1. Advisor: Dr. Tom Brikowski
GIS Analysis of Soil Erosion and Chemical Concentration for White Rock Reservoir Watershed
GIS Master Project By
Ovi Sipos
Advisor: Dr. Tom Brikowski
UT Dallas
November 2007

2. Introduction
This study discuses the possibility of applying GIS to develop and analyze a spatial model of soil erosion and chemical concentration for the area of study.
Several studies have been developed to estimate soil erosion and sediment transport at reduce scales, but just a few attempts have been made to provide direct mapping of spatial models.
The importance of this study comes from the fact that a large scale model can be developed using the methodology investigated here.
The purpose of this study is to determine the influence of agricultural and non-agricultural practices on the water quality correlated with soil erosion due to surface water.
Several factors that influence soil erosion and sediment transportation are identified and analyzed such as runoff, land use, soil types and hydrological soil groups.

3. Objective
The project objective can be divided in three objectives as following:
To establish a GIS model for the soil loss and sediment transportation due to water erosion
To develop a GIS model for the chemical concentration in the study area in correlation with the land use practices
To determine the relationship between the two models

4. Limitations Data availability and accuracy
no spatial data for pesticide use
pesticide data available just by zip code
pesticide and precipitation data available on a monthly or annual average– does not consider variations from year to year which is a negative factor

5. Data sources
Soil type data - Natural Resources Conservation Service
Land use / Land cover
Pesticide data for Texas – Texas Environmental Profile
Precipitation data – Natural Resources Conservation Service
Digital Elevation Model – USGS

6. Literature review
The article by Van Meter, P.C., Land, L.F., and Brawn (1996) titled Water- quality trends using sediments cores from White Rock Lake, Dallas, Texas summarize the principal findings documents in a report on water quality for White Rock Creek Basin using dated sediment cores. The study used sediment cores to reconstruct water-quality conditions.
City of Dallas - Drainage Design Manual provides criteria and design recommendations for storm drainage facilities in Dallas County. This manual is a valuable source of technical information that is specifically tailored for Dallas County and describes concepts and methods used in hydraulic design: runoff, curve number method, rational method etc.
The book Randall J. Charbeneau (2000) titled Groundwater Hydraulic and Pollutant Transport analyses the physical and chemical processes that control the transport and fate of hazardous substances in the subsurface environment. The book also provides important information about hydraulic and erosion cocepts.
The article ArcGIS Hydro Data Model Structure and Definitions by David R. Maidment and Timothy L. Whiteaker (2000) provides and overview of the ArcGIS hydro data model that contains geospatial and temporal data describing the surface water flow system of the landscape.
The article Modeling Agrichemical Transport in Midwest Rivers Using GIS by Pawel Mizgalewicz and David R. Maidment describes a method for regionalizing watershed and scaling water quality estimates.
Several methods have been developed which involve the application of soil erosion equations and models and often require complex calculations.  Some examples include: Journal of Paleolimnology (Bradbury, Van Meter 1996), Water Erosion Prediction Project (Cochrane and Flanagan, 2003), Sediment Delivery Distributed (SEDD) (Fernandez et al., 2003), Soil and Water Assessment Tool (Arnold et al., 1998; Tripathi et al., 2004), Hillslope Erosion Model (Wilson et al., 2001).

7. Analysis
The methodology followed in this paper is divided into the following steps:
Hydrological modeling of the area
Modeling of soil loss as a result of water erosion
Determination of rainfall and runoff relationship
Modeling of chemical concentration for the area of study
Soil Loss – Chemical Concentration relationship

8. 1. Hydrological modeling of the area of study
The purpose of this hydrological analysis is to determine the drainage areas and the discharge points specific for each area. The following steps describe this process.
Flow Direction
Flow Accumulation

9. Hydrological modeling of the area of study-Cont

10. Hydrological modeling of the area of study-Cont

11. Hydrological modeling of the area of study-Cont

12. Hydrological modeling of the area of study-Cont

13. 2. Modeling of soil loss as a result of water erosion
Revised Universal Soil Loss Equation (RUSLE)
A=R*K*L*S*C*P
Where:
A is the annual soil loss (t acre-1 yr-1)
R is the rainfall erosion factor (MJ mm acre-1 h-1 yr-1),
K is the soil erodibility factor (t h MJ-1 mm-1),
L is the slope length (unitless),
S is the slope gradient or steepness (unitless),
C is the crop management factor (unitless) and
P is the erosion control practice factor (unitless) (Wischmeier and Smith, 1978).

14. Modeling of soil loss as a result of water erosion-Cont
A=R*K*L*S*C*P
Support Practice Factor (P) describes the supporting effect of practices like contouring, cropping and terraces. Most of the time this variable is set to 1 in land management applications.
Erosivity Factor (R) value for the study area was derived from precipitation records for Texas and is 291.

15. Modeling of soil loss as a result of water erosion-Cont
A=R*K*L*S*C*P
Soil Erodibility Factor (K) represents the susceptibility of soil to erosion and the rate of runoff. Table below presents the k values specific for certain soil types:

16. Modeling of soil loss as a result of water erosion-Cont
A=R*K*L*S*C*P
Slope Length and Steepness Factor (LS) represents erodibility due to combination of slope length and steepness.
LS = (Flow Accum * Cell Size/22.13)^0.4 * (sin slope/0.0896)^1.3
Flow Accumulation
Slope LS

17. Modeling of soil loss as a result of water erosion-Cont
A=R*K*L*S*C*P
Cover and Management Factor (C) represents the effect of soil cover and soil-disturbing activities on soil erosion. The values for C factor were input manually into the land use shape file based on land use and converted to grid.

18. x x x x P-FACTOR R-FACTOR C-FACTOR K-FACTOR LS-FACTOR A=PxRxCxKxLS
SOIL LOSS

19. Soil Loss

20. 3. Determination of rainfall and runoff relationship
Determination of Rainfall / Runoff Relationship is required in order to assess the transport of chemical load throughout the study area. The volume of runoff for a grid cell is attributed to the quantity of precipitation over that particular cell. The procedure used to calculate this factor is SCS (Soil Conservation Services) Runoff Curve Number equation:
Where Q = runoff
S =potential maximum retention
P =rainfall
CN =curve number

21. Determination of rainfall and runoff relationship-Cont

22. Determination of rainfall and runoff relationship-Cont

23. Determination of rainfall and runoff relationship-Cont

24. Determination of rainfall and runoff relationship-Cont

25. Determination of rainfall and runoff relationship-Cont

26. 4. Modeling of chemical concentration for the area of study
Modeling the chemical concentration in the study area is achieved by defining the mass of chemical components transported per volume of runoff.
The chemical load estimation for the area is calculating by taking the product of the expected chemical concentration and the runoff depth corresponding to that cell:
L (kg/year) = K x Q x CH x A
Where
Q = is the runoff (mm/year or mm/month)
K = is a constant equals to 10^-6
A = drainage area
CH = chemical concentration

27. Modeling of chemical concentration for the area of study - Cont
The pesticide use data for Texas is available on the Texas Environmental Profile website and it can be ranked by state, county or zip code.
The zip code shape file was intersected with the drainage area polygons shape file to determine the chemical distribution for each individual area. The chemical concentration was calculated in units per square meter.
Once the chemical concentration was calculated a new field was added to the attribute table for the Chemical Load.

28. Modeling of chemical concentration for the area of study - Cont

29. Modeling of chemical concentration for the area of study - Cont

30. Modeling of chemical concentration for the area of study - Cont
DA_8

31. 5. SOIL LOSS-CHEMICAL CONCENTRATION RELATIONSHIP
SPATIAL AUTOCORRELATION FUNCTION
Calculates the cross correlation between the chemical load model and the actual soil loss model in this study.
The correlation coefficient between the two models is found to be 0.01 which reflects a weak correlation between the two models.

32. Conclusions
The chemical concentration and soil loss models developed in this study have shown a possible method of characterizing the process of transportation of sediment and chemicals as they actually contribute to the White Rock watershed.
The method uses data that is publicly available and synthesizes the data in a consistent and logical way across the area of study.
The procedure used for this method utilizes standard ArcMap commands and functions.
The spatial correlation between chemical concentration and soil loss due to water erosion is weak therefore the erosion is not a major factor in this model.
The model gives estimates for average monthly and annual flow as well as spatial chemical concentration by zip code. These data don’t consider variation within years and provide accurate pesticide concentration for certain are which is a real restriction.

33. Conclusions-Cont.
The fact sheet The Water- quality trends using sediments cores from White Rock Lake, Dallas, Texas published under the USGS web site provides important information about the sediment deposits found in White Rock Lake and the correlation between the urban land use and use of insecticide. As you can see from these chats, the use of DDT, which is a toxic compound of insecticide, begun in 1939 and widespread use continued until about 1972 when was prohibited.

34. References
City of Dallas, Drainage Design Manual
David R. Maidment ArcHydro: GIS for Water Resources. Redlands California: ESRI Press.
David R. Maidment and Timothy L. Whiteaker (2000) ArcGIS Hydro Data Model Structure and Definitions
U.S. Geological Survey (USGS)
North Central Council of Governments (NCTCOG)
U.S. Department of Agriculture (USDA)
Environmental System Research Institute (ESRI)
Texas Environmental Profile (
Pawel Mizgalewicz and David R. Maidment (2000) Modeling Agrichemical Transport in Midwest Rivers Using GIS