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Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model
Author(s) -
Sorooshian Soroosh,
Duan Qingyun,
Gupta Vijai Kumar
Publication year - 1993
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 - 10.1029/92wr02617
Subject(s) - calibration , surface runoff , global optimization , consistency (knowledge bases) , set (abstract data type) , watershed , data set , function (biology) , runoff curve number , hydrological modelling , mathematics , computer science , hydrology (agriculture) , algorithm , environmental science , mathematical optimization , statistics , geology , ecology , geotechnical engineering , machine learning , climatology , geometry , evolutionary biology , biology , programming language
Conceptual rainfall‐runoff models are difficult to calibrate by means of automatic methods; one major reason for this is the inability of conventional procedures to locate the globally optimal set of parameters. This paper investigates the consistency with which two global optimization methods, the shuffled complex evolution (SCE‐UA) method (developed by the authors) and the multistart simplex (MSX) method, are able to find the optimal parameter set during calibration of the Sacramento soil moisture accounting model (SAC‐SMA) of the National Weather Service River Forecast System (NWSRFS). In the first phase of this study, error‐free synthetic data are used to conduct a comparative evaluation of the algorithms under “ideal” conditions. In 10 independent trials of each algorithm in which 13 parameters of the SAC‐SMA model were optimized simultaneously, the SCE‐UA method achieved a 100% success rate in locating the precise global optimum (i.e., the “true” parameter values) while the MSX method failed in all trials even with more than twice the number of function evaluations. In the second phase, historical data from the Leaf River watershed are used to conduct a comparative evaluation of the algorithms under “real” conditions, using two different estimation criteria, DRMS and HMLE; the SCE‐UA algorithm obtained consistently lower function values and more closely grouped parameter estimates, while using one‐third fewer function evaluations than the MSX algorithm.