An improved GRACE-derived groundwater storage anomaly (igGWSA) dataset over global land with full consideration of non-groundwater components based on current new datasets

Accurate quantification of global groundwater storage anomaly (GWSA) is imperative for global water security and socio-economic sustainability. The Gravity Recovery and Climate Experiment (GRACE) satellite has emerged as a prevailing methodology for estimating GWSA. However, oversimplification of non-groundwater components potentially compromised its accuracy in most previous studies. Here we present an improved GRACE-derived GWSA dataset at the global scale, namely igGWSA, with full consideration of non-groundwater components including glaciers, snow, permafrost, lakes, reservoirs, surface runoff, profile soil moisture (PSM), and plant canopy water based on current new datasets. In particular, PSM was generated based on Catchment Land Surface Model and random forest algorithm. igGWSA demonstrated strong agreement with well-observed groundwater level and model-simulated GWSA in five globally recognized hotspots of groundwater depletion. Compared to igGWSA, simplified estimation would lead to misinterpretations of groundwater storage variations. For example, neglecting glaciers and permafrost would erroneously interpret glacier melting and permafrost degradation as part of changes in groundwater storage, respectively, thereby exaggerating groundwater mass loss in global glaciated areas and concealing groundwater mass gain in global permafrost zone. Failure to account for lakes and reservoirs would also lead to an overestimation of groundwater decline globally. This highlighted the necessity of comprehensively accounting for non-groundwater components when estimating GWSA, especially under a changing environment.