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Updated: Jul 1, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model

Martin Gauch1,2, Frederik Kratzert3, Oren Gilon4

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This summary is machine-generated.

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Area of Science:

  • Hydrology
  • Water Resource Management
  • Environmental Modeling

Background:

  • Accurate rainfall-runoff models are crucial for hydrological science and practice.
  • Numerous evaluation metrics exist, but visual judgment remains prevalent among hydrologists.
  • The agreement between visual judgment and quantitative metrics is not well understood.

Purpose of the Study:

  • To investigate the concordance between expert visual judgment and quantitative metrics in evaluating rainfall-runoff models.
  • To determine if expert opinions align with established quantitative performance metrics.
  • To assess the reliability of quantitative metrics in reflecting human preferences in hydrological model evaluation.

Main Methods:

  • 622 hydrological experts were engaged to evaluate over 14,000 pairs of hydrographs from 13 distinct models.
  • Experts provided visual judgments on model performance.
  • Quantitative metrics were compared against expert opinions.

Main Results:

  • Expert opinions generally align with quantitative metrics, showing a preference for Machine Learning models over traditional hydrological models.
  • Significant inconsistencies were observed within expert opinions.
  • Quantitative metrics can predict expert agreement where consensus exists, indicating they capture human preferences.

Conclusions:

  • Quantitative metrics sufficiently encode human preferences for hydrological model evaluation.
  • While improvements are possible, the hydrologic community should increase reliance on quantitative metrics for benchmarking.
  • Reinforcing benchmarking efforts with greater trust in quantitative metrics is recommended.