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Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed
Author(s) -
Michaud Jene,
Sorooshian Soroosh
Publication year - 1994
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 - 10.1029/93wr03218
Subject(s) - flash flood , watershed , surface runoff , distributed element model , simple (philosophy) , calibration , environmental science , hydrology (agriculture) , rain gauge , hydrological modelling , computer science , meteorology , flood myth , radar , geology , mathematics , geotechnical engineering , engineering , climatology , geography , ecology , statistics , telecommunications , philosophy , archaeology , epistemology , machine learning , electrical engineering , biology
The increasing availability of distributed rainfall data and computational resources is providing the opportunity to use distributed models for rainfall‐runoff forecasting or other applications. This paper compares the accuracy of simulations from a complex distributed model (KINEROS), a simple distributed model (based on the Soil Conservation Service (SCS) method), and a simple lumped model (SCS method). The 150 km 2 , semiarid Walnut Gulch experimental watershed was the test site; models were validated using 24 severe thunderstorms and rain gauge densities similar to those found at flash flood warning sites (one gauge per 20 km 2 ). Under these circumstances, none of the models were able to accurately simulate peak flows or runoff volumes from individual events. Models showed somewhat more skill in predicting time to peak and the ratio of peak flow to volume. When calibration was performed, the accuracy of the complex distributed model was similar to that of the simple distributed model. Without calibration, the complex distributed model was more accurate than the simple distributed model. The spatially lumped model performed very poorly. The complex distributed model was validated under real‐time forecasting conditions; forecasts based on observed rainfall had lead times of 30—75 min.