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Prediction of runoff and soil moistures at the watershed scale: Effects of model complexity and parameter assignment
Author(s) -
Downer Charles W.,
Ogden Fred L.
Publication year - 2003
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2002wr001439
Subject(s) - surface runoff , environmental science , hydrology (agriculture) , watershed , hydrological modelling , water content , soil science , calibration , scale (ratio) , geotechnical engineering , geology , mathematics , statistics , climatology , computer science , geography , ecology , cartography , machine learning , biology
The application of physically based hydrologic models implies they properly simulate processes at the computational scale. A chief criticism is that model predictions are compared only to discharge data. The physically based, hydrologic model CASC2D is reformulated such that soil moistures and fluxes can be computed using Richards' equation. The gridded surface subsurface hydrologic analysis (GSSHA) model is calibrated and verified against outlet discharge measurements during the growing season. The verified model is used to simulate an extended period during which measurements of soil moisture are available. Though soil moisture data are not used in the calibration and verification efforts, the model reproduces both the trends and the magnitude of soil moisture during the growing season. With additional formulation enhancements, soil moistures during the nongrowing season are also reproduced within a root‐mean‐square error of 0.1. However, more work is needed to understand the underprediction of runoff during the nongrowing season.