a large pond just upstream of the station. The
close agreement between the simulated and monitored data indicates the
validity of the delivery ratios used for each land use and the integrity of
the sediment estimates for each subwatershed.
Results of simulations showed that nine subwatersheds (7H, 7A, 8A, 9, 3F,
3H, 3C, 4C and 4D) contributed significant amounts of sediments (Fig. 1-3).
These high source areas, located in the urbanized lower portion of the
Watershed, constitute 16% of the total area (calculated up to station 413005)
but contributed almost 50% of the total sediment loadings. The high sediment
yields from these subwatersheds can be ascribed mainly to developing areas
and—to a certain degree—to medium density residential areas. Developing
areas were present in almost all of the subwatersheds. However, high amounts
of sediments were transported from developing areas in the critical
subwatersheds essentially because of their short distances to the stream and
extensive connected imperviousness. Although high amounts of sediment can be
eroded in other subwatersheds particularly those in the rural portion of the
Watershed, delivery of sediment to the stream could be impeded as a result of
low connected imperviousness and/or greater distance to the stream. Medium
density residential areas, the predominant land use in the critical
subwatersheds, were significant sources of sediment loadings. Due to
extensive impervious surfaces in these areas, dust and dirt washoff was
prevalent.
1-10
-------
673001
• Mainstem station
Menomonee River
and tributaries
413007
Fig. 1-2. Simulated (S) and monitored (M) sediment loadings (kg/ha) from
area adjacent to mainstem monitoring stations—summer, 1977
(monitored data taken from (6)).
1-11
-------
kg/ha
0-150
CD 150-350
mi >350
Menomonee River
and tributaries
Fig. 1-3. Distribution of simulated sediment loadings in the
Menomonee River Watershed—summer, 1977.
1-12
-------
It is evident from the critical subwatersheds (Tables I-A-11, I-A-18,
I-A-22, I-A-25, I-A-34, I-A-35, I-A-38, I-A-41 and I-A-43) that the majority
of the sediment loadings (50 to 85%) originated from small areas (1 to 5%)
that were under development. This also can be seen in Table 1-3, which is an
integration of the loadings from various land uses in the entire Watershed.
Over 50% of the total sediment loadings was contributed by developing areas
occupying just 3% of the total area of the Watershed.
It has been shown that the model is a useful tool in identifying critical
nonpoint source areas of sediment in the Menomonee River Watershed. Results
indicate that developing areas in urbanizing subwatersheds are the most cost-
effective to manage. The method is applicable to other watersheds. However,
the difficulty of simulating sediment loadings on pervious areas requires some
recalibration and reverification of the model in other watersheds using
monitored data.
1-13
-------
Table 1-3. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in the Menomonee River Watershed
(area in ha)--Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO/DENS/RES
HI/DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
LANDFILL
WATER
FREEWAYS
TOTALS
WATER
PERV
108658.
2.3%
530245.
11.4%
1318740.
28. 4%
32057.
. 7%
139462.
3.0%
1087328.
23.4%
77469.
1.7%
1093929.
23.5%
49089.
1.1%
170156.
3.7%
16278.
.4%
26359.
. 6%
0.
.0%
0.
.0%
4649770.
100. 0%
WATER
IMPER
998707.
7.6%
3023765.
23.1%
5813647.
44.5%
4453.
.0%
972818.
7.4%
258787.
2.0%
0.
.0%
677001.
5.2%
0.
.0%
0.
.0%
0.
.0%
0.
.0%
655984.
5. 0%
670673.
5. 1%
13075835.
100. 0%
WATER
TOTAL
1107365.
6.2%
3554010.
20.1%
7132387.
40. 2%
36510.
. 2%
1112280.
6.3%
1346115.
7. 6%
77469.
.4%
1770930.
10. 0%
49089.
. 3%
170156.
1. 0%
16278.
. 1%
26359.
.1%
655984.
3. 7%
670673.
3. 8%
17725605.
100. 0%
SEDIMENT
PERV
5449.
.1%
50976.
1. 3%
592661.
14. 8%
3841.
.1%
30810.
.8%
2802398.
69.8%
316601.
7.9%
178430.
4. 4%
4903.
.1%
6695.
. 2%
19268.
. 5%
1004.
.0%
0.
.0%
0.
.0%
4013036.
100. 0%
DUST/DIRT
IMPER
117205.
7. 8%
350339.
23.4%
662801.
44. 3%
475.
. 0%
110318.
7. 4%
28106.
1.9%
0.
.0%
78421.
5.2%
0.
.0%
0.
. 0%~
0.
.0%
0.
. 0%
72644.
4. 9%
77354.
5. 2%
1497663.
100.0%
SEDIMENT
TOTAL
122654.
2.2%
401315.
7. 3%
1255462.
22.8%
4316.
.1%
1411?fl.
2. 6%
2830504.
51.4%
316601.
5.7%
256851.
4.7%
4903.
.1%
6695.
.1%
19268.
. 3%
1004.
.0%
72644.
1. 3%
77354.
1.4%
5510699.
100. 0%
AREA
PERV
189.
.8%
770.
3.1%
6039,
24. 0%
220.
.9%
245.
1.0%
801.
3.2%
4806.
19.1%
8949.
35.5%
1969.
7 .8%
1069.
4. 2%
32.
.1%
106.
.4%
0.
.0%
0.
.0%
25196.
100.0%
AREA
IMPER
449.
6.4%
1334.
19. 0%
3071.
43.8%
27.
.4%
359.
5.1%
272.
3.9%
0.
. 0%
813.
11.6%
0.
.0%
0.
.0%
0.
.0%
0.
.0%
142.
2.0%
542.
7.7%
7009.
100. 0%
AREA
TOTAL
638
2.
2104.
6.
9110.
28.
247.
604.
1.
1073.
3.
4806.
14.
9762.
30.
1969.
6.
1069.
3.
32.
106.
142.
542.
1.
32205,
100.
0%
5%
3%
8%
9%
3%
9%
3%
1%
3%
1%
3%
4%
7%
0%
-------
REFERENCES - I
1. Novotny, V., M. A. Chin and H. Tran. Description and Calibration of a
Pollutant Loading Model-LANDRUN. Final Report of the Menomonee River
Pilot Watershed Study, Vol. 4, Environmental Protection Agency, 1979.
2. Walesh, S. G. Land Use, Population and Physical Characteristics of the
Menomonee River Watershed. Part I: Land Data Management System. Final
Report of the Menomonee River Pilot Watershed Study, Vol. 2, U.S.
Environmental Protection Agency, 1979.
3. Simsiman, G. V., J. Goodrich-Mahoney, G. Chesters and R. Bannerman. Land
Use, Population and Physical Characteristics of the Menomonee River
Watershed. Part III. Description of the Watershed. Final Report of the
Menomonee River Pilot Watershed Study, Vol. 2, U.S. Environmental
Protection Agency, 1979.
4. American Public Works Association. Water Pollution Aspect of Urban
Runoff. Water Pollution Control Research Journal WP-20-15, Washington,
D.C., 1969.
5. Novotny, V., M. A. Chin and H. Tran. Description and Calibration of a
Pollutant Loading Model-LANDRUN. Part II: Calibration and Verification
of the Model. Final Report of the Menomonee River Pilot Watershed Study,
Vol. 4, U.S. Environmental Protection Agency, 1979.
6. Bannerman, R., J. G. Konrad, D. Becker and G. V. Simsiman. Surface Water
Monitoring Data. Part II: Quality of Runoff from Mixed Land Uses. Final
Report of the Menomonee River Pilot Watershed Study, Vol. 3, U.S.
Environmental Protection Agency, 1979.
1-15
-------
APPENDIX I-A. SIMULATED LOADINGS FOR 48 SUBWATERSHEDS
Table I-A-1 Water Cm3} and sed intent (kg) loadings est imated by LAMDRUN for each land use in Subw^
Summer 1977
LAND USE
INDUSTRIAL
COMflEfltlAl
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORFSTS
WETLANDS
WATER
TOTALS
Table l-f>-7.
LAND USE
INDUSTRIAL
1 r . / . E ' ./----
LO /DENS/RFS
HI /CEliS/PFS
DEVELOPING
ROW CROPS
PK/HEC/PASTR
FOREST0
V-ETLAI'DS
FFEDLC73
KATtK
FREE«'AYS
"OTALS
WATER
PERV
275.
2?,
12615.
10.17,
12850.
10. 6»
633.
.57,
16177
13.37,
12910 .
35.1%
531.
31318.
28. 3%
152
.1%
537.
.51
0 .
.0%
121381
^te- (-,') in
ViATlP
PFFV
11311
1 r",
13 55
1666.
1 IS
1856.
1 27,
16213
30 88
2611 .
1 .7%
29a25.
19 9Z
T 6%
16110
1 1 .07,
2102
1 .1%
'-,%
r f
1 193 39
WATFR
IMPER
2125.
1 71
10260.
8.11
11711.
9 n
102.
.1$
11611.
9 27,
3902
3.1!
0.
.0%
2816.
2.2J
05
0
81111.
66 lil
126638.
'XZ
^."'t.
\' ' ;
'• '-'<:_
1C3
.17,
126.
1 .5%
963.
3, IS
.07,
112.
1 .5%
.05
.OS
07,
9371
33 3S
1152
15 ',%
28123.
WATER
TOTAL
2100 .
1 .0!
22875.
9.2%
21561
9.97,
735.
.3!
27788.
1 1 .2%
16812.
18.9%
53".
37131
15. OS
152.
2%
587.
.2%
31111
33.9%
213019.
„„,„,,-
";'T
"r:,
1 ",',-., 7
•e '7.
1769.
1 .01
2232.
1 is
1 0 .
17,
13
1 .51
97
3.17,
07,
1 1 .
1 5?
.OJ
.0?
oj
939.
33. 3S
116.
15.»S
2818
SEDIMENT
TOTAL
215.
.5%
2136.
3783.
7.0i
109 .
.2%
3150.
5.8%
31131.
67.6%
2005.
3023
22 .
01
11 .
.If,
15.61
51076.
,- ,..,-,
"A i r L'.1"
TOTAL
1 1 ° f
71 1
1 .37,
'7',
56.
74.
. IS
37165.
66. OS
5737.
6191
9 47,
137
3S
<)52.
1 .27,
1683
3. IS
939
1 .77,
146.
3S
55132.
AREA
PERV
0.
.0%
6.
1 7%
25.
7 .1%
3.
.77,
17.
1.71
25.
7.11
74.
20.91
192
51.11
3.21
2
1%
0.
.0%
365.
, ,,!„,,..
AM A
3.5%
26
5. IS
17.
1 .5%
.2%
37
3.3J
432.
38.31
343.
30 33
72.
6.4?,
34
7.57,
6
.5J
os
.OJ
1111.
AREA
IHPtB
2.
2.3%
8.
11 4%
12 .
16.2%
Q
12.
16.1%
8.
10.87,
o .
.01
14.
19 .5%
0.
.0%
0 .
01
23.3%
71.
,!• J 1 /E ( Jri „
AREA
I1PEK
1 3 .
15.5%
12
14.91
1 /.
20.2J
2
2 IS
1 .
l .7%
7
7.7S
o;
9.3S
.OS
.OS
.OJ
2.2%
23.
26.67,
36
AREA
iUTKI.
.n
1 1 .
3.31
37.
8 7%
3.
.71
29.
6.74
33.
7.5J
74.
17.31
207.
ia.1%
1 1 .
d.61
2
.HI
17.
4.0%
429.
in n.J>--
ARF.A
TOTAL
52
1.31
38.
3.2%
75.
6.2S
19.
1.6%
4 .
3J
44 .
3.6Z
30 ',"„
351
^9.35
'J.'.i
34 .
7.07,
6
Ji
1 97,
U'.u
1-16
-------
Table l-A-3. Water Cm3) and sediment (kg) loadings estimated by LANDRUN for each land
Summer 1977
in Subwaterhsed 12C (area in ra)--
LAND USE
INDUSTRIAL
COMMEHCIAL
HED/DEHS/BES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
Table I-A-1.
LAUD USE
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
TOTALS
WATFR
PERV
2687.
2.9J
7356.
7.9%
22955.
24.5*
132*4.
1 . 1J
114661 .
15. 61
865.
.91
35685.
38. U
2621.
2.85
41040 .
It. 3%
H95.
1.6J
0.
.OJ
93692.
Water (m3) ai
Summer 1977
WATER
PERV
6896.
8. 61
10928.
13. 1%
1965.
2.5J
5079.
6 .11*
3395.
1. 31
38135.
17.7*
6381 .
3.0*
5922.
7. "I
1168.
1 .5*
79869.
WATER
IMPER
852.
1.7%
1761 .
9.6J
274)2.
15. OJ
S3.
.5*
1489.
2.71
0.
.0%
•537.
3.5*
0
.0*
0.
.OJ
0.
.OJ
11719.
614.11
18283.
id sediment
WATER
IMPER
1771 .
147. 8J
1395.
37.71
102.
2.8*
'35.
3.6*
0.
.0%
300.
8.1*
0.
.0*
0 .
.OJ
0.
.01
3703.
WATER
TOTAL
3539.
3.2*
91 17.
8.1J
25697.
22. 9J
11407.
1 .3*
15150.
13. 5Z
865.
.8*
36322.
32.14!
262H
2.3*
I401JO .
3.6J
11495.
1 .3*
11719.
10. 5J
111975.
(kg) loadings
WATER
TOTAL
8667.
10. 1J
12323.
11.7*
2067.
2.5*
52141.
6.2*
3395.
1. 1*
38135.
416.0*
6381 .
7.6J
5922.
7.1*
1 168.
1 .14*
83572.
SFDIMEHT
PERV
12
.U
57.
.5J
4168.
14.0*
32.
.3J
3855.
33.3?
21148
18.5*
3007.
26.0*
199.
1.7*
1 18.
1 .0*
1686.
141 6J
0
.0*
1 1582.
estimated by
SEDIMENT
PERV
841.
.55
207.
1 .3J
52.
.35
921 .
6.01
8730.
56. 8J
39H7.
25. 7J
596
3.9*
166.
1 .1J
661.
1.3J
15367.
DUST/DIRT
IMPER
85.
41. 6J
176.
9.6*
2741.
15.0?,
8.
.1*
18.
2 62
0.
.0*
61.
3.5*
0
.OJ
0.
.OJ
0.
.0*
1171.
61.2*
1829
LANDRUN for
DUST/DIRT
IMPFR
178.
17.85
139.
37. 1%
10.
2.7*
11.
3.8*
0.
OJ
31.
8.3*
0.
.OJ
0.
.0*
0.
.0*
372.
SEDIMENT
TOTAL
97.
.7J
233.
1 .75
712.
5.5*
HO.
.35
3903.
29. 11
2118.
16. OJ
3071 .
22. 9J
199.
1.5*
118.
.9*
1686.
12.65
1 171.
8 8J
13111.
each land use in
SEDIKFNT
TOTAL
262.
1 .7*
316
2.2*
62.
.1*
935.
5.95
8730.
55.51
3978.
25. 3 J
596.
3.8*
166
1 .1*
661.
1.25
15739.
AREA
PERV
1 .
.35
5.
1.0J
36
6.95
9.
1.8J
5.
9*
178.
33. 8J
39.15
51
10.31
?7.
5.2J
3.
5J
0.
.0*
526.
Subwatershed
AREA
PERV
19.
2.0J
21.
2.55
13.
1 .1*
3.
.35
111 .
16 1J
305.
31 .8*
116.
12.15
31.
3.2J
3.
3J
958.
AREA
IMPER
2.
1.85
5.
9.3J
19.
10.85
2.
3. 71
3.
7.31
0.
.05
13
28.1*
0 .
.05
0.
.0*
0.
.05
2.
5.21
16.
12D (area ir
AREA
IMPER
5.
19. 5J
10.
11.11
? .
8.9*
1 .
1.0*
0.
.OJ
6.
26. 6J
0.
.05
0
.02
0.
.05
23
',1EA
TST^L
1 .
.n
10.
i .at
55.
9 6J
1 1 .
1.95
3.
1 .15
178.
31.11
220.
38. 5J
51.
9.1*
27.
1.8*
3.
.5%
2.
.15
571 .
i ha)--
AREA
TOTAL
21.
2.1J
31.
3.5*
15.
1 .55
3.
.35
411
15. 3%
311.
31. 7J
1 16.
11 8!
31.
3.27,
3
.3*
981 .
1-17
-------
Table I-A-
5 Water (m3) and sediment (kg) loadings estimated bv LANDRUN for each land use in Sjbwatershed I"L (a
Summer 1977
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
HOW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DEHS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WA 1 <• R
WATER
PERV
2789.
2.0J
20150.
11.35
3170.
2.55
7879.
5 65
9152
6.55
51628
36.7!
16780.
1 1 .95
26113.
18.65
2799.
2. or,
o .
05
110790.
Summer 1977
WATER
PERV
83.
.15
1053.
3.65
31361.
30.25
199.
.25
3128.
3.0!
21385 .
21 .15
381 .
25260.
22.2!
0 .
.05
21212
18 6T,
386.
.3J
0
05
WATER
IMPER
2266.
10.5%
11108.
65.55
570.
2.6!
533.
2.5!
0.
.05
907.
1.5!
0.
.0!
0.
0*
0.
0!
3108.
11.1!
21552
WATER
IMPER
3153
1 05
61763.
19 .35
195952.
60 .OJ
137.
0!
20170.
6.3!
10931.
3.3!
0.
.0!
16625
5. 11
.0!
0.
0%
0 .
.0!
11583.
1 . 55
WATER
TOTAL
5055.
3.1!
31258.
21. 1!
1010
2.55
5112.
5.25
9152.
5.63
52595.
32.1!
16780.
10.3',
26113.
10.1%
2799.
1 7!
3108.
1 .95
162312
WATER
TOTAL
3236.
.7!
68816
15. 65
230313.
52. 35
336.
.15
23898.
5.1!
35316.
8 OJ
381 .
11
11885.
9.5!
0
.05
21212
1.85
386
.1!
11583.
3.3!
SEDIMENT
PERV
169.
.5%
6108.
7.5S
1110.
1.6!
5.1?
51 125.
59 5!
11111 .
13.35
1993
2.35
1376.
1 .65
7290.
8.5«
0 .
.0!
85865.
SEDIMENT
PFRV
6.
.01
1227.
1 .9!
11001
68 1%
50
.1!
1 162.
1.85
15332.
23.9!
1 0!
1022.
1 65
0.
05
786.
1 25
7.
.OJ
0.
.0!
DUST/DIRT
IMPER
228
10.65
1113.
65.15
57.
2.6!
53.
2.5J
0.
.0!
97.
1.5!
0 .
.0!
0.
0!
0.
.05
311.
11 ir,
2159.
DUST/DIRT
IMPER
317.
1 .05
7118
19.8!
21539.
60.05
15.
2250 .
6 33
1201 .
3.35
0
.or,
1827
5.1!
0 .
.OJ
0.
.05
0
.OJ
1603.
1.5!
SEDIMENT
TOTAL
697.
.85
7821 .
8 9!
1167.
1 .7!
1136.
5.0!
51 125
58.1!
1 1508.
13.U
1993.
2.3!
1376.
1 .6!
7290.
3.35
311.
.1!
S8021.
SEDIMENT
TOTAL
353.
.1!
8315.
8.3!
65510
65.1%
65
1!
3112.
3.1!
16583.
16.5!
669.
.7!
2B19
2.85
0.
.05
786
.85
7.
.05
1603.
1 65
AREA
PERV
9.
.65
51.
3.1!
21.
1.35
5.
35
193.
31 .55
172.
30.1!
373.
23.8!
137.
5.7!
3
.25
0 .
.01
1567.
AREA
PFRV
0
.OJ
3.
.65
112.
30. 7J
1 .
25
1
1 .03
13.
1.05
1 .73
136.
29 . 13
8
1 .75
111
30.55
1 .
.35
0.
.OJ
AREA
I M P F R
7.n
1 1 .
58.0!
7.8!
1 .
1.1!
0.
.05
19.9%
0.
.0!
0.
.0!
0.
.05
1 .
2.6!
25.
AREA
IMPtR
1 .
.7!
21 .
15.3!
8 1 .
58.61
0 .
.25
8 .
5 6!
8.
6.0J
. 03
15
1 1 .05
0
.03
n
.OS
p
3.
2.113
AREA
TOTAL
1 1
.71
68.
1.3J
23
1 .1!
6.
.1!
193.
31 .0!
177.
30.0!
373.
23.5!
137.
8.6!
3.
.25
1 .
.0%
1592
AREA
TOTAL
1
.2!
21 .
1 05
223.
37.2!
1 .
12.
2 0!
l'«
S
1 . (5
151 .
1 .3!
111.
1
i.
61312.
1-18
-------
Table I-A-7. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwater-shed 10B (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DEHS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
WATER
FREEWAYS
TOTALS
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
WATER
FREEWAYS
TOTALS
WATER
PERV
199.
.5*
6230.
6.21
55286.
55. 21
1873.
1 .91
5193.
5.2J
1 1101 9
11.0}
2315.
2.31
11583.
11.61
0.
.0%
187.
.21
0.
.01
0.
.01
100185.
Summer 1977
WATER
PERV
81.
.11
9362.
13.11
15113.
21.71
966.
1 .14*
12810.
18.1%
2985.
1.31
18555.
26.7$
1111.
5.91
5628.
8.11
0.
.0%
0.
.0%
69617.
WATER
INFER
1619.
3.9*
5872.
11.1 %
7901.
18. 9}
68.
.21
1215.
3.01
618.
1 .61
0.
.01
323.
.81
0.
.0»
0.
.0%
19302.
16. 2J
1753.
11 .U
11761.
WATER
IMPER
115.
1 9J
2105.
27.21
1159.
18. 8S
32-
.11
291.
3.81
0.
.01
100.
1.31
0.
.01
0.
.01
980.
12.61
2635.
31. OS
7750.
WATER
TOTAL
2118.
1 .51
12102.
8.51
63190.
11. 5J
1911 .
1 .11
6138.
1.5%
11667.
10.31
2315.
1.6J
11906.
10.51
0.
.01
187
.11
19302.
13.61
1753.
3.31
111919.
WATER
TOTAL
229.
.31
1 1167
11.81
16572.
21 1%
998.
1 .31
13101.
16.91
2985.
3.91
18655.
21. U
1111.
5.31
5628.
7.31
980.
1.31
2635.
3.11
77367.
SEDIMENT
PERV
2.
.OJ
73.
.61
1627.
12.71
63.
.51
30.
.21
2823.
22.0}
6368.
19.71
1817
11.21
0.
.01
2.
.01
0.
.0%
0.
.01
12805.
SEDIMENT
PERV
0.
.01
97.
51
3114 .
1 8$
22
.11
6793.
36.14}
6577.
16.01
2307.
12 HI
351.
1 .91
159.
.91
0.
.01
0.
.0}
18650.
DUST/DIRT
IMPER
181.
3.91
615.
11.01
869.
18.91
8.
21
137.
3.01
71.
1 .51
0.
.01
36.
.81
0.
01
0.
.01
2122.
16.21
522.
11 .11
1591 .
DUST/DIRT
IMPER
15.
1 91
21 1
27.21
116.
18.81
3.
.11
29.
3 71
0.
01
10.
1 .31
0.
.01
0.
.01
98.
12.61
261.
31.01
776
SEDIMENT
TOTAL
183.
1 .11
718.
1.11
2"496.
11.31
71 .
.11
167.
1.01
2891.
16.61
6368.
36.61
1853.
10.71
0.
.01
2.
.01
2122.
12.21
522.
3.01
17396.
each land use
SEDI1ENT
TOTAL
15.
.11
308.
1.6}
190.
2.51
25.
.11
6822.
35.1}
8577.
11.2}
2317.
11 .91
351.
1 .8}
159
.8}
98
.51
261.
1 .11
19126 .
AREA
PERV
0.
.1}
1 .
1.31
107.
32.51
10.
3.01
1 .
1.11
U .
1 11
70.
21 21
107.
32.71
21
6.31
2.
.51
0.
.08
0 .
.0$
328.
in Subwatershed
AREA
PERV
0.
.01
10.
2.2}
38.
8 11
6.
1 .1}
7.
1 .5}
157.
33.51
159
31.0}
61
13.71
26.
5 6}
0 .
.05
0.
.01
167.
AREA
IMPER
5.
3.6}
17.
12.7}
60.
15.71
2
1 .2}
5.
3-61
5.
3.71
0.
.01
7.
5.61
0.
.0}
0.
.0}
14 .
3 3}
27.
20.61
131
IOC (area
ARES
IMPER
0
1.11
5.
15.81
10.
29.21
1
1.91
2
5.91
0
0}
6 01
0.
.0}
0
.0?
0.
.61
13
39. 5S
31.
AREA
TOTAL
5.
1 .11
21 .
1.6}
166.
36.31
12.
2.51
8.
1 81
9.
1 .91
70.
15.2}
115.
25.01
21.
1.51
2.
.31
14
1 .0}
27.
5.91
159.
in ha)--
AREA
TOTAL
0.
.11
16.
3.11
18.
9.61
7
1 .11
9.
l .81
157.
31 .21
161
32. 1}
61
12.71
?6.
5.31
.0%
13.
2.71
502
1-19
-------
Table I-A-9. Water Cm3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 10D (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
FREEWAYS
TOTALS
Table I-A-10.
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DEHS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTP
FORESTS
WETLANDS
WATER
FRFEU'AYS
TOTALS
HATER
PERV
9196.
3.7%
17305.
6.9%
48704.
19.5%
2114.
.8%
217.
. 11
82225.
33.05
3346.
1.3%
64926.
26. 1%
5562.
2.2%
13998.
5.61
1594.
.6%
0
.0%
0.
.0%
249187.
Water (m 3) at
Summer 1977
WATER
PEPV
112 .
.5%
23418.
25.41
1658.
1 8*
20250.
22.0»
5060.
5.5*
19326
21 .01
3264.
3.5*
18737.
20.3%
0
01
0 .
.0%
92157.
WATER
IMPER
3674.
2.9%
3603.
2.9%
6054.
4.81
113.
.11
61.
.0%
69689.
55.3*
0.
.0*
492.
.41
0.
.0%
0.
.0%
0.
.0%
35385
28.11
6920.
5 51
125991.
id sediment
WATER
IMPER
4718.
6.61
51309
75.51
585.
.8%
3689.
5.1%
0.
01
2475.
3 4%
0.
.0%
0 .
.0%
1582.
6.41
1562.
2.21
71920.
WATER
TOTAL
12870.
3.4%
20908.
5.61
54758.
14.6*
2227.
.6%
278.
.1*
151914.
40.5%
3316.
.9%
65418
17.1%
5562.
1.5%
13998.
3.7%
1591.
.1%
35385.
9.4%
6920.
1 .8%
375178.
(kg) loading:
WATER
TOTAL
5160.
3.1%
77727.
47.1%
2243.
1 41
23939.
14 61
5060.
3 1%
21603.
13.31
3264.
2.0%
18737.
1 1 .4*
4582.
2.81
1562.
1 .01
164077.
SEDIMENT
PERV
165.
.11
187.
.1*
3292.
2.3*
102.
.1*
5.
.0*
88698.
63.31
29194.
20.8*
15516.
11. U
597.
.41
832.
.61
1603.
1 .11
0.
.01
0.
.01
140191 .
; estimated by
SEDIMENT
PERV
33.
. 1*
16736.
28.41
826.
1 .4%
12386.
21.0*
24973.
42.4%
2966.
5.01
272.
.51
677.
1 .21
0.
.0%
0.
.0%
58869.
DUST/DIRT
IMPER
368.
2.9%
361.
2.9%
606.
4.8%
1 1 .
.1%
6.
.01
6981 .
55.3*
0.
.01
49.
.4%
0.
.0%
0.
.01
0.
.01
3545.
28.11
693.
5.51
12620.
LA»IDPUN for
DUST/DIRT
IMPER
472.
6 61
5440
75.51
58.
.81
370.
5.H
Q
.01
248.
3.4%
0.
.01
0.
.0%
459.
6 4%
156.
2.21
7203.
SEDIMENT
TOTAL
533.
.31
548.
.41
3898.
2.61
113.
.11
11 .
.01
95679.
62. 6i
29194.
19.1%
15565.
10.21
597.
.4%
832.
.5%
1603.
1 .0%
3545.
2.31
693
.51
15281 1 .
each land use
SEDIMENT
TOTAL
505
81
22176.
33.61
881.
1 .31
12756.
19 3%
24973.
37.81
3214.
4.91
272.
.4%
677.
1 .01
459.
.7%
156.
.2%
66072.
AREA
PERV
19.
1.3%
9.
.6%
152.
10.3%
20.
1 .3%
1 .
.0%
75.
5.1%
313.
21.21
609.
41.31
203.
13.11
72.
4.91
3.
.21
0.
.01
0.
.0*
1475.
in Subwatersh*
AREA
PERV
0.
.01
88.
10.91
9.
1 .11
1 1 .
1.31
317.
39.3*
218.
27 11
73
9.0%
90.
1 1 .21
0.
.09
0.
.0*
806.
AREA
IMPER
9.
6.9*
9.
6.8*
41 .
30.51
2.
1 .7*
0.
.2%
20.
15.0*
0.
.0*
10.
7.4*
0.
.0*
0.
.0*
0.
.01
7.
5.31
35.
26. 1*
136.
;d 10E (area
AREA
IMPER
2.
4.11
28.
59.01
1 .
2.51
1 .
8.0%
0.
.01
5
10 81
0.
.0*
0.
.01
1 .
2.01
6.
13.61
47.
AREA
TOTAL
28.
1.81
18.
1 .1*
193.
12.01
22.
1 .11
1 .
.11
96.
5.91
3'3.
19.41
619.
38.5%
203.
12.61
72.
4.41
3.
.21
7.
.5*
35.
2.2*
1610.
in ha)-
AREA
TOTAL
2.
.21
1 16.
13.61
10.
1 .21
14.
1 .71
317.
37.11
224.
26.21
73.
8. 55
90
10.6%
1 .
. 1%
6.
.8J
853.
1-20
-------
Table I-A-11. Water (m*) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 7A (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/BES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
FREEWAYS
TOTALS
Table I-A-12.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
FREEWAYS
TOTALS
WATER
PERV
3395.
6.9*
7571.
15.5*
516.
1 .1*
0.
.0*
5.
.0*
36366.
71.31
0.
.01
861.
1.8*
0.
.0*
197.
.1*
25.
.1*
0.
.0*
0.
.01
18969.
Water Cm1)
Summer 1977
WATER
PERV
7325.
6.11
28818.
23.81
12156.
10. OS
1133.
1 .21
905.
.7*
12722.
10.5*
1860.
1.0*
15791.
37.8*
0 .
.0*
958.
.8*
6062.
5.0*
0.
.0*
0.
.0*
121033-
WATER
IMPER
97857.
20.0*
118520.
30.3*
110135.
22.5*
523.
.11
399.
.1*
16232.
3.3*
0.
.0*
9110.
1.9*
0.
.01
0.
.0*
0.
.0*
72111.
11.7*
31516.
7.0*
190066.
and sediment
WATER
IMPER
2606.
6.1*
10310.
21.2*
1658.
3.9*
36.
. 1*
190.
.1*
110.
1.0*
0.
.01
291 .
.7*
0.
.0*
0.
.0*
0.
.0*
23219.
51.1*
3901.
9.1*
12681.
WATER
TOTAL
101252.
18.81
156091.
29.0*
110981.
20.6*
523.
.1*
101.
.1*
52598.
9.8*
0.
.0*
10301 .
1.9*
0.
.0*
197.
.0*
25.
.01
72111.
13.1*
31516.
6.1*
539035.
(kg) loadinj
WATER
TOTAL
9931 .
6.1*
39158.
23.9*
13811.
8.1*
1169.
.91
1095.
.7*
13162.
g.OJ
1860.
3.0*
16085.
28.1*
0.
.0*
958.
.6*
6062.
3.7*
23219.
11.2*
3901.
2.1*
163717.
SEDIMENT
PEBV
325.
.11
1707.
.51
229.
.11
0.
.0*
1 .
.0*
328391.
99.3*
0.
.01
30.
.0*
0.
.0*
7.
.0*
22.
.0*
0.
.0*
0.
.0*
330715.
is estimated by
SEDIMENT
PERV
81.
.3*
255.
.9*
286.
1 .01
12.
.11
6.
.0*
5737.
19.6*
11809.
50.61
7910.
27.2*
0.
.01
18.
.11
63.
.2*
0.
.0*
0.
.0*
29210.
DUST/DIRT
IMPER
11752.
20.0*
17835.
30.3*
13261 .
22.5*
63.
.1*
18.
.1*
1919.
3.3*
0.
.0*
1131.
1.9*
0.
.01
0.
.0*
0.
.0*
8660.
11.71
1118.
7.01
58850.
LANDRUN for
DUST/DIRT
IMPER
286.
6.11
1136.
21.21
181.
3.9*
1.
.1*
21 .
.1*
19.
1.0*
0.
.0*
33.
.7*
0.
.0*
0.
.0*
0.
.0*
2552.
51.1*
129.
9.11
1691 .
SEDIMENT
TOTAL
12077.
3.1*
19512.
5.01
13190.
3.51
63.
.01
19.
.01
330313.
81.81
0.
.0*
1 161.
.31
0.
,•01
7.
.01
22.
.0*
8660.
2.2*
1118.
1.1*
389565.
each land use
SEDIMENT
TOTAL
370.
1.1*
1391.
1.1*
167.
1.1*
16.
1*
27.
.1*
5786.
17.11
11809 .
13.6*
7973.
23.5*
0.
.0*
18.
.11
63.
.21
2552.
7.51
129.
1.31
33931.
AREA
PERV
2.
.31
11 .
1.91
135.
17.8*
5.
.7*
0.
.0*
39.
5.1*
87.
11.51
381.
50.81
68.
9.0*
21.
2.8*
0.
.0*
0.
.0*
0.
.0*
756.
in Subwatershed
AREA
PERV
11 .
5.6*
19.
2.6*
11 .
5.9*
10.
1.3*
3.
.3*
9.
1.31
111 .
19.61
113.
56.11
23.
3.2*
6.
.8*
22.
2.9*
0.
.01
0.
.0*
733.
AREA
IMPER
28.
12.3*
12.
18.71
12.
18 .6*
1 .
.3*
0.
.1*
11 .
1.7*
0.
.0*
7.
3.21
0.
.01
0.
.01
0.
.0*
16.
7.3*
79.
31.9*
225.
7B (area
AREA
IMPER
7.
8.1*
29.
33.3*
13.
11.21
1 .
.9*
1 .
.8*
3.
3.81
0.
.0*
7.
7.5*
0.
.0*
0.
.0*
0.
.0*
5.
6.0*
22.
25.1*
88.
AREA
TOTAL
30.
3.1*
56.
5.7*
177.
18.0*
6.
.6*
0.
.0*
19.
5.0*
87.
8.9*
391.
39.8*
68.
7.01
21.
2.21
0.
.0*
16.
1.7*
79.
8.0*
981.
in ha)--
AREA
TOTAL
18.
5.9*
19.
5.9*
56.
6.8*
10.
1 .3*
3.
.1*
12.
1.51
111.
17.5*
119.
51 .1*
23.
2.81
6.
.81
22.
2.6J
5.
.61
22.
2.7*
820.
1-21
-------
Table I-A-13. Water (ma) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 7C (area in ha)--
Summer 1977
LAND USE
COMMERCIAL
NED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
BOW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
TOTALS
Table I-A-14.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
TOTALS
WATER
PERV
3701 .
2.41
70236.
46.21
713.
.5%
501.
.31
"47753.
31 .1*
2292.
1 .51
25882.
17.01
1 1 .
.01
79«.
.5%
151876.
Water Cm3) and
Summer 1977
WATER
PERV
7258.
5. 61
6571.
5.01
27006.
20.71
0 .
.01
344 .
.3»
88197.
67. 5J
0.
05
0.
.01
0.
.01
1257.
1 .0%
21
.0%
0.
.0$
130657.
WATER
IMPER
8651.
26. OJ
20238.
60.71
10.
.11
335.
1 .OJ
2022.
6.11
0.
.01
2019.
6.11
0.
.01
0.
.01
33338.
sediment
WATER
IMPER
3098.
5.U
11169.
21.61
20569.
35. 7J
16.
.0$
109.
.2%
2595.
4.51
0.
.OJ
1031.
1 .81
0.
.01
0
.01
0 .
.OJ
16086.
27. 9J
57676.
WATER
TOTAL
12355.
6.7J
90161.
18. 8J
753.
.U
839.
.51
19775.
26.91
2292.
1.2J
27931.
15.11
11 .
.OJ
791.
.11
185211.
(kg) loading
WATER
TOTAL
10356.
5.5J
20713.
11. OJ
17575.
25. 3J
16.
.OJ
153.
.21
90792.
48.2*
0.
.OJ
1031.
.5*
0.
.01
1257.
.71
21 .
.01
16086.
8.51
188333.
SEDIMENT
PERV
273.
.3*
20207.
21.21
108.
.1J
18.
.01
51264.
61.51
6554.
7. 91
4912.
5.91
31.
.01
20.
.01
83387.
;s estimated by
SEDIMENT
PERV
24.
.OJ
78.
.11
1677.
1 .91
0.
.01
2.
.01
S6153
97.91
0.
.01
0.
.01
0.
.01
38.
.01
0.
.01
0.
.01
87972.
DUST/DIRT
IMPER
951.
26.01
2225.
60.71
1 .
.11
37.
1.0*
222.
6.1J
0.
.01
225.
6.11
0.
.01
0.
.01
3664.
LANDRUN for
DUST/DIRT
IMPER
373.
5 41
1701 .
21.61
2170.
35.71
.01
13.
.21
312.
4.51
0.
.01
124.
1 .81
0.
.01
0
.01
0 .
.OJ
1932.
27. 9J
6927.
SEDIMENT
TOTAL
1224.
1.1J
22132.
25.81
112.
.1J
55.
.1*
51186.
59.1*
6551.
7.5*
5137.
5.91
31.
.01
20.
.01
87051.
each land use in
SEDIMENT
TOTAL
397.
.11
1779.
1 .9*
1117.
1 .1*
2 .
.01
15.
.01
86165.
91.11
0.
.01
124.
.11
0.
0*
38.
.01
0.
.0*
1932.
2.01
94899.
AREA
PERV
2.
.4*
272.
45.5*
1 .
.71
0.
.11
37.
6.21
65.
10.81
193.
32.21
19.
3.15
5.
.91
598.
Subwatershed
AREA
PERV
6.
.51
5.
.11
626.
51.31
5.
.41
1 .
.01
71 .
6.21
71 .
6.2J
247.
21 .4J
50.
4.41
70.
6. 11
0.
.01
0.
.01
1 154.
AREA
IMPER
20.
16.31
76.
63.4*
0.
.4*
1 .
.8*
1 1 .
9.5*
0.
.01
12.
9.6*
0.
.0*
0.
.0*
120.
7D (area
AREA
IMPER
9.
3.5*
40.
15.9*
156.
61.7*
0 .
.1*
0.
.2J
20.
7.81
0.
.01
23.
9.31
0.
.01
0.
.01
0.
.01
4 .
1 .11
253.
AREA
TOTAL
22.
3.11
348.
48.51
5.
.71
1 .
.21
48.
6.7*
65.
9.0*
204.
28.5*
19.
2.6*
5.
.7*
718.
in ha)--
AREA
TOTAL
15.
1 .1*
45.
3.2*
782.
55.6*
5.
.1*
1 .
.1*
91 .
6.5*
71 .
5.11
271 .
19.31
50.
3.61
70.
5.01
0.
.01
4 .
.31
1106.
1-22
-------
Table I-A-15. Water (m1) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 7E (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
NED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WATER
FREEWAYS
TOTALS
Table I-A- 16 .
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
WATER
PERV
562.
1.0%
10031 .
18.71
16385.
30.6*
415.
.81
11152.
21 .11
116.
.85
11278.
26. 6J
0.
.0%
0.
.01
0.
.01
53599.
Summer 1977
WATER
PERV
1028.
1.3*
29611 .
37. OJ
9619.
12.01
3.
.01
39379.
19 . !%
0.
.01
0 .
.0%
0.
.0%
15.
.01
168.
.6*
0.
.OJ
80153.
WATER
IMPER
13748.
14.81
25003.
27.01
36544.
39. U
66.
.1*
1426.
1.5*
0.
.01
1189.
1.3*
0.
.0*
11040.
11.9*
3682.
1.0*
92698.
WATER
IMPER
2827.
9.71
11915.
40.8%
5561.
19.01
34.
.11
972.
3-3%
0.
.0%
233.
.8%
0.
.01
0.
.01
0.
.0%
7692.
26. 3J
29234.
WATER
TOTAL
14310.
9.8%
35034.
23.91
52929.
36.2%
511.
.3%
12878.
8.8%
116.
.3%
15467.
10.6%
0.
.0%
1 1040.
7.5%
3682.
2.5%
146297.
WATER
TOTAL
3855.
3. 55
41556.
38.0%
15180.
13.9%
37.
.0%
40351.
36.9%
0.
.0%
233.
.2%
0.
.0%
15.
.0%
168.
.4%
7692.
7.0*
109387.
SEDIMENT
PERV
43.
.1%
2011 .
2.6%
8711 .
11.2%
116.
.2%
63728.
82.2%
936.
1.2%
1943.
2.5%
0.
.0%
0.
.0%
0.
.0%
775)8.
SEDIMENT
PERV
3.
.0%
190.
1.1%
233.
1 .3%
0.
.0%
16675.
93.8%
0.
.0%
0.
.0%
0.
.0%
0.
.0%
675.
3.8%
0.
.0%
17776.
DUST/DIRT
IMPER
1511 .
14.8%
2749.
27.0%
1017.
39.1%
7.
.1%
157.
1.5%
0.
.0%
130.
1.3%
0.
.0%
1211.
11.9%
105.
4.0t
10190.
' LANDRUN for
DUST/DIRT
IMPER
340.
9.7%
1430.
10.7%
668.
19 0%
1 .
.1%
117.
3.3%
0 .
.0%
28.
.8%
0.
.0%
0.
.0%
0.
.0%
921.
26.3%
3511.
SEDIMENT
TOTAL
1551.
1.8*
1760
5.1%
12728.
11.5%
153-
.2%
63885.
72.8*
936.
1 . 1%
2073.
2.1%
0.
.0%
1214.
1 .4%
405.
.5%
87708
each land use
SEDIMENT
TOTAL
313.
1 .6%
1620.
7.6%
901 .
4.2%
4 .
.0%
16792.
78 .9J
0.
.0%
28.
.1%
0.
.0%
0.
.0%
675
3.2%
921.
4.3%
21287.
AREA
PERV
0.
.2%
1 1 .
4.6J
58.
25.2%
3.
1 .2*
6.
2.6*
16.
7.2%
118.
51.3%
18.
7.7%
0.
.0*
0.
.0%
229.
in Subwatersh
AREA
PERV
1 .
.1%
21 .
2.97.
157.
21 .4%
7.
1 .0%
31.
4.6%
173.
23 6%
301.
11.1%
33.
1.5%
3.
.1%
1
.2%
0.
.0%
733.
AREA
IMPER
8.
10.9%
11.
19.8%
28.
38.7%
0.
.4%
3.
4.5%
0.
0%
5.
7.5%
0.
.0%
3.
3.5%
10.
14.6%
71 .
ed 7F (area
AREA
IMPER
8.
8.1%
31.
34.1%
42.
12.5%
1
.8%
7.
7.4%
0.
.0%
5.
5.3%
0.
.0%
0.
.0%
0 .
.0%
2.
1.8*
99.
AREA
TOTAL
8.
2.7%
25.
8.2%
85.
28.1%
3.
1 .0%
9.
3.0%
16.
5.5%
123.
40.9*
18.
5.9%
3.
.8%
10.
3.5*
301.
in ha)--
AREA
TOTAL
9.
1 .1*
55.
6.6%
199.
24.0%
8.
.9%
4 1 .
1.9%
173.
20.7%
309.
37 1%
33.
3 9%
3.
.4%
1
.2%
? .
.21
832.
1-23
-------
Table I-A-17. Water (m3) and sediment (kg) loadings estimated by LANDRUM for each land use in Subwatershed 7G tared in h,i)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
FREEWAYS
TOTALS
Table I-A-18.
LAUD USE
INDUSTRIAL
COMMERCIAL
HED/DENS/RES
HI /DENS/RES
DEVELOPING
BO'* CROPS
PK/REC/PASTR
FORESTS
WETLANDS
TOTALS
WATER
PERV
1208.
2.11
31306.
15.91
23601 .
12. OJ
3019.
1.51
1976.
1 .OJ
63605.
32.31
12778.
6.51
17920.
21. 3J
0.
.OJ
7906.
1.01
525.
.31
0.
.01
0 .
.01
196811.
Water (m3) and
Summer 1977
WATER
PERV
27.
.1%
5716.
11. 3J
25606.
50.31
3398.
6.7%
10610.
20.81
7.
0%
51 18.
10. 11
0.
.OJ
396.
.81
50908.
WATER
IMPER
11938.
31.11
9709.
25.31
3130.
8. 91
86.
.2»
122.
1 .11
1552.
1.0%
0.
.OJ
295.
.8%
0.
.OJ
0.
.OJ
0.
.OJ
5315.
13.91
5651.
11.71
38131.
sedinent
WATER
IMPER
1097
.6%
18920.
25. 7J
116513.
61 .21
16076.
8.11
3760.
2.01
0.
.Oil
3913.
2.1J
0.
.OJ
0.
.OJ
190309.
WATER
TOTAL
16116.
6.9J
11015.
17.11
27031 .
11 ,5J
3105.
1.3J
2398.
1 .OJ
65157.
27.71
12778.
5.U
1B215.
20. 5J
0.
.OJ
7906.
3. 11
525.
.21
5315.
2 3J
5651.
2.1J
235275.
(kg) loading
WATER
TOTAL
1121.
.51
51666.
22. 7J
112119 .
58. 9J
19171.
8.1J
11370.
6.01
7.
.05
9031 .
3.7%
0.
.OJ
396.
.2%
211217.
SEDIMENT
PERV
21 .
.OJ
153.
.31
1051 .
,8J
132.
.11
5.
.OJ
61963.
18, OJ
51788.
10.5%
12702.
9.11
0 .
,0%
310.
.3J
779
.6J
0.
.01
0.
.01
135231.
?s estimated by
SEDIMENT
PERV
2.
.0%
1119.
1 .21
28355.
23 5%
2006.
1 .71
88167.
73.21
10.
.01
570.
.51
0 .
01
16.
.01
120875.
DUST/DIRT
IHPER
1312.
31. U
1067 .
25. 3J
378.
8.9%
9.
.21
17.
1.1*
171 .
1.01
0.
.01
32.
.81
0.
.OJ
0.
.0%
0.
.01
588.
13.91
621 .
11.71
1225.
LA!ID°UN for
DUST/DIRT
IMPER
121 ,
.6%
5378.
25. 7J
12811.
61 .2*
1767.
8.1%
113.
2.01
0.
.01
130.
2.1J
0.
.OJ
0.
.01
20920.
SEDIMENT
TOTAL
1333.
1 .OJ
1520.
1 .1J
1129.
1.01
111.
.11
52.
.01
65131.
16.7%
51788.
39.31
12731.
9.11
0.
.0%
310.
.21
779.
.61
568.
.11
621 .
.11
139159.
each land use in
SEDIMENT
TOTAL
123.
.1J
6827.
1.81
11 166.
29.01
3773.
2.71
88880
62.71
10.
.0%
1000.
.7%
0.
.01
16
.0%
111795.
AREA
PERV
3.
.2J
38.
3.2%
86.
7.2J
18.
1.5J
1 .
.1%
76.
6.3%
381.
31.8%
121 .
35. OJ
120.
10. OJ
55
1.61
1 .
. U
0.
.0%
0.
.0%
1201 .
Subwate
AREA
PERV
0 .
.OJ
It .
2.31
1 10.
66. OJ
5.
2.8J
8
1.91
0,
.21
35.
21.2%
1 .
.6%
3-
1.91
166.
AREA
IMPER
31.
23.71
27.
19.3%
26.
18.2J
2.
1 .11
2.
1.1J
12.
8. 21
0.
.OJ
7.
1.71
0.
.01
0.
.01
0.
.01
1 .
.91
32.
22.51
112.
rshed 7H (area
AREA
IMPER
0 .
.11
17
20.11
53.
62 3%
7.
7.81
3.
1.01
0
.OJ
n .
5.21
0
.01
0.
.0%
85.
AREA
TOTAL
36.
2. 71
65.
1.9%
1 12.
8.3%
20.
1.5J
3.
.21
88.
6.5%
381.
28.1%
128.
31 .8%
120.
B.9%
55.
1.1%
1 .
.1%
1 .
.1%
32.
2.1J
1313.
in ha) —
AREA
TOTAL
0.
.21
21 .
8.3%
162.
61. Tl
1.5J
12.
1 6%
0.
.1%
10.
15.8%
1 .
.1%
3.
1 .31
251 .
1-24
-------
Table I-A-19. Water (mJ) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 11A (area in ha)-
Summer 1977
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DENS/BES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
WATER
TOTALS
Table I-A-20.
LAND USE
COMMERCIAL
MED/DENS/BES
LO /DENS/BES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
WATEB
PERV
1113.
2.51
6985.
15. 5»
1 194.
9.31
1595.
3.5*
6122.
13-6J
22187.
«9.31
1092.
2.11
1761 .
3.91
0.
.01
15019.
Water (m3) and
Summer 1977
WATER
PEBV
378.
.11
20120.
19.5*
1789.
1.7J
803.
.81
33161.
31. 6t
1925.
1.7H
33H1.
31. 91
1298.
1 .21
5391.
5.1J
3201.
3.11
0.
.OJ
101786.
WATEB
IMPER
249.
1.0*
627.
10. 2J
115.
1.9J
15.
.7J
0.
.01
741.
12.1*
0.
.0*
0.
.0*
1370.
71.1*
6150.
sediment
WATER
IMPER
698.
1.1*
1907.
2.9>
61.
.11
176.
.3*
725.
1 .1*
0.
OJ
296.
.1*
0.
.01
0.
.01
0.
.01
61998.
91.11
65861.
WATER
TOTAL
1362.
2.71
7612.
11. 9t
1309.
8.11
1610.
3.21
6122.
12.01
22931.
11.81
1092.
2.11
1761 .
3.11
1370.
S.51
51199.
(kg) loadings
WATER
TOTAL
1076.
.61
22327.
13.11
1853.
1.11
979.
.6J
33889.
19.91
1925.
2.91
33710.
19.81
1298.
.81
5391 .
3.21
3201.
1.91
61998.
36.31
170650.
SEDIMENT
PERV
22.
.11
101.
.31
79.
.31
978.
3.21
27093.
89.91
17«5.
5.81
67.
.2*
36.
.11
0.
.0*
30121.
estimated by
SEDIMENT
PERV
2.
.01
953.
1 .31
35.
.01
3.
.01
13988.
58.31
21793.
28.91
6028.
8.01
83.
.11
181.
.21
2317.
3.11
0.
.01
75416.
DUST/DIRT
IMPEB
21.
3.91
63.
10.21
12.
1.91
5.
.81
0.
.01
75.
12.21
0.
.01
0.
.01
138.
71.01
617.
LANDRUN for
DUST/DIRT
IMPER
70.
1.11
191 .
2.91
7.
.11
18.
.31
73.
1.11
0.
.01
30.
.51
0 .
.OJ
0.
.01
0.
.OJ
6210.
91.11
6599.
SEDIMENT
TOTAL
16.
.11
167.
.51
91 .
.31
983.
3.21
27093.
88.11
1820.
5.91
67.
.21
t .
36.
.11
138.
1 .11
30711 .
each land use
SEDIMENT
TOTAL
72.
.11
Till.
1.11
12.
.11
21 .
.01
14061 .
53.71
21793.
26.61
6058.
7.11
83.
.11
181.
.21
2317.
2.91
6210.
7.61
82015.
AREA
PERV
6.
1.11
8.
1 .71
11 .
2.11
1 .
.11
358.
71 .31
79.
15.71
32.
6.11
8.
1.51
0.
.01
503.
in Subwatershed
AREA
PEBV
0.
.01
51 .
6.31
1 1 .
1.31
1 .
.11
18.
2.2J
281 .
31.61
321.
39.51
88.
10.81
31-
1.21
7.
.91
0 .
.OJ
812.
AREA
IMPER
1 .
2.71
1.
18.01
2.
10.01
0.
1.31
0.
.01
15.
64.21
0.
.01
0.
.OJ
1 .
3.8*
21.
11B (area
AREA
IMPER
2.
4.11
13.
32.21
1 .
3.31
1 .
1.51
5.
12.21
0.
.01
6.
15.01
0 .
.01
0.
.01
0.
.01
13.
31 .1J
10.
AREA
TOTAL
6.
1.21
13.
2.11
13.
2.51
1 .
.21
358.
68.11
91.
17.91
32.
6.21
8.
1.51
1 .
.21
527.
an ha)-'
AREA
TOTAL
2.
.21
61.
7.61
12.
1.11
1 .
.21
23.
2.71
281 .
33. OJ
327.
38.11
88.
10.31
31.
1.0J
7.
.8J
13.
1 .51
852.
1-25
-------
Table I-A-21.
Water (m!) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 11C (area in ha)-
Summer 1977
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
Table I-A-22.
LAND USE
INDUSTRIAL
COMMERCIAL
MFD/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/PEC/PASTR
FORESTS
WFTLANDS
WATER
FRFEWAYS
TOTAI S
WATER
PERV
2831 .
2.7%
29743.
28.7%
1517.
1 .5%
24852.
24.0%
6591 .
6.4%
28551.
27.6%
5113.
4.9%
1455
1 .4%
2827.
2.7%
0.
.0%
103480.
Water (m3) and
Summer 1977
WATER
PERV
54.
.1%
600.
1 . 1%
14151 .
25.1%
116.
.2%
1487.
2.6%
24033.
42. 71
0.
.0%
15772.
28.0%
0.
0",
85
.2!
0.
.0%
0
.0%
56298.
WATER
IMPER
667.
12.0%
2418.
43 4%
50.
.9%
456.
8.2%
0.
.0%
1 15.
2.1%
0.
.0%
0.
.0%
0.
.0%
1868.
33.5%
5574.
sediment
WATER
IMPER
2304.
.8%
19906.
6.9%
183197.
63.6%
321.
. l*
30127 .
10.5%
12969.
4 .5%
0
.0%
3903.
1 4%
0.
0%
0.
.0%
13644.
4.7%
21525.
7.5%
287896.
WATER
TOTAL
3498.
3.2%
32161 .
29.5%
1567.
1.4%
25308.
23.2%
6591 .
6.0J
28666.
26.3%
5113.
4.7%
1455.
1.3%
2827 .
2.6%
1868.
1 .7%
109054.
(kg) loading:
WATER
TOTAL
2358.
7%
20506.
6.0%
197348.
57.3%
437.
1%
31614.
9.2%
37002.
10.8%
0.
.0%
19675.
5.7%
0.
.0%
85.
.0%
13644.
4.0%
21525.
6 3%
344194 .
SEDIMENT
PERV
72.
.1%
1265.
2.4%
52.
.1%
18312.
35 4%
23519.
45.5%
5486.
10.6%
460.
.9%
28.
.1%
2490.
4.8%
0.
.0%
51684.
; estimated by
SEDIMENT
PERV
7.
.0%
252.
.1%
14107.
5.7%
70.
.0%
679.
.3%
231996.
93.5%
0.
.0%
924.
.4%
0.
0%
6.
.0%
0.
.0%
0.
.0%
248041 .
DUST/DIRT
IMPER
66.
11.9%
242.
43.5%
5.
.9%
45.
8.1%
0.
.0%
1 1 .
2.0%
0.
.0%
0.
.0%
0.
.0%
187.
33.6%
556.
LANDRUN for
DUST/DIRT
IMPER
254.
.8%
2195.
6.9%
20203.
63.6%
35.
.1%
3323.
10.5%
1431 .
4.5%
0.
.0%
431 .
1 .4%
0
.0%
0.
.0%
1505.
4.7%
2374.
7.5%
31751 .
SEDIMENT
TOTAL
138.
.3%
1507.
2.9%
57.
.1%
18357.
35.1%
23519.
45.0%
5497.
10.5%
460.
.9%
28.
.1%
2490.
4.8%
187.
.4%
52240.
each land use in
SEDIMENT
TOTAL
261 .
.1%
2447.
.9%
34310.
12.3%
105.
.01
4002.
1 4%
233427.
83.4%
0.
.0%
1355.
.5%
0.
.0%
6.
.0%
1505.
.5%
2374.
.8%
279792.
AREA
PERV
8.
1 .1%
75.
10. 1%
1 1 .
1 .5%
13.
1 .7%
299.
40. 3J
234.
31 .6%
90.
12.2%
6.
.9%
5.
.7%
0 .
.0%
740.
Subwatershed
AREA
PERV
0.
.0%
6.
1.5%
148.
33.4%
2.
.4%
8.
1 9%
19.
4.3%
16
3 5%
222.
50.0%
18.
4 1%
4 .
.9%
0.
.0%
0.
.0%
444 .
AREA
IMPER
2.
6.8%
17.
65.9%
1 .
4.0%
3.
12.4%
0.
.0%
2.
9.3%
0.
.0%
0.
.0%
0.
.0%
0.
1 .5%
25.
8A (area
AREA
IMPER
1 .
.4%
5.
3.5%
66.
42.3%
0.
.2%
9.
6 0%
8.
5.1%
0.
.0%
17.
10.8%
0.
.0%
0.
.0%
3.
1 .9%
46.
29.8%
155.
AREA
TOTAL
10.
1.3%
91.
11.9%
12.
1.5%
16.
2.1%
299.
39.0%
236.
30.8%
90.
1 1 .8%
6.
.8%
5.
.7%
0.
.0%
765.
in ha)--
AREA
TOTAL
1 .
.1%
12.
2.0%
214.
35.7%
2.
.4%
18.
2.9%
27.
4.5%
16.
2.6%
239.
39.8%
18.
3.1%
4 .
.6%
3
.5%
46.
7.7%
599.
1-26
-------
Table I-A-23.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
TOTALS
Table I-A-24.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
FEEDLOTS
WATER
TOTALS
Water (m'>
Summer 1977
WATER
PERV
1878.
2.61
"4152.
5.71
7522.
10. 3J
362.
.5*
1867.
2.61
10988.
56. 2-,
0.
.01
13631.
18.7*
0.
.01
655.
.91
1905.
2.6%
0.
.0%
72963.
Water (m3)
Summer 1977
WATER
PERV
8125.
14.11
53612.
27. 9J
8329.
1.3J
1388.
.7?
12913.
6.71
62075.
32.31
6081.
3. 21
29966.
15. 61
0.
.0%
9291.
1.8J
71 .
.0*
0.
.01
192187.
and sediment
WATER
IMPER
31352.
9.6J
117919.
15.1*
86638.
26.1*
753.
.21
10752.
12.1*
18633.
5.7*
0.
.0*
1713.
.51
0.
.0*
0.
.0*
0.
.0*
396.
.1*
328156.
and sediment
WATER
IMPER
3211.
6.5*
22036.
11 .1*
1205.
2.11
10.
.1*
2510.
5.U
2096.
1.2J
0.
.01
202.
.1*
0.
.01
0
.0*
0.
.0*
18335.
36.9*
19668.
(kg) loadins
WATER
TOTAL
33230.
8.3*
152071 .
37.9*
91160.
23.5*
1115.
.3*
12619.
10.6*
59621.
11.9*
0.
.0*
15317.
3.81
0.
.0*
655.
.2*
1Q05.
.51
396.
.1*
101119.
(kg) loadinj
WATER
TOTAL
11639.
1.8*
75678
31.3*
9531.
3.9*
1128.
.6*
15153.
6.1%
61171 .
26.5*
6081.
2.5*
30168.
12.5*
0 .
.0*
9291.
3.8*
71 .
.0*
18335.
7.6*
211855.
SEDIMENT
PERV
312.
.2*
1793.
1.0*
1303.
2.3*
330.
.21
797.
.11
177718.
95.11
0.
.01
839.
.51
0.
.01
31.
.01
39.
.0*
0.
.0*
186225.
*s estimated by
SEDIMENT
PERV
60.
.1*
698.
.7*
319.
.1*
30.
.0*
111.
. 1*
68911
72.11
21010.
22.01
1237.
1 .11
0.
.01
199.
.2*
21.
.0*
0.
.0*
95659.
LANDRUN for
DUST/DIRT
IMPER
3157.
9.61
16313.
15.1*
9555.
26.1*
83.
.2*
1191 .
12.1*
2055.
5.71
0.
.01
189.
.51
0.
.01
0
.01
0.
.01
11 .
.1*
36190.
LANDRUN for
DUST/DIRT
IMPER
351.
6.51
2122.
11 .11
132.
2.11
it .
.11
279.
5.11
230.
1.21
0.
.01
23.
.1*
0.
.0*
0.
.01
0.
.0}
2015.
36.91
5159.
each land use
SEDIMENT
TOTAL
3799.
1 .71
18106.
8.1*
13858.
6 21
113.
.21
5291 .
2.1*
179803.
80.8*
0.
.01
1028.
.5*
0.
.01
31.
.01
39.
.01
11 .
.01
222115.
each land use
SEDIMENT
TOTAL
111.
.«
3120.
3.1*
181 .
.51
31.
.0*
390.
.11
69171 .
68.11
21010 .
20.81
1260.
1.21
0 .
.01
199.
.21
21.
.0!
2015.
2 01
101118.
in Subwatershed
AREA
PERV
2.
.3*
27.
3.6*
81.
10.9*
7.
.9*
12.
1.61
38.
5.1*
116.
15.61
373.
50.3*
17.
6.3*
25.
3.3*
13.
1 .81
0.
.01
711.
in Subwatershed
AREA
PERV
13
1.1*
37.
1.11
31.
3.81
9.
1 .01
10.
1.1%
17.
5.21
258.
28. SI
281.
31.71
135.
15.01
70.
7 81
0.
.0%
0.
.01
896
8B (area
AREA
IMPER
8.
7.61
10.
35.7*
31.
27.81
1 .
.71
13.
11 .21
1 1 .
10.31
0.
.0*
7.
6.6}
0.
.01
0.
.01
0.
.01
0.
.1*
1 12.
8C (area
AREA
IMPER
9.
7.91
62.
51.0*
9.
7.9*
1 .
.8*
10.
8.3*
16.
13.7*
0.
.01
5
3.91
0.
.01
0
.01
0.
.01
4
3.61
116
in ha)--
AREA
TOTAL
1 1 .
1.31
67.
7.81
112.
13.11
8.
.91
25.
2.91
50.
5.81
116.
13.61
381 .
11.61
17.
5.51
25.
2.91
13
1.6*
0.
.0*
853.
in ha)--
AREA
TOTAL
22.
2.21
99.
9.81
13.
1.31
10.
1 .01
19.
1 .91
62.
6.2*
258.
25 5*
288.
28.5*
135
13.31
70.
6.91
0.
.0%
u .
.11
1011 .
1-27
-------
Table I-A-25. Water (m1) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 9 (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
FREEWAYS
TOTALS
Table I-A-26.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
LANDFILL
WATER
FREEWAYS
WATER
PERV
1875.
2.3$
1892.
5.9$
26697.
32.1$
80.
.1$
1706.
5.7$
30852.
37.11
0.
.01
1 1920.
11.31
0.
.01
10.
.0$
2179.
2.6$
0.
.0$
0.
.0$
83211 .
Water (m3)
Summer 1977
WATER
PERV
1705.
.9$
23099.
11 .9$
61007 .
33.0$
31.
.0$
6022.
3.1$
10873.
5.61
76983
39.7$
0.
.0$
11313.
5.8$
0.
.0$
0.
.01
WATER
IMPER
66226.
9.61
151926.
22.01
297535.
13.01
202.
.01
76307.
11.01
19112.
2.8$
0.
.01
28837.
1.21
0.
.01
0.
.01
0.
.01
9530.
1 .11
11169.
6.01
691111.
and sediment
WATER
IMPER
51265.
6.81
192213.
25 51
380539.
50.61
19
0$
39111.
5.21
1160
.6$
60612.
8.11
0
.0$
0.
.0$
5867.
8$
18158.
2 51
WATER
TOTAL
68101 .
8.81
156818.
20.21
321232.
11.91
282.
.01
81013.
10.51
50261.
6.5$
0.
.01
10757.
5.3$
0.
.01
10.
.0$
2179.
.3$
9530.
1 .2$
11169.
5.1$
771655.
(kg) loadings
WATER
TOTAL
52970.
5.61
215312.
22 71
111516 .
17.0$
53.
.0$
15136.
1.8$
15033.
1 61
137625.
11.51
0.
.01
11313.
1 .2$
5867.
.6$
18158.
1.9$
SEDIMENT
PERV
237.
.11
2002.
.7$
11907.
5.2$
60.
.0$
2261.
.8$
269575.
93.2$
0.
.01
213.
.11
0.
.0$
0.
.01
36.
.01
0.
.0$
0.
.0$
289321.
estimated by
SFDIMENT
PERV
1 18.
.11
3773.
3.5$
67925.
62.21
2 .
.01
1 160.
1 . 11
21161.
22.1$
11602.
10.61
0.
.0$
502.
.5$
0.
.0$
0.
.0$
DUST/DIRT
IMPER
7303.
9.6$
16751.
22.0$
32811 .
13.01
22.
.01
8115.
11.0$
2110.
2.81
0.
.01
3180.
1.21
0.
.0$
0.
.0$
0.
.0$
1051 .
1 .1$
1573.
6 0$
76219.
LANDRUN for
DUST/DIRT
IMPER
5932.
6.8$
22216.
25.5$
11031 .
50.61
2.
.01
1561 .
5.21
182.
.61
7017.
8.1$
0.
01
0.
.01
679.
.81
2136
2.51
SEDIMENT
TOTAL
7510.
2.1$
18756.
5.11
17718.
13.11
82.
.01
10676.
2.91
271715.
71.3$
0.
.01
3123.
.9$
0.
.0$
0.
.0$
36.
.0$
1051 .
.3$
"4573.
1 .3$
365570.
each land use
SEDIMENT
TOTAL
6050.
3.1$
26019.
13.3$
11 1959.
57.0$
1 .
.01
5721 .
2.9$
21616.
12.61
18619.
9 5$
0.
.0$
502.
.3$
679.
.3$
2136.
1 .11
AREA
PERV
1 .
.11
"41 .
12.6$
121.
37.5$
1 .
.3$
11.
1.11
10.
3.11
1 .
."41
115.
35.91
2.
.81
1 .
.21
15.
1.51
0.
.01
0.
.0$
322.
in Subwatershed
AREA
PERV
1 .
.21
17.
3.0$
290.
50.7$
0.
.0$
12.
2.2!
6.
1 01
205.
35.81
1.
61
37
6.1$
0.
.01
0
.0$
AREA
IMPER
18.
7.7$
141 .
17.6$
107.
15.91
0.
.1$
23.
10. 1J
12.
5.11
0.
.01
21.
8.91
0.
.01
0.
.0$
0.
.0$
2.
.9$
9.
3.8$
233.
6A (area
AREA
IKPER
18.
14.61
69.
17.31
161.
11 .11
0.
.01
15.
3.91
1 .
.9$
87.
21 .9$
0.
.0$
0.
.0$
1 .
.3$
10.
10.01
AREA
TOTAL
19.
3.11
82.
11.7$
227.
11 .0$
1 .
.2$
38.
6.8$
22.
3.9$
1 .
.2$
136.
21.51
2.
.11
1 .
.11
15.
2.6$
2.
.11
9.
1.61
555.
in ha)--
AREA
TOTAL
20.
2.0$
86.
8.9$
151 .
16.81
0.
.01
28.
2.9$
10.
1 .0$
292.
30.11
1 .
.11
37.
3.81
1 .
.1$
10.
1.1$
191036.
752607.
916613.
109216.
196335.
398.
1-28
-------
Table I-A-27. Water tmj> and sediment (kg) loadings estimated by LANDRUN for each land use
Summer 1977
Subwatershed 6B (area in ha)--
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
FREEWAYS
TOTALS
Table I-A-28.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
WETLANDS
WATER
FREEWAYS
TOTALS
WATER
PERV
13839.
4.5*
71212.
23.91
110468.
35.61
17422.
5.61
39526.
12.7*
51656.
16.61
0.
.01
2569.
.81
950.
.3*
0.
.0*
0.
.0*
310612.
Water (m3) and
Summer 1977
WATER
PERV
281.
.21
26751.
15.11
75625.
42.6*
103.
.11
8697.
4.91
37125.
20.91
27721 .
15.6*
0 .
.01
1382.
.81
0.
.01
0.
.0*
177685.
WATER
IMPER
100737.
13.3*
217597.
32.7*
276116.
36.5*
33109.
1 .1*
10506.
1 .1*
25091.
3.3*
0.
.0*
0.
.0*
0.
.0*
35077.
4.6*
28110.
3.7*
7566"46.
sediment
WATER
IMPER
1246.
3.6*
11903.
31.11
13347.
38.5*
5.
.0*
1968.
5.71
1 179.
3.1*
2176.
6.31
0.
.01
0.
.0*
2766.
8.0*
55.
.2*
34645.
WATER
TOTAL
114576.
10.7*
321809.
30.2*
386584.
36.2*
50831 .
4.8*
50032.
1.7*
76750.
7.21
0.
.0*
2569.
.2*
950.
.1*
35077.
3.31
28110.
2.6*
1067288.
(kg) loading;
WATER
TOTAL
1527.
.7*
38651.
18.21
88972.
41 .9*
108.
.1*
10665.
5.01
38304.
18.01
29897.
11.1*
0.
.0*
1382.
.7*
2766.
1 .3*
55.
.0*
212330.
SEDIMENT
PERV
575.
.6*
7268.
7.3!
13486.
13.71
1116.
1 .41
43354.
13.51
3400.
3.4!
0.
.01
95.
.11
1 1.
.01
0.
.01
0.
.0*
99605.
; estimated by
SEDIMENT
PERV
1.
.0*
397.
1 .0!
7119.
18.8*
3.
.0*
91.
.21
29353.
77.21
1016.
2.7*
0.
.0*
21 .
.1*
0.
.0*
0.
.01
38031.
DUST/DIRT
IHPER
1 1657.
13.31
28651.
32.7*
31950.
36.5*
3865.
4.1*
1216.
1.4*
2901.
3.3*
0.
.0*
0.
.0*
0.
.01
1059.
4.6*
3253.
3.7*
87555.
LANDRUN for
DUST/DIRT
IMPEh
144.
3.6*
1378.
34.41
1545.
38.5*
1 .
.0*
227.
5.7*
137.
3.4*
251 .
6.31
0.
.01
0.
.0*
320.
8.0*
6.
.1*
4009.
SEDIMENT
TOTAL
12232.
6.5*
35919.
19.2*
75436.
40.31
5281 .
2.81
44570.
23.8*
6304.
3.41
0.
.01
95.
.11
11 .
.01
"4059.
2.21
3253.
1 .71
187160.
each Land use
SEDIMENT
TOTAL
115.
.31
1775.
4.21
8694.
20.71
4 .
.01
318.
.81
29490.
70.11
1267.
3.0*
0.
.0*
21 .
.01
320.
.81
6.
.01
12010.
AREA
PERV
11 .
1 .11
81.
10.0*
311.
12.2*
14.
1 .71
25.
3.11
308.
38.11
11 .
1.41
13.
1.61
3.
.4*
0.
.01
0.
.01
808.
in Subwatershed
AREA
PERV
0.
.01
20.
3.41
339.
59.31
1 .
.11
6.
1.11
27.
4.71
125.
21.91
47.
8.21
7.
1.31
0.
.01
0.
.01
572.
AREA
IMPER
48.
9.31
1 18.
23.01
198.
38.41
21 .
4.01
1 1 .
2.21
36.
7.01
0.
.01
0.
.01
0.
.01
8.
1.51
76.
14.71
516.
6C (area
AREA
IMPER
3.
2.01
32.
18.71
96.
56.0*
0.
.11
7.
4.11
8,
4.91
23.
13.71
0 .
.01
0
.01
1
.41
0.
.21
171.
AREA
TOTAL
59.
4.51
199.
15.1*
539.
40.7*
34.
2.61
36.
2.8*
344.
26.0*
11 .
.8*
13.
1.0*
.3*
8.
.61
76.
5.71
1323.
in ha)--
AREA
TOTAL
4 .
.51
52.
6.91
435.
58.51
1 .
.11
13.
1 .81
35.
4.71
1 49 .
20.01
47.
6.31
7.
1.01
1 .
.1*
0.
.01
744 .
1-29
-------
Table I-A-29. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 6D (area in ha>--
Suimer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
WETLANDS
WATER
TOTALS
Table I-A-30.
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PftSTR
FORESTS
WETLAI4DS
WATER
TOTALS
HATER
PERV
6017.
1.6J
3158.
2.6J
51739.
11 .7*
377.
.31
1928.
1 .51
31113.
23.71
25376.
19.3*
0.
.OJ
8220.
6.31
0.
.01
131228.
Water (m3) and
Summer 1977
WATER
PERV
18906.
8.81
77686.
36.01
370.
.21
63025.
29.21
2276.
1 . 11
HU663.
20.75
0 .
.01
8682.
1.01
.OJ
?15608.
WATER
IMPER
3211.
17.01
1136.
6.01
8563.
15.2*
16.
.11
711.
3.91
859.
1.55
1317.
6.9*
0.
.01
0.
.01
3111.
16.11
18960.
sediment
k'ATER
IMPER
21831.
17.61
13700.
26.31
1 0.
.01
1913.
3.75
0.
.01
2651 .
5.11
0.
.05
0.
.05
9063.
17.15
52171.
HATER
TOTAL
9231.
6.11
1591.
3.11
63302.
12.11
393.
.31
2669.
1.81
31972.
21 31
26693.
17.81
0.
.05
8220.
5. 51
3111.
2.1J
150188.
(kg) loading:
WATER
TOTAL
13710.
16.31
91386.
380.
.11
61938.
21.31
2276.
.85
17311.
17.75
n
.OJ
8682.
3.21
9063.
3.11
267779.
SEDIMENT
PERV
53-
.2J
12.
.2J
1558.
18. 3J
.11
15.
.21
19117.
76.61
898.
3.61
0.
.01
231.
.9*
0
.01
21958.
5 estimated by
SEDIMENT
PERV
316.
1 .11
5257.
19 01
7.
.01
18735.
67. 6J
1198.
5.11
1605.
5.81
0 .
.01
311.
i .11
0.
.01
27729.
DUST/DIRT
IMPER
372.
17.01
131.
6.01
991.
15.21
2.
.1*
86.
3.91
99.
1.51
153.
7.01
0.
.01
0.
.05
360.
16.11
2191.
LANDRUN for
DUST/DIRT
IMPER
2871.
17.61
1585
26.31
1 .
.01
222.
3.71
0.
.01
306.
5.11
0
.01
0.
.01
1019.
17.11
6037.
SEDIMENT
TOTAL
125.
1 .61
173.
.61
5519.
20.11
16.
.11
131.
.51
19216.
70.81
1051.
3.91
0.
.01
231.
.91
360.
1.31
27152.
each land use in
SEDIMENT
TOTAL
3190.
9.11
6812.
20.31
8.
.01
18957.
56.11
1198.
1.11
1911.
5.71
0 .
.01
.91
1019.
3.H
33766.
AREA
PERV
26.
1.51
3.
.61
291.
50.81
2.
.11
6.
1 .11
27.
1.71
115.
25.31
30.
5.21
12.
7.11
0.
.01
572.
Subwatershed
AREA
PERV
11 .
1.81
337.
11.11
3.
.11
13.
5.7J
29.
3.81
221 .
29.01
72.
9.51
11.
5.81
0.
.01
765.
AREA
IMPER
9.
8.91
3.
3.21
61.
63.21
0.
.11
2.71
6.
6.31
11 .
11.61
0.
.01
0.
.01
1 .
.71
97.
6E (area
AREA
IMPER
67.
31.91
98.
16.91
0.
.11
1 1 .
6.51
0.
.01
29.
13.61
0.
.01
0.
.01
2.
.95
209
AREA
TOTAL
31.
5.15
6.
1.01
352.
52.61
3-
.11
9.
1.31
33.
1.91
159.
23.8*
30.
14.1*
12.
6.3*
1 .
.1*
669.
in ha)--
AREA
TOTAL
81.
8.3*
135.
11.7*
3.
.3*
57.
5 8*
29.
3.0*
250.
25.7*
72.
7.U
11 .
1.51
2.
.21
971.
1-30
-------
Table I-A-31. Water Cm') and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 6F (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
WETLANDS
TOTALS
WATER
PERV
65.
.11
7721.
16. 3J
1 1322.
23. 9%
110.
• 3J
3«6.
.7J
22125.
16.6%
0.
.OJ
5719.
12. 1J
17168.
WATER
IMPER
288.
5.3J
2603.
17. 7J
1811.
33. 2%
3.
.1J
10.
.21
739.
13. 5J
0.
.01
0.
.OJ
5157.
WATER
TOTAL
353.
.11
10321.
19.51
13136.
21. 8t
113.
.31
356.
.71
22861.
13.21
0.
.01
5719.
10.9%
52925.
SEDIMENT
PERV
0.
.01
88.
1.6J
611.
33.51
2.
.1*
95.
5.01
902.
17.1%
0.
.01
186.
9. 71
1911.
DUST/DIRT
IMPER
33.
5.21
301.
17. 7J
210.
33.3%
0.
.OJ
1 .
.2J
86.
13. 6J
0.
.0%
0.
.OJ
631.
SEDIMENT
TOTAL
33.
1.31
389.
15. 3J
851.
33-U
2.
.11
96.
3.8%
988.
38.81
0.
.OJ
186.
7.3%
2515.
AREA
PERV
0.
.0%
6.
2.1J
59.
22.11
1 .
.3%
0.
.1%
138.
52.3%
29.
11.1%
31.
11 .6%
265.
AREA
IMPER
1 .
2.71
7.
21. 3J
13-
15.1%
0.
.2%
0.
.2%
8.
27.5%
0.
.0%
0.
.OJ
29.
AREA
TOTAL
1 .
.3%
13.
1.3%
72.
21. 7J
1 .
.3%
0.
.1%
116.
19. 9J
29.
10. OJ
31.
10. 5J
291.
Table I-A-32. Water Cm3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 4A (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
WATER
FREEWAYS
TOTALS
WATER
PERV
169S.
2.2J
5816.
7.6%
33928.
11. 1J
1150.
5.1J
1160.
1 .51
29651.
38.81
0 .
.0%
0.
.0%
76106.
WATER
IMPER
71106.
8.9%
11881 1 .
11.3%
337168.
10.8%
57191.
6.9%
837.
.1%
63670.
7.7%
752.
.1%
175253.
21. 2J
828088.
WATER
TOTAL
75801.
8.1%
121627.
13. B%
371396.
11.1%
61311 .
6.8!
1997.
.2%
93321.
10.3%
752.
.1J
175253.
19.1%
901191 .
SEDIMENT
PERV
159.
.7%
1619.
7.0%
11153.
62 8%
817 .
3.7%
2110.
9.2J
3821 .
16 .6%
0.
.0%
0.
.OJ
23009.
DUST/DIRT
IMPER
8768.
8.9%
11057.
11.3%
39926.
10.8%
6766.
6.9J
99.
.1%
7533.
7.7%
89.
. 1%
20735.
21 .2%
97973.
SEDIMENT
TOTAL
8927.
7.1%
15676.
13.0%
51379.
11 -9J
7613-
6.3%
2209.
1 .8%
11351
9. 11
89.
.1%
20735.
17. 1%
120982.
AREA
PERV
1 .
.5%
29.
10 8%
132.
19.3%
12
1.3%
1 .
.2%
93.
31 Bi
0.
.OJ
0.
.0%
267.
AREA
IMPER
20.
7 3%
32.
11 6%
122
It . 1%
18.
6.1%
1
.2J
16.
16 .6%
0.
.1%
38.
13.7*
277.
AREA
TOTAL
22.
1.0%
61 .
11 .2%
251.
16.6%
29
5 U
1 .
.2%
139.
25.5%
0
.01
38.
7.0%
515.
1-31
-------
Table I-A-33. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use
Summer 1977
Subwatershed 4B (area in ha)--
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/BES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
HATER
FREEWAYS
TOTALS
HATER
PERV
1191 .
1 .11
9133.
9.11
59781
67. 5J
21 .
OJ
2381 .
2.3%
3300.
3-2%
16963.
16 1%
0.
.OJ
0.
.OJ
103373.
WATER
IMPER
61051.
6.1J
221850
21 .01
679667.
61.1%
30.
.OJ
31118 .
3.3%
2371.
.2J
30326.
2.9J
9353.
.9%
12817.
1 .21
105U919 .
HATER
TOTAL
65515.
5.7J
231283.
20. OJ
719151.
61. 7J
51 .
OJ
36829.
3.2%
5671.
.55
17289.
1.1%
9353.
.8%
12817 .
1 . 1J
1158292
SEDIMENT
PERV
131.
.3%
1238.
2.5%
37991 .
75.5%
1 .
.OJ
150
.9%
8315.
16.6%
2165.
1 3%
0
.0%
0
.OJ
50321.
DUST/DIRT
IMPER
7578.
6 1%
26218.
21.0%
80113
61.1%
1.
.OJ
1076.
3.3%
281.
2%
3588.
2.9%
1 107.
.91
1516.
1 .2J
12181 1 .
SEDIMENT
TOTAL
7712.
1 . 1J
27186.
15.7%
1 18101 .
67. 6J
5.
.OJ
1526.
2.6J
8626.
1.91
5753.
3.3J
1 107.
.6%
1516.
.9%
175135.
AREA
PERV
1 .
.3%
8.
2.01
276.
70. 8J
0.
.0%
7.
1.8J
2.
.5%
96.
21. 6J
0.
.OJ
0.
.OJ
390.
AREA
IMPER
17.
1.8%
60.
16.6%
216.
67.9%
0.
.OJ
1 1 .
2. 91
1 .
.1%
22.
6. 11
2.
.6%
3.
.8%
363.
AREA
TOTAL
19.
2.5%
68.
9.1%
522.
69.1%
0.
.0%
18.
2.3%
3-
.1%
118.
15.6%
2.
.3%
3.
.1%
752.
Table I-A-3U. Water (rr 3 ) and sediment (kg) loadings est i ma ted Ly LA'iDRUN for each land use in Subwatershed UC (area in ha )--
Summer 1977
LUND USE
INDUSTRIAL
COMMERCIAL
MED/DEIiS/RES
HI /DEHS/KES
DFVELOPI NG
"K/BEC/PASTR
'•HER
TOTALS
WftTEB
PERV
28
OJ
3310.
3.1*
51152.
55.2%
9673.
9.9%
21676.
22 11
9311
9.5%
0%
98153.
WATER
IMPER
1117
.1%
101502.
10.21
680729.
6R.5J
158717.
16.01
15006.
1 .51
32360.
3.3%
1136.
.1J
99«227
WATER
TOTAL
1175.
1%
1 01812.
9 6%
731881
67 3%
168120
15 1%
36682.
3.1%
11671.
3.8%
1136.
.1%
1092380.
SEDIMENT
PERV
1 .
.0%
1093.
5%
53313.
21.5%
1218.
1 9J
157611 .
72. 3J
1657.
8J
0.
.01
217956.
DUST/DIRT
IKPER
151.
. 1%
10815
10 2J
72529
66.5%
16911
16 OJ
1599.
1 5J
3118 .
3. 31
173.
1%
105932.
SEDIMENT
TOTAL
158.
0%
11908
3.7%
125812.
38. 91
21 162
6.5%
159210 .
19.2%
5105.
1 .61
173.
1J
32388B.
AREA
PERV
G.
.01
1 1 .
3.2%
223.
65.6%
27.
8.1J
8
2.5%
70
20 7J
Q _
.OJ
310.
AREA
IMPER
0
.11
28.
7.7%
251.
69.0%
51
13.8%
10
2 6J
21.
6.6%
1 .
.3".
368
AREA
TOTAL
0.
.1%
39.
5.5%
176
67.3%
78.
11 .0%
13.
2.5J
95.
13.1%
1 .
. 11
707.
1-32
-------
Table I-A-35. Water Cm') and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed MD (area in ha)--
Summer 1977
LAND USE
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
WETLANDS
LANDFILL
WATER
FREEWAYS
TOTALS
Table I-A-36.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
FORESTS
LANDFILL
WATER
FREEhAYS
TOTALS
WATER
PERV
2556.
2.7*
25229.
26.3*
50.
.1*
3735.
3.9*
37596.
39.21
0.
.0*
23121 .
21.11
51.
.1*
516.
.5*
2970.
3.1*
0.
.0*
0.
.OJ
95827.
Water (m'J
Summer 1977
WATER
PERV
95.
.1*
5206.
1.6*
28878.
25.3*
3633.
3.3*
318.
• 3*
111 .
.1*
75725.
66.2*
0.
.01
11 .
.0*
0.
.0*
0.
.OJ
111313.
WATER
IKPEfi
108888.
15. MJ
361725.
51.1*
18.
.0*
76399.
10.8*
23520.
3.3*
0.
.OJ
10190.
5.7*
0.
.OJ
0.
.0*
0.
.01
2115.
.3*
95117.
13.1!
708032.
and sediment
WATER
IMPER
1357.
.7!
89686.
15.U
279175.
17.9*
11919.
7.2*
250.
.OJ
0.
126602.
21 .7J
0.
.0*
0.
.OJ
10730.
7.0*
230.
.0*
582919.
WATER
TOTAL
111111.
13.9*
386951.
18.1!
98.
.0*
80131.
10. OJ
61116.
7.6*
0.
.01
63311 .
7.9*
51.
.0*
516.
.1*
2970.
.1*
2115.
.3*
95117.
11.8*
803859.
(kg) loadinj
WATER
TOTHL
1152.
.6*
91892.
13.6*
308053.
11.2*
15552.
6.51
598.
.1*
111 .
.1*
202327.
29.0*
0.
.01
11.
.OJ
10730.
5.8*
230.
.0*
697292.
SEDIMENT
PERV
1088.
.3*
3H30.
8.1*
22.
.0*
1713.
.51
3130H9.
88.9*
0.
.0*
8163.
2.11
45.
.0*
113.
.0*
332.
.1*
0.
.01
0.
.01
385985.
£s estimated by
SEDIMENT
PERV
5.
.0%
1023.
2.11
12363.
25.5*
3392.
7 01
612.
1.31
5015.
10.31
26053.
53.8*
0.
.0*
0.
.0*
0.
.0*
0.
.0*
18163.
DUST/DIRT
IMPEfl
11602.
15.11
38511 .
51.1!
5.
.0*
8110.
10.8*
2506.
3.3*
0.
.0*
1282.
5.7*
0.
.01
0.
.01
0.
.0*
229.
.3*
10131.
13.1*
75139.
LANDRUN for
DUST/DIRT
IMPER
515.
.7*
1061 1 .
15.11
33030.
17.9*
1959.
7.2*
30.
.0*
0.
.0*
11978.
21 .7*
0.
.OJ
0.
.0*
1819.
7.0*
27.
.01
68969.
SEDIMENT
TOTAL
12690.
2.8*
69971.
15.2*
27.
.01
9883.
2.1*
315555.
71.91
0.
.0*
12115.
2.7*
15.
.0*
11 V.
.01
332.
.1*
229.
.OJ
10131.
2.2J
161121.
each land use
SEDIMENT
TOTAL
520.
.1*
11631.
9.9*
15393.
38. 7J
8351 .
7 11
612.
.5*
5015.
1.3*
11031 .
31.9*
0.
.0*
0.
.0*
1819.
1.1*
27.
.0*
117132.
AREA
PERV
21.
3.8J
255.
17. OJ
0.
.11
16.
3.0J
26.
1.9*
15.
2.8J
173.
31.91
11.
2.51
9.
1.6*
13.
2.11
0.
.01
0.
.01
512.
in Subwatershed
AREA
PERV
0.
.01
6.
2.1*
119.
11.5*
8.
2.9*
0 .
.11
8.
2.71
131.
16.31
1 1 .
3.9*
0.
.0*
0.
.OS
0.
.01
286.
AREA
IMPER
30.
11 .91
135.
52.6*
0.
.0*
21.
9.5*
15.
5.9*
0.
.0*
30.
11 .7*
0.
.OJ
0.
.OJ
0.
.0*
0.
.2*
21 .
8.31
256.
3A (area
AREA
IMPER
1 .
.5*
21.
10.1*
101 .
12. 1J
13.
5.11
0.
.11
0.
.0*
92.
38.1*
0.
.01
0.
.0*
9.
3.71
0.
.01
210.
AREA
TOTAL
51 .
6.1*
390.
18.81
0.
.11
11 .
5.1*
11 .
5.21
15.
1.9*
203.
25.11
11 .
1.71
9.
1.11
13.
1.6*
0.
.1*
21 .
2.7*
799.
in ha)--
AREA
TOTAL
1 .
30.
5.8*
220.
11 .8*
21 .
1.11
0.
.1*
8.
1 .51
226.
12.81
11 .
2.1*
0 .
.05
9.
1 .7*
0.
.OS
527.
1-33
-------
Table I-A-37. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 3B (area in ha) —
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
WATER
FREEWAYS
TOTALS
WATER
PERV
18<49.
1 .21
"4512.
3.0J
103569.
69. 11
1 1 .
.01
7072.
1.71
3910.
2.6J
28171 .
18.9%
0
.0%
0.
.01
119151.
WATER
IMPER
39057.
2.9%
111870.
10.81
95931".
71.51
10.
.0%
99972.
7.11
2809.
.21
55112.
1 .11
31053.
2.31
9972.
.7!
1312529.
WATER
TOTAL
10906.
2.7%
119112 .
10. OJ
1062913.
71. 3«
21 .
.OH
107011 .
7.2J
6719.
.51
83613.
5.6%
31053.
2.1%
9972.
.71
1191683.
SEDIMENT
PERV
157.
. 11
TUB.
.6J
100H91.
81. 1J
1 .
.0%
1952.
1 .6%
1 0 <4 1 3 .
8.51
9713.
7.9%
0 .
.OJ
0.
.01
123508
DUPT/DIRT
IMPER
1519.
2.9»
16764.
10.81
1 1101 1 .
71.51
1 .
.OJ
1 1568.
7.1%
325.
.2J
6115.
1.11
3593.
2. 31
1 151 .
7J
155350.
SEDIMENT
TOTAL
1676.
1.7J
17512.
6.31
211505.
75 81
2.
.01
13520.
1.8%
10768.
3. 91
16128.
5.81
3593.
1 .31
115".
.11
278858.
AREA
PERV
1 .
.31
11 .
.81
305.
65.51
0
.01
17.
3.61
1 .
.31
137.
29.51
0.
.01
0.
OJ
166.
AREA
IMPER
10.
2. 21
39.
8.21
311 .
72.51
0.
.01
31.
6.5J
2.
.11
10.
8.11
7.
1.11
2.
.51
175.
AREA
TOTAL
12.
1.31
«3.
1.51
619.
69.01
0.
.01
17.
5.01
3.
.3%
177.
18.81
7.
.71
2.
.21
910.
Table I-A-3S. Water
-------
Table I-A-39. Water (m3 > and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 3D (area in ha) --
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
1.0 /DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
LANDFILL
WATER
FREEWAYS
TOTALS
Table I-A-40.
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
FORESTS
LAMDFILL
WATER
FREEWAYS
TOTALS
WATER
PERV
7830.
10. 5S
38359.
51.71
12366.
16.71
23.
.OJ
1175.
1 .6%
9876.
13.31
«383.
5.9%
0.
.01
218.
.31
0.
.01
0.
.OJ
7*4230.
Water Cr a) a
Summer 1977
WATER
PERV
2069.
21 .1*
1828.
19. 81
855.
8. 8S
«28.
4 .4%
1017.
10. 5J
151.
1.6J
0.
.0%
3»1.
3.5J
0.
.OJ
0.
.0%
9689.
WATER
IMPER
117027.
31.01
210810.
11. 5J
69639.
11.71
1 1 .
.OJ
2170.
.5%
2633.
.6J
19262.
1.1J
0.
.01
0.
.01
16760.
3.51
5151.
1.21
171096.
nd sediment
WATER
IMPER
58718.
16.0J
175151 .
17.71
21561.
6.71
13638.
3.7%
565.
.2J
1501 .
1 .2%
0.
.OJ
0.
.01
13651.
3.7J
76097.
20. 7J
366915.
WATER
TOTAL
151857.
28.21
219199.
15.11
82005.
15. OJ
31.
.0%
3615.
.71
12509.
2.3J
23615.
1.31
0.
.OJ
?18.
.01
16760.
3.1J
5151.
1 .OJ
518326.
(kg) loadinj
WATER
TOTAL
60817
16. 11
179979.
17. 81
25116.
6.7%
11066.
J .7"
1582.
.11
1652.
1 2J
0.
.0",
311.
.1J
13651.
3.6J
76097.
20.2%
37660U.
SEDIMENT
PERV
801.
1.9J
6172.
11.21
6867.
15. 8J
1 .
.OJ
159.
.11
29206.
67. 2%
246.
.6J
0.
OJ
2.
.OJ
0.
.OJ
0
.08
13157
>s estimated by
SEDIMENT
PFRV
217.
3.5J
1121
18.01
211.
3.1%
161 .
2.61
1510.
72. 2%
1Q .
.21
0.
.01
12.
.25
0.
.OJ
0.
.01
6215.
DUST/DIRT
IMPER
17656.
31. OJ
25319.
11.51
8363
11.7%
1 .
.OJ
297.
.5J
316.
.6%
2313.
1 1%
0.
.OJ
0.
.OJ
2013
3 5J
655.
1 .2%
56933.
LANDRUN for
DUST/DIRT
IMPER
7055.
16 OJ
21033.
17 .7*
2950.
6. 71
1638
3 71
68.
.2$
511 .
1 2%
0
.0%
0
.0$
1610.
3.7*
9138
2C.7J
11063.
SEDIMENT
TOTAL
18160.
18.4%
31491 .
31 U
15230.
15.2%
2.
.01
156.
.5%
29522.
29 .1J
2559.
2.5%
0.
.0%
2
.os
2013
2.0J
655.
.7%
100390.
each land use
SEDIMECT
TOTAL
7272.
11.51
22151.
U1.0%
^ 161 .
6 3!
1799.
3.6%
1578
9 1!
551 .
1 15
0 .
.05
12
.05
1610 .
3.3%
9138.
18 2J
50308
AREA
PERV
6.
1.9J
28.
9 2S
102.
31.1%
0 .
.0%
1 .
.1%
f>
1 .9%
119.
19.9%
7.
2.3J
1
.3%
0.
.OJ
0.
0%
298.
in Subwate
AREA
PERV
1 .
1 IS
28.
22 8%
17.
13 3!
it .
3 n
\ .
5J
72.
57.51
1
.5*
1 .
1 .0%
0
.0%
r
1 21
AREA
IMPER
8"
27.35
120.
39.1%
53
17 .21
0 .
0!
2.
.5%
3-
1 .0%
29.
9 5%
0.
.OJ
0.
.OJ
1
1.2%
12.
4.0%
306.
rshed 3E (area
AREA
IMPER
17 .
15.9%
50.
47 .3%
9.
8.8%
1.2J
0 .
1J
3
3.21
0
.0%
0
02
3
2 9J
It'
17.3*
lr'5 .
AREA
TOTAL
89.
11.8%
117.
24.4%
155.
25. 6J
0.
.OJ
3.
.5%
9.
1 .4J
178.
29.5%
7
1 . 1%
1 .
.1%
1 .
.6%
12.
2.1%
605.
in ha)--
AREA
TOTAL
18.
7 9%
78.
34. OJ
26.
1 1 .2%
S .
3.71
1 .
.1%
75.
32 6%
1
11
1
. b%
1 .32
13 .
7 3r»
2l_
1-35
-------
Table I-A-11.
LAND USE
INDUSTRIAL
COMMERCIAL
MFD/DENS/RES
LO /DENS/RES
HI /DENS/RES
DEVELOPING
ROW CROPS
PK/REC/PASTR
WATER
FREEWAYS
TOTALS
LAIIL USE
II.DIJSTRIAL
COMMERCIAL
(.-E^EJiS/RES
MI /CEt.S/RES
""•"EC/PASTR
TCTAI ?
Water (m ) and
Summer 1977
HATER
PERV
1 181.
2.51
1288.
9.21
11920.
32.11
1 .
01
7639.
16.11
13159.
28.91
0.
.01
5017.
10.81
0.
.01
0.
.01
16508 .
HATER
PERV
1 2 .
.21
573.
1C. 51
2852.
52.1!
1668.
30.61
33S
6. 21
5113.
sediment
WATER
IMPER
19650.
7.51
195991.
29.81
218233.
33.21
60.
.01
95226.
11.51
8355.
1 31
0.
.01
37811 .
5.81
13523.
2 11
39161.
6.01
658010.
HATER
IMPER
922.
1 .31
31559.
15 05
21673
30.91
11933.
21.3!
1075.
1 .51
70167.
(kg) loadinj
WATER
TOTAL
50831.
7.21
200279
28.11
233153.
33.11
61 .
.01
102865.
11.61
21811.
3.11
0.
.01
12858
6.11
13523.
1.91
39161 .
5.61
701518.
HATER
TOTAL
931.
1 .21
32132.
12.51
21530.
32.11
16601 .
22.01
1113.
1 .91
75610.
Js estimated by
SEDIMENT
PERV
183.
.11
2950.
1 .11
18119.
8.11
2.
.01
3213.
1.51
191532.
88.31
0.
.01
93".
.U
0.
.01
0.
.01
216963.
SEDIMENT
PERV
1 .
166.
39.61
555.
17.21
111.
12.21
1 1 .
91
1177.
LANDRUN for
DUST/DIRT
IMPER
5962.
7.51
23536.
29.81
26207 .
33.21
7.
.01
1H35.
11.51
1003.
1.31
0.
.01
1511 .
5.81
1621.
2.11
1703.
6.01
79021 .
DUST/DIRT
IMPFR
111.
1 .31
3790.
15.01
2603.
30.91
1793.
21.31
129
1 .51
8126.
SEDIMENT
TOTAL
6115.
2.11
26166.
8.91
11356.
15.01
9.
.0!
11618.
1.91
192535.
65.01
0.
.01
5178.
1 .91
1621.
.51
1703.
1.61
295981.
SEDIMENT
TOTAL
1 12.
1 .21
1256.
11.31
3158.
32.91
1937.
20.21
110 .
1 .51
9603.
AREA
PERV
1 .
.31
30.
11 .11
75.
28.11
0.
.11
11.
5.11
6.
2.11
3.
1 .11
136.
51.21
0.
.01
0.
.01
265.
AREA
PEPV
0.
.01
111.
93.21
5
1.11
2.
1 .11
1 .
1 .21
119.
"!hed 3F (.ire
AREA
IMPER
11 .
6.11
56.
21.21
83.
35.91
0.
.01
31.
13.11
5.
2.11
0.
.01
29.
12.51
3.
1.31
9.
1.11
230.
AREA
IMPER
.81
18.
55.91
8.
25 .61
5.
15.11
1
2.51
32
d in hd)--
AREA
TOTAL
15.
3.01
86.
17.31
157.
31.81
0.
.11
15.
9.11
12.
2.11
.61
165.
33.21
3.
.61
9.
1.91
196.
AREA
TOTAL
0 .
.21
129.
85.31
13.
8.61
7.
1.11
2 .
1 .51
151.
1-36
-------
Table I-A-H3. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwater-shed 3K (area in ha)--
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/BES
HI /DENS/RES
DEVELOPING
PK/BEC/PASTR
WATER
FREEWAYS
TOTALS
WATER
PERV
10322.
15.9*
18102.
28.1*
8718.
13.5*
2395.
3.7*
12800.
19.7*
12170.
18.81
0.
.0*
0.
.0*
61837.
WATER
IMPER
119535.
17.51
272012.
31.9*
212300.
28.11
53681.
6.3*
7902.
.9*
71879.
8.8*
20609.
2.14*
3215).
3.8*
853102.
WATER
TOTAL
159857.
17.11
290111.
31.6*
251018.
27.31
56079.
6.11
20702.
2.3*
87019.
9.5*
20609.
2.2*
32151.
3.5*
918239.
SEDIMENT
PEBV
1066.
.6*
5227.
2.7*
6012.
3.11
716.
.11
177102.
91.91
2626.
1 .11
0.
.0*
0.
.0*
193109.
DUST/DIRT
IMPER
17957.
17.5*
32669.
31.91
29096.
28.11
6116.
6.31
919.
.9*
1993.
8.8*
2175.
2.11
3897.
3.81
102182.
SEDIMENT
TOTAL
19023.
6.11
37896.
12.81
35138.
11.91
7192.
2.1*
178351.
60.31
11619.
3.91
2175.
.81
3897.
1.31
295591.
AREA
PERV
10.
2.81
18.
5.11
113.
33.51
1 1 .
3.21
6.
1 .81
180.
53.3*
0.
.01
0.
.01
338.
AREA
IMPER
12.
11 .01
77.
25.51
92.
30.31
17.
5.8*
5.
1 .7*
57.
18.71
5.
1 .51
8
2.6*
303.
AREA
TOTAL
52.
8.11
96.
11.9*
205.
31.9*
28.
1.1*
1 1 .
1.8*
237.
36.9*
5.
.7*
8.
1.2*
612.
Table I-A-44. Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 5 (are
Summer 1977
LAUD USE
COMMFRCIAL
MFD/DEHS/RES
HI /UENS/BES
DEVELOPING
PK/KEC/PASTR
TOTALS
WATER
PERV
916.
2.9*
21201.
71.11
115.
.1*
1112.
13.6*
2851 .
8.8*
32528.
WATER
IMPER
33039.
12.3*
221901 .
83.8*
1578.
.6*
3117.
1 2*
5611.
2.1*
268252.
WATER
TOTAL
33985.
11.3*
219108.
82.11
1693.
.6*
7529.
2.5*
8165.
2.8*
300780.
SEDIMENT
PERV
66.
.14*
7690.
18.8*
8.
.11
7902.
50.11
107.
.7*
15773.
DUST/DIRT
IMPER
3909.
12.31
26609.
83.8*
187.
.61
369.
1.2*
661.
2.11
31738.
SEDIMENT
TOTAL
3975.
8.11
31299.
72.21
195.
.1*
8271.
17.1*
771 .
1.6*
17511 .
AREA
PERV
1 .
1 .0*
70.
90. OJ
0 .
.1*
2.
2.01
5.
6.61
78.
AREA
IMPER
g .
9.31
81.
81.01
0 .
.5*
2 .
2.01
4 .
1.21
97.
AREA
TOTAL
1 0 .
5.61
152.
86.7*
.11
3 .
2.0*
5.3*
175.
1-37
-------
Table I-A-45. Water (m1) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed 2 Care
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
TOTALS
WATER
PERV
1 1 .
.0%
25«5.
7.51
261433.
77.61
1069.
3.1*
481
i .11
3536.
10. 1%
31075.
WATER
IHPER
23".
.11
39918.
13.95
225880.
78.71
1322"4.
U.6I
33«.
. 1%
7257.
2.5%
2868147.
WATER
TOTAL
2145.
.11
1421463.
13.21
252313.
78. 6J
11293.
1.51
«15.
.3S
10793.
3.11
320922.
SEDIMENT
PERV
0.
.01
180.
2.31
6915.
87.71
105.
1.31
130.
5.51
259.
3.31
7889.
DUST/DIRT
IHPER
28.
.11
1723.
13.91
26721.
78.71
1565.
14.61
10.
.11
859
2.51
33939
SEDIMENT
TOTAL
28.
. IS
1903.
11.71
33639.
80.11
1670.
1.01
1.70.
1 .11
1118.
2.71
11828.
AREA
PERV
0.
.01
2.
2.71
58.
72.31
2.
2 31
0 .
.21
18.
22.51
80.
AREA
IMPER
0.
.11
1 1 .
10.61
S2.
80.01
14 .
il.OI
0.
.21
5.
5 1%
102.
AREA
TOTAL
n.
.01
13.
7.21
139.
76.61
6.
3.31
0.
.21
23.
12.71
182.
Table I-A-U6 Water (m3) and sediment (kg) loadings estimated by LANDRUN for each land us
Sunmer 1977
Subwaterbhed 1A (area in ha)--
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
LO /DENS/RES
HI /DENS/PES
DEVELOPING
PK/REC/PAS1B
WATER
FREEWAYS
WATER
PERV
11)695.
9. 1J
26788.
16.61
60096.
37.31
252
21
1203.
2.6J
713.
IS
51111 1 .
33 81
0.
.nl
0.
.nj
WATER
IMPER
680223.
26 11
963993.
37.01
557160.
21 .11
306.
.01
55930.
2.11
505.
.0%
1 11711
It . Hf,
11208 .
1 .71
1901488 .
7 31
WATER
TOTAL
691918.
25.11
990781 .
35 81
617256.
22.31
558.
.01
60133.
2.21
1218.
.0?
169125.
6.11
11208
1 .6>
190188.
6.91
SEDIMENT
PERV
2122.
14 .1%
1259.
8 .91
23628.
19.21
15.
.01
685.
1 .1*
1117.
2 11}
16195.
33.71
0
.01
0
.01
DUST/DIRT
IMPER
80179.
26. U
111052
37.01
65919.
21 .11
36.
.01
6617.
2. 11
60.
.OJ
13572.
1 .1%
5230.
1 7*
22537.
7.31
SEDIMENT
TOTAL
82601 .
23.21
118311.
33 21
89517.
25. 11
51 .
.01
7302.
2.01
1207
.31
29767.
8.31
5230
1.51
22537.
6.31
AREA
PERV
13.
3.71
23.
6.71
173.
50.81
0.
.11
9.
2.71
n
.11
122.
35.91
0.
.01
0.
.OJ
AREA
IMPER
185.
23 01
262.
32.61
202.
25.11
0.
.01
17.
2.21
0.
.01
83.
10.1%
10.
1 .21
11 .
5 11
AREA
TOTAL
197.
17.31
285.
21.91
375.
32.81
1
.11
27.
2.31
1 .
.01
205.
18.01
10.
.81
11 .
3.81
308502.
356553.
1-38
-------
Table I-A-47. Water Cm3) and sediment (kg) loadings estimated by LANDRUN for each land use in Subwatershed IB (ar
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
HED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTB
LANDFILL
WATER
FREEWAYS
TOTALS
WATER
PERV
2386.
1.01
10073.
16.91
29273.
19. 2»
3551.
6.01
1368.
2.3J
9553.
16.01
3351.
5 6%
0.
.01
0.
.0%
59558.
WATER
IMPER
111205.
15.0%
200712.
27.1?
252217 .
31. OJ
16091.
6.2J
919.
.11
15712.
2.11
0.
.05
13122.
1.8S
100811.
13. 6J
711215.
WATER
TOTAL
113591.
11. 2J
210815.
26. 31
281520.
35.21
19615.
6.2%
2317.
.3%
25295.
3. 21
3351.
.11
13122.
1.7%
100811.
12.61
800773.
SEDIMENT
PERV
222.
1.3%
1510.
9.3J
7811.
17.01
527.
3.2%
2201.
13.3%
3362.
20.2%
965.
5.8%
0.
.OJ
0.
.0%
16631 .
DUST/DIRT
IMPER
13157.
15.0%
23750.
27.1%
29811.
31. OJ
5151.
6.2%
112.
.1%
1863.
2.1%
0.
.01
1588.
1.8%
11928.
13.6%
87696.
SEDIMENT
TOTAL
13379.
12.8%
25290.
24.2%
37655.
36.1%
5981.
5.7%
2316.
2.2%
5225.
5.0%
965.
.9%
1588.
1.5%
11928.
1 1 -1J
104327.
AREA
PERV
2.
1.3%
15.
9.3%
65.
10.8%
7.
4.5%
0.
.2J
58.
36.0%
13.
7.9%
0.
.0%
0.
.0%
160.
AREA
IHPER
30.
13.2%
55.
23.9%
91 .
40.0%
14.
6.3%
1 .
.3%
1 1 .
5.0%
0.
.0%
3.
1.3%
23.
10. 1%
228.
AREA
TOTAL
32.
8.3J
69.
17.91
157.
40.3%
22.
5.5%
1 .
.2%
69.
17.81
13-
3.2%
3.
.8%
23.
5.9%
389.
Table I-A-48. Water (mj) and sediment (kg) loadings estimated by LANDRUfJ for each land use in Subwatershed 19 (area in ha)—
Summer 1977
LAND USE
INDUSTRIAL
COMMERCIAL
MED/DENS/RES
HI /DENS/RES
DEVELOPING
PK/REC/PASTR
LANDFILL
WATER
TOTALS
WATER
PERV
1176.
2.3%
7026.
13.8%
30675
60.1%
1139.
2.2%
4316.
8.5%
6389.
12.5%
292.
.6%
0.
.01
51013.
WATER
IMPER
52661.
10.5%
114516 .
22.91
28897?.
57.8%
15529.
3.1%
3046.
.6%
11238.
2.8J
0.
.0%
11225.
2.2J
500190.
WATER
TOTAL
53810.
9. 81
121512.
22.1%
319647.
58.0%
16668.
3.0%
7362.
1 .3%
20627
3.71
292.
.11
11225.
2.0%
551203.
SEDIMENT
PERV
01 .
.4J
680.
2.8J
1071 8 .
44.4%
142.
.6%
10771 .
11.6%
1695.
7.0J
45.
-2J
0.
.OJ
24142.
DUST/DIRT
IMPER
6231 .
10.5%
13519.
22.9%
3Hfl9.
57.8%
1837.
3.11
360.
.6%
1685.
2.81
0.
.01
1328.
2.2%
59179.
SEDIMENT
TOTAL
6322.
7.6%
11229.
17.1%
44907 .
53.9%
1979.
2.4%
11131.
13.1%
3380.
4.1%
15.
.1%
1328.
1 .6%
83321.
AREA
PERV
1 .
.7%
7.
4.9%
93.
68.6%
3.
2.0%
1 .
1.1%
30.
21. 8J
1 .
.8%
0 .
.0%
135.
AREA
IHPER
14.
8.4J
31.
18. 4J
105.
61 .7J
5.
2.8%
2.
1.11
10.
6.1%
0.
.0%
2.
1.4%
170.
AREA
TOTAL
15.
5.0%
38.
12.4%
197.
64.8%
8.
2.5%
3.
1.1%
40.
13.1%
1 .
.4%
? .
.8%
3C5.
1-39
-------
PART II
MODEL ENHANCED UNIT LOADING (MEUL) - A METHOD
OF ASSESSING POLLUTANT LOADINGS FROM A
SINGLE LAND USE
by
V, NOVOTNY
G, CHESTERS
G, V, SIMSIMAN
Il-i
-------
ABSTRACT
The Model Enhanced Unit Loading (MEUL) method utilizing the LANDRUN
model has been developed to simulate potential pollutant loadings from
urban and non-urban land uses. The simulations for typical land uses are
evaluated as if the land uses are located on hydrologically different soils
representative of standard hydrologic categories. Pollutant loadings vary
considerably among land uses. Sensitivity analyses indicate that the most
significant factors affecting such differences are extent of imperviousness
of urban areas, portion of the impervious areas directly connected to
runoff channels, depression and storage, length of dry period between
rainfall, curb height for urban areas and soil type, slope and vegetative
cover for pervious urban and non-urban areas. The applicability of the
unit loading data obtained by the MEUL method has been tested on several
well-monitored subwatersheds in the Menomonee River Watershed. The
simulated unit loadings for sediment and phosphate-P are of the same order
of magnitude as the measured values.
Il-ii
-------
CONTENTS - PART II
Title Page Il-i
Abstract Il-ii
Contents Il-iii
Figures Il-iv
Tables Il-vi
II-l. Introduction II-l
II-2. Conclusions II-3
H-3. Methodology II-4
Pollutant Transport Process from Non-point Sources ........ II-4
Pollutant Loadings and Transport from Impervious
Urban Areas II-4
Unit Loadings from Pervious Areas II-5
Rainfall factor, R II-6
Soil erodibility factor, K II-7
Slope-length factor, LS II-7
Vegetative cover factor, C II-7
Erosion control practice factor II—7
Delivery ratio factor, D II-8
Application of LANDRUN Model - Model Enhanced Unit
Loading (MEUL) Simulations Based on Land Use H-8
Surface characteristics II—9
Soils 11-10
Soil erosion data 11-18
Pollutant accumulation in urban areas 11-22
Atmospheric pollutant deposition 11-22
Wind erosion 11-29
Motor vehicles 11-29
Litter deposition 11-29
Effect of'vegetation 11-29
Pollutant washout 11-30
Street sweeping practices 11-32
Meteorological inputs 11-32
II-4. Results and Discussion 11-39
Simulated Loadings 11-39
Comparison of Measured Loadings with Estimates
Obtained by the MEUL Method 11-44
References 11-50
Appendices
II-A. Detailed Statistical Evaluation of Street Litter
Accumulation 11-53
II-B. Simulated Loading Diagrams 11-61
II-C. Remedial Measures and Non-Point Pollution Control 11-76
Il-iii
-------
FIGURES
Number Page
II-l Depression storage capacity in relation to degree of
land slope 11-11
II-2 Fraction of impervious areas not directly connected to
channel 11-12
II-3 Moisture characteristics of selected soils 11-15
II-A Soil particle size distribution accepted'by USDA-SCS 11-16
II-5 Relationship between soil permeability and soil texture ... 11-17
II-6 Determination of soil K factor 11-19
II-7 Pollutant accumulation schematic model 11-23
II-8 Curb length-imperviousness relationship 11-25
II-9 Seasonal cumulative frequency of precipitation 11-35
11-10 Seasonal cumulative frequency of R-factor 11-36
11-11 Slope correction factor for sediment and phosphate
loadings from pervious urban areas 11-41
II-12 Loading multiplier for different slope categories 11-42
11-13 Relationship of the size of the area to sediment
loading 11-43
11-14 Sediment delivery ratio versus drainage area 11-47
II-A-1 Effect of dry periods on the quantity of street litters ... 11-59
II-B-1 Sediment loadings from residential areas 11-62
II-B-2 Sediment loadings from commercial areas 11-63
II-B-3 Sediment loadings from industrial areas 11-64
II-B-4 Phosphate-P loadings from residential areas 11-65
Il-iv
-------
II-B-5 Phosphate-P loadings from commercial areas 11-66
II-B-6 Phosphate-P loadings from industrial areas 11-67
II-B-7 Relationship of sediment loadings and R-factor in row
crop-woodland areas 11-68
II-B-8 Probability distribution of sediment loadings in row
crop-woodland areas 11-69
II-B-9 Relationship of sediment loadings and R-facator in
feedlots 11-70
II-B-10 Probability distribution of sediment loadings in
f eedlots 11-71
II-B-11 Relationship of sediment loadings and R-factor in
pastures 11-72
II-B-12 Probability distribution of sediment loadings in
pastures 11-73
II-B-13 Relationship of sediment loadings and R-factor in
wetlands 11-74
II-B-14 Probability distribution of sediment loadings in
wetlands 11-75
II-C-1 Effect of sweeping interval on pollutant loadings
(sweeping efficiency = 50%) 11-78
II-C-2 Effect of sweeping efficiency on pollutant loadings
(sweeping interval = 7 days) 11-79
II-C-3 Effect of curb (median barrier) height on street litter
accumulation 11-80
II-v
-------
TABLES
Number Page
II-l Properties of soils used in the simulation 11-13
II-2 Properties of the major soil types surrounding the
Donges Bay station (463001), Menomonee River Watershed 11-14
II-3 C-value used to compute erosion 11-20
II-4 Metal concentrations of surficial materials of the U.S.A. ... 11-21
11-5 Street refuse accumulation 11-24
II-6 Pollutants associated with street refuse 11-26
II-7 Metal contamination of street refuse 11-27
II-8 Annual and monthly mean deposition rates of particulate
material in Milwaukee County 11-28
II-9 Daily leaf fall 11-31
11-10 Pollutant distribution in various particle sizes 11-33
11-11 Interrelationship of sweeper efficiency and particle size ... 11-33
11-12 Street sweeping removal efficiency of pollutants 11-33
11-13 Urban land use information 11-37
11-14 Non-urban land use information 11-38
11-15 Simulated pollutant loadings for urban land uses under
slope category B (2 to 6%) during an average year (1968) .... 11-40
11-16 Simulated pollutant loadings for land uses on essentially
pervious areas 11-45
11-17 Comparison of simulated and measured sediment and phosphate
loadings in subwatersheds with mixed land uses 11-48
11-18 Comparison of simulated and measured sediment and phosphate
loadings in predominantly single land use areas 11-49
Il-vi
-------
II-A-1 Partial and multiple correlation coefficients between dust
and dirt pollutants and factors affecting their accumulation. 11-57
-------
II-l. INTRODUCTION
The International Joint Commission, through the Great Lakes Water Quality
Board, established the International Reference Group on Great Lakes Pollution
from Land Use Activities (PLUARG) to study and report the effects of land use
on water quality and recommend remedial measures. Several pilot watersheds
subjected to detailed monitoring were selected throughout the Great Lakes
Basin in Canada and the United States. The Menomonee River Watershed located
in the southeastern part of Wisconsin in the Milwaukee metropolitan area was
one of the watersheds selected. The primary task was to establish pollutant
loadings from various land uses and extrapolate these findings to the entire
Great Lakes region.
The investigation discussed in this report presents an effort to develop
unit loadings for typical urban and suburban land uses using a combination of
modeling techniques with measured monitored data. It is true that the best
information on actual loadings can be obtained only from direct field
measurements. However, the applicability of such information is limited by
time and location at which the data were gathered and sometimes by the
sparsity of data. On the other hand, even the most effective models may fail
to provide reliable results if proper calibration and verification is not
guaranteed. Thus, a combination of simulated loadings using a mathematical
model, calibrated and verified by extensive monitoring data and applied to
several hydrologically different seasons and soils, may provide a better
understanding of the variability of the loading figures, their dependence on
meteorological, pedological and environmental factors and may reveal a
possible impact of some remedial measures suggested for reducing pollutant
impact.
Pollution from non-point or diffuse sources originates either from
weathering of minerals, erosion of virgin and forest lands including residues
of natural vegetation, or from artificial or semi-artificial sources. The
latter sources can be related directly to human activities such as fertilizer
application or use of agricultural chemicals for controlling weeds and pests,
erosion of soil materials from agricultural farming areas and animal feedlots,
erosion occurring in urban developments, transportation, atmospheric fallout,
etc. With the gradual elimination of point sources including sewage and
industrial wastewater outfalls, it is becoming obvious that a substantial
portion of surface waters pollution originates from the use of land by man,
i.e. from diffuse sources.
A tendency exists to relate pollutant loadings from non-point sources to
type of land use. In this approach, pollution from diffuse sources is
expressed simply as a value or range of unit loadings (loadings/unit area/unit
time) for the land use. This approach, though justified as an initial rough
approximation may lead to results which deviate markedly from measured
values. More appropriately, it is important to examine and analyze the basic
processes and factors involved in pollutant generation from diffuse sources.
II-l
-------
The Model Enhanced Unit Loading (MEUL) analysis is a method which
assesses pollutant loadings from various land uses on a directly comparative
basis. The loadings are generated by a hydrologic overland pollution
transport model calibrated and verified by extensive field measurements and
monitoring. The loadings generated in this way are abstracted from a
particular location at a particular time and reflect for a typical area mean
pollutant accumulation characteristics and statistically averaged
meteorological conditions subjected to certain land uses. The pollutant
loadings developed in this report do not include background or natural
composition of surface waters caused by its contact with geological layers,
undisturbed soils and natural vegetation.
Limitations of the MEUL method include:
1. The method is intended basically for comparative assessment of
loadings among various land uses.
2. The loadings are related to a few primary variables such as degree of
imperviousness of the area, cleanliness of the area, soil characteristics and
type of land use.
3. The meteorological inputs represent a typical average meteorological
year for the Ilidwest (Milwaukee). The accuracy of the estimates for pervious
areas was improved by considering the 10 and 90 percentile meteorological
seasons selected from 30 years of weather observations in southeastern
Wisconsin.
4. The pollutant accumulation rates on impervious areas represent
average U.S. rates as reported by Sartor and Boyd (1).
5. The loading figures were computed for five typical urban land uses
(residential, commercial, industrial, developing and parks) and five typical
non-urban land uses (row crops, pastures, woodland, wetland and feedlots).
6. The loading figures are not intended to be used for estimating
accurate loadings in areas where no historical or monitoring data are
available.
7. No monitored pollutant loadings from pervious areas and only limited
loadings from impervious areas during winter conditions in Midwestern areas
are available.
II-2
-------
II-2. CONCLUSIONS
Large amounts of pollutants are washed into surface waters from non-point
sources. The factors contributing to non-point pollution from various urban
and non-urban land uses have been investigated using a calibrated and verified
hydrologic transport model capable of simulating overland pollutant loading
and transport. The simulated seasonal loadings provide a comparison of the
variability and potential danger to surface waters of typical land use
activities. The model was calibrated and verified using field data from the
Menomonee River Pilot Watershed Study. The simulated loadings for typical
land use areas were evaluated as if the land uses were located on four
hydrologically different soils representative of standard hydrologic
categories. Developing urban, high density urban areas with no cleaning
practices, livestock feedlots and steep-sloped crop lands yield the highest
pollutant potential while parks and recreational areas, low density
residential and most urban areas with good cleaning practices produce much
less pollutants. The differences in pollution potential among the land uses
were several orders of magnitude. Summer rains in Midwestern areas have the
highest erosion potential; however, spring rains on bare soils with frozen
subsurface generate the highest sediment runoff on row cropland. By
sensitivity analyses, various parameters have been tested as to their effect
on loadings. The most significant parameters are extent of imperviousness of
urban areas, fraction of impervious areas directly connected to surface
runoff, depression and interception storage, average length of the dry period
preceding a rain, curb height for urban areas and soil type, slope and
vegetative cover for pervious urban and non-urban areas.
Various control techniques and their impact on non-point sources
pollutant generation have been discussed.
The loading diagrams which relate sediment and phosphate-P unit loadings
to the most important causative factors have been developed and their
applicability tested on several subwatersheds in the Menomonee River Basin.
Estimated and measured loading values were of the same order of magnitude.
II-3
-------
I1-3. METHODOLOGY
Pollutant Transport Process From Non-Point Sources
Water is the primary mover of pollutants through the environment from
their sources to the place of final disposal. Unlike pollutants from point
sources which enter the hydrologic transport route during a late stage of the
hydrologic cycle (channel or estuary flow), non-point source pollutants enter
the hydrologic route during its early stage, i.e., in precipitation or by
overland flow. The point where the pollutants enter the hydrologic transport
process depends not only on the type and location of the source but also on
the physical form in which the pollutant occurs. Gaseous, emulsified and
dispersed airborne pollutants enter the water transport route following
deposition on the surface by wet or dry fallout. Soluble pollutants mix with
water directly. Relatively insoluble pollutants either are dispersed and
picked up during rain or snowmelt events through subsequent surface runoff, or
are transported by wind and subsequently redeposited. Furthermore, pollutants
can be adsorbed by soil and dust particles and transported by water in the
particulate phase.
It is anticipated that non-point pollutant transport processes in urban
areas may be different from those in non-urban areas because:
1. Large portions of urban areas are impervious resulting in much higher
hydrological activity.
2. With the exception of construction sites most of the pervious
surfaces in residential or city areas are well protected by lawns and are
subject to less erosion.
3. Pollutant loadings in urban areas are affected mainly by litter
accumulation, dry or wet fallout and traffic while in non-urban areas most of
the pollution is due to erosion of soils and soil-adsorbed pollutants.
4. Over a large period of time (season) almost all of the pollutants
deposited on impervious surfaces which have not been removed by street
cleaning practices, wind or decay, eventually end up in surface runoff. On
the other hand, in non-urban areas soil represents an extensive pool of
sediments and pollutants adsorbed by soil and their removal rate depends then
on the energy of rain or runoff which liberates the soil particles and
eliminates surface protection.
Pollutant Loadings and Transport From Impervious Urban Areas
Pollutant accumulation on ground surfaces in urban areas and subsequent
washout by runoff represents a major pollutant contribution from non-point
II-4
-------
urban sources. Since impervious areas are almost fully hydrologically active,
most of the runoff and associated pollutants in highly urbanized areas
originate from these surfaces. The amount of deposited pollutants depends on
various factors and inputs. The major inputs are atmospheric fallout, street
litter deposition, animal and bird fecal wastes, dead vegetation, and road
traffic impacts. The factors which affect the quality of street refuse washed
out to surface waters include land use, population density, traffic flow and
frequency, effectiveness of street cleaning, type of street surface and
condition.
It has been realized that a simple unit loading value related to land use
may not provide an adequate estimation. Instead, the loading values should be
correlated to major causative factors which for various urban land uses can be
listed as follows:
a. Percent impervious area directly connected to a channel (a function
of land use or percent of imperviousness).
b. Population density (a factor related to land use).
c. Dry and wet atmospheric fallout.
d. Litter accumulation (a factor related to population density and land
use).
e. Traffic density (a factor related to land use).
f. Curb height and length/unit area (factors related to land use).
g. Percent open area (a factor related to land use).
h. Average wind velocity.
i. Street cleaning practices and effectiveness.
j. Average number of dry days preceding a rain or rain intensity.
k. Depression and interception storage (a factor related to land use).
With the exception of low density residential areas, other factors such as
slope, soil type, are expected to have little effect on pollutant loads from
urban areas because most of the loading originates from impervious areas.
It can be seen that most—but not all—of the above listed factors are
indeed related to land use. Thus, it may be possible to develop a multi-
dimensional loading factor for various urban land uses which would be a
function of:
a. Dry fallout (primary independent variable).
b. Street cleaning frequency and efficiency. ) parametric
c. Average wind velocity. ) independent variable
d. Average number of dry days preceding a rain. )
Unit Loadings From Pervious Areas
Urban or suburban pervious areas with the exception of those overlain
with heavy clay soils or areas with a very high groundwater table are
hydrologically active only during extreme storms or during spring melt or rain
events when the ground is frozen. Freezing of the surface layers in
Midwestern areas of the United States also provides protection against erosion
and groundwater contamination.
11-5
-------
Sediment and soil-adsorbed pollutants (e.g., P, heavy metals and most
pesticides) can be modeled by the Universal Soil Loss Equation (USLE). The
equation in its original form (2) can be written as:
A = (R) (K) (LS) (C) (P) Eq. (1)
where
A is amount of sediment generated/storm
R is the rainfall energy factor of the storm
K is the soil erodibility factor
LS is the length-slope factor
C is the vegetative cover factor
P is the erosion control factor
In this form the equation represents the amount of soil particles liberated by
rain energy impact. In order to obtain the sediment load to receiving waters
the equation must be multiplied by a delivery ratio:
AS = D * A Eq. (2)
where AS is the sediment load and D is the sediment delivery ratio.
Loadings of some pollutants other than sediment are then estimated by
PL = AS * CP * RP Eq. (3)
where
PL is pollutant loading
CP is pollutant content of the soil
RP is the enrichment factor accounting for the difference
in pollutant content in soil and the sediment
suspended in water
It is possible now to estimate which of the above variables is land use
related.
Rainfall factor, R
This is a function of storm intensity and volume and is not related to
any land use activity.
The rainfall energy factor, R, is computed according to the equation:
R = Zi{[(2.29 + 1.15 log Xi)]Di}l Eq. (4)
II-6
-------
where
X. is rainfall intensity, cm/hr
E. is rainfall hydrograph time interval
D. is rainfall depth during time interval i
I is the maximum 30 rain rainfall intensity of the storm
in cm/hr
It is evident that the rain energy input/season reduced by the amount of
snowpack on the surface is the major independent variable affecting the soil
loss estimation.
Soil erodibility factor, K
This is purely a function of soil characteristics (2,3). For most
Midwestern soils the K factor is in the range 0.1 to 0.4.
Slope-length factor, LS
This is based on formula (2):
LS = L/2 (0.0138 + 0.00974S + 0.0013852) Eq. (5)
where
L is length from the point of origin of the overland
flow, m
S is the average slope over the given overland flow
length, %
The equation indicates that soil loss is more sensitive to slope changes than
to the size of the area.
Vegetative cover factor, C
This variable depends on the crop or vegetative cover and the season. It
varies from 0.005 for heavily wooded areas to 1.0 for bare soils. Besides the
rain energy factor and slope this is a variable to which soil loss is very
sensitive.
Erosion control practice factor, P
This factor depends on erosion practices implemented in the Watershed.
In the absence of such practices the value assigned to this factor is unity.
II-7
-------
Delivery ratio factor, D
This Is probably the most difficult factor to estimate. For larger
watersheds the delivery ratio seems to be a function of watershed size and
configuration. For smaller areas it may be a function of the lot roughness
(depression and interception storage) and, mainly, permeability. For
relatively homogeneous sites, a study by the Midwest Research Institute (4)
related delivery ratio to soil texture and drainage density which is defined
as the ratio of total channel-segment lengths to the basin area.
If a loading function is to be developed it should be related to the
rainfall energy factor as a primary independent variable, with soil type,
slope and depression storage as parametric variables
Application of LANDRUN Model - Model Enhanced Unit
Loading (MEUL) Simulations Based on Land Use
This method used in the study to develop loading functions relied on
field data and system simulation. It has been realized that although the
field data provide the best information on pollutant loadings from a
particular site the information is limited by time and location at which the
data were gathered. On the other hand, even the most complex simulation model
of a watershed can provide results quite far from reality if the model is not
properly calibrated or verified.
A model developed for this study has the code name LANDRUN (5). It is a
deterministic watershed model capable of simulating the following processes:
a. Snowpack-snowmelt by the Holtan or Philip Models.
b. Infiltration by the Holtan or Philip Models.
c. Excess rain can be computed as the difference between precipitation
and evaporation, evapotranspiration, infiltration and surface
storage.
d. Routing of excess rain by an Instantaneous Unit Hydrograph Method.
e. Dust and dirt accumulation in urban areas and washout.
f. Removal of accumulated pollutants on impervious areas by cleaning
practices.
g. Surface erosion by a modified quasi-dynamic USLE which includes
effects of rainfall energy and sheet runoff.
h. Routing of the sediment and sediment-adsorbed pollutants.
The model takes into consideration several parameters including:
a. Land use data.
b. Meteorological parameters.
c. Pollutant input.
II-8
-------
The computer model is capable of estimating:
a. Storm water hydrographs and volume.
b. Sediment transport from pervious areas.
c. Dust and dirt washout from urban impervious areas.
d. Volatile suspended solids in the runoff.
e. Adsorbed pollutant loadings.
A dynamic soil adsorption segment is an optional feature of the model which
enables detailed study of pollutant-soil interactions (6).
Following calibration and verification of the LANDRUN nodel (7),
pollutant loading simulations were conducted for the land uses agreed upon by
PLUARG. The land uses were grouped into urban and non-urban categories:
Urban uses Non-urban uses
Low density residential Row crops
Medium density residential Pasture
High density residential Livestock feedlots
Commercial Woodlands
Industrial Wetlands
Park and recreation
Developing
To simulate pollutant loadings, each land use was assigned typical values
for such variables as degree of imperviousness, fraction of impervious areas
directly connected to a channel, depression storage, permeability of pervious
areas, slope, soil moisture characteristics, etc. In addition, other
variables describing atmospheric fallout, litter accumulation, street sweeping
practices and the USLE inputs were selected. The values were based on
Menomonee River Pilot Watershed data or on literature values typical of
Midwestern urban areas.
Surface characteristics
The model requires a detailed description of the hydrologic
characteristics of the subwatershed surface. Included are: Degree of
imperviousness, depression and interception (surface) storage, subwatershed
slope, surface roughness and extent of impervious areas directly connected to
a channel.
Most of the land surface data was obtained from the SEWRPC Land Data
Management System (Land DMS) (8). Unless otherwise specified default values
were substituted in the model for depression and interception storage and
surface roughness. For combined depression and interception storage
characteristics, default values used are: 6.35 mm (1/4 inch) for pervious
areas and 1.58 mm (1/16 inch) for impervious areas. These values are similar
to those used in the Chicago study (9) and other urban studies. For non-urban
pervious areas a graph developed by Hiemstra (10) served as a guide to
selection of the storage characteristics (Fig. II-l).
II-9
-------
Surface roughness characteristics are necessary if routing of pollutants
is required. The value of the Manning roughness factor for pervious areas is
0.25 and for impervious areas is 0.012.
The impervious areas not directly connected to the surface runoff
channels include rooftops discharging through underground drains, paved areas
overflowing on adjacent pervious surfaces, etc. This factor can be related
approximately to the total imperviousness of the area as shown in Fig. II-2.
The simulated areas were 1 km2 for each land use.
Soils
For simulation purposes, four soils typical of the Menomonee River
Watershed or immediate vicinity were selected. These soils are representative
of each basic hydrologic group ranging from the most permeable hydrologic
group A to the least permeable group D (11).
Table II-l shows the basic soil data used in the simulation; these data
reflect typical values for soils given in SCS soil maps. More exactly
measured values for ten major soil types in the Donges Bay Road subwatershed
(station 463001) are reported in Table II-2.
Some of the data such as 0.3-bar moisture tension (field moisture
capacity) and 15-bar moisture tension (wilting coefficient) are unavailable
from soil maps. In this case, a graph relating moisture characteristics to
median particle diameter of the soils was prepared using data from the
Menomonee River Watershed and literature values (Fig. II-3). The median
particle diameter in mm was computed using a formula suggested by Horn (13):
dm = t°'3 ^% Sand') + °'01 ^% s±lt) + °'002
The particle sizes (Fig. II-4) are the averages of the particle size ranges
recommended by the U.S. Department of Agriculture (USDA).
The permeability ranges related to soil mean particle diameter are shown
in Fig. II-5. Known and measured data for some Wisconsin soils indicate that
a lower range of permeability seems to be typical for Wisconsin rather than an
average theoretical curve. However, data measured by Bouma et al. (14)
represent permeabilities of septic tank seepage fields after several years of
operation and may not provide a good approximation of permeability of typical
undisturbed soils. Such values confirm the lower limits of the permeability-
texture relationship.
Soil erosion data
Use of the USLE requires a knowledge of: the rainfall energy factor (R),
soil erodibility factor (K), cropping management factor (C), erosion control
practice factor (P) and the slope-length factor (LS).
11-10
-------
Contour Furrows
25
Fig. II-l. Depression storage capacity in relation to degree
of land slope (10).
11-11
-------
1.0 --
5-1
O
a
cfl
0.8 ••
0.6 - -
0.4 -•
0.2 •-
80
100
Total Impervious Area, %
Fig. II-2. Fraction of impervious areas not directly
connected to channel (12).
11-12
-------
Table II-l. Properties of soils used in the simulation
Soil type
Property
Hydrologic group
Depth of A-horizon, cm
Sand, %
Silt, %
Clay, %
Mean diameter, mm
Organic matter, %
Permeability of A-horizon,
cm/hr
0.3 bar 1^0 content, %
15 bar 1^0 content, %
Porosity, %
K factor*
PO/-P adsorption,** ug/g
Total P content, ug/g
Boyer Is
A
41
80
15
5
0.415
0.5
40
8
0
30
0.09
243
1,000
Hochheim 1
B
20
45
39
16
0.138
2.0
10
20
7
34
0.24
346
1,500
Ozaukee sil
C
28
15
55
20
0.051
3.0
3.0
30
17
43
0.31
403
1,800
Ashkum sicl
D
28
5
56
39
0.021
8.0
0.5
36
24
46
0.15
697
3,100
*K is the soil erodibility factor used in USLE.
**Soil adsorption maximum obtained from the Langmuir isotherm.
11-13
-------
Table II-2. Properties of the major soil types surrounding the Donges Bay station (463001), Menomonee
River Watershed
H
1
I—1
-P-
Property
Area, ha
% of total area*
Depth of A-horizon, cm
Hydrologic group
PH
Clay, %
Organic C,** %
0.3 bar H20 content, 7,
15 bar HjO content, '/.
Available HjO, cm/cm
Extractable Fe, %
Bulk density, g/cm
Permeability, cm/hr
Porosity, %
Cation exchange capacity,
me/100 g
Ozaukee
6
20
1.
7
1
1.
1
41
14
1,
.6
.3
52
.8
.2
44
.5
.0
,018
47
18
C
to
to
to
to
0
to
to
to
to
sil Mequon sil
182
.5 8.5
28
C
7.3 7.4 to 7.8
20.8
1.70
8.1
.20 0.20
1.3
1.55
45.7
14.4
Soil type
Ogden muck Pella sil Theresa sil Sebewa sil Colwood sil Ashkum sicl
162 101 73 47
7.6 4.7 3.4 2.2
90 30 40 40
D B D D
6.6 to 7.8 6.6 to 7.3 6.6 to 7.8 7.4 to 7.8
11.7 to 13.1
3.89 to 4.30
34.6
10.5
>0.20 0.24 0.20 0.20
1.20 to 1.31
21.2 to 23.9
56 50
2.6 2.4
23 25
D D
7.4 to 7.8 7.4 to 7.
39.7
3.66 to 5.
Fox 1 Kibble sil
39 31
1.8 1.4
18
B B
8 6.1 to 7.3 7.4 to 7.8
88
16.2 to 19.2
0.20 0.20 0.16 0.16
1.1 to 1.
1.50 to 1.
1.6 to 5.1 1.6 to 5.
33.2 to 33.
2
85
1 1.6 to 5.1 1.6 to 5.1
9
*Total area of Donges Bay station subwatershed is 2,144 ha.
**To convert organic C to organic matter divide organic C by 0.60
-------
50
I
M
Ul
10
0.002
0.01
Particle Diameter, mm
ty co
d o
g
-------
13
C
n)
T)
C
a)
co
PM T3 0) g (DOC .H
C 3 en cs , -U >>CflQJ'HS-lt^W>
CO .-H W W C T3 cfl M tO
iH -H Cl) -H CU O C) V<
O CO t>pLi;S3OE> O
1 1 1 1 1 1
i-t CN) m
o o o >H CN in in
O O O OO O .-HCNCMinO O OO
o o o oo o oooorH CM mo
Particle size, mm
Fig. II-4. Soil particle size distribution accepted by USDA-SCS.
11-16
-------
Textural Class
USDA-SCS
Sand
Loamy Sand
Sandy Clay Loam
Sandy Loam
Sandy Clay
Loam
Clay Loam
Clay (Fine)
Silt Loam
Silty Clay Loam
Clay (Very Fine)
Silt
Silty Clay
Mean Particle
Diameter, mm
0.285
0.250
0.176
0.167
0.157
0.124
0.103
0.0785
0.0726
0.0362
0.0328
0.0240
0.0236
1.0
PL,
§
OJ
X
0.6 -
0.3 -
0.1
0.06 _
0.03 -
0.003-
o.ooi-
Soils with high exchangeable
sodium percent, highly dispersed
swelling clays.
0.04
Theoretical
as function
size.
Essentially
impervious
Slowly
permeable
I / non-swelling
I I I I I MM I I I I lll'lf I |Ti MR
Puddled soils, poor
structure, highly
compacted.
Permeable
permeability /
of particle /
/ T Soils witt
* good struc-
ture, highly
flocculated
duetto high
Ca organic
matter, iron
oxides, non-
compacted,
non-swelling
clay
0-01 0.03 0.06 0.1 ,, 0.3 0.6 1.0
m/day
10
0.1
1 ' ""| 1 1—T
0.4 1.0 4
cm/hr
Permeability Rates
40.0
Fig. II-5. Relationship between soil permeability and soil texture (13).
-------
The value of R is computed by the LANDRUN model from the rainfall data
and the LS factor is estimated from average slope and area of the subwatershed
for each land use. However, the remaining three factors must be inputted for
each soil and land cover. Figure II-6 is a nomograph for estimating K. The
factor K is determined from the contents of silt and very fine sand (particle
size 0.01 to 0.1 mm), sand (0.1 to 2 mm), organic matter, soil structure and
permeability. The K factors for the selected four soils are:
Soil K factor
Boyer Is 0.09
Hochheim sil 0.24
Ozaukee sil 0.31
Ashkum sicl 0.15
The factor, C, is dependent on type of groundcover, general management
practices and composition of the soil. For simulation purposes, the values
suggested by Brandt (15) were used (Table II-3). For agricultural cultivated
lands C was 1 during the spring season and adjusted -to its tabular value for
summer and fall.
The P factor was 1 for most land uses. Some erosion control was assumed
on croplands.
Organic matter content of soils was selected to reflect typical values in
the Watershed.
Phosphate-P content of soils was based on the known range of P content of
the Ozaukee sil (P - 0.18 %) which was determined from the measured total P-
suspended solids relationship from the spring runoff at the Donges Bay Road
station. The phosphate-P content for other soils was adjusted according to
their adsorption characteristics, Q° (6).
The lead content of average soils is very low. The U.S. Geological
Survey (USGS) has undertaken an in-depth study (16) to determine the elemental
composition of surficial materials in the United States. Soil samples were
collected from 863 sites throughout the 48 conterminous states and analyzed
for 44 elements. The average values for eastern and western parts of the
United States are presented in Table II-4.
11-18
-------
Soil Structure
1 - Very Fine Granular
2 - Fine Granular
3 - tied, or Coarse
Granular
- Blocky, Platy or
Massive S
Percent Sand
(0.1-2.0 mm)
6 - Very Slow
5 - Slow
4 - Slow to Mod.
3 - Moderate
2 - Mod. to Rapid
1 - Rapid
100
Fit;. 11-6. Determination of soil K factor (3).
-------
Table II-3. C-value used to compute
erosion (15)
Land use C-value
Cropland 0.08
Grassland 0.01
Woodland 0.05
Construction 1.00
Urban 0.01
11-20
-------
Table II-4. Metal concentrations of surflcial materials of the U.S.A.(16)
Element
As
Ba
Cd
Ce
Cr
Co
Cu
Fe
Ga
Ge
Au
Hf
In
La
Pb
Mn
Mo
Nd
Ni
Nb
Pd
Ft
Re
Sc
St
Ta
Te
Tl
Th
Ti
U
V
Yb
Y
Zn
Zr
Total
Average, yg/g
*
554
—
86
53
10
25
25,000
19
—
—
—
—
41
20
560
3
45
20
13
—
—
—
10
240
—
—
—
—
3,000
—
76
4
29
54
240
30,100
Range, yg/g Conterminous U.S.A.
< 1,000
15 to 5,000
< 20
<150 to 300
1 to 1,500
<3 to 70
<1 to 300
100 to 100,000
<5 to 70
< 10
< 20
< 100
< 10
<30 to 200
<10 to 700
<1 to 7,000
<3 to 7
<70 to 300
<5 to 700
<10 to 100
< 1
< 30
< 30
<5 to 50
<5 to 3,000
< 200
< 2,000
< 50
< 200
300 to 15,000
< 500
<7 to 500
<1 to 50
<10 to 200
<25 to 2,000
<10 to 2,000
__
430
—
75
37
7
18
18,000
14
—
—
—
—
34
16
340
—
39
14
12
—
—
—
8
120
—
—
—
—
2,500
—
56
3
24
44
200
2,990
Geometric means, yg/g
West of 97th meridian
_—
560
—
74
38
8
21
20,000
18
—
—
—
—
35
18
389
—
36
16
11
—
—
—
9
210
—
—
—
—
2,100
—
66
3
25
51
170
23,858
East of 97th meridian
300
—
78
36
7
14
15,000
10
—
—
—
—
33
14
285
—
44
13
13
—
—
—
7
51
—
—
—
—
3,000
—
46
3
23
36
250
19,263
* Below detection limit.
11-21
-------
Pollutant accumulation in urban areas
The basic feature of urban areas is the extent of imperviousness of the
land surface. Besides the hydrological significance of impervious areas
(higher runoff, shorter duration of high pollutant concentrations, higher
flood peaks), essentially all pollutants are flushed into the receiving waters
whenever runoff takes place.
Pervious urban areas produce pollutant loadings of lesser magnitude
provided that these areas are not steep and are well protected by lawns,
shrubbery and trees. The amount of pollutants deposited on impervious areas
depends on various factors and inputs as mentioned earlier. Pollutants
transported from impervious areas can be carried by wind and traffic impact
and they accumulate near the curb. Thus, it has been reported that street
pollution accumulation rates are related to the unit length of curb (Fig. II-
7; Table II-5). Reporting street refuse loadings/unit length of curb, instead
of a more meaningful area loading, seems to be justified since it has been
observed that almost 80% of refuse can be found within 15 cm and 97% within 1
m of the curb (17). The strong correlation existing between curb length
density and degree of imperviousness of residential areas (Fig. II-8) can be
utilized for simulation purposes.
A recently-developed regression formula (9) between curb length of urban
areas and population density is:
CL = 311.67 - (266.07) (0.839)(2'48 PD) Eq. (7)
where
CL is curb length in m/ha
PD is population density, persons/ha
Refuse washed from streets by runoff contains many hazardous
contaminants. Significant organic pollutants, toxic metals, pesticides and
bacteria are associated commonly with the dust and dirt fraction (Tables II-6
and II-7). It should be noted that these values, though typical, are not
uniform but represent averages from a wide range of refuse deposition and
contamination from a limited number of municipalities which have been studied.
Atmospheric pollutant deposition
Deposition of atmospheric pollutants occurs as dry or wet fallout. The
deposition rates of particulate atmospheric pollutants in United States cities
vary from 3.5 to >35 Tonnes/km2/month. Higher deposition rates can be
expected in congested industrial areas or business districts while lower
deposition rates are common in residential and rural suburban zones (Table II-
8).
11-22
-------
DUST FALLOUT FROM INDUSTRIAL
AND STATIONARY FUEL COMBUSTION PROCESSES
I1 1
'
POLLUTANTS CARRIED
AWAY BY WIND AND
TRAFFIC
MEDIAN
BARRIER
LITTER
DEPOSITS
CURB
POLLUTANTS EMITTED FROM
MOTOR VEHICLES
POLLUTANTS ACCUMULATED
AT ROAD SURFACE
Fig. II-7. Pollutant accumulation schematic model.
11-23
-------
Table II-5. Street refuse accumulation
Solids accumulation, g/curb m/day
Land use Chicago* Eight U.S. cities**
Single family
Multiple family
Commercial
Industrial
Weighted average 22.3
10.4
34.2
49.1
68.4
48
66
69
127
*Taken from (9); data is for dust and dirt only.
**Taken from (1); data is for total solids which contain
75% dust and dirt.
11-24
-------
400 ,
300-
ed
u
60
£ 200 -|
100 -
00
8
O Data from (18)
A Menomonee River Watershed
0 20 40 60 80 100
Imperviousness, %
Fig. 11-8. Curb length-imperviousness relationship.
11-25
-------
Table II-6. Pollutants associated with street refuse (1)
Pollutant
BOD5*
COD
Volatile solids
Total nitrogen
Nitrate-N
Phosphate-P
Total metals
Zn
Cu
Pb
Ni
"g
Cr
p.p'-DDD, ng/g
p,p'-DDT, ng/g
Total coliforms, organisms/g
Fecal coliforms, organisms/g
Concentration, ug/g total solids
Residential Industrial Commercial Total
5,000 3,000 7,700 5,000
33,800 59,000 31,500
78,000 56,500 77,000 71,400
1,020 870 600 1,570
32 41 314 67
600 800 550 780
2,040 1,150 1,800
460
140
410
36
52
78
48
43
71xl06
40xl06
*Taken from (9).
11-26
-------
Table II-7. Metal contamination of street refuse (19)
Contaminant
Concentration, ug/g total solids
Residential
Industrial
Commercial
Total
Cd
Cr
Cu
Ni
Pb
Sr
Zn
3.45
186
95
22
1,468
23
397
2.83
208
55
59
1,339
134
283
3.92
241
126
59
3,924
151
506
2.82
183
101
31
1,324
177
338
11-27
-------
I
NJ
00
Table II-8. Annual and monthly mean deposition rates of particulate material in
Milwaukee County (20)
Annual
Land use
Agricultural and
rural suburbs
Residential
Local business
Commercial
Industrial
1951
58.4
93.6
152.4
191.3
342.8
1957
64.3
82.7
113.2
200.0
235.1
1963
85.
88.
124.
153.
172.
r\
deposition rate, Tonne s/ km /yr
1965 1966 1967
2 102.1 114.5 129.5
5 99.4 97.7 95.2
4 102.5 109.0 121.9
7 173.8 153.7 190.4
4 189.6 174.1 180.0
1968
98.5
94.0
123.6
169.6
177.1
1969
80.4
81.0
96.5
146.5
170.4
Monthly deposition
Jan Feb Mar
Apr
May
rate (
June
1951 to 1969), Tonnes/km2 /mo
July Aug Sept Oct
Nov
Dec
9.85 10.4 12.9 14.1 14.5 12.8 10.5 10.6 10.6 10.2 9.71 8.09
-------
Wind erosion
The effect of wind erosion on surface particulate pollutant loadings
seems to be significant only occasionally. Factors important in the
assessment are: climate, soil characteristics, surface roughness, vegetative
cover and length of the eroding surface (21). In urban areas the primary
source of wind eroded materials are open, ungrassed areas and construction
sites.
Motor vehicles
Traffic can contribute significantly to pollutant deposition in urban
areas. High amounts of some metals in storm water runoff are attributed to
motor vehicle emissions and to the breakdown of road surface materials and
vehicular parts. Motor vehicle usage can influence pollutant accumulation in
urban areas and near high density traffic lanes by emission of pollutants, oil
and gasoline spillage, mechanical impact of traffic, tire abrasion, etc.
Therefore, in addition to traffic density, the pavement composition and
conditions are significant in determining traffic impact on pollution.
Streets paved entirely with asphalt have provided total solids loadings of
about 80% higher than all-concrete streets (17). Streets where conditions
were rated "fair to poor" were found to have total solids loadings ~ 2.5 times
greater than those rated "good to excellent" (1).
Litter deposition
Litter deposits in urban areas include solid wastes dropped from garbage
collectors, animal and bird fecal droppings, fallen tree leaves, grass
clippings, etc. The dust and dirt component of litter (material <3.5 mm) is
regarded as having greatest pollution potential; although most of the litter
is orginally larger in size than dust and dirt, the mechanical fracture of
litter increases the amount of dust and dirt. It has been reported that
residential areas had greater amounts of street surface dust and dirt as
population density increased, reflecting increased pedestrian and roadway
traffic (9). It is also expected that the higher the population density, the
greater the street deposition from garbage collections.
Effect of vegetation
Leaf fall and grass clippings in urban areas contribute significantly to
dust and dirt accumulation. For most of the year, the accumulation on
impervious areas arises from erosion of soils from surrounding pervious areas,
atmospheric pollution and litter accumulation and during the fall season, leaf
fall increases the organic solids accumulated at the surface.
11-29
-------
Heaney and Huber (22) estimated from the study of Carlisle et al. (23)
that average leaf fall was 14 to 26 kg/tree/year. The area investigated was
stocked with trees ranging in age from 40 to 120 years with a 90 to 95% closed
canopy, and 155 trees/ha; species were mainly oak and birch. Typical values
for leaf fall in Minnesota are ~380 Tonnes/km2/year in a forested area with
~420 trees/ha with 65% occurring during the fall season. Fallen leaves are 90
to 97% organic matter and contain about 0.04 to 0.28% P (24).
For loading simulations, values of leaf fall for various land uses were
estimated (Table II-9). Organic and P contents of leaves were assumed to be
90 and 0.1%, respectively.
A detailed statistical evaluation of street litter accumulation is
contained in Appendix II-A.
Pollutant washout
Not all pollutants accumulated during a period preceding a rainfall are
washed off the impervious surface during the initial moments of the rain. The
rate at which loose particulate matter is washed from street surfaces depends
on three factors, namely, rainfall intensity, street surface characteristics
and particle size (17). It can be expected that the amount of pollutants
washed off generally will follow the equation:
PL = 4^ = - K L Eq. (8)
dt p
where
PL is pollutant washout rate
L is amount of pollutant present on the surface
K is a coefficient depending on rain intensity and
street surface characteristics
The coefficient, K , which was found to be independent of particle size in
the range of 10 to 1000 pin is approximated as follows:
where
Kp = EUR Eq. (9)
E is urban washout coefficient
R is the surface runoff rate, cm/hr
11-30
-------
Table II-9. Daily leaf fall
o
Leaf fall, Tonnes/km /day
Land use
Forest
Parks
Low density residential
Medium density residential
Spring-Summer
2.45
1.22
0.17
0.08
Fall
7.0
3.5
0.35
0.18
High density residential,
commercial and industrial 0.016 0.036
11-31
-------
Values close to 1.81 1 have been reported for the washout coefficient, E
(25). U
Not all litter is available for transport by surface runoff. Therefore
sediment washout rate should be multiplied by an availability factor (25) as:
Ag = 0.57 + 0.5 R1'1 Eq. (10)
It is obvious that a limit must be placed on the availability factor as runoff
rate increases. A suggested value for the maximum A is 0.75, which implies
that about 25% of urban litter is unavailable for transport.
Street sweeping practices
Street sweeping is a common practice in American cities whereas in
European cities streets are washed. Most of street sweeping is done
mechanically either by brush or vacuum. Removal efficiencies with brush
sweepers are shown in Table 11-10; removal of deposited suspended solids is
~50% with one pass of a sweeper. Some pollutants are associated more with
finer particle fractions (Table 11-11). By cumulative multiplication of
sweeping efficiency for each fraction and pollution concentrations on
particles of the fraction, overall efficiency can be estimated (Table 11-12),
e.g., the efficiency of sweeping for P control would be 22% compared to 50%
for total solids. Street washing is more effective for fine materials.
Meteorological inputs
The climate of the Milwaukee area is influenced by the general storms
which move eastward across the upper Ohio River valley and the Great Lakes
region.
Annual precipitation is about 762 mm (30 in); two-thirds of which occurs
during the growing season. Thunderstorms, which carry the highest erosion
potential, occur less frequently and with less severity than in areas to the
south and west. The maximum rainfall which occurred in a 24-hr period is 172
mm (5.76 in) in June 1917. As much as 20 mm (0.79 in) has fallen in 5 min, 28
mm (1.11 in) in 10 min, 34 mm (1.34 in) in 15 min, 42 mm (1.86 in) in 30 min,
and 57 mm (2.25 in) in 1 hr.
The average yearly rainfall energy factor, R, for sediment loss
estimation by the USLE assigned for the Milwaukee area is R = 125 (2).
It has been realized that pollutant loadings shall be representative of
an average season, i.e., they express loadings which would be a mathematical
average over a long time period. In order to obtain such averages, at least
20 to 30 yr of data is necessary. In the absence of such a data base, as is
almost always the case, water quality (loading) data time series can be
generated by a properly calibrated and verified model using a measured
meteorological time series as input. Hourly precipitation data for the
11-32
-------
Table 11-10. Pollutant distribution in various particle sizes (17)
Particle size, Pollutant distribution, %
Vm Total solids Volatile solids COD TKN
>2000 24.9 11.0 2.9 9.9
840-2400 7.6 17.4 4.5 11.6
246-840 24.6 12.0 13.0 20.0
104-246 27.8 16.1 12.4 20.2
43-104 9.7 17.9 45.0 19.6
<43 5.9 25.6 22.7 18.7
Table 11-11. Interrelationship of sweeper efficiency
and particle size (17)
Particle size, ym Sweeper efficiency, 7,
>2000 79
840-2000 66
246-840 60
104-246 48
43-104 20
<43 50
Overall 18
Table 11-12. Street sweeping removal efficiency of
pollutants (17)
Pollutant Removal efficiency, %
Total solids 50.0
Volatile solids 42.5
COD 31.0
TKN 43.9
PO^-P 22.2
PO^-P
0
0.9
6.9
6.4
29.6
56.2
11-33
-------
Milwaukee area are available and a time series covering 37 yr was prepared.
In an ideal case, the simulation period would cover an entire 37 yr of
data, but with more complex models such simulation periods may prove to be
prohibitively expensive requiring considerable computer time and storage
capacity.
To avoid the expensive, long simulation runs, the 37 yr series of
meteorological data was analyzed as to its distribution of seasonal wetness
and erosion potential.
The wetness analysis utilized a simple summation of precipitation per
calendar season; the seasonal erosion potential is based on the USLE R factor
as expressed by Eq. (4). In analyzing the erosion potential, only rain events
were counted, snowfall was omitted.
The probabilistic distributions of seasonal wetness and erosion potential
are shown in Figs. II-9 and 11-10. The arrows indicate the probabilistic
expectancy of season from the monitoring period 1975-1977. It should be
pointed out that the graphs are typical for the storm patterns in the
Milwaukee area and should not be generalized to other areas.
Summaries of the final land data used for simulation are in Tables 11-13
and 11-14.
11-34
-------
400.-
300. _
200'-
IOO--
'16
1977'
SUMMER
12
\
WINTER
10
50
PL (%)
Fig. II-9. Seasonal cumulative frequency of precipitation.
--8
--4
0
95
11-35
-------
Fig. 11-10. Seasonal cumulative frequency of R factor .
11-36
-------
Table 11-13. Urban land use information
1— t
1— 1
1
UI
Category
Housing, dwelling/ha
Curb, m/ha
Impervious area, %
Impervious area not
connected, %
Street litter accumulation,
g/m/day
Dust and dirt fallout,
t onne s /km /day
Spring and summer
Fall
Low density
0.3 to 5
95
25
90
45
0.25
0.6
Residential
Medium density
5 to 16
270
60
55
66
0.25
0.5
Land
High density
>16
300
95
10
60
0.28
0.3
use
Commerical
300
90
10
65
0.46
0.48
Industrial
300
90
10
100
0.50
0.5
Park and
recreation Developing
2.0 3.0
90 90
1.4** 0.5
3.5** 0.5
Sweeping frequency, days
Well maintained
Poorly maintained
Sweeping efficiency, %
1000
1000
1000
1000+ 1000+
) 30
) 1000"*
Solids
P04-P
Impervious area affected
by sweeping, %
C factor for pervious
area*
50
22
50
0.01
50
22
50
0.01
50
22
50
0.01
50
22
50
0.01
50
22
50
0.01
50
22
50
0.01
50
22
50
0.01
*C is the cropping factor used in USLE.
**Includes leaf fall and vegetation.
+Denotes the absence of maintenance.
-------
Table 11-14. Non-urban land use information
u>
CO
Land use
Category
Impervious area, %
Litter and atmospheric
fallout, tonnes /km2 /day
Spring and summer
Fall
C factor*
Spring
Summer and fall
P factor**
Soil
Row crop
1.0
0.2
0.2
1.0
0.08
0.8
average
Feedlots Pasture
1.0 1.0
0.2 0.2
0.2 0.2
1.0 0.03
1.0 0.03
1.0 1.0
compacted; average
high
organic
matter and
P contents
Wetlands
1.0
0.2
0.2
0.5
0.2
1.0
average;
high water
table
Woodlands
1.0
2.4
7.0
0.005
0.005
1.0
average
*C is the cropping factor in USLE.
**P is the conservation factor in USLE.
-------
RESULTS. AND DISCUSSION
Simulated Loadings
The simulation results for each land use and characteristic season
produced loading diagrams which related loadings of pollutants (sediment,
volatile suspended solids and phosphate-P) to the R-factor for mostly pervious
areas and to atmospheric fallout for impervious areas. These loading diagrams
are presented in Appendix II-B.
Loadings for urban areas were related to the degree of imperviousness and
accumulation rates established for relatively clean areas (i.e., areas which
are swept about once a week) and areas with on cleaning. The upper curves
represent loadings from poorly-maintained areas based on a uniform daily rate
of pollutant accumulation which decreases with prolonged dry periods similar
to the rates reported (1,17). The loadings for urban land uses were plotted
separately for impervious and pervious areas. It should be remembered that
the loading from the impervious areas was estimated assuming an atmospheric
fallout rate of 0.8 Tonnes/km2/day and curb litter loadings similar to those
obtained by Sartor and Boyd (1) and Sartor et al. (17). If significantly
different accumulation rates are anticipated the loadings from impervious
areas should be adjusted accordingly to reflect the change in curb loading
rate due to increased or decreased atmospheric fallout.
Since impervious urban areas were simulated for an average year and the
loadings appear to have no correlation with rainfall intensity, the average
loading values can be read directly from the diagram and values are presented
in Table 11-15. In order to obtain average loadings for pervious areas, the
loading diagram related to the R-factor must be transformed to a probability
distribution loading plot using the cumulative frequency chart of the R-factor
as given in Fig. 11-10. The area under the R-factor-probability curve can be
graphically or numerically integrated according to the equation:
= J1 Lipidp Eq. (11)
c
where
I is the average loading, kg/ha
L^ is the loading function
PJ is the assigned probability of L. being less or equal.
It also should be noted that the loading diagrams in Appendix II-B
reflect loadings from a 1 km2 area under slope category B (2 to 6%) for the
impervious urban areas and slope category C (6 to 12%) for pervious areas. To
transform these values to other slopes and areal units, the loadings
corresponding to pervious areas should be multiplied by slope or area
11-39
-------
I
-P-
o
Table 11-15. Simulated pollutant loadings* for urban land uses under slope category B (2 to 6%)
during an average year (1968)
Soils and maintenance
Imperv. ,
Sediment,
Winter
Spring
kg/ha
Summer
Volatile susp. solids,
kg /ha
Fall
Winter Spring Summer
Fall
Winter
PO»-P,
Spring
kg/ha
Summer
Pb, kg/ha
Fall
Winter
Spring
Summer
Fall
Low Density Residential
Poor soils, poorly
maintained area
Poor soils, well
maintained area
Permeable soils, poorly
maintained area
Permeable soils, well
maintained area
25
25
25
25
24
16
24
16
300
130
225
55
450
365
240
180
150
100
130
35
2.0 19.0 22.0
1.25 5.0 13.0
2.0 15.0 13.0
1.25 3.0 4.0
**
**
**
**
0.016
0.01
0.016
0.01
0.44
0.36
0.15
0.04
1.10
1.00
0.18
0.12
0.34
0.20
0.12
0.03
0.035
0.035
0.23
0.023
0.29
0.036
0.29
0.036
0.25
0.057
0.25
0.056
0.24
0.012
0.24
0.012
Medium Density Residential
Poorly maintained area
Well maintained area
60
60
221
141
900
275
1,100
540
600
120
17 70 80
11 19 34
98
19
0.14
0.09
1.25
1.10
1.36
1.00
0.98
0.13
0.32
0.21
1.31
0.31
1.43
0.50
0.90
0.11
High Density Residential
Poorly maintained area
Well maintained area
Poorly maintained area
Well maintained area
Poorly maintained area
Well maintained area
95
95
90
90
90
90
294
187
264
167
403
256
2,090
304
1,950
283
2,970
420
2,040
800
1,920
516
2, no
1,200
1,700
200
1,720
200
2,600
330
22 180 158
14 20 60
Commercial
16 121 115
10 17 28
Industrial
29 229 201
18 298 83
498
28
287
34
520
65
0.20
0.13
0.11
0.07
0.21
0.13
1.62
0.33
1.00
0.30
2.00
0.60
1.44
0.70
1.30
0.60
2.40
1.10
1.50
0.16
1.00
0.20
2.18
0.30
0.43
0.27
1.06
0.66
0.54
0.33
3.40
0.49
8.16
1.03
4.25
0.54
2.98
0.67
7.08
1.60
3.71
1.50
2.80
0.28
6.63
0.25
3.48
0.40
*Slmulated loadings were obtained assuming dust fallout rates of 0.8 tonnes/kra2/day except for park and recreational areas where the value was increased to
1.4 in the Spring and to 3.5 tonnes/km2/day in the Fall because of the effect of dead vegetation.
**60 to 85% of the total sediment was in the form of vegetation.
-------
M
-e-
H-
OQ
M
M
ri O
H- re
O
c o
01 O
n
C i-i
n to
cr o
(D rt
3 H-
o
B> 3
M
(D Mi
p, fa
01 O
n MI
o
«: i-t
H-
rf 01
3* re
a.
»-3 H-
&g
I-- 3
(D rt
M (»
M 3
i a.
o
01
T3
g"
o
CB
a-
H-
3
TO
01
Slope Correction Factor
O
•a
n
o -
-------
Slope Correction Factor
I
-C-
po
09
t-h O
O Pi
i-! CU
H-
C 3
cn OQ
0)
rt rt
^T H-
T)
H M
(H p,
cr n>
M i-i
(D
t-h
M O
M CU
ON H-
(D
i-j
m
3
en
M
o
n>
n
(D
OQ
o
o
o
w
i—>
o
m
K)
O
o
o
tD
CO
-------
Loading Correction Factor
8?
M
to
rt
H-
O
3
0)
g"
CO
H-
N
n>
o
Hi
o
o
(6
to
i-<
to
to
rt
O
CO
(T)
CL
CD
to
O
to
a,
H-
3
TO
-------
that erosion potential of soils in the slope category D (12 to 20%) is about
20 times greater than that for soils in slope category A (0 to 2%).
Table 11-16 shows the average potential loading values for typical
pervious land (non-urban) uses situated on the four hydrologic soil groups.
The loadings for each land use, soil and season are long-term average
simulation results.
It is seen from Tables 11-15 and 11-16 that developing urban, industrial,
commercial and high density residential land uses with poor maintenance and
street cleaning practices, produce the highest potential loadings in urban
areas while low density residential and park and recreation land uses
contribute the least. For non-urban land uses, livestock feedlots are
expected to have the highest pollution potential and woodlands the lowest.
However, simulated loadings for feedlots may be unrealistic because of the
impossibility of arriving at reasonable values for the soil credibility
factor, K, due to the unusually high organic matter content and unknown
compactness of feedlot soils.
Differences between the pollution potentials for various land uses
indicate that pollution control measures should be concentrated intensively on
hazardous land uses; i.e., developing and high density residential areas,
unprotected non-urban areas located on soils with low permeability and steep
slopes and feedlots. Discussion of remedial measures is given in Appendix
II-C.
Comparison of Measured Loadings with Estimates
Obtained by the MEUL Method
One purpose of the Menomonee River pilot project was to establish
loadings from various land use activities. Although at the conclusion of the
research it can be stated that the loadings should be related to various
causative factors such as imperviousness of the area, type and slope of the
soils, vegetative factors etc., some of these factors may indeed be related to
land use. For example, the imperviousness of the area which is one of the
primary factors defining residential land uses can be correlated with housing
density. However, it must be realized that great loading variations should be
expected within one particular land use based upon soil type and slope
category, atmospheric fallout and litter accumulation and type of activities
taking place in the area. This is especially true for such land uses as low
density residential where most of the loadings originate from pervious areas
thereby involving soil type and slope as principal causative factors.
Furthermore, commercial and industrial land categories seem to be too broadly-
defined and need further subcategorization (e.g., type of industry or type of
commercial activities, degree of imperviousness).
Another problem which can arise when comparing estimated and measured
loadings is that each season has a different erosion potential. This is shown
in Fig. 11-10 where cumulative rainfall energy factors defined by the USLE
were arranged on a probabilistic scale of seasons. More than one order of
magnitude of sediment loss can be expected based on whether the season is dry
or has a significant number of high intensity storms. The measured values
11-44
-------
Table 11-16. Simulated pollutant loadings for land uses on essentially
pervious areas
Soil and
slope*
Sediment, kg/ha
Spring
Summer Fall
Spring
Park and Recreation — SC+ = 0
BMA
8MB
BMC
HMA
HUB
HMC
OUA
OUB
OUC
OUD
ASA
ASB
ASC
BMA
8MB
BMC
HMA
HMB
HMC
QUA
OUB
OUC
OUD
ASA
ASB
ASC
BMA
BMB
BMC
HMA
HMB
HMC
QUA
OUB
OUC
OUD
ASA
ASB
ASC
BMA
BMB
BMC
HMA
HMB
HMC
OUA
OUB
OUC
OUD
ASA
ASB
ASC
18
44
120
30
94
275
55
172
501
1,290
61
184
532
<1
1.5
14
<1
3.3
28
<1
8.3
85
1,400
<1
7.1
94
<10
303
2,800
<10
655
5,500
<10
1,665
17,000
280,000
<10
1,420
18,700
936
2,450
7,200
2,440
6,390
18,800
8,200
21,000
61,400
142,000
3,380
8,840
26,000
23 ' 17
64 26
186 82
52 26
160 46
477 174
64 30
235 55
692 217
1,770 599
115 31
340 57
1,010 225
Woodland— SC -
<1 <1
1.0 <1
35 9.4
<1 <1
2.2 <1
80 19
<1 <1
6.2 <1
150 32
1,300 2,850
2.9 <1
32 2.1
334 50
Row Crops — SC
<10 <10
16 <10
560 150
<10 <10
36 <10
1,280 296
10 <10
100 <10
2,400 518
20,900 4,565
46 <10
505 34
5,340 800
Feedlots — SC -
1,490 452
3,240 1,360
8,750 5,430
3,600 1,130
7,860 3,395
21,200 13,600
18,200 3,000
39,600 9,000
107,000 36,000
245,000 100,000
8,700 1,380
18,900 4,130
51,200 16,500
0.02
0.04
0.12
0.04
0.14
0.41
0.09
0.30
0.80
2.31
0.17
0.55
1.63
0.005
<0.001
0.0015
0.014
<0.001
0.005
0.041
<0.001
0.015
0.153
2.52
<0.001
0.022
0.28
- 1.0 or
<0.01
0.30
2.8
<0.01
0.98
8.25
<0.01
3.00
30.6
505
<0.01
4.39
57.9
1.0
1.82
5.89
14.4
7.33
19.2
56.4
29.5
75.6
221
511
21.0
54.8
161
PO..-P
Summer
.01
0.03
0.07
0.10
0.08
0.24
0.72
0.13
0.42
1.25
3.19
0.35
1.05
3.11
Sediment, kg/ha
Fall
0.02
0.03
0.07
0.03
0.06
0.25
0.05
0.09
0.38
1.07
0.08
0.15
0.68
Spring
25
102
330
60
252
795
134
487
1,470
3,830
152
522
1,560
Summer
54
178
543
142
466
1,420
206
690
2,060
5,300
330
1,000
3,000
Fall
Pasture — SC
21
47
216
48
107
492
60
135
620
1,770
62
140
645
POn-P
Spring Summer
= 0.03
0.02
0.10
0.33
0.09
0.36
1.19
0.23
0.87
2.65
6.89
0.47
1.60
4.85
0.05
0.17
0.54
0.21
0.68
2.12
0.37
1.22
3.71
9.53
1.03
3.11
9.30
Fall
0.02
0.05
0.22
0.07
0.16
0.73
0.11
0.24
1.11
3.18
0.19
0.43
1.99
Wetland— SC - 0.03
<0.001
0.001
0.035
<0.001
0.003
0.012
<0.001
0.011
0.270
2.34
0.009
0.098
1.35
0.08
<0.01
0.02
0.56
<0.01
0.05
1.92
0.02
0.18
4.31
37.5
0.14
1.56
16.9
2.97
6.48
17.4
10.8
23.6
63.8
65.5
142
385
882
52.1
117
317
<0.001
<0.001
0.010
<0.001
<0.001
0.027
<0.001
<0.001
0.059
0.52
<0.001
0.007
0.16
<0.01
<0.01
0.15
<0.01
<0.01
0.44
<0.01
<0.01
0.94
8.28
<0.01
0.11
2.50
0.90
2.71
10.9
3.39
10.2
40.7
10.8
32.4
129
360
8.53
25.6
102
26
97
**
69
256
**
119
441
**
**
140
519
**
830
3,400
11,000
2,000
8,400
26,500
4,500
16,200
49,100
128,000
5,100
17,400
52,200
45
144
**
124
395
**
248
655
**
**
350
1,090
**
1,800
5,900
18,100
4,700
15,500
47,200
6,900
23,000
68,700
177,000
11,000
33,500
100,000
4
12
**
11
34
**
19
58
**
**
25
80
**
Developing
700
1,600
7,200
1,600
3,600
16,400
2,000
4,500
20,700
59,000
2,100
4,700
21,500
0.03
0.10
**
0.10
0.38
**
0.21
0.79
**
**
0.43
1.61
**
Urban — SC
0.83
3.40
11.0
3.00
12.6
39.7
8.10
29.2
88.4
229
15.8
54.0
161
0.05
0.14
**
0.19
0.59
**
0.45
1.18
**
**
1.09
3.37
**
- 1.0
1.80
5.90
18.1
7.05
23.3
71.0
12.4
41.4
123
34.1
104
3JO
<0.001
0.01
**
0.02
0.05
**
0.03
0.11
**
**
0.08
0.25
**
0.70
1.60
7.20
2.40
5.40
24.6
3.60
8.10
37.3
106
6.51
14.6
66.7
*BM is Boyer Is, HM is Hochheim 1, 00 is Ozaukee sll, and AS is Ashkum sicl; A is 0 to 2%, B is 2 to 6%, C is 6 to 12% and D
is 12 to 20% slope.
ope
**Not applicable.
+SC is the cropping factor used in USLE.
11-45
-------
average meteorological conditions on which the MEUL method is based.
The following correction factors based on Fig. 11-10 should be applied to
sediment loadings from pervious areas.
Season Erosion Correction Factor
Spring 1975 0.44
Summer 1975 4.00
Fall 1975 1.25
Spring 1976 0.31
Summer 1976 2.3
Fall 1976 5.0
Spring 1977 1.0
Summer 1977 0.66
The loading values must be further adjusted by the delivery ratio (DR)
relating loadings at the watershed outlet to those potentially liberated from
the source area. The DR is still an unknown quantity which includes such
factors as sedimentation and resettling during overland and channel flow,
flocculation and agglomeration of suspended particles and removal of
pollutants by infiltration during overland flow. An inaccurate method of DR
estimation relates DR to the areal size of the watershed as shown in Fig. II-
14. Although the method is inaccurate it is as good as any other available.
Another factor which must be included is type of drainage. Natural drainage
systems with low or no curbs will yield low delivery ratios approximately
proportional to the fraction of impervious (e.g., storm sewer) and pervious
drainage ditches. Areas with no curbs may show loadings reduced as much as
50% or more as compared to typical urban landscapes of impervious areas (i.e.,
streets draining into impervious drainage gutters). The loading figures
presented in this report are based on the assumption that most of the street
pollutants will accumulate near the curb.
Tables 11-17 and 11-18 present a comparison of measured and estimated
sediment and phosphate-P loadings for some major pilot subwatersheds and for
areas in a predominantly single land use in the Menomonee River Watershed. In
almost all cases the estimated values were higher than the measured ones, a
fact partially attributable to assigning a DR-value. For most of the
simulated land uses the DR (ratio of measured:estimated loadings) is within
the ranges indicated in Fig. 11-14. The measured loadings for the fall
seasons were low and do not conform to estimated values. It should be noted
that Fall 1975 and 1976 seasons were very dry with minimal runoff.
It can also be expected that DR for highly impervious areas will be
higher than for largely pervious areas of the same size and DR will be higher
in sewered than in unsewered areas with natural drainage ditches.
Simulated unit loadings agree fairly well with measured values under
similar meteorological conditions and land use characteristics. An exception
has been noted for livestock feedlots where it was impossible to arrive at
reasonable values of the soil erodibility factor, K, due to unusually high
organic content of feedlot soils and unknown degree of compactness. Available
measured loading values from feedlots (28,29) deviate significantly from
simulated ranges; however, more research is necessary to obtain more realistic
data.
11-46
-------
i i 11 ni| 1—i i i inn 1—i i 11 mi 1—i I I n
0.026 0.26 2.6 26 260
Drainage Area, km2
O Red Hills Physiographic Area - Texas and Oklahoma
® Missouri Basin Loess Hills - Iowa and Nebraska
A Blackland Prairies - Texas
n Sand-Clay Hills - Mississippi
• Piedmont Physiographic Area - North Carolina,
South Carolina and Georgia
Fig. 11-14. Sediment delivery ratio versus drainage area (% of eroded
soil material transported to the downstream outlet of
streams based upon their drainage area) (27).
11-47
-------
Table 11-17.
Comparison of simulated and measured sediment and phosphate
loadings in subwatersheds with mixed land uses (measured
loadings are taken from (26))
Impervious Sediment, kg/ha
Land Use
Area, %
areas, % Spring
Summer
Fall
POi.-P
, kg/ha
Spring Summer
Fall
Donges Bay Rd. (463001), 2144 ha
Commercial
High density residential
Medium density residential
Low density residential
Row crops
Contributing
Pasture A
Pasture B
Wetlands
Feedlots
Developing
Estimated mean
Measured, arithmetic mean
weighted mean
Delivery ratio, weighted
Industrial
Commercial
High density residential
Medium density residential
Low density residential
Park and recreation A
Woodlands A
Developing A
Landfill A
Water
Estimated mean
Measured, arithmetic mean
weighted mean
Delivery ratio, weighted
Industrial
Commercial
High density residential
Medium density residential
Low density residential
Developing A
Row crops
Parks and recreation A
Woodlands
Wetland
Landfill
Estimated mean
Measured, arithmetic mean
weighted mean
Delivery ratio, weighted
Commercial
High density residential
Medium density residential
Low density residential
Developing A
Parks and recreation
Estimated mean
Measured, arithmetic mean
weighted mean
Delivery ratio
*Corrected for the area used
**No cleaning in spring, medi
2.6
0.05
3.9
4.7
74
32
5
5
2.3
0.5
1.6
1.8
35
3.8
15.8
14.6
23
-
2.7
2.7
0.3
0.9
27.9
3.3
24.2
15.6
1.8
0.07
18.6
0.6
0.3
0.5
26.6
0.5
39.1
27.2
3.0
9.0
200
400
200
120
1,655
134
487
119
2,100
2,800
597
304
107
0.18
Noyes Creek** (413011), 552
60 880(80)***
60 700(100)
70 730(130)
40 360(160)
10 180(160)
2 55
-
2 3,000
2 3,000
35 547
840
566
1.0
Honey Creek (413006), 2,803
855(55)
655(55)
655(55)
255(55)
75(55)
3,500
-
55
-
-
2,500
368
417
294
0.80
Schoonmaker Creek1"1"1" (413010),
90 350(50)
90 350(50)
60 200(50)
25 200(170)
1.6 1,500
5.0 27
54 277
157
120
0.43
400
800
400
250
100
206
690
248
4,525
4,150
212
39
62
0.29
ha
1,020(220)
650(150)
820(170)
470(270)
290(270)
64
-
6,600
6,600
762
389
566
0.74
ha
864(64)
564(64)
714(64)
264(64)
84(64)
7,000
-
64
-
-
5,500
425
225
287
0.68
179 ha
500(50)
800(50)
600(200)
280(200)
2,300
32
531
147
210
0.40
200
60
150
50
10
60
135
18
750*
1,200*
50
460(60)
250(50)
350(50)
140(60)
70(60)
30
-
1,260*
1,260*
155
136"""
153
0.99
460(60)
250(50)
350(50)
115(35)
45(45)
1,200
-
30
-
-
1,050**
258
28
41
0.16
190(10)
210(30)
160(60)
75(60)
660*
15*
120
33
45
0.38
0.30
0.60
0.50
0.35
3.0
0.23
0.87
0.21
5.90
5.67
1.18
0.61
0.20
0.17
0.70
0.50
0.60
0.45
0.30
0.09
-
4.1
4.1
0.56
0.61
0.70
0.80
0.80
0.50
0.18
0.37
1.22
0.45
12.89
7.45
0.40
0.07
0.06
0.15
1.1
0.60
0.80
0.60
0.45
0.13
-
6.2
6.2
0.78
0.36
0.25
0.70
0.40
0.10
0.00
0.11
0.24
0.03
2.0
2.5
0.09
0.30
0.16
0.16
0.15
0.12
0.09
-
3.6
3.6
0.32
0.01
urn maintenance in summer and fall.
***( ) amount contributed by pervious
+Assume that 50% originated
44-Data for Fall 1976 excluded
1 1 1 Assume good cleaning.
areas.
fromm pervious areas.
due to
unusually dry weather.
11-48
-------
H
*»
VO
Table 11-18. Comparison of simulated and measured sediment and phosphate
loadings in predominantly single land use areas (measured loadings
are taken from (26))
Type of loading
140 ha, 1%
Estimated mean
Measured, arithmetic mean
weighted mean
Delivery ratio
Sediment, kg/ha POi^-P,
kg /ha
Spring Summer Fall Spring Summer
Timmerman Airport (413614):
mean slope, 18% impervious, commerical
36 55 15 0.06
16 68 4.2 0.03
16 55 6.
0.44 1.0 0.40
Brookfield Square (6830089):
0.10
0.09
Fall
0.05
0.03
61 ha, 2% mean slope, 50.4% impervious, commercial
Estimated mean
Measured, arithmetic mean
weighted mean
200 310 120 0.3
350 136 5 0.26
350 180
Stadium Interchange (413615):
0.3
0.16
0.1
0.02
64 ha, 2% mean slope, 44.6% impervious, transportation
Estimated mean
Measured, arithmetic mean
49
Estimated mean
Measured, arithmetic mean
250 450 100 0.42
230 353 28 0.24
Allis Chalmers (413616);
ha, 89.9% impervious, industrial
1,200 1,600 1,600 0.9
79 913 - 0.45
0.60
0.32
1.3
2.38
0.15
0.04
0.5
0.08
-------
REFERENCES-II
1. Sartor, J. D. and G. B. Boyd. Water Pollution Aspects of Street Surface
Contaminants. U.S. Environmental Protection Agency Report No. EPA-R2-72-
081, Washington, D.C., 1972.
2. Wischmeier, W. II. and D. D. Smith. Predicting Rainfall-Erosion Losses
from Cropland East of the Rocky Mountains. U.S. Dept. of Agriculture
Handbook 282. Washington, D.C., 1965. 47 pp.
3. Wischmeier, W. H., C. B. Johnson and B. V. Cross. A Soil Erodibility
Nomograph for Farmland and Construction Sites. J. Soil and Water
Conserv. 26(5):189-193, 1971.
4. Midwest Research Institute. Loading Functions for Assessment of Water
Pollution from Non-point Sources. U.S. Environmental Protection Agency
Report No. EPA-600/2-76/151, Washington, D.C., 1976.
5. Novotny, V., 11. A. Chin and H. Tran. Description and Calibration of a
Pollutant Loading Model-LANDRUN. Part I: Description of the Model.
Final Report of the Menomonee River Pilot Watershed Study, Vol. 4, U.S.
Environmental Protection Agency, 1979.
6. Novotny, V., H. Tran, G. Simsiman and G. Chesters. Mathematical
Modelling of Land Runoff Contaminated by Phosphorus. J. Water Pollution
Control Fed. 50(1) :101-112, 1978.
7. Novotny, V., M. A. Chin and H. Tran. Description and Calibration of a
Pollutant Loading Model-LANDRUN. Part II: Calibration and Verification
of the Model. Final Report of the Menomonee River Pilot Watershed Study,
Vol. 4, U.S. Environmental Protection Agency, 1979.
8. Walesh, S. G. Land Use, Population and Physical Characteristics of the
Menomonee River Watershed. Part I: Land Data Management System. Final
Report of the Menomonee River Pilot Watershed Study, Vol. 2, U.S.
Environmental Protection Agency, 1979.
9. American Public Works Association. Water Pollution Aspect of Urban
Runoff. Water Pollution Control Research Journal WP-20-15, Washington,
D.C., 1969.
10. Hiemstra, L. Frequencies of Runoff for Small Basins. Ph.D. Thesis,
Colorado State University, Fort Collins, Colorado, 1968. 151 pp.
11. Simsiman, G. V., J. Goodrich-Mahoney, G. Chesters and R. Bannerman. Land
Use, Population and Physical Characteristics of the Menomonee River
Watershed. Part III: Description of the Watershed. Final Report of the
Menomonnee River Pilot Watershed Study, Vol. 2, U.S. Environmental
Protection Agency, 1979.
11-50
-------
12. Hydrocomp International. Hydrocomp Simulation Programming Operation
Manual. Hydrocomp International, Palo Alto, California, 1972.
13. Horn, M. E. Estimating Soil Permeability Rates. J. Irrigation and
Drainage Div. Proc., Amer. Soc. Civil Engineers 97(IR2):263-274, 1971.
14. Bouma, J. , W. A. Ziebell, W. G. Walker, D. G. Olcott, E. McCoy and F. D.
Hole. Soil Absorption of Septic Tank Effluents. University of Wisconsin
Extension, Geological and Natural History Survey Information Circular No.
20, Madison, Wisconsin, 1972.
15. Brandt, G. H., E. S. Conyers, M B. Ettinger, F. J. Lowes, J. W. Mighton
and J. W. Pollack. An Economic Analysis of Erosion and Sediment Control
Methods for Watersheds Undergoing Urbanization. Final Report, OWPJl
Contract No. 14-31-001-3392, Dow Chemical Co., Midland, Michigan, 1972.
16. Shacklette, H. T., J. C. Hamilton, J. G. Boernagen and J. M. Bowles.
Elemental Composition of Surficial Materials in the Conterminous United
States. U.S. Geological Survey Proc. Paper 574-P, Washington, D.C.,
1971.
17. Sartor, J. D., G. B. Boyd and F. J. Agardy. Water Pollution Aspects of
Street Surface Contaminants. J. Water Pollution Control Fed. 46(3):458-
467, 1974.
18. Graham, D. H., L. S. Costello and H. J. Mallon. Estimation of
Imperviousness and Specific Curb Length for Forecasting Storm Quality and
Quantity. J. Water Pollution Control Fed. 46(4):717-725, 1974.
19. Pitt, R. and G. Amy. Toxic Material Analysis of Street Surface
Contaminants. U.S. Environmental Protection Agency Rep. No. EPA-R2-73-
283, Washington, D.C., 1973.
20. Milwaukee County Department of Air Pollution. Ambient Air Quality
(Particulates and Sulfur Dioxides) in Milwaukee County. Milwaukee County
Department of Air Pollution, Milwaukee, Wisconsin, 1970.
21. Beaseley, R. B. Erosion and Sediment Pollution Control. The Iowa State
University Press, Ames, Iowa, 1972.
22. Heaney, J. P. and W. C. Huber. Storm Water Management Model:
Refinements, Testing and Decision-making. Department of Environmental
Engineering Sciences, University of Florida, Gainesville, Florida, 1973.
23. Carlisle, A. A., H. F. Brown and E. J. White. Litter Fall Leaf
Production and the Effects of Defoliation by Tortrix viridanal in a
Sissile Oak (Quercus petrala). Woodland J. Ecology 54:65-98, 1966.
24. Lutz, H. J. and R. I. Chandler. Forest Soils. John Wiley and Sons, New
York, N.Y., 1976.
25. U.S. Army Corps of Engineers. Urban Storm Water Runoff Model STORM. The
Hydrologic Engineering Center, U.S. Army Corps of Engineers, Davis,
California, 1975.
11-51
-------
26. Konrad, J. G. and G. Chesters. Menomonee Paver Pilot Watershed Study;
Summary Pilot Watershed Report. Submitted to PLUARG Task Group C (U.S.),
Activity 2. Windsor, Ontario, Hay 1978. 77 pp.
27. Roehl, J. W. Sediment Source Areas, Delivery Ratios and Influencing
Morphological Factors. J.A.S.H. Commission on Land Erosion Publ. No. 59,
1962.
28. Daniel, T. C., W. Wendt and P. E. McGuire. Pollutant Loadings from
Selected Rural Land Uses. Trans. Amer. Soc. Ag. Eng. (submitted for
publication), 1979.
29. Coote, D. R. and F. R. Hore. Pollution Potential of Cattle Feedlots and
Manure Storages in the Canadian Great Lakes Basin. Final Report
Agricultural Watershed Studies Project 21. Submitted to PLUARG, Windsor,
Ontario, 1978.
II-52
-------
APPENDIX II-A
DETAILED STATISTICAL EVALUATION OF STREET LITTER ACCUMULATION
It has been realized that a simple unit loading value may not be
representative of the surface pollution accumulation process. Instead, a mass
balance model can be developed which may better represent the dynamic
character of the street refuse accumulation. The model is based on the
following simple mass balance equation (see Fig. II-7 for more detail):
L is the polllutant accumulation on the surface, g/curb m/day
LD is the pollutant deposition rate, g/curb m/day
LR is the pollutant removal rate from the surface, g/curb m/day
The simple mass balance equation presented above can be expanded by
identifying the significant factors which affect deposition and removal from
street surfaces. The primary sources can be related to fallout of atmospheric
pollutants, motor vehicle usage and deposition of street litter.
Traffic can contribute significantly to pollutant deposition in urban
areas. Large amounts of toxic metals in storm water runoff are often
attributed to motor vehicle emissions and to the breakdown of road surface
materials and vehicle parts.
The variables affecting the pollutant deposition rate on impervious urban
areas can be combined to yield the following equation:
LD = (ATFL) (SW/2) + AL A (SW/2) (POA) + A2 (RD) + AS (TD) (RCC)
Eq. (A-2)
where
ATFL is a coefficient reflecting deposition from stationary
combustion processes and atmospheric fallout, g/ha/day
SW is the street width, m
Aj is a coefficient reflecting the effect of open areas on
pollutant deposition
POA is % open area in the vicinity of the site
A is a coefficient reflecting the effect of residential
density on pollutant accumulation
RD is the residential density, dwelling units/ha
11-53
-------
A is a coefficient reflecting the effect of traffic on
pollutant accumulation
TD is traffic density, thousand axles/day
RCC is road composition and conditions which is a value based on
scale determined from regression analysis
At the same time that pollutants are being deposited on the surface they
are being removed. Factors which should be investigated as affecting the
removal rate include wind speed, traffic speed, and curb and average height of
buildings. The equation for street surface refuse removal can be formulated
as:
LR = A1+[f1(H) f2(WS,TS)]L Eq. (A-3)
where
A is a coefficient reflecting the rate of pollutant removal due
to the combined effect of wind and traffic speed
II is curb height, cm
WS is average wind speed, km/hr
TS is average traffic speed, km/hr
The function f (H), describes the effect of curb height on pollutant
removal and can be modeled as:
f^H) = e"BH Eq. (A-4)
where 3 is a statistical coefficient.
The above model was applied to a set of field data. Since the Menomonee
River Watershed data do not yet provide a representative data sample, the data
sample was supplemented by field measurements of street refuse accumulation in
the Washington, D.C. area (A-l).
The solution to Eq. 12 will yield the following formula:
A —Rt
L = |(1 - e *C) + C Eq. (A-5)
where
t is time from last street cleaning or rain
A and B are variables determined for each constituent
C is a constant
The Washington, D.C. data (A-l), contain about 73 measurements on 7
different sites. Although the number of sites is probably too low to provide
a sufficient spread of independent variables the statistical analysis did
provide some answers as to the significance of the variables involved.
The best fit equations for four typical constituents, i.e., which were
statistically significant are as follows:
11-54
-------
Dust and dirt suspended solids -
A —Rt
DDSS -£(1 - e ) + C Eq. (A-6)
A = ATFL(-r) - 5.02(RD) - 6.29(POA) + 1.15(TD)
B = 0.0116e~°'°88H (TS + WS)
C = 0.0
Multiple correlation coefficient R = 0.86
Similarly:
Dust and dirt chemical oxygen demand -
A — Rt
DDCOD = f (1-e ) + C Eq. (A-7)
A = 2.60(— ) - 0.28(RD - 0.51(POA) + 0.52(TD)
— n
B = 0.142e (TS + WS)
C = 0
Multiple correlation coefficient R = 0.71
Dust and dirt volatile suspended solids -
A A A
DDVSS =~(1 - e'V) -^-(1 - e~V) +-^-(1 - e'V) + C Eq. (A-8)
Bl B2 B3
•&l = 0.024 e~°'05H (TS + WS)
= 0.25(RD) + 0.31(POA)
= 0.048 e °>05H (TS + WS)
= 0.069(TD)
11-55
-------
= 0.105 e~°'05H (TS + WS)
C = 0
Multiple correlation coefficient R = 0.65
Dust and dirt lead -
A A A
1 — R t- 9 R t- X — R *-
DD Lead = -(1 - e V) - (1 - e~V ) + -=±(1 - e V) + C
0.131-^
0.036 e~°-03H (xs + WS)
A = 0.027(RD)
— n
0.026 e (TS + WS)
A3 = 0.013 (TD)
0.053 e~°'°3H (TS + WS)
C = -0.825
Multiple correlation coefficient R = 0.80
Table Il-A-1 lists the partial correlation coefficients for the above
variables. From the table it can be seen that in all four cases the overall
functional relationship is at a significant level. The dependent variables
which have the most significant effect on the independent variables vary with
the character of the variables. As might be expected, traffic density may
have a very significant effect on the magnitude of the accumulation of dust
and dirt constituents, particularly lead. On initial inspection it may seem
surprising that the regression coefficients have a negative value for PDA and
RD. One would expect that quantity of street refuse would increase with
increasing housing density or open area (i.e., area without significant
vegetation). On the other hand, just the opposite can be true if one realizes
that a significant portion of street refuse originates from vegetation — lawns,
trees and shrubs — which are inversely proportional to housing density (RD) or
open area (POA). Thus, it seems that trees and vegetation near impervious
areas may contribute significantly (especially during the fall season) to
pollutant loading.
The above equations represent the best combination of variables which
II-56
-------
M
I
Ln
Table 1I-A-1. Partial and multiple correlation coefficients between dust and dirt
pollutants and factors affecting their accumulation
Partial r of dependent
Independent variable
Suspended solids
COD
Volatile suspended solids
Lead
SW
0.28
0.26
0.13
0.067
RD
-0.30
-0.16
-0.23
-0.113
POA
-0.34
-0.27
-0.20
0.0018
variable*
TD
0.34
0.15
0.26
0.40
Multiple R
0.86
0.71
0.65
0.80
*SW is street width, RD is residential density, POA is percent of open area,
TD is traffic density, H is curb height, TS is traffic speed and WS is wind
speed.
-------
were investigated. Other combinations which yielded lower statistical
correlations included the effect of traffic speed on pollutant accumulation
(as in the form of TD x TS or TD x TS ), excluding some insignificant
variables and others.
Equations (A-6) to (A-9) indicate that as the quantity of deposited
pollutants increases with prolonged dry periods, more particles can be removed
by wind and traffic and the actual differential deposition rate decreases.
This fact was also observed by Sartor et al. (A-2) and is documented in
Fig. II-A-1.
11-58
-------
H-
OQ
Street Litter Quantity, g/curb m
rt rt
rt o
H rt
CO
• o
Ml
TJ
rt
H
H-
O
a
en
rt
cr
g
3
O
Hi
rt
rt
rt
p
CO
rt
a-
I
to
H-
3
O
rt
(u
en
O
l-1
rt
H-
O
OQ
H-
0
CL
(U
^
cn
N3 -
00 -
00
O O O
Average Daily Accumulation Rate, g/m-day
-------
REFERENCES-APPENDIX II-A
A-l. Shaheen, D. Contribution of Roadway Usage to Water Pollution. U.S.
Environmental Protection Agency Report No. EPA 600/2-75-004, Washington,
D.C., 1975.
A-2. Sartor, J. D. and G. B. Boyd. Water Pollution Aspects of Street Surface
Contaminants. U.S. Environmental Protection Agency Report No. EPA-R2-
72-081, Washington, D.C., 1972.
A-3. Sartor, J. D., G. B. Boyd and F. J. Agardy. Water Pollution Aspects of
Street Surface Contaminants. J. Water Pollution Control Fed. 46(3):458-
467, 1974.
II-60
-------
APPENDIX II-B
SIMULATED LOADING DIAGRAMS
Loadings for impervious urban land uses (Figs. II-B-1 to II-B-6) reflect
values from areas under slope category B (2 to 6%). Average loadings can be
read directly from the loading diagrams. Loading diagrams for volatile
suspended solids and Pb are available but are not presented in this report.
Loadings from pervious areas shown in Figs. II-B-7 to II-B-16 reflect
values from a 1 km2 area under soil slope category C (6 to 12%). To obtain
average loadings, loading diagram related to the R-factor must be transformed
to a probability distribution loading plot using the cumulative frequency
chart in Fig. 11-10. The cropping factor, SC, on all loading diagrams is
0.01. To obtain loadings for each land use with SC other than 0.01 multiply
the values from the graph by 100 and SC factors in Table 11-16. To transform
loadings to other slopes and areal units, values should be multiplied by slope
or area correction factors presented in Figs. 11-11 to H-13. Loading
diagrams for phosphate-P are available but are not given in this report.
11-61
-------
H
I
Fig. II-B-1. Sediment loadings from residential areas.
-------
M
M
I
OJ
rvicivs -x
Imperviousness,
40 60
Imperviousness, %
80 100
-1 1 1 1
Imperviousness, %
Fig. II-B-2. Sediment loadings from commercial areas.
-------
i i 1 1 1 1 ) 1
20 40 60 80
Fig. II-B-3, Sedinent loadings from industrial areas.
-------
H
M
I
Ul
Poorly maintained
Itnperviousness, %
Imperviousness,
I-nperviousness, 1
Fig. II-B-4. Phosphate-P loadings from residential areas.
-------
I
ON
In.perviousness, I
Fig. II-B-5. Phosphate-P loadings from commercial areas.
-------
Fig. II-B-6. Phosphate-P loadings from industrial areas.
-------
500--
M
I
00
3000"
1400-
. 1000- -
600- -
200-
40
Fig. II-B-7. Relationship of sediment loadings and R-factor in row crop-woodland areas.
-------
500- -
300..
100
3000-
100
MOO P
IOOO- -
600--
200--
50
Fig. II-B-8. Probability distribution of sediment loadings in row crop-woodland areas.
-------
I
•~J
o
20 30
Fig. II-B-9. Relationship of sediment loadings and R-factor in feedlots.
-------
1000'
500--
0 1000- •
no
100
Fig. II-B-10. Probability distribution of sediment loadings in feedlots.
-------
I
^4
S3
_ 500.
' 1000--
Spring
50 .,300--
Fig. II-B-11. Relationship of sediment loadings and R-factor in pastures.
-------
M
I
U>
° 1000
Fig. II-B-12. Probability distribution of sediment loadings in pastures,
-------
; too-•
E 300--
20
30
Fig. II-B-13. Relationship of sediment loadings and R-factor in wetlands,
-------
I
~J
Ln
- 100--
I 1 1 1 1 1 1— 1
7004.
r BOO..
E 300 -
100
Fig. II-B-14. Probability distribution of sediment loadings in wetlands.
-------
APPENDIX II-C
REMEDIAL MEASURES AND NON-POINT POLLUTION CONTROL
Remedial measures can be categorized using a macro or micro scale. The
former may result in better land use practices and zoning, legislation
limiting marketing certain potentially-hazardous pollutants or better farming
practices. These measures are usually long-term remedies and take longer
periods of time to implement. Micro-scale remedial measures include better
management and control of existing land uses. In urban settings, limiting the
non-point pollution can take place either at the source (maintenance and
cleaning) or at the area outlet (storage and treatment). In non-urban
settings, the control is limited to better farming practices and erosion
control.
A literature review by the Wisconsin Department of Natural Resources
(C-l) compiled and presented possible management practices to control water
quality of urban runoff. The control techniques mentioned included:
Source control
Increased infiltration
Retention of runoff
Reduction of erosion
Reduction of contaminant deposition
Street sweeping
Outfall treatment and collection control
Reduction in channel erosion
Infiltration and sedimentation basins
Storage basins to equalize flow
Physical, chemical and biological treatment
The study concluded that in low density urbanizing areas the quality of
stormwater runoff is most efficiently handled by systems incorporated into the
development stage such as zoning, control of developing areas, increased
perviousness and optimal design of stormwater conveyance systems. In high
density, developed areas, runoff is handled by good street cleaning practices
and through one of a series of treatment methods subsequent to collection.
Source control of urban-related pollution, which reduces on-site
pollutant generation or prevents pollutants from leaving the small drainage
areas at which a disturbance occurs, is less expensive and more effective than
remedial measures once the pollutants leave the site and move downstream.
Control of runoff pollution by collection systems is more expensive than on-
site source control but less costly than treatment at the outfall.
Treatment of urban runoff may be feasible only for highly developed areas
where source control and collection control are not possible.
11-76
-------
The difference between frequently cleaned and poorly maintained (no
cleaning) urban areas can be seen in Table 11-15. Although Table 11-15
represents simulated pollutant loadings the importance of street cleaning is
evident. Figures II-C-1 and II-C-2 show the simulated effect of street
cleaning frequency and efficiency on sediment loadings. The average
efficiency of street sweepers for the suspended particulate materials (dust)
is about 50% (C-2) but due to the fact that P is associated mostly with the
fine fractions of street dust and dirt the expected efficiency of P removal is
only about 22%. The effects of street sweeping are much higher during a dry
season and when a linear accumulation of street pollutants is assumed.
Other remedial measures include increasing pervious areas within urban
settings and reducing impervious areas directly connected to surface runoff
channels. Installing pervious parking areas, introducing seepage beds and
basins, and disconnecting roof drains from storm sewers can be listed as
possible examples. These measures can be ineffective if the area is located
on impermeable soils or on steep slopes since the conveyance of runoff from
the pervious area would create more erosion and pollutant washout from these
soils. Pervious areas should not be left bare. Permanent or temporal surface
protection, such as lawns, temporary seeding, or application of mulch or
chemicals should be practiced to control erosion and pollutant washout.
Street curbs and highway barriers represent obstacles at which surface
suspended pollutants (dust) can accumulate. Studies by Sartor et al. (C-2)
and Sartor and Boyd (C-3) indicated that 90% of surface suspended pollutants
are located within 1 m of the curb. One would suspect that the curb height
can—to some degree—affect the amount of pollutants accumulated. To provide
some insight into the validity of this hypothesis, a sensitivity analysis of
Eq. (A-6) was performed (Fig. II-C-3). Thus, lower curb heights may result in
less pollutant accumulation near the curb since some of the deposits can be
removed by wind and traffic and deposited in adjacent pervious areas where
they are less available for transport. Obviously, lowering curb sizes would
be effective only if the streets are surrounded by pervious areas.
11-77
-------
100-1
M
I-H
oo
O
3
•a
&
OB
O
PL,
20-
Linear pollutant
Decreasing rate
accumulation
Sweeping Interval, days
Fig. II-C-1. Effect of sweeping interval on pollutant loadings (sweeping efficiency = 50%).
-------
100 -,
1
--J
VD
-d
0)
00
80 -
60 -
40 H
High Density Residential
Medium Density Residential
Low Density Residential
10 20 30 40 50
Sweeping Efficiency, %
Fig. II-C-2. Effect of sweeping efficiency on pollutant loadings (sweeping interval = 7 days)
-------
0)
T3
I
90 -I
70 -
c
o
•H
Cti
i-H
3
a
o
50 _
Medium density residential
street width = 10 m
percent open area = 40
residential density = 10 units/ha
traffic density = 1000 ax/day
wind and traffic speed = 60 km/hr
4J
•H
0)
0)
30 -
to
40
1
80
Curb (Median Barrier) Height, cm
II-C-3. Effect of curb (median barrier) height on street litter
accumulation.
11-30
-------
REFERENCES-APPENDIX II-C
C-l. Oberts, G. L. Water Quality Effects of Potential Urban Best Management
Practices. Dept. of Natural Resources Tech. Bull. No. 97, Madison,
Wisconsin, 1977.
C-2. Sartor, J. D., G. B. Boyd and F. J. Agardy. Water Pollution Aspects of
Street Surface Contaminants. J. Water Pollution Control Fed. 46(3):458-
467, 1974.
C-3. Sartor, J. D. and G. B. Boyd. Water Pollution Aspects of Street Surface
Contaminants. U.S. Environmental Protection Agency Report No. EPA-R2-72-
081, Washington, D.C., 1972.
11-81
-------
PART III
A SIMPLE, EMPIRICAL MODEL FOR PREDICTING
RUNOFF QUALITY FROM SMALL WATERSHEDS
by
D, S, CHERKAUER
Ill-i
-------
ABSTRACT
A simple model for calculating the time distribution of suspended solid
loads in a runoff event is presented. Instantaneous solids concentrations
are related to discharge per unit drainage area, rainfall intensity,
antecedent dry period, and stage of urban development. A set of empirical
curves developed from observations on small watersheds within the Menomonee
and Milwaukee River watersheds allows calculation of suspended solids
concentrations for any percentage of urbanization. These concentrations can
then be combined with discharges predicted by some standard means to provide
loading. The model has been tested in watersheds from a variety of
climatic, geologic and topographic regions. For storms within the calibra-
tion limits of the model, it predicts loads with reasonable accuracy.
Ill-ii
-------
CONTENTS - PART III
Title Page Ill-i
Abstract Ill-ii
Contents Ill-iii
Figures Ill-iv
Tables Ill-vi
III-l. Introduction III-l
III-2. Conclusions III-2
III-3. Methods and Procedures III-6
III-4. Results and Discussion III-9
References 111-22
Ill-iii
-------
FIGURES
Number Page
III-l Regression coefficients for model for total suspended
solids III-5
III-2 Comparison of observed and predicted suspended solids
concentrations for Brown Deer Creek on 6/8/77. Total
precipitation was 1.32 cm, the antecedent dry period
was 3 days and rainfall intensity was 0.25 cm/hr 111-10
III-3 Comparison of observed and predicted suspended solids
loads for Brown Deer Creek on 6/8/77. Total precipi-
tation was 1.32 cm, antecedent dry period was 3 days
and rainfall intensity was 0.25 cm/hr III-ll
III-4 Comparison of observed and predicted suspended solids loads
for Underwood Creek on 4/23/76. Total precipitation is
5.4 cm, antecedent dry period was 1 day and rainfall
intensity was 0.30 cm/hr III-l2
III-5 Comparison of observed and predicted suspended solids
loads for Event 32 at Third Fork Creek, Durham,
North Carolina. Total precipitation was 2 cm, the
antecedent dry period was 2.5 days and rainfall
intensity was 0.48 cm/hr 111-14
III-6 Comparison of observed and predicted suspended solids
loads for Event 29, at Third Fork Creek, Durham,
North Carolina. Total precipitation was 6 cm, the
antecedent dry period was 5 days and rainfall
intensity was 0.86 cm/hr 111-15
III-7 Comparison of observed and predicted suspended solids
loads for Event 27 at Third Fork Creek, Durham,
North Carolina. Total precipitation was 3.8 cm,
the antecedent dry period was 11.3 days and rainfall
intensity was 1.14 cm/hr 111-16
III-8 Comparison of observed and predicted suspended solids
loads for Bloody Run, Cincinnati, Ohio on 9/25/70.
Total precipitation was 1.7 cm, antecedent dry period
was 1 day and rain fall intensity was 0.73 cm/hr 111-17
Ill-iv
-------
Number rage
III-9 Comparison of observed and predicted suspended solids loads
for Bloody Run, Cincinnati, Ohio on 10/20/70. Total
precipitation was 2.3 cm, antecedent dry period was 6 days
and rainfall intensity was 0.45 cm/hr 111-18
111-10 Comparison of observed and predicted suspended solids loads for
Baker Street Basin, San Francisco, California on 11/5/69.
Loads have been predicted using the general model (x) and
also a modified model which reduces the importance of
antecedent conditions (o). Total precipitation was 1.6 cm,
antecedent dry period was 19 days and rainfall intensity
was 0.33 cm/hr 111-19
III-ll Comparison of observed and predicted suspended solids
concentrations and flow for Baker Street Basin,
San Francisco, California. Total rainfall was 1.6 cm,
antecedent dry period was 19 days and rainfall intensity
was 0.33 cm/hr 111-20
III-v
-------
TABLES
Number Page
III-l Comparisons of predictive capabilities of model for
suspended solids loads III-3
III-2 Coefficients for final regression equations for various
degrees of urbanization III-8
Ill-vi
-------
III-l. INTRODUCTION
One of the objectives of the Menomonee River Pilot Watershed Project was
to synthesize the collected data into a form useful to planners and others
concerned with the effects of runoff quality from future urban development.
Models, calibrated with data gathered from the Menomonee River Study can be
extrapolated to project the effects of developemnt. The LANDRUN digital model
represents the primary modeling effort and like most available digital runoff
models for calibration, it requires detailed input of the hydraulics of the
Watershed and its channels. When precise inputs can be provided, the model
produces precise results. However, in many urban areas in the Great Lakes
Watershed, either the necessary input data is not available or time and
budget constraints do not allow development and/or calibration of a digital
model.
With these concerns in mind, a methodology is presented for development
of a simple empirical model for predicting runoff quality from small watersheds,
This model is less precise than LANDRUN in its final product, but it is one
which can be calibrated for a particular urban area with data which is easily
obtainable.
III-l
-------
III-2. CONCLUSIONS
Table III-l summarizes the investigation and provides a comparison of the
observed and predicted suspended solids loads for each event discussed. After
calibration in an area, the model is able to predict suspended solids loads to
about + 20%. It cannot be used on watersheds (such as Underwood Creek) which
are substantially larger than those used for calibration without introducing
a substantial error (Table III-l). In addition, the model is valid only for
the range of rainfall intensities and totals for which it is calibrated. It
would probably be advisable to calibrate it locally for small, intermediate
and large storms, but insufficient data has been analyzed to determine the
value of multiple calibrations.
Extrapolation of the model to areas of vastly different climatic, geo-
logic and topographic conditions produced surprisingly good results. Admit-
tedly, predicted solids loads were generally substantially different from the
observed ones (error range of 8 to 80%, Table III-l). However, within the
constraints of its calibration, the model was always within the proper order
of magnitude for watersheds and events that produced from 1,100 to 46,000 kg
suspended solids/km2. In addition, it cannot be determined from the published
watershed descriptions the extent of active construction in these areas. Such
construction is not accounted for in the model.
Two conclusions can be drawn from the apparent flexibility of this
statistical model. First, the regression coefficients developed for the
Menomonee River Watershed are valid for a wide range of conditions. Local
calibrations should be made to refine the coefficients for local conditions.
Secondly, it can be inferred that rainfall conditions (intensity and duration
of antecedent dry conditions), amount of runoff and degree of urbanization
are much more important in determining suspended solids in urban areas than
are such local conditions as topography, geology and vegetation. If this
were not the case, the regression information transferred from one area to
another would bear no relationship with reality.
Furthermore, it has been suggested that the model produces reasonably
accurate estimations of suspended solids loads after it has been calibrated
for local conditions. The principal value of the model is the ease with
which it can be calibrated. Runoff samples must be collected from a variety
of small streams for which the following is known:
a. Intensity and quantity of rainfall capable of producing runoff.
b. antecedent rainfall conditions,
c. discharge at the time of sample collection, and
d. land usage information for the sampled watersheds.
Multiple regression relations are then developed for suspended solids
concentrations and Items a. and c. for each stream. The regression coeffi-
cients are plotted as functions of urban development (Fig. III-l). The
III-2
-------
Table III-l. Comparisons of predictive capabilities of model for suspended solids loads
Drainage basin Rainfall
Date or Area, km2 Urban, % Amount, Intensity, Antecedent
event no. cm cm/hr dry period, days
Loads
Observed, Predicted, Difference,
kg/km2 kg/km2 %
Comments
Brown Deer, Milwaukee, Wisconsin
6/8/77 7.5 65 1.3 0.25 3
2,900 2,290 -21
Meets all conditions
of calibration
Underwood, Milwaukee, Wisconsin
M
H 4/23/76 49.7 54 5.4 0.30 1
U>
Third Fork, Durham, North
27* 4.3 80 3.8 1.14 11
29* 6.0 0.86 5
32* 2.0 0.48 2.5
Bloody Run, Cincinnati
9/25/70 9.6 77 1.7 0.73 1
10/20/70 2.3 0.45 6
Baker Street, San Francisco,
11/5/69 0.73 100 1.6 0.33 19
11/5/69 1.6 0.33 1**
2,100 850 -60
Carolina
46,200 27,400 -41
14,300 19,300 +35
3,800 3,500 +8
, Ohio
3,220 5,280 +64
2,800 5,000 +79
California
1,130 6,765 +500
1,130 1,730 +53
In calibration
watershed but too
large
Outside calibration
area
Outside calibration
area
Outside calibration
area
*Taken from Colston (1).
**Antecedent dry period of 1 day was substituted for the 19 days.
-------
suspended solids concentration model is then interfaced with whatever method
is used locally to predict runoff quantities.
Relatively few samples are needed; only 15 to 20 from each of 5 to 8
watersheds as a minimum should be collected from a range of storm events.
However, it is unnecessary to monitor the runoff events continuously. As
long as discharge is known spot sampling is adequate because each sample is
treated independently by the model. If continuous monitoring data is avail-
able, the precision of the model should be markedly enhanced by separate
consideration of the rising and falling limbs of the hydrograph.
III-4
-------
+4000 -
+2000-
0-
Location coding: BD - Brown Deer; BV - Beaver; DB - Donges Bay; HO - Honey;
NO - Noyes; SC - Schoonmaker; T - Trinity
(a)
-1000
M 0 20
—i 1 r~
40 60
I I 1
80 100
oo -20
—T"
20
(c)
• BD
—I 1 1 1 1~
40 60 80
100
M
I
Ul
+2400 -
+1600-
+800
0-
-400
(b)
0 20 40 60 80 100
Urban, X
o 0
-600 •
20
(d)
III
40 60 80
Urban, %
100
Fig, III-l. Regression coefficients for model for total suspended solids.
-------
III-3. METHODS AND PROCEDURES
Efforts have been concentrated on small watersheds (<28 km2) tributary
to the Menomonee and Milwaukee Rivers. The watersheds are small enough to
have simple hydraulic responses to precipitation events and these responses
are amenable to the type of analysis proposed. Also, small streams are more
dramatically affected by the processes of urbanization than larger receiving
streams, because urban development will occupy a greater percentage of the
watershed. Furthermore, concentration on small watersheds provides flexibi-
lity in the model, because larger watersheds can be modeled as the composites
of the small ones. On the other hand, a model developed for large watersheds
is not easily adapted to smaller watersheds.
The Menomonee River monitoring stations used for development of this
model were Noyes, Schoonmaker and Honey Creeks and the Little Menomonee River
at Donges Bay Road. In addition, three tributaries to the Milwaukee River,
which are adjacent to the Little Menomonee at Donges Bay Road and Noyes Creek,
were used (2,3). Water quality and flow in these watersheds were monitored
manually from 1974 until 1977.
The initial step in the data analysis was to determine what independent
factors most closely control the quality of water in surface runoff. Data
were handled independently for each stream. Furthermore, analysis was
restricted to rainfall runoff events and each sample for a particular stream
was treated as an independent input and were all combined in a multiple
regression analysis. A variety of rainfall and watershed parameters were
tested as independent variables in the regression to determine whether they
were statistically related to the dependent variable, i.e., the concentration
of the chemical of interest. Only the procedure used in establishing and
testing a model for total suspended solids is described here. However,
similar development could be done for other water quality parameters.
With suspended solids concentration as dependent variable, total preci-
pitation, rainfall intensity and duration, precipitation event recurrence
interval, antecedent rainfall, instantaneous runoff/unit area of drainage,
and temporal position of the sample within a runoff event were all tried as
independent variables in a multiple regression analysis. Consistently, for
the watersheds considered, the most important independent variables proved
to be instantaneous runoff/unit area of drainage, rainfall intensity and
antecedent rainfall conditions, in order of descending correlation. For
comparison, the significant independent variables for total P concentration
were total precipitation, instantaneous runoff and antecedent rainfall, again
in descending order of importance.
It should also be pointed out that the position of the sample within
the time framework of the runoff event may merit further attention. A
relative time parameter was used, namely, a ratio of elapsed time since the
III-6
-------
start of runoff to an average response time for the watershed. Response
time was defined as the time elapsed between the start of runoff and the
crest of the hydrograph. As a result, samples on the rising limb had relative
time ratios < 1.0, those on the descending limb were > 1.0. Separation of
samples into rising and falling limb categories improves the statistical
significance of the multiple regressions. However, this separation has not
been included in the model because it may reduce the availability of data
for calibration at other sites.
After the initial determination of primary independent variables,
multiple regressions were run in each watershed. The regression coefficients
for each independent variable were plotted as a function of the extent of
the watershed which was urbanized (Fig. III-l). The extent of urbanization
is the sum of residential, commercial, industrial and transportation land
uses. This factor was used—rather than extent of imperviousness—because
it is more readily obtainable from literature or from local or regional
planning agencies.
The graphs in Fig. III-l can be used to create a multiple regression
equation for a small watershed for which degree of urbanization is known.
Table III-2 lists equations for several levels of urban development. Thus
a user need know only the following to operate the model:
a. Watershed drainage area (km2),
b. area urbanized (%),
c. instantaneous discharge for the time suspended solids concentration
is desired (m3/sec),
d. rainfall intensity (cm/hr), and
e. antecedent rainfall period, i.e., number of days since preceding
rain which produced runoff rain (days).
The degree of urbanization determines which equation to use (Table III-2;
Fig. III-l), and the equation provides the instantaneous suspended solids
concentration after Items a, c, d and e are entered in the model. The
instantaneous discharge values can be obtained from any runoff predicting
system available to the user, from the basic "Rational Method" to the more
sophisticated digital models. Any error inherent in discharge prediction
will be additive in this water quality model.
However, a word of caution is essential for developing the suspended
solids model. It has been found that the regression coefficient for
instantaneous discharge is sensitive to active construction. For those
watersheds where construction is underway (Brown Deer) coefficients are
produced which fall above the line in Fig. Ill-la. Data were insufficient
to determine the extent to which construction activity affects the coeffi-
cient, but it is known that the model will produce erroneous results under
such conditions.
III-7
-------
Table III-2. Coefficients for final regression equations for various
degrees of urbanization*
Watershed
urbanized, %
0
20
40
60
80
100
Coefficient
for QA,
m3 /sec/km2
(a)
+700
+550
+400
+250
+100
-50
Coefficient
for I, cm/hr
(b)
0
+80
+200
+520
+1420
+3000
Coefficient
for A, days
(c)
-12
-3.5
-4.5
+12.5
+21
+29
Regression
constant
(d)
+160
+80
0
-120
-400
-820
*SS = a(QA) + b(I) + c(A) + d, where SS is suspended solids concentration
(mg/L), QA is discharge/unit drainage area (m3/sec/km ), I is rainfall
intensity (cm/hr), A is antecedent dry period (days).
III-8
-------
III-4. RESULTS AND DISCUSSION
In an attempt to determine the reliability of the model and limitations
of its use, several tests have been tried. The model has been used to predict
suspended solids loads for streams in the study area, one of which was used in
calibrating the model. Also, it was tested against published data for small
watersheds outside the Great Lakes Watershed. It would have been desirable to
also test in the Great Lakes area outside of southeastern Wisconsin, but data
for small watersheds were not available. The model also was tested on watersheds
having different geological and hydraulic conditions from those used to calibrate
it and for storms of different magnitudes and intensities from the studied storms.
For each test, measured flow rather than predicted flow was used because
the model provides no method of flow prediction, and the use of any runoff
predictor introduces an error in the final load calculations. That error com-
pounds with any error due to the suspended solids prediction. Separation of
these two errors is difficult and clouds the validity of the test of the
empirical model. Thus, it is assumed that each user will interface the sus-
pended solids model with his own method of obtaining flow.
Comparison of observed suspended solids loads with those predicted by
the model for the Brown Deer Watershed for a storm event on 6/8/77 is shown in
Fig. III-2. The Watershed is one used for calibration of the model, but data
from this event were not used in the calibration. The Watershed is 65%
urbanized and the equation derived from Fig. III-l is:
SS = 200 QA + 6801 + 14.5A - 170,
where SS is suspended solids concentration (mg/L)
QA is discharge/unit area (m3/sec/km2)
I is rainfall intensity (cm/hr)
A is antecedent dry period (days)
The agreement is obviously good. The comparative suspended solids loads/unit
area (QA x SS) are shown in Fig. III-3, and agreement again is good. All
further tests compare loads because they are more reliable indicators of
average stream conditions during an event. Concentrations tend to fluctuate
dramatically in the early and late stages of an event when discharge is very
low. However, these fluctuations are of little importance because the stream
does not carry large quantities of suspended solids at these times. Comparison
of loads attaches more importance to the bulk of the sediment transported.
A second test (Fig. III-4) was run on Underwood Creek, one of the larger
(49.7 km ) tributaries to the Menomonee River. In this case, agreement is
poor likely because the Watershed is outside the size range of watersheds for
which the model was calibrated. Because of its size, Underwood Creek is not
simply a single stream with ephemeral tributaries, but has two main branches
which complicate its hydraulics. The model does not work well on complex or
large stream systems.
III-9
-------
250 •
200 '
oo
0)
•a
c
«
a
w
3
150
100
800
1000 1200 1400
Time, hr
1600
3000
3200
Fig. III-2. Comparison of observed and predicted suspended solids
concentrations for Brown Deer Creek on 6/8/77. Total
precipitation was 1.32 cm, the antecedent dry period
was 3 days and rain fall intensity was 0.25 cm/hr.
111-10
-------
w
•a
•H
rH
O
ra
•a
ai
T3
a
01
20.0
10.0 -
800
3200
Fig. III-3.
Comparison of observed and predicted suspended solids
loads for Brown Deer Creek, Milwaukee, Wisconsin on 6/8/77,
Total precipitation was 1.32 cm, antecedent dry period
was 3 days and rainfall intensity was 0.25 cm/hr.
III-ll
-------
o
to
00
ft
C^J
o
i-H
01
(U
-O
8
50
40
30
20
10 '
7 11 15 19 23 27 31 33
39 43 47 51
Fig. III-4. Comparison of observed and predicted suspended solids
loads for Underwood Creek on 4/23/76. Total precipi-
tation is 5.4 cm, antecedent dry period was 1 day and
rainfall intensity was 0.30 cm/hr.
111-12
-------
Other tests were run using data from Durham, North Carolina (1),
San Francisco, California (4) and Cincinnati, Ohio (5). The purpose of these
tests was to determine whether the coefficients established in Wisconson could
be transferred to other urban areas where topographic, climatic and geologic
conditions were different. It was anticipated that these conditions would
each play major roles in defining the coefficients and consequently the degree
of transferability that could be achieved.
The Durham, North Carolina data is most complete, providing runoff and
suspended solids for a wide range of rainfall events on a 4.3 km watershed
which is 80% urban. The terrain is steeper than that in Milwaukee (average
land slope of 6 to 7% in Durham, 2% in Milwaukee) and geologic conditions are
entirely different. However, for storms which fall within the range of intens-
ity and total precipitation of storms used to calibrate the model, there is
remarkably good agreement (Figs. III-5 to III-7) .
The model was calibrated in the Menomonee River Watershed using storms
which had intensities > 0.25 cm/hr and total precipitation > 1.0 cm. With
the Durham data, the model was used to predict suspended solids for each of
the 34 events for which rainfall data was available (1). It was found that
the model did not agree with observed data for events of intensity < 0.25
cm/hr (19 events). Of the remaining 15 events, 7 had precipitation of < 1.0
cm, and were not handled well by the model. However, for the 8 events which
had intensity > 0.25 cm/hr and total precipitation > 1.0 cm, the model worked
well (Figs. III-5 to III-7). It seems that rainfall conditions and percentage
development may play a larger role in controlling the sediment regression
coefficients than local topography and geology.
Data from the Bloody Run Watershed in Cincinnati (5) also provided an
opportunity for investigating the transferability of the model. This Water-
shed is 9.63 km2 in size, is 80% urban and has an average slope of about 5%.
Again it is topographically and geologically different from the Menomonee
Watershed. Data for several events are published, but only four fall within
the total precipitation and intensity range valid for the model. Use of the
model to predict suspended solids loads for Bloody Run, Cincinnati are shown
in Figs. III-8 and III-9. Agreement with observed values is not particularly
good. The results for the 9/25/70 event (Fig. III-8) reveal a major short-
coming of the model, i.e., the model is extremely insensitive to changes in
suspended solids during events when discharge remains relatively constant.
The Bloody Run flow response to a rainfall of 1.65 cm (intensity of 0.73 cm/hr)
on 9/25/70, varied only from 0.27 m3/sec/km2 to 0.30 m3/sec/km2 over a 3.5 hr
period. Consequently, the model, which is discharge dependent, predicted a
relatively constant solids load while observed values were variable. Such a
response from an urban watershed is probably anomalous, but nonetheless, the
model does not handle it well.
The San Francisco data (4) provides a less comprehensive test than
Durham or Cincinnati. Only one storm fits in the intensity and total rainfall
conditions for the model, and it has an anomalous antecedent dry period of
19 days. For a watershed of 0.73 km2 which is 100% developed, the model
greatly overpredicted suspended solids (Fig. 111-10). However, it does
properly predict for this Watershed the unusual conditions where suspended
solids concentrations increase when runoff decreases (Fig. III-ll). This
dilution effect is anomalous for suspended solids. In fact, if an antecedent
111-13
-------
200 •
•3
o
CO
•o
OJ
•a
C
cu
a,
CO
160 -
120
Time, hr
Fig. III-5.
Comparison of observed and predicted suspended solids
loads for Event 32 at Third Fork Creek, Durham, North
Carolina. Total precipitation was 2 cm, the antecedent
dry period was 2.5 days and rainfall intensity was
0.48 cm/hr.
111-14
-------
5000
4
o
QJ
03
60
-a
o
i-t
CO
•H
rH
o
CO
4000
3000 •
« 2000
0)
I
en
1000
Observed
Predicted
1 2
Time, hr
Fig. III-6. Comparison of observed and predicted suspended
solids loads for Event 29, at Third Fork Creek,
Durham, North Carolina. Total precipitation
was 6 cm, the antecedent dry period was 5 days
and rainfall intensity was 0.86 cm/hr.
111-15
-------
o
CO
->»
M
cfl
O
to
13
•H
H
O
CO
§
a
co
8000 •
6000 .
4000 •
2000 •
2 3
Time, hr
Fig. III-7. Comparison of observed and predicted suspended solids
loads for Event 27 at Third Fork Creek, Durham,
North Carolina. Total precipitation was 3.8 cm, the
antecedent dry period was 11.3 days and rainfall
intensity was 1.14 cm/hr.
II1-16
-------
o
0)
CO
toO
13
n)
O
O
CO
13
0)
T3
0)
a
CO
500 •
7:40
10
Time, hr
Fig. III-8. Comparison of observed and predicted suspended solids loads for Bloody Run,
Cincinnati, Ohio on 9/25/70. Total precipitation was 1.7 cm, antecedent dry
period was 1 day and rainfall intensity was 0.73 cm/hr.
-------
1000
I
M
00
4
o
0)
CO
60
•a
o
rH
w
•rl
.H
O
w
C
0)
cx
500 '
A—A
5:15
6:30 7
Time, hr
7:30
Fig. III-9. Comparison of observed and predicted suspended solids loads for Bloody Run, Cincinnati
Ohio on 10/20/70. Total precipitation was 2.3 cm, antecedent dry period was 6 days and
rainfall intensity was 0.45 cm/hr.
-------
4
a
a)
M
oo
13
CO
O
0)
•a
•rl
rH
O
CO
0)
'O
§
a.
to
1000
Predicted
A
p • ^*V ^E^» Predicted
^. t~ <-*?*^
" ^^X^^ Observed
, . , |
i 5 6
Time, hr
— n — — —
i
7
— — n
•
i
8
Fig. 111-10.
Comparison of observed and predicted suspended solids
loads, Baker Street Basin, San Francisco, California on
11/5/69. Loads have been predicted using the general
model (x) and also a modified model which reduces the
importance of antecedent conditions (o). Total precip-
itation was 1.6 cm, antecedent dry period was 19 days
and rainfall intensity was 0.33 cm/hr.
111-19
-------
3 -«
2 ~
I -
Observed
750
60
e
- 500 £
n)
M
4J
g
O
C
o
o
•8
- 250
a)
cu
to
V)
Fig. III-ll.
5678
Time, hr
Comparison of observed and predicted suspended solids
concentrations and flow for Baker Street Basin,
San Francisco, California. Total rainfall was 1.6
cm, antecedent dry period was 19 days and rainfall
intensity was 0.33 cm/hr.
111-20
-------
dry period of 1 day is entered into the equation, the model produces very
reasonable results. Exactly what this means is not understood. Perhaps the
model does not work for such a steep (average slope 8 to 10%) watershed or for
such long antecedent dry periods. Or perhaps on steep watersheds, the
antecedent dry conditions become unimportant or the model is unaffected after
1 or 2 days. The interpretation of the test remains unresolved.
111-21
-------
REFERENCES-III
1. Colston, N. V. Jr. Characterization and Treatment of Urban Land Runoff.
U.S. Environmental Protection Agency Report No. EPA 670/2-74-096, 1974.
157 pp.
2. Konrad, J. G., G. Chesters and K. W. Bauer. Menomonee River Pilot
Watershed Study: Semi-Annual Report. IJC-Pollution from Land Use
Activities Reference Group. Sponsored by U.S. Environmental Protection
Agency. April 1976. 135 pp.
3. Cherkauer, D. S. The Hydrologic Response of Small Watersheds to Suburban
Development: Observations and Modeling. In: Urbanization and Water
Quality Control, W. Whipple Jr., ed., American Water Resources Association,
Minneapolis, Minn., 1975. pp. 110-119.
4. Yen, B. C., V. T. Chow and A. 0. Akran. Stormwater Runoff on Urban
Areas of Steep Slope. U.S Environmental Protection Agency Report No.
EPA 600/2-77-168, 1977. 91 pp.
5. University of Cincinnati, Department of Civil Engineering. Urban Runoff
Characteristics. U.S. Environmental Protection Agency Report No. EPA
11024 DQU 10/70, 1970. 340 pp.
111-22
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-905/4-79-029E
2.
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Simulation of Pollutant Loadings and Runoff Quality-
Volume 5
6. REPORT DATE
November 1979
0. PERFORMING ORGANIZATION CODE
[7.AUTHOR^) V.- Novotny, P. Balsiger, P. S. Cherkauer,
G.V. Simsiman, G. Chesters, R. Bannerman and
.1 r,
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Wisconsin Department of Natural Resources
P.O. Box 7921 .
Madison, Wisconsin 53707
10. PROGRAM ELEMENT NO.
A42B2A
11. CONTRACT/GRANT NO.
R005142
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency
Great Lakes National Program Office
536 South Clark Street, Room 932
Chicago, Illinois 60605
13. TYPE OF REPORT AND PERIOD COVERED
Final 'Report 197/4-1978
14. SPONSORING AGENCY CODE
U.S. EPA
15. SUPPLEMENTARY NOTES
University Wisconsin System Water Resources Center and
Southeastern Wisconsin Regional Planning Commission.
16. ABSTRACT
Simulations of sediment loadings for various land uses in 48 subwatersheds of the
Menomonee River Watershed are preformed using the LANDRUN model. In order to
determine ratios estimated for pervious areas in each subwatershed.
The Model Enhanced Unit Loading (MEUL) method utilizing the LANDRUN model has been
developed to simulate potential pollutant loadings from urban and non-urban land
uses.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
Sediment Loading
Nonpoint source pollution
Watershed
Monitored
Storm sewer
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Report I
Available to Public through Technical
Information Service, Springfield, VA 22161
21. NO. OF PAGES
172
20. SECURITY CLASS (Thispage)
22. PRICE
EPA Form 2220—1 (R«v. 4—77) PREVIOUS EDITION is OBSOLETE
U S. Government Printing Office 1981 750-804
------- |