Terrestrial Water Storage Anomalies Research Articles

A novel dynamic scale factor designed for recovering global TWS changes

For time-variable satellite gravity solutions of GRACE and GRACE-FO in terms of spherical harmonics coefficients, the Scale Factor (SF) is often used to recover the close to “true” signal of Terrestrial Water Storage (TWS) anomalies. However, the conventional SF method has some limitations that may hinder its effectiveness, including: (1) their dependency on input hydrological models that may lead to divergent estimations of SFs; (2) the arbitrary choice of filter strength, which may not be representative in different regions; (3) limited consideration of SF in the temporal dynamics (the conventional SF was fixed value) for monthly varied TWS. Here, we propose a new Dynamic SF method to overcome these limitations and increase accuracy of the restored global TWS changes. This method involves: (1) the Bayesian Three-Cornered Hat (BTCH) method is applied to merge three sets of hydrological products into an optimal hydrological dataset to be used for estimating a unique SF, (2) the anisotropic DDK3 filter (found to be numerically optimal) is applied to suppress the correlated noise, and (3) an iterative Kalman filter process is formulated and implemented to estimate monthly Dynamic SF corresponding to monthly global TWS fields. The Dynamic SF outperformed an ordinary SF method in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), and the Signal to Noise Ratio (SNR), which were found to be improved by 30.8%, 32.2%, and 41.3%, respectively. Moreover, the recovered TWS using the Dynamic SF method showed a good agreement with the GRACE/GRACE-FO RL06 mascon solutions of the CSR and that of JPL regarding the long-term trend, seasonal, and interannual variations.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.

Monitoring spatio-temporal variations of terrestrial water storage changes and their potential influencing factors in a humid subtropical climate region of Southeast China

Identifying the changes in terrestrial water storage is essential for a comprehensive understanding of the regional hydrological mass balance under global climate change. This study used a partial least square regression model to fill the observation gaps between GRACE and GRACE-FO and obtained a complete series of terrestrial water storage anomaly data from April 2002 to December 2020 from southeast China. We investigated the variations in terrestrial water storage anomalies in the region and the influencing factors. The study revealed that terrestrial water storage (TWS) anomalies have been increasing in the region, with an average increase of 0.33 cm/yr (p < 0.01). The intra-annual variation showed a positive anomaly from March to September and a negative anomaly in other months. Terrestrial water storage anomalies increased in most regions (especially in the central and northern parts), whereas they decreased in the southern parts. In terms of the components, the soil moisture storage (SMS) contributes 58.3 % and the surface water storage (SWS, especially reservoirs water storage) contributes 41.4 % to the TWS. The study also found that changes in the precipitation explain approximately 71.7 % of the terrestrial water storage variation, and reservoirs contributes to the remaining 28.3 %. These results are essential for understanding the changes in the hydrological cycle and developing strategies for water management in Southeast China.

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.

Drought susceptibility mapping in Iraq using GRACE/GRACE-FO, GLDAS, and machine learning algorithms

Drought susceptibility mapping is crucial for mitigating water scarcity and its cascading effects. This study utilizes machine learning (ML) and Gravity Recovery and Climate Experiment (GRACE) data to identify drought-prone areas in Iraq. Terrestrial water storage anomaly (TWSA) data derived from GRACE observations was utilized, in conjunction with various meteorological and topographical factors (temperature, precipitation, wind speed, potential evapotranspiration, aridity index, normalized difference vegetation index, groundwater storage, root zone moisture, elevation, and slope) sourced from diverse global datasets, primarily those comprising the Global Land Data Assimilation System (GLDAS). These combined datasets served as input for five machine learning models to identify the drought prone area of Iraq: linear discriminant analysis, classification and regression trees, support vector machine, random forest, and k-nearest neighbor. All models exhibited exceptional performance (>0.9 accuracy, >0.8 kappa values, >0.9 areas under the receiver operating characteristic curve) highlighting the effectiveness of the combined TWSA and multi-factor approach. The analysis identified five drought susceptibility zones across Iraq, ranging from very low to very high. An area of approximately 160,650 km2, primarily concentrated in the northeastern and Mesopotamian plains of central and southern Iraq, was identified as exhibiting high to very high drought susceptibility, highlighting the necessity for targeted mitigation strategies. This study pioneers the use of GRACE-derived TWSA and GLDAS data for drought susceptibility mapping in Iraq, paving the way for data-driven water management interventions.

Characterizing the drought events in Yangtze River basin via the insight view of its sub-basins water storage variations

Yangtze River Basin (YRB) is the most important strategic base of water resources in China, which has the characteristics of complex terrain and unbalanced water distribution in spatial and temporal domain. Drought events have occurred frequently in this basin due to the influence of global climate change and human activities. In this context, it is critical to formulate drought policies based on the characteristics of drought events in different sub-basins. Based on the latest HUST-Grace2020 model released by Huazhong University of Science and Technology, we calculate the terrestrial water storage anomaly (TWSAs) in YRB during January 2003 to July 2016, and combine water storage deficit index (WSDI) with a modified run theory to extract precisely drought events in terms of sub-basins. On this basis, the characteristics of drought events are identified in different sub-basins and different seasons. Subsequently, the links between drought events and topography as well as climate factors are explored in the YRB. The results indicate that: (1) Soil moisture storage anomaly (SMSA) is the uppermost component (41%) of TWSA but the largest contributor (59%) to TWSA trend is groundwater storage anomaly (GWSA). Precipitation is the main factor affecting the composition and trend of TWSC. (2) Eight drought events (one severe level, four moderate level and three mild level) are extracted across YRB and the most severe event can be monitored in each sub-basin which begins from May 2006 to August 2007. (3) The frequency of drought events is higher in the lower region of YRB, but serious drought events are more likely to occur in the source region of YRB. (4) Droughts are more frequent in summer and winter, while severe and extreme situations mainly occur in autumn. (5) Drought severity in the YRB is positively correlated with topography, and drought events are influenced by El Niño, especially in the downstream YRB. This study sheds a theoretical basis for drought early warning of different levels in YRB across the various sub-basins.

Satellite observed dryland greening in Asian endorheic basins: Drivers and implications to sustainable development

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEBs). As these retention basins are only being replenished by the intermittent and scarce rainfall, global warming coupled with ever-rising human demand for water is exerting unprecedented pressures on local water and ecological security. Recent studies revealed a persistent and widespread water storage decline across the AEBs, yet the response of dryland vegetation to this recent hydroclimatic trend and a spatially explicit partitioning of the impact into the hydroclimatic factors and human activities remain largely unknown. To fill in this knowledge gap, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53 %), encompassing Mongolia Plateau, Northwest China, Qinghai Tibet Plateau, and Western Asia (except the Arabian Peninsula), exhibited a significant greening trend over the past two decades. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3 %) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the AEB, our results therefore raised a significant concern on the ecological and societal sustainability in this region, where a mild increase in precipitation cannot catch up the rising evaporative demand and water consumption resulted from global warming and agriculture intensification.

Assessment of anthropogenic and climate-driven water storage variations over water-stressed river basins of Ethiopia

Abstract Globally, surface water, groundwater, soil moisture, snow storage, canopy water, and wet biomass constituents make up water storage, which plays a significant role in the hydrological water balance. Evaluating the variations in water storage anomalies associated with climate forcing and human activities over river basins is crucial for assessing water scarcity and predicting potential pressures on water resources in the future. In this study, we assessed the impacts of climatic and anthropogenic drivers on the change in water storage in the river basins of Ethiopia by using the independent component analysis to examine Gravity Recovery and Climate Experiment with the Global Land Data Assimilate System-based water storage and comparing the independent component analysis with hydro-meteorological data and statistical data related to human activities. It is of great significance for helping people better understand the evaluation of terrestrial water storage anomalies under the combined influence of climatic change and anthropogenic activities and providing information for better protection and utilization of water resources at river basin level. It is crucial to take effective measures to protect these precious land and water resources and prevent their further deterioration. The estimated result will be essential for sustainable water management and protection.

Detection of extreme hydrological droughts in the poyang lake basin during 2021–2022 using GNSS-derived daily terrestrial water storage anomalies

Poyang Lake is the largest freshwater lake in China, serving as a natural reservoir and playing a paramount role in climate regulation, ecological environment, and water resource management. However, in recent years, Poyang Lake has approached desiccation multiple times, with severe droughts becoming increasingly common. Consequently, precise quantification and analysis of the terrestrial water storage anomalies (TWSA) and drought characteristics of the Poyang Lake basin (PLB) are of profound scientific and practical significance. This paper, for the first time, utilizes data for the period 2021–2022 from 77 newly-established GNSS observation stations in the PLB to precisely determine its vertical crustal displacement, invert daily and monthly TWSA, and investigate extreme hydrological drought. The results reveal the following: 1) The annual amplitude range of vertical surface displacements at GNSS stations in the Poyang Lake basin is from 7 to 14 mm, with the most substantial seasonal vertical displacements occurring during the months of June and July; 2) monthly GNSS-TWSA maintains a commendable consistency with TWSA data obtained from the Gravity Recovery and Climate Experiment (GRACE), the Global Land Data Assimilation System (GLDAS), and precipitation, with correlation coefficients of 0.67, 0.55, and 0.62, respectively; 3) at daily scale, the GNSS-derived Drought Severity Index (GNSS-DSI) accurately recorded the severity and intensity of eight drought events in the PLB during 2021–2022, in particular the period of extensive drought between October 2021 and February 2022, when drought intensity reaching a notable 1.03, which is classified as an extreme and prolonged drought event. Additionally, at local temporal scales, daily GNSS-DSI exhibits heightened sensitivity to drought signals. This study provides novel technological tools and datasets for multi-source satellite-based drought monitoring in the PLB.

Is groundwater running out in the Western Cape, South Africa? Evaluating GRACE data to assess groundwater storage during droughts

Study regionThe Western Cape (WC), South Africa. Study focusThe WC has become increasingly dependent on groundwater in recent years due to repeated droughts. A framework to monitor the regional groundwater levels is urgently required to sustainably manage the WC’s water resources, since the region has inconsistent or unavailable monitoring data. Therefore, this study aims to understand how Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data can be used to monitor groundwater storage variations (ΔGWS) in the WC. In-situ ΔGWS time-series from twelve aquifers in the WC were compared to GRACE and GLDAS data. New hydrological insights for the regionGRACE terrestrial water storage anomalies (ΔTWS) showed moderate positive correlation (r = 0.69) with in-situ ΔGWS from the Adelaide Subgroup Aquifer (ASA), an unconfined aquifer with large areal extent and large ΔGWS. The Table Mountain Group Upper Aquifer Unit (TMG UAU) and Cape Flats Aquifer (CFA) also showed significant positive correlations with GLDAS ΔGWS of 0.83 and 0.73, respectively. Our results suggest that ΔGWS in the ASA can be monitored using GRACE ΔTWS, while GLDAS ΔGWS data can be used to monitor ΔGWS in the unconfined TMG UAU and CFA. GRACE and GLDAS data may be suitable to monitor groundwater availability in other water- and data-scarce regions of Africa.

Comparative evaluation of the dynamics of terrestrial water storage and drought incidences using multiple data sources: Tana sub-basin, Ethiopia

Abstract 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.

Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses

AbstractLateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle.

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for understanding our climate system. This study proposes a self-supervised data assimilation model with a new loss function to provide global TWSAs with a spatial resolution of 0.5°. The model combines hydrological simulations as well as measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions. The efficiency of the high-resolution information is proved by closing the water balance equation in small basins while preserving large-scale accuracy inherited from the GRACE(-FO) measurements. The product contributes to monitoring natural hazards locally and shows potential for better understanding the impacts of natural and anthropogenic activities on the water cycle. We anticipate our approach to be generally applicable to other TWSA data sources and the resulting products to be valuable for the geoscience community and society.

Hydrological Assessment of Iraq for the Period 2002-2020 Using GRACE Terrestrial Water Storage Deficit

The research examines the effectiveness of remote sensing for monitoring drought conditions, specifically focusing on the utilization of satellite missions like GRACE to detect alterations in water levels. The study analyzes GRACE data for 10 Jet Propulsion Laboratory (JPL) grid cells alongside precipitation data from 11 meteorological stations. It employs the DrinC program to calculate the standard precipitation index (SPI) at different time intervals. Non- Non-parametric Mann-Kendall tests and Sen's slope statistics are used to determine the trend and magnitude of the GRACE-based anomaly in terrestrial water storage. The statistical findings reveal a significant decrease in Iraq's terrestrial water storage anomaly between 2007 and 2017, indicating drier climatic conditions. However, there is an increase in anomaly between 2002 and 2006, as well as from 2018 to 2020, indicating wetter conditions. The water storage deficit varies among grid cells, each exhibiting distinct characteristics and patterns. The study also highlights that more intense and prolonged droughts tend to occur in the northern and southern regions of Iraq, while the central region experiences more frequent but less severe droughts. In most grid cells, the Mann-Kendall test illustrates a substantial decrease in the water storage deficit index and a significant increase in SPI-06. On the other hand, only one grid cell shows a noticeable precipitation trend. Comparatively, trends in the water storage deficit index and SPI-06 are similar. Overall, the water storage deficit and its index prove valuable in predicting drought severity in locations lacking hydrological measurements.

Analysis of gap filling techniques for GRACE/GRACE-FO terrestrial water storage anomalies in Canada

Terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) have become indispensable for hydrological monitoring since the first mission launched in 2002. Evaluation of basin dynamics is enabled through TWSA integration with independent hydrological and climate datasets such as precipitation, evapotranspiration, and surface runoff. With over 20 years of observations, long-term statistical analysis reveals water storage trends, provided the 11-month gap between the two missions is filled. This study compares four methods for reconstructing GRACE-like TWSA over Canada, namely: extreme gradient boosting, artificial neural networks, automated machine learning, and projection onto convex sets. The GRACE mascon product released by the Jet Propulsion Laboratory is used over the study region. Nine sets of hydrological and climate parameters are used as predictors for the machine learning models, namely GRACE average seasonal signals, TWSA from the GLDAS Catchment Land Surface Model, precipitation, air temperature, glacier surface mass balance, ocean tides, the North Atlantic Oscillation, the Multivariate ENSO Index, and sea surface temperature. For each of the four methods, 20 % of the GRACE TWSA data (the ‘testing data’) is omitted from the model training, and the model is used to fill the artificial gap. Root mean square error (RMSE) is computed using the difference between testing data and model predictions. Normalized RMSE expresses the RMSE as a proportion of the range of the GRACE TWSA data. The automated machine learning algorithm performs best in terms of mean normalized root mean square error, with 6 % (2.4 cm equivalent water height RMSE), followed by projection onto convex sets, extreme gradient boosting, and artificial neural networks. Inclusion of glacier surface mass balance models derived from the GMAO Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis and the Randolph Glacier Inventory improved the automated machine learning average root mean square error by 70 %. Results indicate that filling the gap between missions allows for more comprehensive analysis of terrestrial water storage changes, including basin-by-basin analysis, which is ongoing.

Evaluating dynamics of land water storage and its response to climate variation in the Deccan Plateau, India

Inter-state river water disputes are frequent over the rain shadow (RS) region of the Western Ghats (WG), where the annual rainfall is much lower (75.2 cm) than that of overall India (118 cm). This study investigated the TWS (Terrestrial Water Storage) dynamics of the entire Deccan plateau (DP) and RS regions, located at the leeward side of the WG mountain range, with the help of Gravity Recovery and Climate Experiment (GRACE) based TWS anomalies (TWSA) data. Here, the latest TRMM (Tropical Rainfall Measuring Mission) rainfall product was evaluated at different time scales against gauge-based measurements to check its applicability as an alternative to gauge-based observed data. Trends in climate variables and TWSA were estimated to understand their variations during 2003–2016 in both regions. The response of GRACE-based TWSA and its constituent components to rainfall was determined using time-lagged correlation analysis. The contribution of constituent variables to TWSA was estimated using the component contribution ratio (CCR) method. It was observed that the TRMM-3B43 data captured the rainfall pattern over the entire DP with great accuracy. Results showed that the increasing trend of TWS in 2003–2009 had reversed in 2010–2016 due to the reduction of climate moistening in both regions. The warming trend of climate is more than twice in the RS region than that found in the DP, leading to a higher decreasing rate of TWS in this region than in DP. For both DP and RS regions, the time lags of groundwater storage anomalies (GWSA), total runoff anomalies (RA), canopy water storage anomalies (CWSA), and soil moisture storage anomalies (SMSA) to rainfall can be arranged as CWSA ≤ RA ≤ SMSA ≤ GWSA. SMSA and GWSA are the main contributors to the TWSA in both areas. However, SWSA also played a vital role in TWS variability, particularly in the RS region.

GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model

Abstract. We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30 arcsec; ∼ 1 km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is the successor of our 5′ (5 arcmin; ∼ 10 km at the Equator) PCR-GLOBWB 2 (PCRaster Global Water Balance model) groundwater model, based on MODFLOW having two model layers. The current version of GLOBGM (v1.0) used in this study also has two model layers, is uncalibrated, and uses available 30′′ PCR-GLOBWB data. Increasing the model resolution from 5′ to 30′′ creates challenges, including increased runtime, memory usage, and data storage that exceed the capacity of a single computer. We show that our parallelization tackles these problems with relatively low parallel hardware requirements to meet the needs of users or modelers who do not have exclusive access to hundreds or thousands of nodes within a supercomputer. For our simulation, we use unstructured grids and a prototype version of MODFLOW 6 that we have parallelized using the message-passing interface. We construct independent unstructured grids with a total of 278 million active cells to cancel all redundant sea and land cells, while satisfying all necessary boundary conditions, and distribute them over three continental-scale groundwater models (168 million – Afro–Eurasia; 77 million – the Americas; 16 million – Australia) and one remaining model for the smaller islands (17 million). Each of the four groundwater models is partitioned into multiple non-overlapping submodels that are tightly coupled within the MODFLOW linear solver, where each submodel is uniquely assigned to one processor core, and associated submodel data are written in parallel during the pre-processing, using data tiles. For balancing the parallel workload in advance, we apply the widely used METIS graph partitioner in two ways: it is straightforwardly applied to all (lateral) model grid cells, and it is applied in an area-based manner to HydroBASINS catchments that are assigned to submodels for pre-sorting to a future coupling with surface water. We consider an experiment for simulating the years 1958–2015 with daily time steps and monthly input, including a 20-year spin-up, on the Dutch national supercomputer Snellius. Given that the serial simulation would require ∼ 4.5 months of runtime, we set a hypothetical target of a maximum of 16 h of simulation runtime. We show that 12 nodes (32 cores per node; 384 cores in total) are sufficient to achieve this target, resulting in a speedup of 138 for the largest Afro–Eurasia model when using 7 nodes (224 cores) in parallel. A limited evaluation of the model output using the United States Geological Survey (USGS) National Water Information System (NWIS) head observations for the contiguous United States was conducted. This showed that increasing the resolution from 5′ to 30′′ results in a significant improvement with GLOBGM for the steady-state simulation when compared to the 5′ PCR-GLOBWB groundwater model. However, results for the transient simulation are quite similar, and there is much room for improvement. Monthly and multi-year total terrestrial water storage anomalies derived from the GLOBGM and PCR-GLOBWB models, however, compared favorably with observations from the GRACE satellite. For the next versions of GLOBGM, further improvements require a more detailed (hydro)geological schematization and better information on the locations, depths, and pumping rates of abstraction wells.

Integrating GRACE/GRACE Follow-On and Wells Data to Detect Groundwater Storage Recovery at a Small-Scale in Beijing Using Deep Learning

Groundwater depletion is adversely affecting Beijing’s ecology and environment. However, the effective execution of the South-to-North Water Diversion Project’s middle route (SNDWP-MR) is anticipated to mitigate Beijing’s groundwater depletion. Here, we propose a robust hybrid statistical downscaling method aimed at enhancing the capability of the Gravity Recovery and Climate Experiment (GRACE) to detect the small-scale groundwater storage anomaly (GWSA) in Beijing. We used three deep learning (DL) methods to reconstruct the 0.5° × 0.5° terrestrial water storage anomaly (TWSA) between 2004 and 2021. Moreover, multiple processing strategies were used to downscale the GWSA to 0.25° from 2004 to 2021 by integrating wells and GRACE/GRACE follow-on data from the optimal DL model. Additionally, we analyzed the spatiotemporal evolution trends of GW in Beijing before and after the implementation of the SNDWP-MR. The results show that the long short-term memory model delivers optimal performance in the TWSA reconstruction of Beijing, with the correlation coefficient (CC), Nash–Sutcliffe coefficient (NSE), and root mean square error (RMSE) being 0.98, 0.96, and 10.19 mm, respectively. The GWSA before and after downscaling is basically consistent with wells data, but the CC and RMSE of downscaling the GWSA from 2004 to 2021 are improving by 34% and 31%, respectively. Before the SNDWP-MR (2004–2014), the trend of GWSA in Beijing was −17.68 ± 4.46 mm/y, with a human contribution of 69.30%. After SNDWP-MR (2015–2021), GWSA gradually increased by 10.00 mm per year, with the SNDWP-MR accounting for 18.30%. This study delivers a technical innovation reference for dynamically monitoring a small-scale GWSA from GRACE/GRACE-FO data.

GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present

Abstract. Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).

Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.