View article

High-resolution, continuous groundwater data is important for place-based adaptive aquifer management. This information is unavailable in many areas due to spatial sparsity of and temporal gaps in groundwater monitoring. This study advances the ability to generate high-resolution (1 km²), temporally continuous estimates of groundwater level (GWL) changes by incorporating 1 km² covariates and existing piezometer observations into predictive modeling. We employed a hybrid machine learning (ML) model, primarily using the geographically weighted random forest (RF_gw) model. To assess the performance of the RF_gw model, we conducted a comprehensive comparison with the SGS geostatistical method and non-spatial ML models (RF and XGBoost). The framework was implemented across the Indus Basin using biannual (July and Oct) GWL data from piezometers and local covariates from 2003 to 2020 …