Storage Anomalies Research Articles

Total water storage anomalies reconstruction using noise-augmented u-shaped network: A case study in the Yangtze River Basin

The satellite mission of Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) have characterized global total water storage anomalies (TWSA) with unprecedented accuracy. However, the data gap between GRACE and GRACE-FO from July 2017 to May 2018 represents challenges for interpreting long-term water storage changes. In this study, we present a deep learning model, a noise-augmented u-shaped network (NA-UNet), to bridge the gap over the Yangtze River Basin (YRB). This model has been trained on precipitation, temperature, and hydrological model data. We show that the NA-UNet model achieves a state-of-the-art TWSA reconstruction, outperforming the standard UNet model and previous studies. At the basin scale, the NA-UNet model agrees favorably well with GRACE observations, with a correlation coefficient (CC) of 0.99, Nash-Sutcliff efficient (NSE) of 0.97, and normalized root-mean-square error (NRMSE) of 0.04 during the testing period. At the grid-cell scale, our model has a much more stable performance with median CC/NSE/NRMSE values of 0.99/0.96/0.05. This significant improvement in the gap-filling ability addresses the issue with discontinuous TWSA observations while laying the foundation for predicting future changes in total water storage.

Deriving the Terrestrial Water Storage Anomaly From GRACE Spherical Harmonic Coefficients Using a Convolutional Neural Network

AbstractTerrestrial water storage anomaly (TWSA), derived from Gravity Recovery and Climate Experiment (GRACE) satellites, has been widely used in hydrology studies. The inversion is commonly achieved by truncating and filtering spherical harmonic coefficients (SHC), whereby the result is characterized by leakage error and low resolution. It remains unclear whether machine learning methods can help resolve this challenging issue. In this study, we present a convolutional neural network (CNN) approach to correct TWSA from GRACE SHC by leveraging the knowledge of the leakage effect determined from global hydrological models (GHMs) and land surface models (LSMs). The CNN approach is implemented in three representative regions in China, that is, the human‐impacted Haihe River Basin, the nature‐impacted Yangtze River Basin and the model‐limited Tibetan Plateau. The results show the following: (a) The recovery performance of CNN at the basin scale is better than that at the grid scale, and the grid‐scale recovery is significantly influenced by the spatial heterogeneity of TWSA and the input GHM/LSMs; (b) The more accurate the GHM/LSMs used for training, the better the recovery performance of CNN; and (c) The trained model retains comparable performance in deriving the TWSA time series from GRACE SHC when compared to that derived from other methods (i.e., scaling factor and mass concentration solutions) with average r = 0.90 and RMSE = 21.50 mm. This study highlights the potential of machine learning to supplement conventional correction methods when deriving the TWSA from GRACE SHC by utilizing signal restoration knowledge learned from multiple accurate GHMs/LSMs.

Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models

Groundwater storage changes in the Amazon River Basin (ARB) play an important role in the hydrological behavior of the region, with significant influence on climate variability and rainforest ecosystems. The GRACE and GRACE-FO satellite missions provide gravity anomalies from which it is possible to monitor changes in terrestrial water storage, albeit at low spatial resolution. This study downscaled GRACE and GRACE-FO data from machine learning models from 1° (110 km approx) to 0.25° (27.5 km approx). It estimated the spatiotemporal variability of terrestrial and groundwater storage anomalies between 2002 and 2021 for the Amazon River Basin. In parallel, the Random Forest and AdaBoost algorithms were compared and analyzed. The results reflected a good fit of the models with a very low error and a slight superiority in the predictions obtained by AdaBoost. On the predictions at 0.25°, spatial patterns associated with the strong influence on storage changes of some rivers and snow-capped mountains were identified, as well as an increase in the accuracy of the scaled data of the original ones. Positive long-term behavior was also obtained in terrestrial and groundwater storage of 14.26 ± 1.18 km3/yr and + 22.24 ± 1.18 km3/yr, respectively. Validation of the time series of groundwater anomalies to water levels in the monitoring wells obtained maximum correlation coefficients of 0.85 with confidence levels of 0.01. These results are promising for satellite information in water management, especially in regional monitoring of unconfined aquifers. The obtained data is stored in a dedicated repository (Satizábal-Alarcón et al., 2023).

Twenty-Year Spatiotemporal Variations of TWS over Mainland China Observed by GRACE and GRACE Follow-On Satellites

Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC > 0.60) and low root-mean-square errors (RMSE < 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management.

Assessing the interannual and subseasonal variabilities in water storage using multi-source soil moisture products and GRACE/GRACE-FO satellites and its applications

Since 2002, the terrestrial water storage anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-on (GRACE-FO), have been widely used in water storage studies. Interannual and subseasonal variabilities in terrestrial water storage are critical for understanding the change of water cycle and interpretation of GRACE error. In this study, we evaluate the interannual plus subseasonal terms of global TWSA from GRACE satellite data and soil moisture storage estimated from multi-source soil moisture data. Results show that soil moisture change dominated total water storage change in most regions, especially in relatively arid regions. We further reconstruct the continuous TWSA by combing the trend and seasonal terms from the GRACE(-FO) TWSA data with the interannual and subseasonal variabilities of multi-source soil moisture products on a global scale from April 2002 to December 2021. Our reconstruction result is further compared with the TWSA from GRACE(-FO) and two recent reconstruction products. The comparisons show that the reconstruction result has great consistency with GRACE(-FO), with 82% of the grids showing Nash-Sutcliffe efficiency coefficient values greater than 0.6 based on JPL Mascon solution. This study emphasizes that the information from soil moisture can provide an efficient approach for tracking the interannual and subseasonal variabilities of long-term terrestrial water storage.

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (−15.21 ± 7.25 mm/yr), Xingtai (−14.98 ± 7.25 mm/yr), and Shijiazhuang (−14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.

Combined monthly GRACE-FO gravity fields for a Global Gravity-based Groundwater Product

SUMMARY The Combination Service for Time-variable Gravity fields (COST-G) operationally provides combinations of monthly Earth gravity field models derived from observations of the microwave ranging instrument of the GRACE Follow-on (GRACE-FO) satellite mission, applying the quality control and combination methodology originally developed by the Horizon 2020 project European Gravity Service for Improved Emergency Management for the data of the GRACE satellites. In the frame of the follow-up Horizon 2020 project Global Gravity-based Groundwater Product (G3P), the GRACE-FO combination is used to derive global grids of groundwater storage anomalies. To meet the user requirements and achieve optimal signal-to-noise ratio, the combination has been further developed and extended to incorporate: • new time-series based on the alternative accelerometer transplant product generated in the frame of the project by the Institute of Geodesy at the Graz University of Technology, which specifically improves the estimation of the C30 coefficient and also reduces the noise at medium to short wavelengths, and • the new time-series AIUB–GRACE-FO–RL02 of monthly GRACE-FO gravity fields, which is derived at the Astronomical Institute of the University of Bern by applying empirical noise modelling techniques. The COST-G quality control confirms the consistency of the contributing GRACE-FO time-series concerning the signal amplitude of seasonal hydrology in large river basins and the secular mass change in polar regions, but it also indicates rather diverse noise characteristics. The difference in the noise levels is taken into account in the combination process by relative weights derived by variance component estimation on the solution level. The weights are expected to be inverse proportional to the noise levels of the individual gravity field solutions. However, this expectation is violated when applying the weighting scheme as developed for the GRACE combination. The reason is found in the high-order coefficients of the gravity field, which are poorly determined from the low–low range-rate observations due to the observation geometry and suffer from aliasing due to the malfunctioning accelerometer onboard one of the GRACE-FO satellites. Hence, for the final G3P-combination a revised weighting scheme is applied where the gravity field coefficients beyond order 60 are excluded from the determination of the weights. The quality of the combined gravity fields is assessed by comparison of the noise content and the signal-to-noise ratio with the individual time-series. Independent validation is provided by the COST-G validation centre at the GFZ German Research Centre for Geosciences, where orbit fits of the low-flying Gravity and steady-state Ocean Circulation Explorer satellite are performed that confirm the high quality of the combined GRACE-FO gravity fields. By the end of the G3P project, the new combination scheme is implemented by COST-G as the new COST-G–GRACE-FO–RL02 and continued to be used for the operational GRACE-FO combination.

A Two‐Step Linear Model to Fill the Data Gap Between GRACE and GRACE‐FO Terrestrial Water Storage Anomalies

AbstractThe Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On (GRACE‐FO) missions have revolutionized global terrestrial water storage anomalies (TWSA) measurements. However, the 11‐month data gap between the two GRACE missions disrupts the measurement continuity and limits its further applications. Previous attempts to fill this data gap require further improvement in terms of method robustness and product quality. Here, we propose a novel two‐step linear model using precipitation, temperature data, and hydrological model‐simulated TWSA as predictors to fill the 11‐month data gap between the two GRACE missions and generate six global gridded GRACE‐like TWSA products from April 2002 to July 2021. These products are evaluated at grid scale globally and also basin scale for the world's largest 72 river basins. Results indicate that our GRACE‐like data show great consistency with the GRACE/GRACE‐FO observations. While most basins exhibit consistent performance across the six GRACE‐like TWSA products, certain areas with lower signal‐to‐noise ratios show significant variability. Furthermore, we assess the performance of our GRACE‐like data during the data gap using one previous reconstruction, a hydrological model simulation, and the Swarm satellite measurement. The results confirm that our GRACE‐like data exhibit equivalent performance within and outside the data gap. This study introduces a more simple and robust method for predicting the missing data between the two GRACE missions and provides readily applicable continuous GRACE‐like TWSA products for hydrologic applications.

Monitoring hydrological changes with satellite data: Spring thaw's effect on soil moisture and groundwater in seasonal Freezing-Thawing zones

Due to the dense vegetation coverage in mountainous areas and difficulties in field monitoring in mid-high-latitude freezing-thawing zones, satellite and remote sensing technologies can be used to monitor long-term global hydrological changes. This study uses multiple satellite data of snow water equivalent (SWE), soil moisture (SM), and groundwater storage anomaly (GWSA) to analyse their spatial–temporal changes in the seasonal freezing-thawing Tumen River Basin. Water transformation during the thawing period is determined through groundwater level fluctuations and correlation analysis methods; thus, the recharge effects of snowmelt and seasonally frozen soil thawing on soil moisture and groundwater are identified. The snow begins to melt in February, and snow water equivalent decreases gradually from upstream to downstream after reaching the peak. Furthermore, seasonally frozen soil melts gradually from March. Soil moisture downstream is higher than that in the upper and middle reaches. Additionally, the GWSA increases gradually in the thawing period, which is high in mountainous areas and low in plains. The Spearman correlation coefficient among ΔSWE, ΔSM, and ΔGWSA is more significant than 0.5 and dependent on altitude. With snow and frozen soil melting, vertical infiltration and meltwater percolation increase soil moisture and GWSA. This phenomenon is predominant at low altitudes (<600 m) and pore phreatic groundwater in the middle and lower reaches. However, snowmelt is the dominant factor in groundwater recharge at high-altitude upper reaches. For seasonal freezing-thawing zones, a close water transformation was observed between snow melting, soil water thawing, and groundwater in spring. Thus, the environmental and ecological impact of spring snowmelt and floods in freezing-thawing zones can be monitored using multi-source data.

Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China

Climate change and excessive exploitation of water resources exert pressure on groundwater supply and the ecosystem in drylands. Although The Gravity Recovery and Climate Experiment (GRACE) satellites has demonstrated the feasibility of quantifying global groundwater storage variations, monitoring regional-scale groundwater has been challenging due to the coarse resolution of GRACE. Previous GRACE downscaling studies focused on develop new algorithms based on the perspective of pixel spatial correlation to improve resolution, which cannot better capture the temporal evolution of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER) algorithm and the multi-scale geographically weighted regression (MGWR) model to establish two different downscaling schemes: pixel temporal continuity downscaling and pixel spatial correlation downscaling. These schemes achieve spatial resolution downscaling of GRACE-derived groundwater storage anomalies (GWSA) from 0.5° to 0.1°. Additionally, the applicability of the PCR-GLOBWB model in drylands is verified. Furtherly, the spatiotemporal distribution patterns of GWSA are analyzed. The results show that (1) Both the temporal and spatial downscaling methods produced consistent results, with data correlations ranged from 0.94 to 0.98 observed in over 80 % of the range before and after downscaling; (2) The groundwater storage change rate in the northern Tarim River Basin (TRB) is 25 times greater than the model results, while in other regions, the average deviation is 2.6 times; (3) The two schemes enhance the correlation (0.27) between GWSA and groundwater level anomaly (GWLA) to 0.59 and 0.52, respectively, with a three-month lag in GWSA relative to GWLA. The temporal downscaling approach exhibited higher CC and lower RMSE, outperforming the spatial downscaling approach. The high-resolution results in this study can well complement groundwater level prediction efforts in arid regions and provide quantitative information for local water resource management.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Improved Drought Characteristics in the Pearl River Basin Based on Reconstructed GRACE Solution with Enhanced Temporal Resolution

As global warming intensifies, the damage caused by drought cannot be disregarded. Traditional drought monitoring is often carried out with monthly resolution, which fails to monitor the sub-monthly climatic event. The GRACE-based drought severity index (DSI) is a drought index based on terrestrial water storage anomalies (TWSA) observed by the gravity recovery and climate experiment (GRACE) satellite. DSI has the ability to monitor drought effectively, and it is in good consistency with other drought monitoring methods. However, the temporal resolution of DSI is limited by that of GRACE observations, so it is necessary to obtain TWSA with a higher temporal resolution to calculate DSI. We use a statistical method to reconstruct the TWSA, which adopts precipitation and temperature to obtain TWSA on a daily resolution. This statistical method needs to be combined with the time series decomposition method, and then the parameters are simulated by the Markov chain Monte Carlo (MCMC) procedure. In this study, we use this TWSA reconstruction method to obtain high-quality TWSA at daily time resolution. The correlation coefficient between CSR–TWSA and the reconstructed TWSA is 0.97, the Nash–Sutcliffe efficiency is 0.93, and the root mean square error is 16.57. The quality of the reconstructed daily TWSA is evaluated, and the DSI on a daily resolution is calculated to analyze the drought phenomenon in the Pearl River basin (PRB). The results show that the TWSA reconstructed by this method has high consistency with other daily publicly available TWSA products and TWSA provided by the Center for Space Research (CSR), which proves the feasibility of this method. The correlation between DSI based on reconstructed daily TWSA, SPI, and SPEI is greater than 0.65, which is feasible for drought monitoring. From 2003 to 2021, the DSI recorded six drought events in the PRB, and the recorded drought is more consistent with SPI-6 and SPEI-6. There was a drought event from 27 May 2011 to 12 October 2011, and this drought event had the lowest DSI minimum (minimum DSI = −1.76) recorded among the six drought events. The drought monitored by the DSI is in line with government announcements. This study provides a method to analyze drought events at a higher temporal resolution, and this method is also applicable in other areas.

GRACE-based dynamic assessment of hydrological drought trigger thresholds induced by meteorological drought and possible driving mechanisms

Determining the threshold at which meteorological drought triggers hydrological drought is critical for early warning and proper mitigation of drought. However, drought trigger thresholds are difficult to determine owing to the nonlinearity between meteorological and hydrological drought, and their dynamics have not been explored. To this end, we introduce a precipitation-driven drought trigger threshold framework. This framework considers the multiscale characteristics of cumulative precipitation anomalies and incorporates the drought severity index of terrestrial water storage anomalies to characterize hydrological drought. The dynamics of trigger thresholds over time and the main drivers of these variations are further explored over China. The results show that hydrological drought is more sensitive to meteorological drought in south China, with some regions showing weak or negative correlations mainly determined by the differences between climate change and human activities. The risk of drought outbreak in the central, northeastern and southern regions of China is high, with trigger thresholds showing a dynamic decreasing trend over time (corresponding to lower cumulative precipitation anomalies), indicating weakened resistance to meteorological drought. Rising temperature is the main factor affecting dynamic changes in the trigger threshold. The drought trigger threshold framework proposed in this study is also applicable for assessments in other regions around the world. This study provides valuable insights and new approaches for understanding the mechanisms of hydrological drought formation. Furthermore, these results are expected to severe as a scientific basis for government departments to reduce water supply stress on human and natural systems and to develop adaptive management strategies.

Response of vegetation to terrestrial water storage in Southwest China

To explore the responses of vegetation growth to change in terrestrial water storage in Southwest China, we analyzed the change trend and relationship between vegetation and terrestrial water storage anomaly (TWSA) in Southwest China from January 2003 to December 2021 by using TWSA data of Gravity Recovery and Climate Experi-ment (GRACE) satellite and normalized differential vegetation index (NDVI) data. The results showed that NDVI in Southwest China during the study period showed an overall upward trend. Meanwhile, TWSA showed a significant downward trend in central and southern Tibet, and a significant upward trend in northwest Tibet and southeast region of Southwest China. Results of Pearson correlation analysis showed that there were significant spatial differences in responses of NDVI to TWSA changes in Southwest China. NDVI had a significant negative response to TWSA changes in most regions of Tibet, but a significant positive response to TWSA changes in most regions of southeast region of Southwest China. Such differences were driven by climate change and topography.

Complex Effects of Tides on Coastal Groundwater Revealed by High‐Resolution Integrated Flow Modeling

AbstractRiver deltas typically have high population density and support a wide range of intensive and prosperous socioeconomic activities. The hydrological processes in these regions are complex, primarily due to the interactions among the river, aquifer, and sea. However, a systematic and quantitative elaboration of the river‐aquifer‐sea interactions is still lacking. Here, we developed an integrated hydrological flow model for the Pearl River Delta (PRD), which contains the world's largest urban area in both size and population, to gain a deeper understanding of the complexities in the river‐aquifer‐sea interactions. The model performance was validated and cross‐checked via observations at gauging stations and independent remote‐sensing products (e.g., soil moisture, evapotranspiration, and total water storage anomalies). Based on the 10‐year simulation results (2004–2013), the major findings of this study are as follows: (a) accurate representation of the tidal effect is important not only for simulating short‐term flow dynamics but also for capturing the characteristics of long‐term hydrological fluxes and states; (b) the flow‐model‐computed average groundwater discharge rate per unit length of the coastline for the PRD is 3.01 m3/d/m, which is comparable with those derived from water budget approaches but 1–2 orders of magnitude lower than the total submarine groundwater discharge (SGD) estimated by using isotope tracer‐based methods; (c) the temporal variation of SGD is controlled by tidal forcing on an hourly time scale, but by terrestrial hydrological processes on monthly and annual time scales; and (d) an integrated hydrological flow model can be used to identify distinct and large subsurface zones sensitive to tidal fluctuations, quantifying the pivotal role of ocean tides in shaping the coastal groundwater system. This study represents a first step in using an integrated hydrological model to explore river‐aquifer‐sea interactions and their effects on the regional groundwater system simultaneously driven by meteorological and tidal forcings.

The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis

AbstractRecent extreme events in the Middle‐Lower reaches of the Yangtze River basin (MLYRB) are proven to be possibly linked to the El Niño‐Southern Oscillation (ENSO) events as indicated by terrestrial water storage anomaly (TWSA). But the relatively short observation time of Gravity Recovery and Climate Experiment series missions (2002–2017; 2018–present) affects the robustness of the evaluation of TWSA. Here, the applicability of four long‐term TWSA data sets (since 1979) in the MLYRB is evaluated first using an evaluation framework including two completely independent tests. After selecting the optimal one, we investigate the effects of ENSO on TWSA in the MLYRB at the basin, subbasin, and grid cell scales, respectively. Results show that ENSO, especially the Eastern Pacific type ENSO has had a significant impact on TWSA variations in the MLYRB and its two subbasins (the Dongting Lake basin and the Poyang Lake basin) since 1979 with correlation coefficients at 0.56–0.65 and time lags at 5–6 months. However, TWSAs in the other two subbasins (the Hanjiang River basin and the Mainstream River basin) have almost no correlation with ENSO. Further analysis reveals that compared with human activity that has a limited impact on TWSA, precipitation is one of the key inducements for regional water storage changes in these two subbasins, and the no correlation between ENSO and TWSA is mainly caused by the weak link between ENSO and precipitation.

Evaluating different predictive strategies for filling the global GRACE/-FO terrestrial water storage anomalies gap

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions provide unprecedented approaches for tracking terrestrial water storage anomalies (TWSA). However, evaluating long-term hydrologic states requires continuous TWSA without the ∼11-month gap between the two GRACE missions. Trend prediction is a challenging problem for TWSA gap-filling. There are three common methods for handling trends in previous efforts, i.e., de-trending the TWSA, adding back the long-term or piecewise trends of GRACE/-FO to the detrended predictions, or not performing such trend replacement. However, a single global application of one of these methods will not produce optimal results. Therefore, we designed a framework to select the optimal trend replacement strategy for each grid in this study. Based on this framework, we better filled the gap (excluding Antarctica) using machine learning techniques adopting the Global Land Data Assimilation System (GLDAS) Noah TWSA, precipitation, and temperature as inputs. The median gridwise Nash-Sutcliffe efficiency of the result generated by our framework improves by 0.08 compared to the result of a single long-term trend replacement strategy. Furthermore, we quantitatively evaluated the impact of three predictive strategies on the results: selection of leader machine learning technique, selection of optimal trend replacement strategy, and selection of most relevant inputs. The results indicate that the selection of trend replacement strategy has the greatest influence, followed by the selection of machine learning technique and then the selection of inputs. In addition, we found that in areas with abundant surface water, utilizing surface water anomalies as an additional predictor benefits the results. Our study is expected to provide suggestions for better TWSA predictive strategies.

How 2022 extreme drought influences the spatiotemporal variations of terrestrial water storage in the Yangtze River Catchment: Insights from GRACE-based drought severity index and in-situ measurements

Under global warming, extreme climatic events frequently occurred worldwide such as extreme drought and heavy flood. Analysis of drought and flood spatiotemporal evolution and physical mechanism is of great significance for future disaster warning and mitigation. In 2022, the Yangtze River Catchment (YRC) experienced an extreme drought, which profoundly affected the regional economy, ecological environment and anthropogenic activities. In this study, we quantified the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA) response to this severe drought event using Gravity Recovery and Climate Experiment-based drought severity index (GRACE-DSI), with the inspection of hydrological model and in-situ measurements. The results showed that the 2022 YRC drought first occurred in July at the upstream and midstream, then spread to downstream and finally occupied almost the whole YRC in August and ∼ 94% of the YRC in September. The effectiveness of GRACE-DSI for drought monitor can be validated by in-situ hydrological and meteorological observations. Good correlations among El Niño-Southern Oscillation (ENSO), precipitation anomalies (PA), and TWSA were found, demonstrating the regional atmospheric circulation anomaly was the driving factor of this extreme drought event, which triggered the changes in precipitation further to influence the pattern of terrestrial water storage. The consecutive La Niña events induced the extension of western Pacific subtropical high (WPSH) and Iranian high, the precipitation deficiency in 2022 summer declined by ∼ 46% and ∼ 36% compared to 2020 and 2021 respectively, which was the main cause of the extreme drought.

Evaluating main drivers of terrestrial water storage depletion in the agro-pastoral ecotone of northern China

With the heightened threat of declining terrestrial groundwater, determining the effects of changing environments on terrestrial water storage anomaly (TWSA) is essential for water resource management. As a critical element of the water cycle, the multi-effects of the natural and artificial factors on TWSA were still not fully understood and quantified. Using multiple high-resolution data products and the structural equation model (SEM), this study discussed how climate, vegetation, and human activities affected terrestrial water storage (TWS) in the West Liao River basin (WLRB), China, from 1990 to 2020. The results confirmed that vegetation showed a greening trend while TWS depleted at 5.93 cm/10a in the WLRB. TWS in greening areas has a negative correlation with vegetation growth. The decline in TWS is mainly dominated by precipitation and runoff variability. By influencing eco-hydrological cycle processes, climate change and human activities affect TWS through direct and indirect(by influencing NDVI) pathways. Land use change, manifested as conversion of grassland into cropland, contributed to the most decline of TWS (51.2 %). Moreover, the crop yield increasing, climate warming, and vegetation greening could lead to TWS depletion. Spatially, the overexploitation of the groundwater for irrigation to support crop production was probably the most critical cause of TWS depletion over upstream of the WLRB. Furthermore, in the West Liao Plain, the intensified vegetation greenness also led to the increasing ecosystem water demand, resulting in the decrease of TWS. This study improves our understanding of the interaction of the basinal eco-hydrological system and provides a hint that reducing groundwater extraction and implementing agriculture fallow is essential to recovering water storage and alleviating the water resource crisis in the agro-pastoral ecotone.

Reconstructing GRACE-derived terrestrial water storage anomalies with in-situ groundwater level measurements and meteorological forcing data

Study regionNorth China Plain (NCP), China, a semi-arid region with intense groundwater withdrawals. Study focusThis paper developed a framework using meteorological data, model-simulated terrestrial water storage anomalies (TWSA), and additional in-situ (groundwater level, GL) data to improve the unsatisfactory GRACE-TWSA reconstruction in arid and semi-arid regions due to the intense anthropogenic influence on groundwater. The inconsistency between point-scale data (GL) and grid-scale data (GRACE-TWSA and predictors other than GL) is handled by feature extraction techniques. Moreover, to deal with temporal non-stationarity, the time series are separated into trend and detrended components, the patterns of which are further learned by linear and nonlinear machine learning models, respectively.New hydrological insights for the region: Multi-site GL observations in NCP can not only serve as validation data but also as predictors providing invaluable information on human effects for the reconstructed TWSA improvement (from 6.51 to 3.86 cm for Root Mean Square Error and from 0.56 to 0.82 for Nash-Sutcliffe Efficiency). Our results show that multi-site GL data in NCP are highly inter-correlated and can be represented by several principal components, demonstrating the strong hydraulic connectivity in NCP. We also find a significant one-month lag and linear relationship between the trends of GRACE-TWSA and GL changes in NCP. These deeper understandings of hydrologic processes have implications for enhancing the GRACE-TWSA estimations in other similar regions.