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CALIBRATION OF RAINFALL-RUNOFF MODELS USING GRADIENT-BASED ALGORITHMS AND ANALYTIC DERIVATIVES

In 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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/614186
Date05 1900
CreatorsHendrickson, Jene Diane, Sorooshian, Soroosh
ContributorsDepartment of Hydrology & Water Resources, The University of Arizona
PublisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Source SetsUniversity of Arizona
Languageen_US
Detected LanguageEnglish
Typetext, Technical Report
SourceProvided by the Department of Hydrology and Water Resources.
RightsCopyright © Arizona Board of Regents
RelationTechnical Reports on Hydrology and Water Resources, No. 87-010

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