Calculating a Pollution Potential Index for Storm Water Runoff at the Watershed Scale Ranking watersheds for potential non-point pollution

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1 Calculating a Pollution Potential Index for Storm Water Runoff at the Watershed Scale Ranking watersheds for potential non-point pollution Philip Dougherty GISC Introduction Non-point source (nps) pollution might be defined as the small amounts of contaminants from a large number of sources that are carried into the watercourse during rainfall events. These contaminants may be washed off lawns, construction areas, farms, or highways during rain storms (Texas Commission on Environmental Quality). It is widely agreed that diffuse pollution is having an effect on water quality and aquatic life (Environmental Protection Agency). Pollution from diffuse sources is decidedly difficult to measure and manage, though; a great deal of research has identified the factors most commonly associated with nps. Utilizing GIS tools, we can measure the potential for nps pollution by mapping and analyzing these factors. By mapping potential nps pollution sources and the landscape characteristics that allow pollutants to reach receiving waters, a theoretical model of pollution potential can be developed. The objective of this model will be to identify the North Central Texas watersheds that are most likely to contribute pollutants to their receiving waters through storm water run-off. In order to meet this objective the model must output a non-point pollution potential index map for the region. The central tendency of index values will be calculated for each watershed in order to summarize and rank each watershed for pollution potential. This measure will aid in our theoretical understanding of watershed and waterway health. Further, the measure could help guide pollution prevention efforts. Literature In developing this model, several key pieces of literature were consulted. Foremost amongst them was River pollution from non-point sources: a new simplified method of assessment by Michele Munafo et al. The general modeling framework used has been derived from the work of Munafo et al., who calculate pollution potential as a function of the land cover indicator, the run-off indicator, and the distance indicator (Munafo et al., 94). The distance indicator is measured by flow length and accounts for pollution dumping as mitigating pollution potential (Munafo et al., 95). Finally, this study suggests normalizing the input data to values of between 0 and 1 before combining the datasets (Munafo et al., 96). This normalization serves to equally rank all values in the input datasets. A second critical source document was Buffer Zone versus Whole Catchment Approaches to Studying Land Use Impact on River Water Quality by Lucie Sliva and

2 Dudley Williams. Sliva and Williams study concludes that water quality is correlated with catchment scale landscape slightly better than with buffer landscape (Sliva and Williams, 3471). According to this result, it is valid to estimate pollution potential by looking at an entire watershed, as opposed to only a buffer zone along each waterway. Lastly, Prakash Basnyat et al. suggest in their work, The use of remote sensing and GIS in watershed level analyses of non-point source pollution problems, that forests act as a sink (Basnyat et al., 65). This study suggests that forests chemically alter certain pollutants as they pass through the forest zone and come into contact with root systems etc. (Basnyat et al., 66). Considering forests as mitigating factors on pollution potential helps to establish an ordinal ranking for land use/land cover types. Forests could be considered to have a negative value for pollution potential. Data The data required by this study includes land use/land cover, soil permeability, slope, flow length, and watershed boundaries. The land use/land cover data was sourced from the Texas Natural Resource Information System (TNRIS) as vector polygons and converted to a raster Grid. Soil unit data was also acquired from the TNRIS and converted to raster Grid using a permeability rate variable. Slope and flow length are derived datasets calculated from a Digital Elevation Model (DEM) also sourced from TNRIS. The watershed boundaries were obtained from the North Central Texas Council of Governments (NCTCOG). The boundaries were utilized as vector polygons. Though the watersheds could also have been derived from the DEM, it made more sense to use the established NCTCOG boundaries because they are in the public domain and most likely recognized as being accurate. Soil permeability, slope, and land use/land cover are used in the calculation of run-off potential, which is a model input. Flow length and land use/land cover (ranked for pollution potential) are input directly to the model. Analysis & Results Run-off potential (ROP) was calculated using simple raster addition, after normalizing all values in each dataset to between 0 and 1, 1 being the greatest potential. The data was normalized using a linear transformation written by: (Value i - Value Min ) / (Value Max -Value Min ) This equation is used throughout for normalization. Soil permeability data was recorded in inches drained per hour. Since high permeability rates are associated with low run-off potential, the inverse of this variable was taken before normalization. Greater values for slope are associated with greater run-off, so no additional processing was required. Lastly, generalized run-off coefficients for each land use/ land cover (LULC) type were sourced from a similar study in Indiana (Indiana Geological Survey). The run-off coefficients were applied to the LULC data before converting to raster and normalizing. Flow length (FL) was calculated for each location within each watershed using standard tools available in the Spatial Analyst extension to ArcGIS software. There were considerable edge effects noted when calculating these flow lengths, as the software tool

3 tends to force flow outward at the edge of the calculation area (in this case, the edge of a given watershed). The edge effects were removed using a filter that employed a focal window and statistic. The edge values of the raster were re-written as the maximum flow length found within the focal neighborhood. A buffer of the watershed boundary was used as an analysis mask to prevent needlessly re-writing all values in the raster. Since longer flow distances mean pollution dumping and less pollution potential, the inverse of flow distance was taken before finally normalizing. The LULC data was converted to raster a second time based, not on run-off coefficient, but LULC type. The types (urban, agricultural, forest etc.) were then ranked in a simple ordinal scale. Forest was ranked the lowest, while urban and agricultural land was ranked the highest on pollution potential. Again, these values were normalized between 0 and 1. A weighted combination of the inputs was then calculated to derive the pollution potential index (PPI). The weights were sourced from Munafo et al. as 5 for LULC, 3 for FL and 2 for ROP (Munafo et al., 96). The combinatorial function is written by:.5(lulc)+.3(fl)+.2(rop)=ppi The resultant pollution potential index map gives the potential for each location in the study area to contribute pollutants to its receiving waters. In a departure from the sourced literature, which essentially stops here, a zonal mean (ZM) of the PPI was calculated for each watershed. The mean pollution potential for each watershed allows for a ranking of watersheds. Considering that the entire catchment has been shown to effect water quality, this ought to be a valid measure. The ranking by mean of PPI is attached in the appendix as Figure 1. A map of this value is also included. Going one step further, a watershed index (WI) value was calculated to represent this mean in a more user-friendly manner. Using the WI, average potential is represented as a value of 100. Values above and below 100 are above and below average. This index looks like a consumer expenditure index that many observers find familiar. The WI value is calculated as: ZM i /μ ZM *100=WI The ranking of watersheds is the same with WI as with PPI, but the WI is a clearer representation. The ranking by WI is attached in the appendix as Figure2. A map of this value is also included. Conclusions The results presented here are an interpretation of water quality and watershed health, as they related to the potential for non-point pollution. The modeling approach outlined here was successful in producing the indices required to rank watersheds for pollution potential. The PPI and WI allow that the watersheds with the highest pollution potential can be identified and mapped. These indices serve to help account for diffuse pollution, which is decidedly difficult to measure and control. A GIS model such as this

4 one could play a role in the management and prevention of non-point pollution, as it can help identify those locations that require the most attention. The contribution of this model, or the way in which it goes beyond the referenced literature, is to employ the pollution potential index in ranking and comparing several catchments in a region. The results of this model, though, cannot be easily validated. We should not, for example, collect water samples in effort to prove that the water quality really is poorest in the predicted poorest watershed. The qualitative approach of this study is most applicable in the general way described above. Further research might investigate the results of alternate weighting schemes in terms of their effect on watershed rank.

5 Appendix: Figure 1 Figure 2

6 References: Articles- Field, Richard and Michael Borst, Thomas P. O connor, Mary K. Stinson, Chi-Yuan Fan, Joyce M. Perdek, Daniel Sullivan Urban Wet-Weather Flow Management: Research Directions. Journal of Water Resources Planning and Management 124 (3): McDonnell, Jeffrey J Where does water go when it rains? Moving beyond the variable source area concept of rainfall-runoff response. Hydrological Processes 17: Munafo, Michele and Giuliano Cecchi, Fabio Baiocco, Laura Mancini River pollution from non-point sources: a new simplified method of assessment. Journal of Environmental Management 77: Prakash, Basnyat and Lawrence D. Teeter, Kathryn M. Flynn, B. Graeme Lockaby Relationships Between Landscape Characteristics and Nonpoint Pollution Inputs to Coastal Estuaries. Environmental Management 23(4): Prakash, Basnyat and Lawrence D. Teeter, B. Graeme Lockaby, Kathryn M. Flynn The use of remote sensing and GIS in watershed level analyses of non-point source pollution problems. Forest Ecology and Management 128: Sivertun, Ake and Lars Prange Non-point source critical area analysis in the Gisselo watershed using GIS. Environmental Modelling & Software 18: Sliva, Lucie and Dudley Williams Buffer Zone Versus Whole Catchment Approaches To Studying Land Use Impact On River Water Quality. Water Research 35 (14): Websites- November National Management Measures to Control Nonpoint Source Pollution from Urban Areas. (accessed July 5, 2006). Nonpoint Source Water Pollution Management Program. (accessed July 5, 2006) Determining runoff coefficients and bacterial loadings from interfluves. (accessed July 10, 2006)

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