Assessment of Water Storage Changes Using GRACE and GLDAS

Moghim, Sanaz

doi:10.1007/s11269-019-02468-5

Cited by 46 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The GLDAS hydrological model, WGHM hydrological model, and ICESat altimetry satellite data are the primary components used here. In the field of hydrology, the GLDAS hydrological model has become the main data source for most researchers (Syed et al, 2008;Moghim, 2020) to obtain information regarding changes in landwater or soil-water reserves. However, the GLDAS hydrological model does not simulate changes in deep groundwater or in surface lakes and reservoirs (Rodell et al, 2004).…”

Section: Time-variable Gravity Field Of Hydrological Signalmentioning

confidence: 99%

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in mainland China

Shen¹,

Wang²,

Rao³

et al. 2022

Earth and Planetary Physics

View full text Add to dashboard Cite

show abstract

Section: Time-variable Gravity Field Of Hydrological Signalmentioning

confidence: 99%

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in mainland China

Shen¹,

Wang²,

Rao³

et al. 2022

Earth and Planetary Physics

View full text Add to dashboard Cite

show abstract

“…For example, infiltration and recharge to groundwater tend to cause a delay in TWSC. In addition, the time-lagged response of TWSC is dependent on prevailing climatic conditions, an abundance of surface water bodies and vegetation density, topographical factors, and many others [73]. It is interesting to note that hydrologic variables always peak earlier than TWSC; for example, monthly TWSC performs a good correlation with precipitation with a time lag of about 2 months [74].…”

Section: Time-lag Effect Of Monthly Twsc Estimationsmentioning

confidence: 99%

Multiple Data Products Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and Its Dominant Factors in Data-Scarce Alpine Regions

et al. 2021

View full text Add to dashboard Cite

As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948–2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = −0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.

show abstract

“…For example, the Noah model in the Global Land Data Assimilation System (GLDAS) only includes soil moisture, snow water equivalent, and total canopy storage components at 0-2 m depth [15]. The influences of other components are ignored in hydrological models, such as surface water, deep groundwater, and anthropogenic factors [16]. It is essential to find an alternative method for monitoring TWS on a large spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

et al. 2022

View full text Add to dashboard Cite

Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

show abstract

Assessment of Water Storage Changes Using GRACE and GLDAS

Cited by 46 publications

References 34 publications

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in mainland China

Spatial distribution characteristics and mechanism of nonhydrological time-variable gravity in mainland China

Multiple Data Products Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and Its Dominant Factors in Data-Scarce Alpine Regions

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Contact Info

Product

Resources

About