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The availability of terrestrial water storage anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) supports many hydrological applications. Five TWSA products are operational and publicly available, including three based on mass concentration (mascon) solutions and two based on the synthesis of spherical harmonic coefficients (SHCs). The mascon solutions have advantages regarding the synthesis of SHCs since the basis functions are represented locally rather than globally, which allows geophysical data constraints. Alternative new solutions based on SHCs are, therefore, critical and warranted to enrich the portfolio of user-friendly TWSA data based on different algorithms. TWSA data based on novel processing protocols is presented with a spatial re-sampling of 0.25 arc-degrees covering 2002–2022. This approach parameterizes the improved point mass (IPM) and adopts the synthesized residual gravitational potential as observations. The assay indicates that the proposed Hohai University (HHU-) IPM TWSA data reliably agree with the mascon solutions. The presented HHU-IPM TWSA data set would be instrumental in regional hydrological applications, particularly enabling improved assessment of regional water budgets.

Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m3/m3, MAE = 0.005 m3/m3, and R = 0.70), during validation period 1 (RMSE = 0.035 m3/m3 and MAE = 0.023 m3/m3, and R = 0.72), validation period 2 (RMSE = 0.054 m3/m3 and MAE = 0.039 m3/m3, and R = 0.51), and validation period 3 (RMSE = 0.046 m3/m3 and MAE = 0.032 m3/m3, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m3/m3, average MAE of 0.023 m3/m3, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia.

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

The Gnangara groundwater system is a highly productive water resource in southwestern Australia. However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation‐based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission. The random forest regression (RFR) model was used to downscale the spatial resolution of GRACE to 0.05° (approx. 5 km), providing estimable signals over the relatively small calibration site (∼2,200 km2) in order to discern any meaningful signals from the original GRACE resolution. Our study reveals that downscaled signals from GRACE can be used to provide precipitation‐based recharge estimates for groundwater systems accurately. However, the growing impacts of climate change, which has led to sporadic precipitation patterns over Western Australia, can limit the efficiency of satellite remote sensing methods in estimating recharge, especially in deep and complex aquifers. Key Points Groundwater Recharge GRACE Downscaling Perth Basin

Satellite‐based monitoring of groundwater changes and their surface effects is crucial for understanding hydrological processes and managing water resources in an era of increasing water scarcity. The Gravity Recovery and Climate Experiment follow‐on (GRACE‐FO) mission provides global measurements of terrestrial water storage anomalies, while Interferometric Synthetic Aperture Radar (InSAR) detects millimeter‐scale land surface displacements caused by groundwater fluctuations. Integrating these complementary data sets offers unprecedented opportunities to study groundwater‐induced surface deformation at local scales, particularly in regions where groundwater management strategies are being implemented. However, the coarse spatial resolution of GRACE‐FO (∼300 km) limits its effective integration with high‐resolution InSAR observations for local‐scale studies. Here, we show that machine learning‐based downscaling can enhance GRACE terrestrial water storage data to 0.05° (5 km) resolution, enabling detailed analysis of mass changes in the Gnangara aquifer system, Western Australia, where a Managed Aquifer Recharge (MAR) program has been operated since 2017. We found that while the downscaled GRACE data showed upward mass trends due to rainfall with limited correlations to InSAR‐detected surface uplift at the MAR site, it demonstrated strong agreement with independent GPS‐measured land displacement trends across the broader region. Validation against in situ groundwater measurements revealed that the Yarragadee aquifer exhibited the strongest correlation with downscaled GRACE data (r = 0.48) with a similar trend, indicating that this aquifer drives dominant mass changes, likely influenced by the ongoing MAR operations. Our results demonstrate that GRACE downscaling provides a valuable tool for regional groundwater monitoring, even when direct correlation with localized surface deformation remains limited.

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions.

The expansion of cultivated land in place of natural vegetation has a substantial influence on hydrologic characteristics of a watershed. However, due to basin characteristics and the nature and intensity of landscape modification, the response varies across basins. This study aims to evaluate the performance of a soil and water assessment tool (SWAT) model and its applicability in assessing the effects of land use land cover (LULC) changes on the hydrological processes of the upper Genale River basin. The results of satellite change detection over the past 30 years (between 1986 and 2016) revealed that the landscape of the basin has changed considerably. They showed that settlement, cultivated, and bare land areas had increased from 0.16% to 0.28%, 24.4% to 47.1%, and 0.16% to 0.62%, respectively. On the contrary, land cover units such as forest, shrubland, and grassland reduced from 29.6% to 13.5%, 23.9% to 19.5%, and 21.8% to 18.9%, respectively. Based on monthly measured flow data, the model was calibrated and validated in SWAT-CUP using the sequential uncertainty fitting (SUFI-2) algorithm. The result showed that the model performed well with coefficient of determination (R2) ≥ 0.74, Nash–Sutcliffe efficiency (NSE) ≥ 0.72, and percent bias (PBIAS) between −5% and 5% for the calibration and validation periods. The hydrological responses of LULC change for the 1986, 2001, and 2016 models showed that the average annual runoff increased by 13.7% and 7.9% and groundwater flow decreased by 2.85% and 2.1% between 1986 and 2001 and 2001 and 2016, respectively. Similarly, the total water yields increased from 324.42 mm to 339.63 mm and from 339.63 mm to 347.32 mm between 1986 and 2001 and 2001 and 2016, respectively. The change in hydrological processes, mainly the rise in runoff and total water yield as well as the reduction in lateral and groundwater flow in the watershed, resulted from LULC changes. This change has broader implications for the planning and management of the land use and water resource development.

It is known that representing wetland dynamics in land surface modeling improves models ‘capacity to reproduce fluxes and land surface boundary conditions for atmospheric modeling in general circulation models. This study presents the development of the full coupling between the Noah-MP land surface model (LSM) and the HyMAP flood model in the NASA Land Information System and its application over the Inner Niger Delta (IND), a well-known hot-spot of strong land surface-atmosphere interactions in West Africa. Here, we define two experiments at 0.02o spatial resolution over 2002-2018 to quantify the impacts of the proposed developments on simulating IND dynamics. One represents the one-way approach for simulating land surface and flooding processes (1-WAY), i.e., Noah-MP neglects surface water availability, and the proposed two-way coupling (2-WAY), where Noah-MP takes surface water availability into account in the vertical water and energy balance. Results show that accounting for two-way interactions between Noah- MP and HyMAP over IND improves simulations of all selected hydrological variables. Compared to 1-WAY, evapotranspiration derived from 2-WAY over flooding zones doubles, increased by 0.8mm/day, resulting in an additional water loss rate of ~18,900km(exp 3)/year, ~40% drop of wetland extent during wet seasons and major improvement in simulated water level variability at multiple locations. Significant soil moisture increase and surface temperature drop were also observed. Wetland outflows decreased by 35%, resulting in a substantial a Nash-Sutcliffe coefficient improvement, from -0.73 to 0.79. It is anticipated that future developments in water monitoring and water-related disaster warning systems will considerably benefit from these findings.

This study analysed the spatio-temporal variability in the trend of 9 extreme rainfall indices (ERIs) and 4 extreme temperature indices (ETIs) over India, estimated from 1° × 1° resolution daily datasets of 1951–2015 period. The trend analysis showed that 20% of grids exhibited a significant increase in Simple Daily Intensity Index (SDII) series, while Consecutive Wet Day (CWD) index of 32% of the grids exhibited significant reduction. The CWD index showed significant decrease in all the rainfall homogeneous regions of India, while the indices of heavy rainfall (R95p and R99p) and SDII showed significant increase in North East and North West regions. All the ETIs of North West India showed significant rise while the warm and cold spell duration indices showed significant increase in the North Central region and Eastern Coast. The temporal variability of trend analysed about the global climate shift of 1977 revealed that tropical nights of 21% grids with significant reduction prior to the shift showed a significant rise after the shift. In general, a significant change in statistical characteristics in ETI series is noticed after the climatic shift deciphering that the non-stationarity of ETI is more apparent than that of ERI across the Indian mainland.

Monitoring changes in evapotranspiration (ET) is useful in the management of water resources in irrigated agricultural landscapes and in the assessment of crop stress and vegetation conditions of drought-vulnerable regions. Information on the impacts of climate variability on ET dynamics is profitable in developing water management adaptation strategies. Such impacts, however, are generally unreported and not conclusively determined in some regions. In this study, changes in MODIS (Moderate Resolution Imaging Spectroradiometer)-derived ET (2000–2014) over large proportions of Sub-Sahara Africa (SSA) are explored. The multivariate analyses of ET over SSA showed that four leading modes of observed dynamics in ET, accounting for about 90% of the total variability, emanated mostly from some sections of the Sudano-Sahel and Congo basin. Based on Man-Kendall’s statistics, significant positive trends ( α = 0.05) in ET over the Central African Republic and most parts of the Sahel region were observed. Over much of the Congo basin nonetheless, ET showed significant ( α = 0.05) distributions of widespread negative trends. These trends in ET were rather found to be consistent with observed changes in model soil moisture but not in all locations, perhaps due to inconsistent trends in maximum rainfall and land surface temperature. However, the results of spatio-temporal drought analysis confirm that the extensive ET losses in the Congo basin were somewhat induced by soil moisture deficits. Amidst other prominent drivers of ET, the dynamics of ET over the terrestrial ecosystems of SSA appear to be a more complex phenomenon that may transcend natural climate variations.