Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Wu, Qingxian; Cheng, Bing; He, Hailong; Wu, Pute

doi:10.3390/rs11020154

Cited by 56 publications

(44 citation statements)

References 121 publications

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“…Geomorphology (GM): GM of an area reflects various landforms, which includes their description, species and physical processes that help assess groundwater recharge potential and evaluate possible groundwater areas [20,23,25,50]. Geomorphic features of the study area were categorized into five units namely: shallow flood plain and beach, shallow buried pediplain, moderately buried pedipalin, deep buried pediplain and sedimentary high ground (Figure 7d).…”

Section: Evaluation Of Factors Governing Groundwater Recharge Zonesmentioning

confidence: 99%

“…Geographic information system (GIS) and Remote Sensing (RS) techniques facilitate estimating surface and subsurface water over large areas [15,18,[20][21][22][23]. A variety of methods have been implemented for groundwater recharge zone mapping worldwide [24][25][26][27] such as frequency ratio (FR), certainty factor (CF), weights-of-evidence (WOE), fuzzy logic index models, analytical hierarchy process (AHP) and multi-influencing factors (MIF).…”

Section: Introductionmentioning

confidence: 99%

“…The MIF is an MCDM technique that computes spatial relationships between a dependent variable and independent variables according to scores that are assigned based on major and minor influential factors affecting the GWRZ [38,39]. These two approaches have gained popularity because they are relatively simple and useful for practical applications prior to launching costly field explorations [19,20,23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

Zghibi

Mirchi

Msaddek

et al. 2020

Water

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Mapping groundwater recharge zones (GWRZs) is essential for planning artificial recharge programs to mitigate groundwater decline and saltwater intrusion into coastal aquifers. We applied two multi-criteria decision-making approaches, namely the analytical hierarchy process (AHP) and the multi-influencing factors (MIF), to map GWRZs in the Korba aquifer in northeastern Tunisia. GWRZ results from the AHP indicate that the majority (69%) of the area can be classified as very good and good for groundwater recharge. The MIF results suggest larger (80.7%) very good and good GWRZs. The GWRZ maps improve groundwater balance calculations by providing estimates of recharge-precipitation ratios to quantify percolation. Lithology, land use/cover and slope were the most sensitive parameters followed by geomorphology, lineament density, rainfall, drainage density and soil type. The AHP approach produced relatively more accurate results than the MIF technique based on correlation of the obtained GWRZs with groundwater well discharge data from 20 wells across the study area. The accuracy of the approaches ultimately depends on the classification criteria, mean rating score and weights assigned to the thematic layers. Nonetheless, the GWRZ maps suggest that there is ample opportunity to implement aquifer recharge programs to reduce groundwater stress in the Korba aquifer.

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Section: Evaluation Of Factors Governing Groundwater Recharge Zonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

Zghibi

Mirchi

Msaddek

et al. 2020

Water

View full text Add to dashboard Cite

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“…Furthermore, when the Gaussian filter radius was greater than or equal to 200 km, there was no appreciable difference in the spatial variations in the hydrological signal. (1) Due to issues such as poor sensor performance and insufficient energy supply [42], there are some data gaps in the GRACE satellite data; however, using data from adjacent months, we used interpolation to retrieve these missing data points [43][44][45]. Because GRACE data cannot obtain the degree-1 coefficients, the degree-1 coefficients calculated by Swenson et al can be used [46].…”

Section: Data Processingmentioning

confidence: 99%

Monitoring Terrestrial Water Storage Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of the Chinese Mainland

2021

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Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an optimized short-arc approach at Tongji University. We analyzed the changes in the TWS and groundwater storage (GWS) in each of the nine major river basins of the Chinese mainland from April 2002 to August 2016, using Tongji-Grace2018, the Global Land Data Assimilation System (GLDAS) hydrological model, in situ observations, and additional auxiliary data (such as precipitation and temperature). Our results indicate that the TWS of the Songliao, Yangtze, Pearl, and Southeastern River Basins are all increasing, with the most drastic TWS growth occurring in the Southeastern River Basin. The TWS of the Yellow, Haihe, Huaihe, and Southwestern River Basins are all decreasing, with the most drastic TWS loss occurring in the Haihe River Basin. The Continental River Basin TWS has remained largely unchanged over time. With the exception of the Songliao and Pearl River Basins, the GWS results produced by the Tongji-Grace2018 model are consistent with the in situ observations of these basins. The correlation coefficients for the Tongji-Grace2018 model results and the in situ observations for the Yellow, Huaihe, Yangtze, Southwestern, and Continental River Basins are higher than 0.710. Overall, the GWS results for the Songliao, Yellow, Haihe, Huaihe, Southwestern, and Continental River Basins all exhibit a downward trend, with the most severe groundwater loss occurring in the Haihe and Huaihe River Basins. However, the Yangtze and Southeastern River Basins both have upward-trending modeled and measured GWS values. This study demonstrates the effectiveness of the Tongji-Grace2018 model for the reliable estimation of TWS and GWS changes on the Chinese mainland, and may contribute to the management of available water resources.

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“…So far, the groundwater change has been investigated via removing other components from TWS using LSM models [35,36,[65][66][67][68][69] or integrated with geographic information system [70]. This is because of the lack of the groundwater measurements.…”

Section: Analysis Of Tws Anomalous Change In 2006mentioning

confidence: 99%

Characterization of the Recharge-Storage-Runoff Process of the Yangtze River Source Region under Climate Change

Fok

Chen

et al. 2020

Water

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Storage and runoff are the two fundamental surface hydrological variables of a catchment. Research studies have been focused on the storage-runoff (S-R) hysteretic relationship of a catchment and its explanation very recently, thanks to satellite gravimetry. However, a complete analysis of a hydrological process starting from recharge to runoff has not been investigated. The S-R hysteretic relationship of Yangtze River Source Region (YRSR) situated in the northeast Tibetan Plateau is also unexplored. This study aims to investigate the Recharge-Storage-Runoff relationship of this catchment using gravimetrically-derived terrestrial water storage (TWS), satellite-derived and gauged precipitation, land surface modeled and gauged evapotranspiration, and runoff data measured during 2003–2012. We found that the Pearson correlation coefficient (PCC) of S-R relationship is 0.7070, in addition to the fact that the peak of runoff every year comes earlier than that of the storage. This finding enables us to further calculate equivalent runoff based on water balance equation using the above data, while comparing to measured runoff time series. The comparison of Global Land Data Assimilation System (GLDAS)-derived (gauge-derived) equivalent runoff against measured runoff reveals a PCC of 0.8992 (0.9402), respectively, indicating both storage and runoff are largely controlled by the recharge derived from precipitation and evapotranspiration. This implies the storage is not coupled with runoff prominently due to steep topography in YRSR unable to hold the water in the form of storage. Exceptional anomalous water storage time series in 2006 has also been investigated. We speculate that the low rainfall might partly be related to an El Niño Southern Oscillation event. The low rainfall and abrupt groundwater transfer are likely to be the causes of the anomaly in 2006.

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Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Cited by 56 publications

References 121 publications

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

Monitoring Terrestrial Water Storage Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of the Chinese Mainland

Characterization of the Recharge-Storage-Runoff Process of the Yangtze River Source Region under Climate Change

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