CALIBRATION OF RAINFALL-RUNOFF MODELS USING GRADIENT-BASED ALGORITHMS AND ANALYTIC DERIVATIVES
AffiliationDepartment of Hydrology & Water Resources, The University of Arizona
KeywordsRunoff -- Mathematical models.
Rain and rainfall -- Mathematical models.
Groundwater -- Mathematical models.
Soil moisture -- Mathematical models.
Soil moisture -- Measurement -- Mathematical models.
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Collection InformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact email@example.com.
AbstractIn the past, derivative-based optimization algorithms have not frequently been used to calibrate conceptual rainfall -riff (CRR) models, partially due to difficulties associated with obtaining the required derivatives. This research applies a recently- developed technique of analytically computing derivatives of a CRR model to a complex, widely -used CRR model. The resulting least squares response surface was found to contain numerous discontinuities in the surface and derivatives. However, the surface and its derivatives were found to be everywhere finite, permitting the use of derivative -based optimization algorithms. Finite difference numeric derivatives were computed and found to be virtually identical to analytic derivatives. A comparison was made between gradient (Newton- Raphsoz) and direct (pattern search) optimization algorithms. The pattern search algorithm was found to be more robust. The lower robustness of the Newton-Raphsoi algorithm was thought to be due to discontinuities and a rough texture of the response surface.
Series/Report no.Technical Reports on Hydrology and Water Resources, No. 87-010
SponsorsThe authors wish to extend thanks to Dr. Vijai Opta and Mr. Larry Brazil for their help and assistance. They also wish to acknowledge the technical support provided by the Center for Computing Information and Technology at the Univeristy of Arizona. Support for this research was provided by the Hydrologic Research Lab of the National Weather Service (Project #NA85AP- H- HY088) and the National Science Foundation (Grant DGF.8610584). Finally, the authors would like to thank Ms. Corsa Thies for typing of the manuscript.
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