H13H-1214:
Risk Assessment and Mapping of Fecal Contamination in the Ohio River Basin
Abstract:
Decisions in many problems in engineering planning are invariably made under conditions of uncertainty imposed by the inherent randomness of natural phenomena. Water quality is one such problem. For example, the leading cause of surface-water impairment in the US is fecal microbial contamination, which can potentially trigger massive outbreaks of gastrointestinal disease. It is well known that the difficulty in prediction of water contamination is rooted in the stochastic variability of microbes in the environment, and in the complexity of environmental systems.To address these issues, we employ a risk-based design format to compute the variability in microbial concentrations and the probability of exceeding the E. Coli target in the Ohio River Basin (ORB). This probability is then mapped onto the basin’s stream network within the ArcGIS environment. We demonstrate how spatial risk maps can be used in support of watershed management decisions, in particular in the assessment of best management practices for reduction of E. Coli load in surface water.
The modeling environment selected for the analysis is the Schematic Processor (SP), a suite of geoprocessing ArcGIS tools. SP operates on a schematic, link-and-node network model of the watershed. The National Hydrography Dataset (NHD) is used as the basis for this representation, as it provides the stream network, lakes, and catchment definitions. Given the schematic network of the watershed, SP adds the capability to perform mathematical computations along the links and at the nodes. This enables modeling fate and transport of any entity over the network.
Data from various sources have been integrated for this analysis. Catchment boundaries, lake locations, the stream network and flow data have been retrieved from the NHDPlus. Land use data come from the National Land Cover Database (NLCD), and microbial observations data from the Ohio River Sanitation Committee. The latter dataset is a result of a 2003-2007 longitudinal study. Samples for E. coli analysis were collected approximately every five miles along the entire length of the Ohio River, with additional samples collected at the mouths of over 125 direct tributaries to the Ohio River.