Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

Tangdamrongsub, Natthachet; Han, Shin‐Chan; Tian, Siyuan; Schmied, Hannes Müller; Sutanudjaja, Edwin H.; Ran, Jiangjun; Wang, Feng

doi:10.3390/rs10030483

Cited by 53 publications

(32 citation statements)

References 67 publications

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“…We have to admit that the method used in this study to derive GWS is imperfect and uncertainties exist. Recently, data assimilation techniques have been proposed to improve the simulation of hydrological models by assimilating the GRACE observation into hydraulic models [65][66][67][68][69]. We will use these new techniques in our future studies to enhance the accuracy of the GWS estimate.…”

Section: Discussionmentioning

confidence: 99%

Seasonal and Interannual Variations in China’s Groundwater Based on GRACE Data and Multisource Hydrological Models

Zhang

Wang

2020

Remote Sensing

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In this study, we used in situ measurements for the first time to analyze the applicability and effectiveness of evaluating groundwater storage (GWS) changes across China using Gravity Recovery and Climate Experiment (GRACE) satellite products and hydrological data derived from the WaterGap Global Hydrological Model (WGHM), Global Land Data Assimilation System (GLDAS) and eartH2Observe (E2O). The results show that the GWS derived from GRACE JPL Mascons products combined with GLDAS Noah V2.1 data most accurately reflect the overall distribution of GWS changes in China and the correlation coefficient between the in situ measurements reaches 0.538. The empirical orthogonal function decomposition for GWS indicates clear interannual variation and seasonal variation in China. The trends of China’s GWS changes showed a clear regional characteristic from 2003 to 2016. The GWS in the northeast, central-south, and western junction of Xinjiang-Qinghai-Tibet had increased significantly, and the North China Plain (NCP) had a severe decline. The correlation coefficient between the annual trends of precipitation and GWS was 0.57, and it reached 0.73 when four provinces (Beijing, Tianjin, Shanxi, Hebei) that are wholly or partially located in the NCP were excluded. The seasonal variability of GWS in China was obvious and the volatilities in Jiangxi, Hunan and Fujian provinces were the highest, reaching 6.39 cm, 6.33 cm and 5.20 cm, respectively. The empirical orthogonal function decomposition for GWS and precipitation over China indicated seasonal consistency with a correlation coefficient of 0.76. The awareness of areas with significant depletion and large seasonal fluctuation of GWS help adaptations to manage local GWS situation.

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Section: Discussionmentioning

confidence: 99%

Seasonal and Interannual Variations in China’s Groundwater Based on GRACE Data and Multisource Hydrological Models

Zhang

Wang

2020

Remote Sensing

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“…In recent years, new GRACE products based on regional mass concentration (mascon) functions have been developed with constraints applied during the inversion of satellite ranging observations (as opposed to after inversion) to better preserve GRACE signals (Rowlands et al, 2005;Save et al, 2012;Watkins et al, 2015). Direct assimilation of gridded GRACE data, which has been performed in recent studies (Tangdamrongsub et al, 2018;Eicker et al, 2014;Girotto et al, 2016Girotto et al, , 2017Kumar et al, 2016;Tangdamrongsub et al, 2015;Tian et al, 2017;van Dijk et al, 2014), not only preserves more of the observed information but also enables better alignment with existing infrastructures of land surface modeling, which are constructed on regular grids rather than irregular basins. , which is important for continuous drought monitoring.…”

Section: Introductionmentioning

confidence: 99%

Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges

Rodell

Kumar

et al. 2019

Water Resources Research

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The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low‐flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM‐simulated groundwater correlates strongly with 12‐month precipitation anomalies in low‐latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity.

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“…However, considering that the quality of GRACE estimates increases with decreasing resolution (i.e. larger areas), they can be of great use to constrain and improve global and regional hydrological models, as proved by many studies (Güntner, 2008;Tangdamrongsub et al, 2018;Werth and Güntner, 2010).…”

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Assessing global water mass transfers from continents to oceans over the period 1948–2016

Cáceres¹,

Marzeion²,

Malles³

et al. 2020

Preprint

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Abstract. Continental water mass change affects ocean mass change (OMC). Assessing the net contribution, however, remains a challenge. We present an integrated version of the WaterGAP global hydrological model that is able to simulate total continental water storage anomalies (TWSA) over the global continental area (except Greenland and Antarctica) consistently by integrating the output from the global glacier model of Marzeion et al. (2012) as an input to WaterGAP. Monthly time series of global mean TWSA obtained with an ensemble of four variants of the integrated model, corresponding to different precipitation input and irrigation water use assumptions, were validated against an ensemble of four TWSA solutions based on GRACE satellite gravimetry over January 2003 to August 2016. The overall fit to GRACE, measured by the Nash–Sutcliffe efficiency (NSE) coefficient, was found to be 0.87. By decomposing the original TWSA signal into its seasonal, linear trend and inter-annual components, we find that the seasonal amplitude and phase are very well reproduced (NSE = 0.88), the linear trend is overestimated by 30–50 % (NSE = 0.65) and inter-annual variability is captured to a certain extent (NSE = 0.57) by the integrated model. During the period 1948–2016, we find that continents lost 34–41 mm of sea level equivalent (SLE) to the oceans, with global glacier mass loss accounting for 81 % of the cumulated mass loss and glacier-free land water storage anomalies (LWSA) accounting for the remaining 19 %. Over 1948–2016, the mass gain on land from impoundment of water in man-made reservoirs, equivalent to 8 mm SLE, was offset by the mass loss from water abstractions, amounting to 15–21 mm SLE and reflecting a cumulated groundwater depletion of 13–19 mm SLE. Climate-driven LWSA are highly sensitive to precipitation input and correlate with El Niño Southern Oscillation multi-year modulations. Significant uncertainty remains in trends of modelled LWSA, which are highly sensitive to simulation of irrigation water use and man-made reservoirs.

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Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

Cited by 53 publications

References 67 publications

Seasonal and Interannual Variations in China’s Groundwater Based on GRACE Data and Multisource Hydrological Models

Seasonal and Interannual Variations in China’s Groundwater Based on GRACE Data and Multisource Hydrological Models

Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges

Assessing global water mass transfers from continents to oceans over the period 1948–2016

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