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