The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.5° spatial resolution, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks. Our models accurately reproduce GRACE/GRACE-FO observations at the global scale and effectively capture the impacts of climate extremes. Overall, GRAiCE outperforms a previous reference TWSA reconstruction in predicting observed TWSA and provides reliable water budget estimates at the river basin scale. By generating long-term continuous TWSA time series, GRAiCE will offer valuable insights into the impacts of climate variability and change on freshwater resources.

Urbanization is one of the main drivers of surface water loss, which implies a transition from water to land. However, it is still unclear how urban areas affect the spatial pattern of surface water loss. Here, we use remotely sensed data to analyze and model the decrease of surface water extent and, in particular, the frequency of surface water loss as a function of distance from urban areas across the contiguous United States (CONUS). We employ an exponential distance‐decay model that confirms the presence of a higher frequency of surface water loss in the proximity of human settlements and provides innovative insights on surface water loss patterns at different spatial scales (i.e., river basins, water resource regions, and the CONUS). These spatial patterns are found to be influenced by climatic conditions, with more widely distributed losses in arid regions with respect to temperate and continental climates. Our results provide a new and deeper understanding of the spatial influence of urban areas on surface water loss, which could be effectively integrated in the definition of sustainable strategies for urbanization, water management, and surface water restoration, focused on both human and environmental water needs.

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows.