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With permafrost warming, the observed discharge of the Kolyma River in northeastern Siberia decreased between 1930s and 2000; however, the underlying mechanism is not well understood. To understand the hydrological changes in the Kolyma River, it is important to analyze the long-term hydrometeorological features, along with the changes in the active layer thickness. A coupled hydrological and biogeochemical model was used to analyze the hydrological changes due to permafrost warming during 1979–2012, and the simulated results were validated with satellite-based products and in situ observational records. The increase in the active layer thickness by permafrost warming suppressed the summer discharge contrary to the increased summer precipitation. This suggests that the increased terrestrial water storage anomaly (TWSA) contributed to increased evapotranspiration, which likely reduced soil water stress to plants. As soil freeze–thaw processes in permafrost areas serve as factors of climate memory, we identified a two-year lag between precipitation and evapotranspiration via TWSA. The present results will expand our understanding of future Arctic changes and can be applied to Arctic adaptation measures.

We assess the accuracy of global‐gridded terrestrial water storage (TWS) estimates derived from temporal gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) satellites. The TWS data set has been corrected for signal modification due to filtering and truncation. Simulations of terrestrial water storage variations from land‐hydrology models are used to infer relationships between regional time series representing different spatial scales. These relationships, which are independent of the actual GRACE data, are used to extrapolate the GRACE TWS estimates from their effective spatial resolution (length scales of a few hundred kilometers) to finer spatial scales (∼100 km). Gridded, scaled data like these enable users who lack expertise in processing and filtering the standard GRACE spherical harmonic geopotential coefficients to estimate the time series of TWS over arbitrarily shaped regions. In addition, we provide gridded fields of leakage and GRACE measurement errors that allow users to rigorously estimate the associated regional TWS uncertainties. These fields are available for download from the GRACE project website (available at http://grace.jpl.nasa.gov). Three scaling relationships are examined: a single gain factor based on regionally averaged time series, spatially distributed (i.e., gridded) gain factors based on time series at each grid point, and gridded‐gain factors estimated as a function of temporal frequency. While regional gain factors have typically been used in previously published studies, we find that comparable accuracies can be obtained from scaled time series based on gridded gain factors. In regions where different temporal modes of TWS variability have significantly different spatial scales, gain factors based on the first two methods may reduce the accuracy of the scaled time series. In these cases, gain factors estimated separately as a function of frequency may be necessary to achieve accurate results. Key Points We present gridded gain factors and error maps for GRACE Measurement and leakage errors are taken into account The new method does not require the use of spherical harmonics by the users

Land water storage plays a key role for the Earth’s climate, natural ecosystems, and human activities. Since the launch of the first Gravity Recovery and Climate Experiment (GRACE) mission in 2002, spaceborne observations of changes in terrestrial water storage (TWS) have provided a unique, global perspective on natural and human-induced changes in freshwater resources. Even though they have become much used within the broader Earth system science community, space-based TWS datasets still incorporate important and case-specific limitations which may not always be clear to users not familiar with the underlying processing algorithms. Here, we provide an accessible and illustrated overview of the measurement concept, of the main available data products, and of some frequently encountered technical terms and concepts. We summarize concrete recommendations on how to use TWS data in combination with other hydrological or climatological datasets, and guidance on how to avoid possible pitfalls. Finally, we provide an overview of some of the main applications of GRACE TWS data in the fields of hydrology and climate science. This review is written with the intention of supporting future research and facilitating the use of satellite-based terrestrial water storage datasets in interdisciplinary contexts.

GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities. Plain Language Summary We used GRACE data to understand how water storage has changed over time across the Indus Basin at a resolution of 1 square kilometer. We generated the new high‐resolution data using machine learning techniques that implemented statistical methods. The new data for analyzing water storage matched well with the original data on a larger scale. Additionally, comparing this detailed data with measurements from 2,200 wells showed that our new method works well. The new high‐resolution data help us detect hotspots of water storage decline where water availability may face challenges in the future if status quo continues. Human activities like more farming, using more water, and building more areas for people to live are a major driver of the water storage decline. In upstream areas less influenced by human impacts, the decline is driven more by climatic factors. By improving understanding of local vulnerabilities, our study supports planning interventions for specific regions based on the need to reduce the impact of human activities or adapt to climate change. Key Points Terrestrial water storage (TWS)/groundwater storage (GWS) derived from downscaled GRACE data show a declining trend across most sub‐regions of the Indus Basin between 2002 and 2023 Anthropogenic stressors explain 47%–83% of TWS decline in the majority of sub‐regions TWS/GWS changes in upstream sub‐regions, where shortwave radiations mainly control the TWS changes, are well explained by climatic factors

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.

Estimating groundwater storage (GWS) anomalies by subtracting model‐derived components from Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) anomalies is a common practice in hydrology. Typically, land surface model (LSM) simulated soil moisture (SM), snow water equivalent, and canopy water content are removed from GRACE‐derived TWS, and the residual is interpreted as GWS. However, this method implicitly assumes that LSMs account for all non‐groundwater storages within distinct, physically meaningful compartments. In this comment, we examine the assumptions, semantic and structural challenges embedded in this approach, suggesting that users consider the consequences of model simplifications and conventions on the results. We encourage careful interpretation and advocate for more sophisticated methods, including making use of data assimilation and models that represent physical hydrological processes more completely.

This study demonstrates the critical role of precipitation intensity in groundwater recharge generation and terrestrial water storage (TWS) change. We conducted two experiments driven by precipitation products with close annual totals but distinct intensity in Arizona, using the Noah‐MP model with advanced soil hydrology. The experiment with higher precipitation intensity (EXPHI) produces an annual groundwater recharge of 6.91 mm/year in Arizona during 2001–2020, ∼15 times that of the experiment with lower precipitation intensity (EXPLI). Correspondingly, EXPLI produces a declining groundwater storage (GWS) trend of − ${-}$0.51 mm/month, nearly triple that of EXPHI. GWS change dominates the TWS trend. EXPLI shows a declining TWS trend of − ${-}$0.57 mm/month, nearly twice that of EXPHI. Higher precipitation intensity reduces evapotranspiration and enhances infiltration and percolation, allowing more precipitation to recharge groundwater. This study underscores the need to ensure the accuracy of precipitation intensity in hydrological modeling for reliable water resources assessment and projection.

The Gravity Recovery and Climate Experiment (GRACE/GRACE‐FO) mission have been providing global data on terrestrial water storage (TWS) for over 20 years. This study aimed to assess the effectiveness of GRACE/GRACE‐FO observations for monitoring groundwater storage (GWS) in Poland. We proposed new method for estimating GWS by incorporating hydrodynamic zoning. Our approach utilized hydrodynamically conditioned relationships between in situ GWS, GRACE/GRACE‐FO‐derived TWS, and modeled TWS to derive spatial patterns for the separate estimation of satellite‐based GWS within distinct hydrodynamic zones. The GWS was evaluated by comparing it with in situ groundwater measurements from national monitoring points. We showed that for a proper evaluation of the satellite‐based GWS using in situ observations, it is necessary to select monitoring points that adequately represent aquifer systems with rapid water exchange. We analyzed the impact of measurement point location on the agreement between satellite‐based and in situ GWS, taking into account the physiographic region, and hydrodynamic zone. We found highest agreement between satellite‐based and in situ estimates of GWS in aquifer systems with rapid water exchange. In discharge zones, satellite‐based GWS changes aligned well with TWS‐GRACE data in terms of the amplitudes and phases of the seasonal signal as well as for long‐term trends. We demonstrated the existence of long‐term negative trends in GWS changes across the entire country (up to −2.45 mm equivalent water height/year in the southeast region of Poland). These trends have been significantly influenced by climate change and the resulting predominance of evapotranspiration over precipitation.

Terrestrial Water Storage (TWS) changes have been estimated at basin to continental scales from gravity variations using data from the Gravity Recovery and Climate Experiment (GRACE) satellites since 2002. The relatively low spatial resolution (∼300 km) of GRACE observations has been a main limitation in such studies. Various data processing strategies, including mascons, forward modeling, and constrained linear deconvolution (CLD), have been employed to address this limitation. Here we develop a revised CLD method to obtain a TWS estimate that combines GRACE observations with much higher spatial resolution land surface models. The revised CLD constrains model estimates to agree with GRACE TWS when smoothed. As an example, we apply the method to obtain a high spatial resolution TWS estimate in Australia. We assess the accuracy of the approach using synthetic GRACE data. Plain Language Summary The estimation of terrestrial water storage (TWS) changes using gravity recovery and climate experiment (GRACE) satellites suffers from low spatial resolution, making it challenging to interpret local‐scale mass changes. In this study, we improved the sparse resolution of GRACE observations by incorporating high‐resolution land surface models (LSM) that provides detailed hydrological information. Through synthetic experiments, we confirmed the accuracy of our estimations in regional‐ and local‐scale. When applied to real GRACE data, our new TWS estimations show better spatial resolution compared to conventional GRACE products. Further, our estimations consistently yield reliable results although different LSM were used. Key Points High‐resolution terrestrial water storage was estimated by combining gravity recovery and climate experiment and land surface models Our new estimates reduced both land‐ocean and inter‐basin leakages simultaneously