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Development and application of the spatially explicit load enrichment calculation tool (select) to determine potential E. coli loads in watershedsRiebschleager, Kendra Jean 15 May 2009 (has links)
According to the USEPA National Section 303(d) List Fact Sheet, bacterial pathogens
are the leading cause of water quality impairments in Texas. The automated Spatially
Explicit Load Enrichment Calculation Tool (SELECT) uses spatially variable factors
such as land use, soil condition, and distance to streams to characterize pathogen sources
across a watershed. The results support development of Total Maximum Daily Loads
(TMDLs) where bacterial contamination is of concern. SELECT calculates potential E.
coli loads by distributing the contributing source populations across suitable habitats,
applying a fecal production rate, and then aggregating the potential load to the
subwatersheds. SELECT provides a Graphical User Interface (GUI), developed in
Visual Basic for Applications (VBA) within ArcGIS 9.X, where project parameters can
be adjusted for various pollutant loading scenarios.
A new approach for characterizing E. coli loads resulting from on-site wastewater
treatment systems (OWTSs) was incorporated into the SELECT methodology. The
pollutant connectivity factor (PCF) module was created to identify areas potentially
contributing E. coli loads to waterbodies during runoff events by weighting the influence
of potential loading, runoff potential, and travel distance.
Simulation results indicate livestock and wildlife are potentially contributing large
amounts of E. coli in the Lake Granbury Watershed in areas where these contributing
sources are not currently monitored for E. coli. The bacterial water quality violations near Lake Granbury are most likely the result of malfunctioning OWTSs and pet waste
in the runoff.
The automated SELECT was verified by characterizing the potential E. coli loading in
the Plum Creek Watershed and comparing to results from a prior study (Teague, 2007).
The E. coli potential load for the watershed was lower than the previous study due to
major differences in assumptions. Comparing the average ranked PCF estimated by
physical properties of the watershed with the statistical clustering of watershed
characteristics provided similar groupings.
SELECT supports the need to evaluate each contributing source separately to effectively
allocate site specific best management practices (BMPs). This approach can be used as a
screening step for determining areas where detailed investigation is merited. SELECT in
conjunction with PCF and clustering analysis can assist decision makers develop
Watershed Protection Plans (WPPs) and determine TMDLs.
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