Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)

Mehrnegar, Nooshin; Jones, Owen D.; Singer, Michael Bliss; Schumacher, Maike; Bates, Paul; Forootan, Ehsan

doi:10.1016/j.advwatres.2020.103528

Cited by 21 publications

(26 citation statements)

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“…In the light of our results in the previous section, we believe that the reconstruction results from Approach 2 have better quality, i.e., contain less noise and the mass anomalies are well localized. Therefore, these fields are used to assess the global mass redistribution of 2003-2018 in terms of trends, seasonal and sub-seasonal cycles, as well as those related to teleconnection evens, see similar studies, e.g., [14,44,50,56,78]. To illustrate the benefit of the iterations in Approach 2 to reduce the noise in TWSC fields, a comparison between the results of the first iteration with those of 250th iteration in terms of basin-averaged TWSC is shown in Figure A1.…”

Section: 7082mentioning

confidence: 99%

“…Validating GRACE TWSC fields was performed by comparisons with geophysically relevant signals from independent data sources. For example, hydrological models were used to assess GRACE data over land area (e.g., [9,49]); however, many studies, e.g., [16,50], indicate that the trend and seasonality of these models are uncertain and such comparisons might be performed with care. Other studies, e.g., [51][52][53] suggested the use of in-situ data such as ocean bottom pressure and global navigation satellite systems (GNSS)-derived vertical loads to assess GRACE signals.…”

Section: Introductionmentioning

confidence: 99%

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An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

et al. 2020

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Observing global terrestrial water storage changes (TWSCs) from (inter-)seasonal to (multi-)decade time-scales is very important to understand the Earth as a system under natural and anthropogenic climate change. The primary goal of the Gravity Recovery And Climate Experiment (GRACE) satellite mission (2002–2017) and its follow-on mission (GRACE-FO, 2018–onward) is to provide time-variable gravity fields, which can be converted to TWSCs with ∼ 300 km spatial resolution; however, the one year data gap between GRACE and GRACE-FO represents a critical discontinuity, which cannot be replaced by alternative data or model with the same quality. To fill this gap, we applied time-variable gravity fields (2013–onward) from the Swarm Earth explorer mission with low spatial resolution of ∼ 1500 km. A novel iterative reconstruction approach was formulated based on the independent component analysis (ICA) that combines the GRACE and Swarm fields. The reconstructed TWSC fields of 2003–2018 were compared with a commonly applied reconstruction technique and GRACE-FO TWSC fields, whose results indicate a considerable noise reduction and long-term consistency improvement of the iterative ICA reconstruction technique. They were applied to evaluate trends and seasonal mass changes (of 2003–2018) within the world’s 33 largest river basins.

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Section: 7082mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

et al. 2020

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“…Currently, most studies mainly rely on a single hydrological model to separate GWS components from GRACE-derived TWS [23][24][25]. However, the accuracy of these models is restricted by uncertainties in climate forcing (particularly precipitation), model parameters, and deficiencies in model structure [26][27][28][29][30][31]. Therefore, the effective combination of multiple models can improve the performance of hydrological simulations relative to a single model.…”

Section: Introductionmentioning

confidence: 99%

“…Long et al [33] used the Bayesian model-averaging technique, which can merge multiple TWS products to analyze the spatiotemporal variability of TWS. Mehrnegar [27] presented the dynamic model-data-averaging method, which can be used to merge multiple TWS simulations. The result indicated that linear trends and seasonality within global hydrological models can be improved by using the dynamic model-dataaveraging method.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Groundwater Storage Estimates Based on Groundwater Weighted Fusion Model

Zheng

Yin

et al. 2022

Remote Sensing

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It is an effective measure to estimate groundwater storage anomalies (GWSA) by combining Gravity Recovery and Climate Experiment (GRACE) data and hydrological models. However, GWSA results based on a single hydrological model and GRACE data may have greater uncertainties, and it is difficult to verify in some regions where in situ groundwater-level measurements are limited. First, to solve this problem, a groundwater weighted fusion model (GWFM) is presented, based on the extended triple collocation (ETC) method. Second, the Shiyang River Basin (SYRB) is taken as an example, and in situ groundwater-level measurements are used to evaluate the performance of the GWFM. The comparison indicates that the correlation coefficient (CC) and Nash-Sutcliffe efficiency coefficient (NSE) are increased by 9–40% and 23–657%, respectively, relative to the original results. Moreover, the root mean squared error (RMSE) is reduced by 9–28%, which verifies the superiority of the GWFM. Third, the spatiotemporal distribution and influencing factors of GWSA in the Hexi Corridor (HC) are comprehensively analyzed during the period between 2003 and 2016. The results show that GWSA decline, with a trend of −2.37 ± 0.38 mm/yr from 2003 to 2010, and the downward trend after 2011 (−0.46 ± 1.35 mm/yr) slow down significantly compared to 2003–2010. The spatial distribution obtained by the GWFM is more reliable compared to the arithmetic average results, and GWFM-based GWSA fully retain the advantages of different models, especially in the southeastern part of the SYRB. Additionally, a simple index is used to evaluate the contributions of climatic factors and human factors to groundwater storage (GWS) in the HC and its different subregions. The index indicates that climate factors occupy a dominant position in the SLRB and SYRB, while human factors have a significant impact on GWS in the Heihe River Basin (HRB). This study can provide suggestions for the management and assessments of groundwater resources in some arid regions.

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“…GRACE and GLDAS (Global Land Data Assimilation System) have provided useful observations for studying water resources around the world, particularly over regions with insufficient in-situ measurements (e.g., Chen et al, 2019;Scanlon et al, 2018). Recently several studies (e.g., Hasan et al, 2018;Mehrnegar et al, 2020;Seyoum, 2018;Shamsudduha et al, 2017) have used GRACE observations to explore changes in TWS in the Nile River Basin (NRB).…”

mentioning

confidence: 99%

Integrating GRACE products and land surface models to estimate changes in key components of terrestrial water storage in the Nile River Basin

Nigatu¹,

Fan²,

You³

2020

Preprint

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The Nile River Basin (NRB) is facing extreme pressure on its water resources due to an alarmingly increasing population that is extremely vulnerable in aspects of irrigation and hydropower. The NRB ascends itself to remotely sensed approaches with high resolution of spatial and temporal coverage as disparate to ground-based in-situ observations due to its size and limited access from basin countries. The Gravity Recovery and Climate Experiment (GRACE) allow a unique opportunity to investigate the changes in key components of Terrestrial Water Storage (TWS). Differences in tuning parameters and processing strategies result in GRACE TWS solutions with regionally specific variations and error patterns. We explored the spatiotemporal changes of the TWS time series, trend, uncertainties, and signal-to-noise ratio (SNR) among different GRACE TWS. We had also investigated the key terrestrial water storage components (surface water, soil moisture, and groundwater storage changes). The results show that the uncertainty of GRACE spherical harmonic (SH) solutions are higher than the mass concentration (mascon) over the NRB, and the Center for Space Research-mascons (CSR-M) noted the first best performance. Substantially, significant long-term (2003–2017) negative groundwater and soil moisture trend demonstrates a potential depletion over NRB. Despite an increase in precipitation and TWS time series, the rate of decline noted to increase rapidly from 2008, thus indicating the possibility of human-induced change ( e.g., for irrigation purposes). Thus, the result of this study provides a guiding principle for future studies in TWS change-related hydro-climatic change over NRB and similar basins.

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Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)

Cited by 21 publications

References 100 publications

An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

Improving the Accuracy of Groundwater Storage Estimates Based on Groundwater Weighted Fusion Model

Integrating GRACE products and land surface models to estimate changes in key components of terrestrial water storage in the Nile River Basin

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