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Automatic Regionalization of Model Parameters for Hydrological Models.

Moritz Feigl1, Stephan Thober2, Robert Schweppe2

  • 1Department of Water, Atmosphere and Environment Institute for Hydrology and Water Management University of Natural Resources and Life Sciences, Vienna Vienna Austria.

Water Resources Research
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces automatic transfer function (TF) estimation for hydrological model parameters, successfully applying Function Space Optimization (FSO) to a complex model. The method achieves high predictive performance in ungauged basins, comparable to expert-developed TFs.

Keywords:
distributed modelsmachine learningoptimizationrainfall‐runoff modelingregionalizationtransfer functions

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Area of Science:

  • Hydrology and Computational Modeling
  • Environmental Science

Background:

  • Parameter estimation in large-scale distributed hydrological models is complex due to high dimensionality.
  • Relating model parameters to landscape characteristics improves physical realism and parameter transferability.
  • Automatic estimation of transfer functions (TFs) for hydrological model parameters is an emerging area.

Purpose of the Study:

  • To present the first large-scale application of automatic TF estimation for a complex hydrological model.
  • To apply the Function Space Optimization (FSO) method for estimating TFs for key hydrological parameters.
  • To evaluate the performance of automatically estimated TFs in ungauged basins.

Main Methods:

  • Applied the Function Space Optimization (FSO) method to automatically estimate TF structures and coefficients.
  • Utilized the mesoscale Hydrologic Model (mHM), a distributed model with a priori TFs.
  • Focused TF estimation on saturated hydraulic conductivity and field capacity parameters.
  • Used a benchmark setup of mHM for comparison.

Main Results:

  • Achieved a median Nash-Sutcliffe Efficiency (NSE) of 0.68 in 222 validation basins.
  • Demonstrated high predictive performance in ungauged basins even with limited calibration data (5 years).
  • Automatic TF estimation yielded results comparable to TFs developed over years by experts.

Conclusions:

  • Automatic TF estimation is a viable approach for complex hydrological models.
  • The FSO method shows promise for enhancing the efficiency and transferability of hydrological model parameters.
  • This work represents a significant step towards automating parameter estimation in distributed hydrological modeling.