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Parameter Estimation and Predictive Uncertainty Quantification in Hydrological Modelling

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Handbook of Hydrometeorological Ensemble Forecasting

Abstract

The majority of hydrological and environmental models contain parameters that must be specified before the model can be used. Parameter estimation is hence a very common problem in environmental sciences and has received tremendous amount of research and industry attention. This chapter reviews some of the key principles of parameter estimation, with a focus on calibration approaches and uncertainty quantification. The distinct approaches of manual calibration, optimization, multi-objective optimization, and probabilistic approaches are described in terms of key theory and representative applications. Advantages and limitations of these strategies are listed and discussed, with a focus on their ability to represent parametric and predictive uncertainties. The role of posterior diagnostics to check calibration and model assumptions that impact on parameter estimation is emphasized. Auxiliary tricks and techniques are described to simplify the process of parameter estimation in practical applications. The chapter concludes with an outline of directions for ongoing and future research. It is hoped that this chapter will help hydrologists and environmental modellers get to the current state of research and practice in model calibration, parameter estimation, and uncertainty quantification.

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Authors and Affiliations

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

    Dmitri Kavetski

  2. School of Engineering, University of Newcastle, Callaghan, NSW, Australia

    Dmitri Kavetski

  3. Department of Systems Analysis, Integrated Assessment and Modelling (SIAM), Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    Dmitri Kavetski

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Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, 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

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Kavetski, D. (2018). Parameter Estimation and Predictive Uncertainty Quantification in Hydrological Modelling. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_25-1

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