Spatio-Temporal Evaluation of Water Storage Trends from Hydrological Models over Australia Using GRACE Mascon Solutions

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

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.

Using Satellite-Based Terrestrial Water Storage Data: A Review

Land water storage plays a key role for the Earth's climate, natural ecosystems, and human activities. Since the launch of the first Gravity Recovery and Climate Experiment (GRACE) mission in 2002, spaceborne observations of changes in terrestrial water storage (TWS) have provided a unique, global perspective on natural and human-induced changes in freshwater resources. Even though they have become much used within the broader Earth system science community, space-based TWS datasets still incorporate important and case-specific limitations which may not always be clear to users not familiar with the underlying processing algorithms. Here, we provide an accessible and illustrated overview of the measurement concept, of the main available data products, and of some frequently encountered technical terms and concepts. We summarize concrete recommendations on how to use TWS data in combination with other hydrological or climatological datasets, and guidance on how to avoid possible pitfalls. Finally, we provide an overview of some of the main applications of GRACE TWS data in the fields of hydrology and climate science. This review is written with the intention of supporting future research and facilitating the use of satellite-based terrestrial water storage datasets in interdisciplinary contexts.

Projected changes in terrestrial water storage and associated flood potential across the Yangtze River basin

Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031-2060) and far (2071-2100) future with reference to the historical period (1985-2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

Desiccation of a saline lake as a lock-in phenomenon: A socio-hydrological perspective

Understanding of how anthropogenic droughts occur in socio-hydrological systems is critical in studying resilience of these systems. This is especially relevant when a "lock-in" toward watershed desiccation occurs as an emergent outcome of coupling among social dynamics and surface and underground water processes. How the various processes collectively fit together to reinforce such a lock-in and what may be a critical or ignored feedback worsening the state of the socio-hydrological systems remains poorly understood. Here we tackle this gap by focusing on the case of Lake Urmia in Iran, a saline lake that faces the same fate as that of Aral Sea due to over-extraction of water sources that feed the lake. We develop an integrative, system-level understanding of how various anthropogenic, surface and underground environmental processes collectively generate the water scarcity and soil salinization issues in the study case. To this end, we investigate a paradoxical phenomenon wherein the increase of soil salinity has not noticeably affected the level of vegetation cover in Lake Urmia Basin. The outcome of our analysis may provide useful insights for informing policymakers how to cope with drought and water scarcity issues in many fragile saline lakes around the world that are currently under threat by overexploitation.

Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r > 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.

Mapping terrestrial water storage changes in Canada using GRACE and GRACE-FO

This study focused upon the estimation and analysis of terrestrial water storage (TWS changes) across the Canadian landscape. The estimation was performed using Gravity Recovery and Climate Experiment (GRACE) data from April 2002 to June 2017, and GRACE Follow-On (GRACE-FO) observations from June 2018 to December 2019. Removing the gravity effects of Glacial Isostatic Adjustment (GIA) signals and leakage is required to have realistic estimations of TWS changes in the Canadian landmass. In this study, GIA correction was based on a regional-scale modeling of uplift rate. To evaluate the performance compared to the latest GIA models, a comparison was made to uplift rate derived from 149 GPS stations over the study area. Refined TWS changes showed strong seasonal patterns (between -160 mm and 80 mm). The slope of the trend was positive (6.6 mm/year) for the period combining both GRACE and GRACE-FO. The trend increases to 45 mm/year over the 17-year period across central Canada, especially in regions surrounding Hudson Bay. For GRACE, maximum TWS variations occurred between February and April; for GRACE-FO, it occurred with a 2-month lag earlier during the short period being considered. Uncertainties in TWS variations that were derived by GRACE increased towards the end of the mission. Uncertainty for GRACE-FO is lower than that at the beginning of GRACE. The TWS changes extracted from the used approach were compared to Mascon solutions TWS changes products (GRCTellus JPL MSCNv02 and CSR MSCNv02), by using two steps: 1) the Water Global Assessment Prognosis hydrological model (WGHM), and 2) TWS changes derived from in-situ precipitation and potential evapotranspiration data. In all the cases our approach provided the best correlations and lower root mean square errors, compared to the Mascon products.