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A new data-driven random forest model accurately estimates water table depth (WTD) across the contiguous United States (CONUS). This approach provides a reliable alternative to traditional models for large-scale freshwater resource assessment.

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Area of Science:

  • Hydrology and Hydrogeology
  • Geospatial Data Science
  • Environmental Modeling

Background:

  • Water table depth (WTD) significantly influences groundwater dynamics and land surface interactions.
  • Limited WTD observations pose challenges for accurate, large-scale groundwater modeling.
  • Existing physically-based models struggle to precisely represent observed WTD.

Purpose of the Study:

  • To develop and validate a purely data-driven method for estimating WTD at a continental scale.
  • To assess the performance of a random forest (RF) model for WTD estimation across the contiguous United States (CONUS).
  • To quantify the uncertainty associated with the RF model's WTD predictions.

Main Methods:

  • Applied a random forest (RF) model using available WTD observations for estimation across the CONUS.
  • Utilized quantile regression forests to quantify prediction uncertainty.
  • Evaluated model performance using Pearson correlation coefficient (r), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE).

Main Results:

  • The RF model achieved high agreement with well observations (r=0.96, NSE=0.93).
  • Location was identified as the most crucial feature for WTD estimation, potentially acting as a spatial surrogate.
  • High prediction uncertainties were generally found in areas with shallow WTD.

Conclusions:

  • The data-driven RF model offers a robust and accurate alternative to physics-based approaches for large-scale WTD estimation.
  • The model provides valuable insights into continental-scale freshwater resources.
  • The trained RF model shows potential for transferability to other regions with similar hydrologic regimes and limited data.