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A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox

Author

Listed:
  • Mojtaba Sadegh

    (University of California
    Boise State University)

  • Amir AghaKouchak

    (University of California)

  • Alejandro Flores

    (Boise State University)

  • Iman Mallakpour

    (University of California)

  • Mohammad Reza Nikoo

    (Shiraz University)

Abstract

We present a framework and toolbox for multi-model (one at a time) nonstationary modeling of rainfall-runoff (RR) transformation. The designed time-varying nature of the five available conceptual RR models in the toolbox allows for modeling processes that are nonstationary in essence. Nonstationary Rainfall-Runoff Toolbox (NRRT) delivers insights about underlying watershed processes through interactive tuning of model parameters to reflect temporal nonstationarities. The toolbox includes a number of performance metrics, along with visual graphics to evaluate the goodness-of-fit of the model simulations. Our analysis shows that the proposed time-varying RR modeling framework successfully captures the nonstationary behavior of the Wights catchment in Australia. A multi-model analysis of this catchment, that has endured deforestation, provides insights on the functionality of different conceptual modules of RR models, and their representation of the real-world.

Suggested Citation

  • Mojtaba Sadegh & Amir AghaKouchak & Alejandro Flores & Iman Mallakpour & Mohammad Reza Nikoo, 2019. "A Multi-Model Nonstationary Rainfall-Runoff Modeling Framework: Analysis and Toolbox," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3011-3024, July.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02283-y
    DOI: 10.1007/s11269-019-02283-y
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    References listed on IDEAS

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    1. Mojtaba Sadegh & Morteza Shakeri Majd & Jairo Hernandez & Ali Torabi Haghighi, 2018. "The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1867-1881, March.
    2. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    3. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
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