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Methods to Estimate Optimal Parameters

  • Tiantian Yang
  • Kuolin HsuEmail author
  • Qingyun Duan
  • Soroosh Sorooshian
  • Chen Wang
Reference work entry

Abstract

Model, data, and parameter estimation are three fundamental elements in hydrologic process modeling and forecasting. Recent progresses in hydrologic modeling have been made toward more efficient and effective estimation of model parameters. In this chapter, classical and recently developed parameter optimization methods and their applications in hydrological model calibration are reviewed. Those methods include gradient-based optimization methods, direct search methods, and recently developed stochastic global optimization methods. A recently developed surrogate model approach, with the purpose to reduce computational burden of model which runs through replacing the hydrologic process model with a cheaper-to-run surrogate model, is also discussed. Extending from a single objective function parameter optimization, multiobjective optimization methods and their core concept in deriving trade-offs are also summarized. Examples are provided to demonstrate the strengths and limitations of optimization algorithms summarized in this chapter.

Keywords

Optimization Hydrologic Model Evolutionary Algorithm Automatic Parameter Estimation Surrogate Model 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tiantian Yang
    • 1
  • Kuolin Hsu
    • 2
    Email author
  • Qingyun Duan
    • 3
  • Soroosh Sorooshian
    • 1
  • Chen Wang
    • 4
  1. 1.University of CaliforniaIrvineUSA
  2. 2.Civil and Environmental Engineering, The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineUSA
  3. 3.Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  4. 4.South China Botanical GardenChinese Academy of SciencesRichlandUSA

Section editors and affiliations

  • Dmitri Kavetski
    • 1
  • Kuolin Hsu
    • 2
  • Yuqiong Liu
    • 3
  1. 1.School of Civil, Environmental and Mining Engineering, University of AdelaideAdelaideAustralia
  2. 2.Civil & Environmental Engineering, The Henry Samueli School of Engineering, University of CaliforniaIrvineUSA
  3. 3.NASA Goddard Space Flight CenterWashington D.C.USA

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