Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Yin, Wenjie; Hu, Litang; Jiao, Jiu Jimmy

doi:10.1155/2017/8254824

Cited by 31 publications

(15 citation statements)

References 55 publications

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“…In the Ordos Basin, there has been a large volume of work on soil water storage, but the measurements were not systematic enough in depth and frequency to obtain root zone soil water storage over the period of 2002 to 2016. Instead, we adopted a similar approach to others [30,36,38,41,42] by capitalizing on the GLDAS data and obtaining root zone SWSA with the four well-tested, large-scale hydrological models, Community Land Model (CLM), National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab model (NOAH), Mosaic and Variable Infiltration Capacity model (VIC) of GLDAS model from NASA [63]. GLDAS integrates satellite-based and ground-based data sets for parameterizing, forcing and constraining the hydrological models.…”

Section: Isolation Of Gwsa From Twsamentioning

confidence: 99%

Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Cheng

et al. 2019

Remote Sensing

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Groundwater recharge (GR) is a key component of regional and global water cycles and is a critical flux for water resource management. However, recharge estimates are difficult to obtain at regional scales due to the lack of an accurate measurement method. Here, we estimate GR using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data. The regional-scale GR rate is calculated based on the groundwater storage fluctuation, which is, in turn, calculated from the difference between GRACE and root zone soil water storage from GLDAS data. We estimated GR in the Ordos Basin of the Chinese Loess Plateau from 2002 to 2012. There was no obvious long-term trend in GR, but the annual recharge varies greatly from 30.8 to 66.5 mm year−1, 42% of which can be explained by the variability in the annual precipitation. The average GR rate over the 11-year period from GRACE data was 48.3 mm year−1, which did not differ significantly from the long-term average recharge estimate of 39.9 mm year−1 from the environmental tracer methods and one-dimensional models. Moreover, the standard deviation of the 11-year average GR is 16.0 mm year−1, with a coefficient of variation (CV) of 33.1%, which is, in most cases, comparable to or smaller than estimates from other GR methods. The improved method could provide critically needed, regional-scale GR estimates for groundwater management and may eventually lead to a sustainable use of groundwater resources.

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Section: Isolation Of Gwsa From Twsamentioning

confidence: 99%

Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Cheng

et al. 2019

Remote Sensing

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“…The water storage variations are computed via timevariable gravity field model provided by CSR, GFZ and JPL difference is existed in few months, but the total results are constant. The GRACE which is known as (Tom and Jerry) satellite measures the monthly gravity field changes by a series of complicated inversion of relative ranging observations (Sun et al, 2010;Yin et al, 2017). For processing different institutions adopted various solution strategies, such as the precise orbit determination from on-board GPS and the corrections for spacecraft platform accelerations.…”

Section: Resultsmentioning

confidence: 99%

Groundwater Storage Change Estimation Using Grace Satellite Data in Indus Basin

Salam¹,

Cheema²,

Zhang³

et al. 2020

Big. data. water. resour.

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Over exploitation of Ground Water (GW) has resulted in lowering of water table in the Indus Basin. While waterlogging, salinity and seawater intrusion has resulted in rising of water table in Indus Basin. The sparse piezometer network cannot provide sufficient data to map groundwater changes spatially. To estimate groundwater change in this region, data from Gravity Recovery and Climate Experiment (GRACE) satellite was used. GRACE measures (Total Water Storage) TWS and used to estimate groundwater storage change. Net change in storage of groundwater was estimated from the change in TWS by including the additional components such as Soil Moisture (SM), Surface water storage (Qs) and snowpack equivalent water (SWE).

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“…The redistribution of surface load alters the gravity field over a region, which can be monitored by Gravity Recovery and Climate Experiment (GRACE) satellites [6][7][8][9]. The GRACE mission is organized by National Aeronautics and Space Administration (NASA) in 2002, which provides a useful and effective method for the large-scale spatiotemporal detection of TWSA [10].…”

Section: Introductionmentioning

confidence: 99%

Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China

et al. 2021

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Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.

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Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Cited by 31 publications

References 55 publications

Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Groundwater Storage Change Estimation Using Grace Satellite Data in Indus Basin

Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China

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