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Handbook of Hydrometeorological Ensemble Forecasting pp 523–561Cite as

Methods to Estimate Optimal Parameters

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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.

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Author information

Authors and Affiliations

  1. University of California, Irvine, CA, USA

    Tiantian Yang & Soroosh Sorooshian

  2. Civil and Environmental Engineering, The Henry Samueli School of Engineering, University of California, Irvine, CA, USA

    Kuolin Hsu

  3. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Qingyun Duan

  4. South China Botanical Garden, Chinese Academy of Sciences, Richland, WA, USA

    Chen Wang

Authors
  1. Tiantian Yang
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  2. Kuolin Hsu
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  3. Qingyun Duan
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  4. Soroosh Sorooshian
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  5. Chen Wang
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Corresponding author

Correspondence to Kuolin Hsu .

Editor information

Editors and Affiliations

  1. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, UK

    Florian Pappenberger

  3. National Center for Atmospheric Research, Boulder, CO, USA

    Andy Wood

  4. Department of Meteorology, Reading University, Reading, UK

    Hannah L. Cloke

  5. U.S. National Weather Service (retired), Annapolis, MD, USA

    John C. Schaake

Section Editor information

  1. School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia

    Dmitri Kavetski

  2. Civil & Environmental Engineering, The Henry Samueli School of Engineering, University of California, Irvine, California, USA

    Kuolin Hsu

  3. NASA Goddard Space Flight Center, Washington D.C., USA

    Yuqiong Liu Assistant Research Scientist

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Yang, T., Hsu, K., Duan, Q., Sorooshian, S., Wang, C. (2019). Methods to Estimate Optimal Parameters. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_26

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