ML-based Monthly Water Table Depth Anomalies over the Seine River Basin

ML-based Monthly Water Table Depth Anomalies over the Seine River Basin 

Associated publication:
  Avila, L., de Lavenne, A., Ramos, M.-H., Kollet, S. (2025).
  Estimation of Monthly Water Table Depth Anomalies Based on the Integration of
  GRACE and ERA5-Land with Large-Scale Simulations Using Random Forest and LSTM Networks.
  Water Resources Management, 39(6), 2899–2918.
  DOI: 10.1007/s11269-025-04097-7

Full output dataset (NetCDF files) available at: https://datapub.fz-juelich.de/slts/Stars4Water/WP3/ML_Seine/

Dataset description:
  This dataset contains monthly water table depth anomaly (wtda) maps over the
  Seine River Basin, estimated using Random Forest (RF) and
  Long Short-Term Memory (LSTM) networks. 

  Spatial resolution: 0.11° × 0.11° (CORDEX EUR-11 grid)
  Temporal coverage: 2004–2022 (monthly)
  Format: NetCDF-4

Variables included:
  - wtda_RF  : Water table depth anomaly estimated by Random Forest
  - wtda_LSTM: Water table depth anomaly estimated by LSTM networks