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WATERSHED CONFIGURATION AND GEOGRAPHIC INFORMATION SYSTEM PARAMETERIZATION FOR SPUR MODEL HYDROLOGIC SIMULATIONS 1
Author(s) -
Sasowsky Kathryn Connors,
Gardner Thomas W.
Publication year - 1991
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1991.tb03108.x
Subject(s) - watershed , surface runoff , environmental science , digital elevation model , runoff curve number , geographic information system , hydrology (agriculture) , watershed area , elevation (ballistics) , computer science , geography , remote sensing , mathematics , geology , geometry , ecology , geotechnical engineering , machine learning , biology
A grid cell geographic information system (GIS) is used to parameterize SPUR, a quasi‐physically based surface runoff model in which a watershed is configured as a set of stream segments and contributing areas. GIS analysis techniques produce various watershed configurations by progressive simplification of a stream network delineated from digital elevation models (DEM). We used three watershed configurations: ≥ 2nd, ≥ 4th, and ≥ 13th Shreve order networks, where the watershed contains 28, 15, and 1 channel segments with 66, 37, and 3 contributing areas, respectively. Watershed configuration controls simulated daily and monthly sums of runoff volumes. For the climatic and topographic setting in southeastern Arizona the ≥ 4th order configuration of the stream network and contributing areas produces results that are typically as good as the ≥ 2nd order network. However both are consistently better than the ≥ 13th order configuration. Due to the degree of parameterization in SPUR, model simulations cannot be significantly improved by increasing watershed configuration beyond the ≥ 4th order network. However, a range of Soil Conservation Service curve numbers derived from rainfall/runoff data can affect model simulations. Higher curve numbers yield better results for the ≥ 2nd order network while lower curve numbers yield better results for the ≥ 4th order network.