Variations in water storage in China over recent decades from GRACE observations and GLDAS

Mo, Xun; Wu, Jingwen; Wang, Qianfeng; Zhou, Huakun

doi:10.5194/nhess-16-469-2016

Cited by 73 publications

(40 citation statements)

References 59 publications

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“…Additionally, increasing trend in groundwater is an important component of total magnitude of trend in TWS, which facilitates the DBH_GW to capture the trend of GRACE TWS data. Increasing trend of TWS in the upstream of the Yellow River basin has been reported in many studies (Mo, Wu, Wang, & Zhou, ; Jiao, Zhang, Liu, & Kuang, ; Zhong et al, ). Increasing in TWS can be motived by various factors, including an increase in precipitation and a decrease in ET.…”

Section: Resultsmentioning

confidence: 74%

The influence of groundwater representation on hydrological simulation and its assessment using satellite‐based water storage variation

Huang

Tang

et al. 2019

Hydrological Processes

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The interaction between surface water and groundwater is an important aspect of hydrological processes. Despite its importance, groundwater is not well represented in many land surface models. In this study, a groundwater module with consideration of surface water and groundwater dynamic interactions is incorporated into the distributed biosphere hydrological (DBH) model in the upstream of the Yellow River basin, China. Two numerical experiments are conducted using the DBH model: one with groundwater module active, namely, DBH_GW and the other without, namely, DBH_NGW. Simulations by two experiments are compared with observed river discharge and terrestrial water storage (TWS) variation from the Gravity Recovery and Climate Experiment (GRACE). The results show that river discharge during the low flow season that is underestimated in the DBH_NGW has been improved by incorporating the groundwater scheme. As for the TWS, simulation in DBH_GW shows better agreement with GRACE data in terms of interannual and intraseasonal variations and annual changing trend. Furthermore, compared with DBH_GW, TWS simulated in DBH_NGW shows smaller decreases during autumn and smaller increases in spring.These results suggest that consideration of groundwater dynamics enables a more reasonable representation of TWS change by increasing TWS amplitudes and signals and as a consequence, improves river discharge simulation in the low flow seasons when groundwater is a major component in runoff. Additionally, incorporation of groundwater module also leads to wetter soil moisture and higher evapotranspiration, especially in the wet seasons.

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Section: Resultsmentioning

confidence: 74%

The influence of groundwater representation on hydrological simulation and its assessment using satellite‐based water storage variation

Huang

Tang

et al. 2019

Hydrological Processes

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“…The latest terrestrial water storage data (RL05) were processed at the Center for Space Research at the University of Texas (CSR), the Jet Propulsion Laboratory (JPL), and the GeoForschungsZentrum (GFZ). According to previous studies [40], we selected the data in CSR and performed stripe filtering and Gaussian filtering in order to reduce the uncertainties in estimating ∆S.…”

Section: Et Productsmentioning

confidence: 99%

Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

Wang

Pan

Shen

et al. 2018

Water

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Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation.

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“…It drives four land surface models: Mosaic, Noah, Community Land Model (CLM) and Variable Infiltration Capacity (VIC) [17]. We obtained GLDAS2.0 and GLDAS2.1 Noah outputs to investigate TWS change in the Jing-Jin-Ji region from 1979-2010s for its high spatial resolution of 0.25 × 0.25 • .…”

Section: Data Sourcesmentioning

confidence: 99%

The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration

et al. 2017

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Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change.

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Variations in water storage in China over recent decades from GRACE observations and GLDAS

Cited by 73 publications

References 59 publications

The influence of groundwater representation on hydrological simulation and its assessment using satellite‐based water storage variation

The influence of groundwater representation on hydrological simulation and its assessment using satellite‐based water storage variation

Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration

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