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MODELING POTENTIAL PATHOGEN INFECTED WATERS UTILIZING LANDSCAPE INDICES
Citation:
Smith, J H. AND J D. Wickham. MODELING POTENTIAL PATHOGEN INFECTED WATERS UTILIZING LANDSCAPE INDICES. Presented at Linkages between biodiversity, ecosystem health and human health, Washington, DC, June 6-11, 2002.
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
The federal Clean Water Act (CWA) requires states, territories and tribal lands to assess their waters on a biennial schedule and identify, list and prioritize impaired waters not meeting water quality standards. Once a water body is listed, the state is required to develop Total Maximum Daily Loads (TMDLS) for the pollutants causing the impairment. A TMDL is a pollution budget for a specific river, lake or stream and is calculated to estimate the amount of pollutant a water body can receive and still meet the standards set. One non-point source (NPS) pollutant that has caused many water bodies to be listed as impaired are pathogens derived from animal wastes, including humans. The potential presence of pathogens is identified by testing the water for fecal coliform, a bacteria also associated with animal wastes. Water contaminated by animal wastes are most often associated with areas that contain large concentrations of animals, such as urban and agricultural areas. It has been postulated that by utilizing landscape indices, measures of various land covers and associated landscape characteristics, those water bodies that my be at risk of fecal coliform contamination may be identified. This study utilized land cover information derived from the National Land Cover Data (NLCD) set to analyze fecal coliform contamination in South Carolina, Various landscape induces were developed for individual watersheds and then analyzed using a step-wise logistic recession. The results reveal the probability of each of the watersheds exceeding TMDL limits. Watersheds with large proportions of urbanization and agriculture on steep slopes had very high probabilities of exceeding these limits, This model will allow South Carolina watershed managers to make knowledgeable decisions on resource use and assess impacts of future land cover changes.