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Watershed Planning within a Quantitative Scenario Analysis Framework
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Explaining Great Lakes water level variability through interpretable ensemble machine learning.

Rahim Barzegar1, Ehsan Raei2, Jan Adamowski3

  • 1Groundwater Research Group (GRES), Research Institute on Mines and Environment (RIME), Université du Québec en Abitibi-Témiscamingue (UQAT), Amos, Québec, Canada.

The Science of the Total Environment
|January 4, 2026
PubMed
Summary
This summary is machine-generated.

Great Lakes water levels are primarily controlled by inflow and outflow, with other factors like evaporation playing a secondary role. Machine learning models accurately predict these fluctuations, aiding water management.

Keywords:
Great LakesInterpretable machine learningSensitivity analysisWater level

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Last Updated: Jan 7, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

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Area of Science:

  • Environmental science
  • Hydrology
  • Machine learning

Background:

  • Great Lakes water-level variability is critical for resource planning, ecosystem health, and policy.
  • Understanding environmental drivers is essential for effective management.

Purpose of the Study:

  • To develop an interpretable machine learning framework to quantify environmental driver impacts on Great Lakes water levels.
  • To analyze immediate and lagged influences on monthly lake-level fluctuations (1982-2022).

Main Methods:

  • Trained eight tree-based algorithms (e.g., XGBoost, LightGBM) with time-aware cross-validation and lagged predictors.
  • Integrated models using a Supervised Committee Machine Learning (SCML) ensemble.
  • Employed SHapley Additive exPlanations (SHAP) and Variogram Analysis of Response Surfaces (VARS) for interpretability.

Main Results:

  • Boosting models outperformed others; the SCML ensemble achieved high prediction accuracy (RMSE as low as 0.118 m).
  • Inflow and outflow were dominant drivers; evaporation, runoff, and air temperature were secondary modulators.
  • Hydrological memory was significant, with fluxes influencing levels at longer lags and atmospheric drivers at shorter lags.

Conclusions:

  • The study provides a robust framework for understanding and predicting Great Lakes water levels.
  • Findings support adaptive management strategies for climate variability and anthropogenic pressures.
  • Lake-specific characteristics influence predictability and driver sensitivity.