Explore the vast range of titles available.

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

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

Enhanced drought modeling is crucial for realistic prediction and effective management of water resources, especially with climate change anticipated to exacerbate drought frequency and severity. Global water models (GWMs) simulate historical and future terrestrial water storage (TWS) with continuous spatial and temporal coverage. However, a global evaluation of TWS simulations by GWMs focused on drought is lacking. Here we evaluate, for the first time, GWMs' capability to represent TWS droughts by comparing simulations with Gravity Recovery and Climate Experiment satellite data. We find notable underestimation of drought severity and coverage by GWMs, across diverse regions, including North America, South America, Africa, and Northern Asia. When examined without trend removal, the underestimation of TWS droughts is more pronounced in recent years (2016–2019) compared to 2002–2015, especially in northern latitudes. This underrepresentation highlights the necessity to improve GWMs to simulate TWS droughts. Our results imply that previously reported future TWS projections could have underestimated droughts.

The Iberian region relies heavily on groundwater and is highly vulnerable to climate variability, making it crucial to understand factors influencing water availability. The aim of this research was to assess how large-scale climate patterns affect total water storage anomalies (TWSAs) in Iberia, particularly in relation to persistent droughts and floods. To address this, I analyzed TWSAs derived from a reconstructed dataset (GRACE-REC) spanning from 1980 to 2019, first at the scale of the entire Iberian Peninsula and then across its main river basins. The links between the North Atlantic Oscillation (NAO), East Atlantic (EA) and Scandinavian (SCAND) patterns, TWSAs, and hydrological extremes were quantified using wavelet and principal component analysis. The results reveal that the NAO exerts the strongest multiyear influence on TWSAs, with periodicities of approximately 10 and 6.5 years, particularly in the southern river basins (Tagus, Guadiana, and Guadalquivir). EA and SCAND have stronger influences in the northern basins (Douro, Minho, and Ebro), driving 2- to 3.5-year cycles. Coupled phases of climate patterns, such as NAO+ and EA− (or SCAND−), correspond to extreme droughts, whereas NAO− and EA+ (or SCAND+) correspond to wet conditions.

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R 2 ), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R 2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R 2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R 2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment.

Quantitative assessment of the terrestrial water storage (TWS) changes and the major driving factors have been hindered by the lack of direct observations in Inner Mongolia, China. In this study, the spatial and temporal changes of TWS and groundwater storage (GWS) in Inner Mongolia during 2003–2021 were evaluated using the satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow On combined with data from land surface models. The results indicated that Inner Mongolia has experienced a widespread TWS loss of approximately 1.82 mm/yr from 2003–2021, with a more severe depletion rate of 4.15 mm/yr for GWS. Meteorological factors were the driving factors for water storage changes in northeastern and western regions. The abundant precipitation increased TWS in northeast regions at 2.36 mm/yr. Anthropogenic activities (agricultural irrigation and coal mining) were the driving factors for water resource decline in the middle and eastern regions (especially in the agropastoral transitional zone), where the decrease rates were 4.09 mm/yr and 3.69 mm/yr, respectively. In addition, the severities of hydrological drought events were identified based on water storage deficits, with average severity values of 17 mm, 18 mm, 24 mm, and 33 mm for the west, middle, east, and northeast regions, respectively. This study established a basic framework for water resource changes in Inner Mongolia and provided a scientific foundation for further water resources investigation.

Evaluating water storage changes and addressing drought challenges in areas like the Tana sub-basin in Ethiopia is difficult due to limited data availability. The aim of this study was to evaluate the dynamics of terrestrial water anomaly and drought incidences by employing multiple data source. The Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets were used to assess the long-term water storage dynamics and drought incidences using the weighted water storage deficit index (WWSDI). WWSDI was used to identify drought periods, which ranged from severe to extreme drought. Despite the overall increase in average annual total water storage anomaly (TWSA) by 0.43 cm/year and a net gain of 50.68 cm equivalent water height from 2003 to 2022, there were instances of terrestrial water storage deficits, particularly in 2005, 2006, and 2009, during historical drought periods. The TWSA exhibited a strong correlation with Lake Tana water storage and precipitation anomalies after adjusting lag times. WWSDI displayed a high correlation with WSDI but a weak correlation with SPI and SPEI. Therefore, utilization of GRACE and GLDAS data is promising for evaluating terrestrial water storage and monitoring drought in data-deficient regions like the Tana sub-basin in Ethiopia.

For 15 years, the Gravity Recovery and Climate Experiment (GRACE) mission have monitored total water storage (TWS) changes. The GRACE mission ended in October 2017, and 11 months later, the GRACE Follow-On (GRACE-FO) mission was launched in May 2018. Bridging the gap between both missions is essential to obtain continuous mass changes. To fill the gap, we propose a new approach based on a remove–restore technique combined with an autoregressive (AR) prediction. We first make use of the Global Land Data Assimilation System (GLDAS) hydrological model to remove climatology from GRACE/GRACE-FO data. Since the GLDAS mis-models real TWS changes for many regions around the world, we further use least-squares estimation (LSE) to remove remaining residual trends and annual and semi-annual oscillations. The missing 11 months of TWS values are then predicted forward and backward with an AR model. For the forward approach, we use the GRACE TWS values before the gap; for the backward approach, we use the GRACE-FO TWS values after the gap. The efficiency of forward–backward AR prediction is examined for the artificial gap of 11 months that we create in the GRACE TWS changes for the July 2008 to May 2009 period. We obtain average differences between predicted and observed GRACE values of at maximum 5 cm for 80% of areas, with the extreme values observed for the Amazon, Alaska, and South and Northern Asia. We demonstrate that forward–backward AR prediction is better than the standalone GLDAS hydrological model for more than 75% of continental areas. For the natural gap (July 2017–May 2018), the misclosures in backward–forward prediction estimated between forward- and backward-predicted values are equal to 10 cm. This represents an amount of 10–20% of the total TWS signal for 60% of areas. The regional analysis shows that the presented method is able to capture the occurrence of droughts or floods, but does not reflect their magnitudes. Results indicate that the presented remove–restore technique combined with AR prediction can be utilized to reliably predict TWS changes for regional analysis, but the removed climatology must be properly matched to the selected region.