Gravity Recovery And Climate Experiment Research Articles

GRACE Data Quantify Water Storage Changes in the Shiyang River Basin, an Inland River in the Arid Zone

Global changes and human activities have significantly altered water cycle processes and water resource patterns in inland river basins in arid zones. New tools are needed to conduct more comprehensive and scientific assessments of basin water cycle processes and water resource patterns. Based on GRACE satellite and Landsat data, this study investigated terrestrial water storage changes and surface water area in the Shiyang River Drainage Basin from 2002 to 2021. It explored the effects of climate change and water conservancy construction on terrestrial water storage changes in the basin. The results of the study show that, although the surface water quantity in the Shiyang River basin has increased in the past 20 years, the overall decreasing trend of terrestrial water storage in the basin of the Shiyang River has an interannual decreasing rate of 0.01 cm/a. The decreasing trend of water storage in the midstream and downstream areas is more prominent. The change in precipitation controls the change in water storage in the Shiyang River Drainage Basin. Artificial water transfer has changed the spatial distribution of water resources in the basin of the Shiyang River. However, it still has not completely reversed the trend of decreasing water storage in the middle and lower reaches of the Shiyang River.

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

A copula-supported Bayesian framework for spatial downscaling of GRACE-derived terrestrial water storage flux

The GRACE and GRACE-FO satellite missions provide mass variations as a fundamentally new observation type for a broad spectrum of novel applications in Earth science disciplines, including oceanography, geophysics, hydrology, and hydrometeorology. Despite all the key findings in hydrology, the utility of GRACE-derived Terrestrial Water Storage Anomaly (TWSA) and its time derivative Terrestrial Water Storage Flux (TWSF) have mainly been limited to large catchments due to their coarse spatial resolution. Here, we propose a method to downscale TWSF by incorporating available finer-resolution data. We determine the downscaled TWSF and its uncertainty within a proposed Bayesian framework by incorporating the fine-scale data of TWSF and Soil Moisture Change (SMC) from different available sources. For the Bayesian ingredients, we rely on GRACE data to obtain the prior and rely on copula models to obtain nonparametric likelihood functions based on the statistical relationship between GRACE TWSF with fine-scale TWSF data and SMC. We apply our method to the Amazon Basin and assess the performances of our products from various fine-scale input datasets of TWSFs and SMCs. Given the lack of ground truth for TWSF, we validate our results against space-based Surface Water Storage Change (SWSC) in the Amazon river system and also against the Vertical Crustal Displacements Rate (VCDR) observed by the Global Positioning System (GPS). Overall, the results show that the proposed method is able to estimate a downscaled TWSF, which is informed by GRACE and fine-scale data. Validation shows that our downscaled products are better anticorrelated with VCDR (−0.81) than fine-scale TWSF (−0.73) and show a mean relative RMSE of 26% with SWSC versus 70% for fine-scale TWSF. The proposed methodology can be extended to other coarse scale datasets, which are crucial for hydrological application at regional and local scales.

Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data

Groundwater storage (GWS) plays a key role in the freshwater supply in China. Revealing the changes in GWS in China is critical for realizing sustainable utilization of groundwater resources. We used Gravity Recovery and Climate Experiment (GRACE) satellite data as well as HydroSHEDS datasets to quantify the variation of GWS in China from 2003 to 2020 at multiple scales. We found that GWS was decreasing at a rate of -2.09 mm/a in China. Three hot-spot areas with dramatic GWS decrease, located in Northwest, Southwest and North China, accounted for 55.52% of the total GWS decrease in China, and the reduction in GWS in these areas was driven primarily by human activities. Additionally, more than half of the urban agglomerations in China potentially impacted by the declining GWS. Therefore, it is suggested that targeted measures should be taken according to local conditions to strengthen the scientific management of groundwater.

Improved real-time cycle-slip detection for Low Earth Orbit satellites based on the dynamic force model

Cycle-slip detection is crucial in achieving high-accuracy Global Navigation Satellite System (GNSS) data processing for Low Earth Orbit (LEO) satellites. The detector based on the dynamic force model has emerged as a promising approach to cycle-slip detection, as it is insensitive to the number of visible tracked satellites and insufficient accuracy of a prior orbit. However, the ionospheric delay limits the applications of the method for high-speed LEO satellites under different scenarios. To complete the method, we propose a new combination of two test parameters: the phase ionosphere-free (IF) combinations in the second-order time-difference model and the Melbourne–Wübbena (MW) combinations. Meanwhile, we highlight the identification of whether the fake-positive cycle slips are derived from the flicker noises and jumps in the receiver quartz-based clock. Considering highly, moderately active and quiet ionospheric activities, comparisons are performed among our proposed two test parameters and the phase second-order time-difference wide-lane (WL) combinations. The results indicate that the detector based on the IF combinations performs optimally under the three levels of ionospheric activities. The time-difference ionospheric delays vary dramatically even under moderately active and quiet ionospheric activities, which leads to the inferiority of the detector based on the WL combinations to one based on the MW combinations. The performances of detectors based on the IF and MW combinations are further evaluated through real-time kinematic orbital determinations comparisons with the Jet Propulsion Laboratory (JPL) precise science orbits (PSO). After using the IF detector, the mean 3-Dimensional (3D) root-mean-squares (RMS) of the orbit differences have slightly decreased by 12% and 6% for the Gravity Recovery and Climate Experiment (GRACE) A and B satellites, respectively. In the case of the GRACE follow-on (GRACE-FO) satellites, the IF detector has achieved a more than 40% improvement over the MW detector. Further, the use of both detectors simultaneously results in a slight improvement in orbit accuracy.

Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales.

Assessing the impact of corrections included in the GRACE Level-3 data on gravimetric polar motion excitation estimates

Between 2002 and 2017, the Gravity Recovery and Climate Experiment (GRACE) mission provided datasets of temporal variations of the Earth’s gravity field, among others in the form of maps of terrestrial water storage (TWS) changes (Level-3 datasets). This paper examines the impact of several corrections included in the GRACE Level-3 data on the estimated series of hydrological plus cryospheric angular momentum (HAM/CAM). We tested the role of removing the glacial isostatic adjustment (GIA) signal, adding degree-1 coefficients of geopotential (DEG1 correction), replacing the degree-2 zonal coefficient with a more accurate estimate from satellite laser ranging (C20 correction), and restoring ocean bottom pressure geopotential coefficients (GAD). The contribution of improved separation of land and ocean signals by using the Coastal Resolution Improvement (CRI) filter was also assessed. We examined the change in agreement between HAM/CAM and the hydrological plus cryospheric signal in geodetically observed excitation (geodetic residuals, GAO) when the corrections are applied. The results show that including GIA, DEG1, C20, and GAD corrections in the GRACE data increases HAM/CAM trends and reduces overall HAM/CAM variability. The exploitation of corrections slightly heightens consistency between HAM/CAM and GAO for χ1 and χ2 in the non-seasonal spectral band and for χ1 in the seasonal spectral band. The results from this study demonstrate how the different corrections combine to make the overall improvement in agreement between HAM/CAM and GAO and which corrections are most valuable.

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

ABSTRACT The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed comparison. Similarities and differences in signal convergence results were compared with land cover data describing soil conditions, vegetation cover, urbanization status, and cultivated land. Convergence was determined using Pearson correlation coefficients and cross-correlation. In order to compare ΔTWS and SM in individual seasons, ΔTWS observations were normalized. Results show that naturally forested areas and large open spaces used for agriculture support the compatibility between GRACE and AMSRE observations and are characterized by a good Pearson correlation coefficient >0.8. Subpolar regions with permafrost present constraints for AMSR-E observations and have little convergence with GRACE observations.

A GRACE/GFO Empirical Low-Pass Filter to Extract the Mass Changes in Nicaragua

Among the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on temporal gravity products, the north–south stripe noise in the spherical harmonic coefficient (SHC) products contaminates the inversion of the Earth’s mass field. In this study, GRACE SHC products are adopted to estimate the mass changes in Nicaragua. To improve this estimation, we propose an empirical low-pass filter to suppress stripe noise. After only using our filter, the Nicaragua regional uncertainty diminishes from 123.26 mm to 69.11 mm, and the mean signal-to-noise ratio of all available months (2002–2021) improves from 1.67 to 1.8. Subsequently, our filter is employed to estimate the basin terrestrial water storage (TWS) change in Nicaragua. In the end, TWS change estimations are compared with various observations such as mascon products, hydrological models, and in situ groundwater observation. The main conclusions are as follows: (1) After using the wavelet coherent analysis, there is a negative resonance between TWS and the climate factor (El Nino–Southern Oscillation) with a period of 2~4 years; (2) The significant ~3.8-year periodic signal in groundwater storage change estimation is contributed by GRACE aliasing error. Our work can provide new knowledge and references for mass change in small areas.

Validation and Evaluation of GRACE-FO Estimates with In Situ Bottom Pressure Array Measurements in the South China Sea

The Gravity Recovery and Climate Experiment (GRACE), and its follow-on mission (GRACE-FO), provides a novel measurement of the variations in ocean bottom pressure (OBP) at global and basin scales, including those in marginal seas. However, these measurements have not yet been validated rigorously for the South China Sea (SCS). In this study, the accuracy in the monthly GRACE-FO mascon solutions in the SCS from the Jet Propulsion Laboratory (JPL), Center for Space Research (CSR), and Goddard Space Flight Center (GSFC) was validated with the results of the comparison with the in situ OBP records from an array of 25 pressure-recording inverted echo sounders (PIESs) that are located west of the Luzon Strait (LS). The correlation coefficient (Cor) and root mean square difference (RMSD) between the 10-month period of GSFC and PIES, spanning from July 2018 to June 2019 (with missing satellite data for August and September 2018), were 0.77 (p-value = 0.005) and 0.41 mbar (1 mbar = 100 Pa), respectively. These values suggest that the accuracy of GSFC in the SCS in this period was substantially better than that of JPL (Cor = 0.35, p-value = 0.16; RMSD = 0.74 mbar) and CSR (Cor = 0.25, p-value = 0.24; RMSD = 0.89 mbar). Moreover, the volume transport anomaly of the SCS abyssal circulation was estimated and compared based on the OBP records from GSFC and PIES observations, indicating that the GRACE-FO OBP (GSFC) can be used to monitor seasonal or longer-period variations in the SCS abyssal volume transport. Additionally, the variations in OBP from GRACE-FO were significantly overestimated on the continental shelf of the SCS, which may be attributed to signal leakage. Our findings provide reliable evidence for the application of long-term, fully covered OBP records from GRACE-FO in the SCS, and also offer a valuable reference for the application of GRACE-FO in other regions.

Relativistic effects of LEO satellite and its impact on clock prediction

Low Earth orbit (LEO) augmentation in the global navigation satellite system has become a focus in the current satellite navigation field. To achieve high precision in positioning, navigation and timing services, relativistic effects should be considered, as they are difficult to distinguish from LEO satellite clock estimates and disturb their predictions. The relativistic effects on LEO satellite clocks are discussed in detail based on both theoretical and empirical results. Two LEO satellite clock prediction strategies are proposed, with and without removing the relativistic effect, using real data from typical LEO satellites: SENTINEL-3B and Gravity Recovery and Climate Experiment Follow-On (GRACE FO-1). For GRACE FO-1 and SENTINEL-3B, the relativistic effects are both on the order of nanoseconds and after removing the relativistic effects, the modified Allan deviations of the clocks are shown to be significantly improved. Based on the prediction strategies proposed, for SENTINEL-3B at around 810 km, with the prediction period increased from 30 to 3600 s, the root mean square error (RMSE) increases from 0.025 ns to about 1.4–1.6 ns. For the lower LEO satellite GRACE FO-1 at around 500 km, the RMSE of the predicted clocks increases more rapidly, i.e. from 0.012 ns at 30 s to about 4.5 ns at 3600 s. Results showed that the LEO satellite relativistic effects developed based on the theory could correct the majority, but not all of the once- and twice-per-revolution terms in the LEO satellite clocks. Although the corrections have exhibited effective improvements in the clock stability, they do not behave better than simply applying the mathematical model to the clock predictions. The latter model, however, does not have physical foundations as the former one.

Quantifying Multi-Source Uncertainties in GRACE-Based Estimates of Groundwater Storage Changes in Mainland China

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Difference analysis of ice sheet mass balance retrieved from various Gravity Recovery and Climate Experiment time-variable gravity field models: a case study in Antarctica and Greenland

To analyze the differences in the inversion of Gravity Recovery and Climate Experiment (GRACE) time-variable gravity field models, our study compares the results of ice sheet mass changes from six versions of GRACE monthly gravity field solutions, including the Center for Space Research (CSR) RL06, Jet Propulsion Laboratory (JPL) RL06, German Research Centre for Geoscience (GFZ) RL06, WHU-GPD01s, Tongji-Grace 2018, and HUST-Grace 2020 models, in Antarctica and Greenland. We analyzed the differences of ice sheet mass predicted rates in the temporal, spatial, and spectral domains. In the temporal domain, these differences have a systematic bias among various models and increase over time, especially after 2011. In the spatial domain, the Antarctic ice sheet mass loss rate of WHU-GPD01s is the most consistent with CSR, and the results of GFZ are significantly different than other models (∼15 Gt / year). The Greenland ice sheet mass loss rates of various models are consistent. In the spectral domain, the JPL and CSR models are in great accordance. The differences between the CSR model and the HUST-Grace2020, WHU-GPD01s, and Tongji-Grace2018 models are mainly distributed in the degrees of 20 to 30, 10 to 45, and 25 to 55, respectively. Moreover, the HUST-Grace2020 and CSR models have the smallest spatial discrepancy except for polar regions, and the strip errors for the trend rate of the global equivalent water height are small, which is suitable for research in the mid and low latitudes.

Identification of Extreme Droughts Based on a Coupled Hydrometeorology Index from GRACE-Derived TWSA and Precipitation in the Yellow River and Yangtze River Basins

Global climate change and human activities have exacerbated droughts’ environmental and socioeconomic threats. However, there is still a lack of effective techniques to consider their combined impacts on drought identification. Therefore, a new copula-based multivariate standardized drought index (CMSDI) was proposed, which integrates precipitation data and terrestrial water storage anomaly (TWSA) data observed by Gravity Recovery and Climate Experiment (GRACE) satellites. The applicability of the CMSDI was assessed compared with the water storage deficits index (WSDI), the self-calibration Palmer drought severity index (sc-PDSI), the standardized precipitation evapotranspiration index (SPEI), and the standardized precipitation index (SPI) in the Yellow River Basin (YRB) and the Yangtze River Basin (YZRB) for 2002–2020. The assessments were conducted regarding both temporal evolution and spatial distribution. The results showed that the CMSDI was more synchronized with the WSDI and SPI than with the other two indices and presented different trends and correlations in the YRB and YZRB. The CMSDI outperformed the other drought indices due to the limitations of the sc-PDSI, SPEI, and SPI in detecting certain drought events, and the greater inaccuracy of the WSDI in identifying extreme droughts. Furthermore, the CMSDI revealed a clear upward trend in parts of the middle and lower YRB and a clear downward trend in the upper YZRB, emphasizing the need for more attention to droughts in the YRB. This study presents a new perspective on the integrated use of satellite and measured data in drought monitoring across different regions.

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

Groundwater depletion occurs when the extraction exceeds its recharge and further impacts water resource management around the world, especially in developing countries. In India, most groundwater level observations are only available on a seasonal scale, i.e., January (late post-monsoon), May (pre-monsoon), August (monsoon), and November (early post-monsoon). The Gravity Recovery and Climate Experiment (GRACE) data are available to estimate the monthly variation in groundwater storage (GWS) by subtracting precipitation runoff, canopy water, soil moisture, and solid water (snow and ice) from the GLDAS model. Considering GRACE-based GWS data, the data fusion is further used to estimate monthly spatial maps of groundwater levels using time-varying spatial regression. Seasonal groundwater monitoring data are used in the training stage to identify spatial relations between groundwater level and GWS changes. Estimation of unknown groundwater levels through data fusion is accomplished by utilizing spatial coefficients that remain consistent with the nearest observed months. Monthly groundwater level maps show that the lowest groundwater level is 50 to 55 m below the earth’s surface in the state of Rajasthan. The accuracy of the estimated groundwater level is validated against observations, yielding an average RMSE of 2.37 m. The use of the GWS information enables identification of monthly spatial patterns of groundwater levels. The results will be employed to identify hotspots of groundwater depletion in India, facilitating efforts to mitigate the adverse effects of excessive groundwater extraction.

Multi-Lagrange multiplier method to improve the region-specific GRACE estimation of water storage change in eleven sub-basins of the Yangtze River

Study RegionYangtze River basin Study FocusTo improve the potential of the Gravity Recovery and Climate Experiment (GRACE) mission for estimating terrestrial water storage variations (ΔTWS) in smaller basins than its typical footprints (150,000 – 200,000 km2), an improved method named the Multi-Lagrange multiplier method (MLMM) is proposed to decrease leakage errors and restore signals from an independent hydrologic model. The MLMM is applied to estimate ΔTWS in the Yangtze River basin (YRB) and eleven of its sub-basins, and is assessed from different GRACE solutions and ground-based precipitation (P), evapotranspiration (ET), and runoff (R). Results indicate that MLMM estimates the leakage correction satisfactorily for small sub-basins. In the Tai Lake basin, the leakage ratio is improved by 16%. Excluding the Han River basin, ΔTWS from MLMM over the YRB are the best among different tested methods with a Nash-Sutcliffe Efficiency larger than 0.80, and the best root means square error (1.34 cm). New Hydrological Insights for the RegionThe main contributor to ΔTWS over the YRB is P with 60%. The contribution of ET to ΔTWS is more extensive than that of R over the middle of YRB. The ΔTWS and the total groundwater and surface water were all increasing over the YRB and eleven of its sub-basins. This paper concludes that the proposed MLMM is helpful for ΔTWS estimation in the small ungauged basins.

Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models

Estimation of total water availability has paramount importance in planning sustainable development of a region, particularly in arid water-scarce areas. Coarse-resolution of existing total water availability or terrestrial water storage anomaly (TWSA) data is the major limitation of their applications in different sectors. An attempt has been made to downscale Gravity Recovery and Climate Experiment (GRACE) TWSA data to develop a high-resolution gridded data product of the total water availability of Iraq. European reanalysis (ERA5) precipitation, evapotranspiration, surface runoff, subsurface runoff and soil water contents data were used to downscale GRACE 1.0° spatial resolution monthly TWSA to 0.1° spatial resolution for the period 2002–2020. A machine learning (ML)-based recursive feature elimination algorithm was used to identify the optimum input combination according to the nonlinear relationship of ERA5 variables with GRACE water equivalence data. The selected subset of inputs was used to develop the downscaling models using three classical ML algorithms for the available GRACE measurement points over Iraq. The models were calibrated at 70% of GRACE grid point locations and validated in the rest of the points. Finally, the model was used to predict TWSA at each ERA5 grid point to generate Iraq's high-resolution water availability dataset. The results showed higher performance of random forest in downscaling TWSA compared to other algorithms. The model estimated the TWSA at validation points with Kling-Gupta Efficiency (KGE) in the range of 0.5–0.91 and Nash-Sutcliff Efficiency (NSE) between 0.54 and 0.88. The modelled high-resolution TWSA data shows higher availability of water resources in the north, particularly northeast of Iraq, and the least in the southeast. The technique developed in this study can be implemented in developing a high-resolution gridded water availability dataset from satellite GRACE data in the region where in-situ estimation is very limited.

Changes of terrestrial water storage during 1981–2020 over China based on dynamic-machine learning model

A reliable estimation of long-term change in terrestrial water storage (TWS) is critical for a sustainable management of freshwater resources and infrastructures. Although the Gravity Recovery and Climate Experiment (GRACE) satellites provide unprecedented accurate TWS anomaly (TWSA) observation since 2002, the limited time span and the gaps between the GRACE and GRACE-Follow On datasets hinder their climatological applications. Here we apply LightGBM, a more accurate and efficient machine learning model, to merge the land surface model simulation, human activities, land properties, and establish a dynamic-machine learning model that can reasonably depict the impacts of climate variation and human interventions on TWSA. We apply the hybrid model to reconstruct the historical TWSA during 1981–2020 over China, which shows reliable results and is consistent with other independent datasets including drought index, TWS change based on water balance, and water withdrawal dataset. The reconstructed TWSA shows significant drier (wetter) trend in North (South) China during the past 40 years, while the TWS variability has increased over 60% regions. We find the change of TWSA is mainly attributed to the climate variation in humid regions, but the human interventions dominate the decline of TWSA in arid and semiarid areas, such as the Yellow and Hai river basins (contributing about 70%). Our findings imply the potential of using artificial intelligence to represent human interventions, which is still uncertain in physical models. Our physical-machine learning model can be applied in long-term prediction and projection of TWSA and its components as well.

Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater Basin, Indonesia

Assessing basin-scale groundwater storage changes is often difficult when groundwater table data are scarce. In this study, we quantify the groundwater storage changes by using the Wflow_sbm hydrological model coupled with the MODLOW groundwater model in the data-scarce area of the Bandung groundwater basin, Indonesia. The soil moisture storage change calculated by Wflow_sbm plus the groundwater storage change calculated by MODFLOW is compared to the water storage change estimated by the GRACE satellite between 2005 and 2015. The calculated cross-correlation coefficient is 0.502, and 62.1% of the simulated water storage change falls within GRACE's estimated uncertainty bounds. The important context in the water storage comparison are GRACE temporally local time-lags, data gaps in GRACE datasets, and differences in model seasonal performance and analyzed domain characteristics. The two latter factors highlight the importance of considering local groundwater-related information over large-scale global datasets in basin-scale groundwater storage change assessment. Based on the groundwater flow model, the current predicament of groundwater abstraction in the Bandung groundwater basin is highly unsustainable for future groundwater uses. On average, the groundwater storage in the study area is dwindling at the rate of 87 million m3/year between 2005 and 2018, dominantly a consequence of groundwater abstraction, whose effect is rippled to further impact the whole groundwater flow regime. Agreed by the situation shown by the limited data, the simulated groundwater table drawdown, spatially, is found to be locally and non-uniformly distributed. The capability of a one-way coupled hydrological and groundwater model to investigate basin-scale groundwater storage change, with comparable estimates to the GRACE dataset, unravel the opportunity of using such methods to estimate the behavior of future groundwater storage dynamics under the changing anthropogenic and climatic factors in catchment-scale studies.

The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.