High-resolution global groundwater storage anomalies: a 1 km downscaled dataset

Abstract

Groundwater depletion poses a challenge to water security, but the coarse spatial resolution of Gravity Recovery and Climate Experiment (GRACE) observations limits the characterization of groundwater variability at regional and local scales. Here, we present a 1 km monthly global groundwater storage anomaly (GWSA) dataset covering April 2002–December 2020. The dataset was generated using Singular Spectrum Analysis (SSA) for temporal gap filling, 19 high-resolution hydroclimatic and geophysical predictors, and aquifer-stratified machine-learning downscaling. Extreme Gradient Boosting (XGBoost) and deep neural networks (DNN) were evaluated as candidate models, and XGBoost was selected for dataset production based on internal, cross-scale, and in-situ evaluations. When aggregated to the original GRACE scale, the 1 km product agreed closely with the original GWSA ( R 2  = 0.972, RMSE = 2.10 cm). Independent evaluation used a global archive of 152 069 groundwater-monitoring wells and a 1° spatial-block calibration–validation design. Effective specific yield ( S yeff ) was estimated from calibration blocks and transferred to held-out blocks before comparison at a common 0.25° grid. For XGBoost, the held-out evaluation yielded R 2  = 0.538, RMSE = 0.378 cm yr −1 , and Bias = 0.106 cm yr −1 . The association between modeled and observation-derived trends remained similar under ± 30 % perturbations in S yeff , although amplitude-related metrics were more sensitive to the conversion coefficient. Standardized time-series comparisons and monthly evaluations of representative aquifers showed better agreement for long-term variability than for seasonal variability. Comparisons with published regional estimates showed close agreement in the North China Plain, weaker depletion magnitudes in northwestern India and California, and a trend-sign mismatch in the High Plains Aquifer during 2003–2013. Experiments using CSR, JPL, H08, and WaterGAP also showed that reconstruction performance and predictor importance depend on the target product. The dataset provides kilometre-scale estimates of groundwater-storage variability under the large-scale GRACE constraint. Local interpretation of the reconstructed spatial patterns requires additional groundwater-level, abstraction, and hydrogeological observations. The 1 km monthly global GWSA dataset is openly available from the National Cryosphere Desert Data Center at https://doi.org/10.12072/ncdc.hydrology.db7323.2026 (Fan et al., 2026).