Title: | Effect of soil data resolution on identification of critical source areas of sediment |
Authors: | Singh, H.V., L. Kalin and P. Srivastava |
Year: | 2011 |
Journal: | Journal of Hydrologic Engineering |
Volume (Issue): | 16(3) |
Pages: | 253-278 |
Article ID: | |
DOI: | 10.1061/(ASCE)HE.1943-5584.0000318 |
URL (non-DOI journals): | |
Model: | SWAT |
Broad Application Category: | hydrologic and pollutant |
Primary Application Category: | soil data resolution effects |
Secondary Application Category: | critical source area assessment |
Watershed Description: | 398 km^2 Fish River in southwest Alabama, USA |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
Abstract: | Identification of critical source areas (CSA) of pollution in a watershed is important for
effective implementation of best management practices (BMPs). Process-based,
watershed models are often used for this purpose. One of the main inputs to these models
is the spatially-explicit soils data. The objective of this study was to evaluate if the use of
two commonly used soil datasets, the State Soil Geographic (STATSGO) and the Soil
Survey Geographic (SSURGO) data, can lead to differences in location of CSAs of
sediment. A watershed model, Soil and Water Assessment Tool (SWAT) in combination
with the Tukey-Kramer test was used for locating CSAs in the Fish River watershed,
which is located in coastal Alabama, U.S.A. The model was calibrated and validated
using flow data from a USGS gauging station located within the watershed. The locations
of the CSAs of sediment were analyzed at subwatershed and Hydrologic Response Unit
(HRU) levels. Results show that the locations of the CSAs were different for the two soil
datasets. The locations of the CSAs varied at both subwatershed and HRU levels. The use
of STATSGO soil data resulted in higher soil erodibility factor and surface runoff. As a
result, higher sediment yield was obtained from the use of the STATSGO data as
compared to the sediment yield obtained from the use of the SSURGO data. Therefore,
for accurate identification of CSAs of sediment (and potentially other pollutants) and for
effective implementation of economically-feasible BMPs, it is important to use the most
detailed spatial dataset available. |
Language: | English |
Keywords: | Critical Source Area, Best Management Practice, SWAT, Sediment, Modeling, STATSGO, SSURGO |