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Analysing spatial variability in drought sensitivity of rivers using explainable artificial intelligence.

Joke De Meester1, Patrick Willems1

  • 1Hydraulics and Geotechnics Section, KU Leuven, Kasteelpark Arenberg 40, BE-3001 Leuven, Belgium.

The Science of the Total Environment
|April 26, 2024
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Summary
This summary is machine-generated.

Predicting river drought sensitivity is crucial for water management. Machine learning models show high accuracy, identifying irrigation and human interference as key factors increasing drought vulnerability.

Keywords:
DroughtInteraction effectsRiversSHAPXGBoost

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Area of Science:

  • Environmental Science
  • Hydrology
  • Ecology

Background:

  • Hydrological drought significantly impacts aquatic ecosystems and human life.
  • Understanding spatial drought sensitivity in rivers is vital for effective water resource management.

Purpose of the Study:

  • To develop predictive models for river drought sensitivity using catchment characteristics and human interference data.
  • To gain insights into the spatial variability of drought sensitivity in rivers within Flanders, Belgium.

Main Methods:

  • Evaluated statistical and machine learning methods, including XGBoost, to predict drought metrics.
  • Utilized catchment characteristics and human interference data for model training.
  • Applied explainable AI techniques to interpret model predictions.

Main Results:

  • XGBoost model achieved high performance, explaining 80-90% of the variance in drought metrics.
  • Irrigation emerged as the most significant variable, with higher irrigation correlating to increased drought sensitivity.
  • Increased human interference generally leads to higher drought sensitivity.

Conclusions:

  • Machine learning effectively predicts river drought sensitivity, aiding water managers.
  • Catchment soil properties (sandy vs. clay) influence drought sensitivity, linked to infiltration and water transfer.
  • The complex impact of forest and agriculture on river drought sensitivity requires further investigation.