Detection of large-scale groundwater storage variability over the karstic regions in Southwest China

Huang, Zhiyong; Yeh, Pat J.‐F.; Pan, Yun; Jiao, Jiu Jimmy; Gong, Huili; Li, Xiaojuan; Güntner, Andreas; Zhu, Yunqiang; Zhang, Chong; Zheng, Longqun

doi:10.1016/j.jhydrol.2018.11.071

Cited by 42 publications

(37 citation statements)

References 56 publications

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“…This technological mission is necessarily long term because it does not seem that significant improvements to existing methods are yet on the horizon. There have been some improvements in radar and microwave rainfall estimates (Diederich, Ryzhkov, Simmer, Zhang, & Trömel, ; Rico‐Ramirez, Liguori, & Schellart, ); eddy correlation and remote sensing estimates of evapotranspiration (Franssen, Stöckli, Lehner, Rotenberg, & Seneviratne, ; Maes, Gentine, Verhoest, & Gonzalez Miralles, ); gravity anomaly estimates of storage (Güntner et al, ; Huang et al, ; Richey et al, ), acoustic Doppler measurements of discharge (Farina, Alvisi, & Franchini, ; Moore, Jamieson, Rainville, Rennie, & Mueller, ), and “citizen science” methods of getting more spatially distributed observations (e.g., Le Coz et al, ; Paul et al, ; Starkey et al, ). Significant epistemic uncertainties and some unmeasured states remain for all of these technologies.…”

mentioning

confidence: 99%

Developing observational methods to drive future hydrological science: Can we make a start as a community?

Beven

Asadullah

Bates

et al. 2019

Hydrological Processes

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Hydrology is still, and for good reasons, an inexact science (for a recent discussion, see Beven, 2019a), even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. The limitations of that understanding have, however, become much more apparent and important in the last century as the pressures of increasing populations, and the anthropogenic impacts on catchment forcing and responses, have intensified (see Abbott et al., 2019; Montanari et al., 2013; Sivapalan, Savenije, & Blöschl, 2012; Srinivasan et al., 2017; Wagener et al., 2010; Wilby, 2019). At the same time, the sophistication of hydrological analyses and models has been developing rapidly, often driven more by the availability of computational power and geographical data sets than any real increases in understanding of hydrologicalprocesses. This sophistication has created an illusion of real progress, but a case can be made that we are still rather muddling along, limited by the significant uncertainties in hydrological observations, knowledge of catchment characteristics, and related gaps in conceptual understanding, particularly of the subsurface. These knowledge gaps are illustrated by the fact that for many catchments, we cannot close the water balance without significant uncertainty (e.g.,

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mentioning

confidence: 99%

Developing observational methods to drive future hydrological science: Can we make a start as a community?

Beven

Asadullah

Bates

et al. 2019

Hydrological Processes

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“…The in situ observation data used in this study include the in situ SM, groundwater-level and reservoir water storage measurements from Huang et al [46]. There are 18 in situ SM observations (6 in Yunnan, 5 in Guizhou and 7 in Guangxi, Figure 1) which are obtained from the China Meteorological Data Website.…”

Section: Study Region and Datamentioning

confidence: 99%

“…The SM data are in terms of the degree of relative soil saturation (i.e., the volumetric SM content divided by the soil porosity). The details on the estimation of observed SMS from relative soil saturation are shown in the supporting information of Huang et al [46].…”

Section: Study Region and Datamentioning

confidence: 99%

“…The in situ groundwater storage anomalies are estimated by multiplying groundwater-level anomalies with the specific yields of the unconfined aquifers or the storage coefficients of the confined aquifers [48]. Details on the determination of specific yields and storage coefficients can be found in Section 2.4 of Huang et al [46].…”

Section: Study Region and Datamentioning

confidence: 99%

“…The reservoir water level data are converted to water storage according to the fitted exponential relation between observed water-level data and reservoir storage data provided by the Water Regime Annual Report of the Pearl River [51]. Details can be found in Section 2.3 of Huang et al [46]. The in situ observations are used to estimate TWS.…”

Section: Study Region and Datamentioning

confidence: 99%

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Sensitivity Analysis of Leakage Correction of GRACE Data in Southwest China Using A-Priori Model Simulations: Inter-Comparison of Spherical Harmonics, Mass Concentration and In Situ Observations

Huang

Jiao

Luo

et al. 2019

Sensors

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The Gravity Recovery and Climate Experiment (GRACE) level-2 spherical harmonic (SH) solutions are noisy and thus require filtering. Filtering reduces noise but affects signal quality via signal leakage. Generally, a leakage correction is required for GRACE applications to remove leakage signal and recover the true signal. Forward modelling based on some a priori information is a widely used approach for leakage correction of GRACE data. The a priori information generally relies on global hydrological model simulations. There are many global hydrological models and therefore it is of interest to explore how different global hydrology model simulations influence leakage correction results. This study investigated the sensitivity of three leakage correction methods (additive method, scaling factor method and multiplicative method) to five global hydrology model simulations (four models from the Global Land Data Assimilation System (GLDAS) and the WaterGAP Global Hydrology Model (WGHM)). The sensitivity analysis was performed with observational data in Southwest China and one sub-region, Guangxi. Results show that although large differences were identified among the five global model simulations, the additive and scaling factor methods are less affected by the choice of a priori model in comparison to the multiplicative approach. For the additive and scaling factor methods, WGHM outperforms the other four GLDAS models in leakage correction of GRACE data. GRACE data corrected with the multiplicative method shows the highest amount of error, indicating this method is not applicable for leakage correction in the study area. This study also assessed the level-3 mascon (mass concentration) solutions of GRACE data. The mascon-based results are nearly as good as the leakage corrected results based on SH solutions.

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Multisource remote sensing data facilitate ecohydrological simulations without runoff calibration

Yang

Scanlon

2022

Hydrological Processes

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Observed runoff is typically the only variable applied to calibrate ecohydrological models, which can simulate runoff well but cannot guarantee reliable reproduction of other hydrological components (e.g., evapotranspiration (ET) and total terrestrial water storage change (TWSC)). This study explores the potential of estimating hydrological components (runoff, ET, TWSC, groundwater, and root‐zone water storage.) using a conceptual model driven and calibrated by multisource remote sensing data only. The performances of the model were evaluated in 13 catchments with different geological, climatic and vegetation conditions. Results show that the model was encouraging in simulating monthly runoff (median KGE, 0.71; RMSE, 19.4 mm/mon), ET (median KGE, 0.86; RMSE, 9.0 mm/mon), and TWSC (median KGE, 0.58; RMSE, 26.6 mm/mon). The good positive correlation between root‐zone water storage and gross primary productivity also indicates the effectiveness of the model in simulating root‐zone water storage. This study offers new prospects for estimating key ecohydrological components precisely, especially in catchments that are difficult to monitor (e.g., karst regions) and catchments with limited observation data (e.g., ungauged regions).

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Detection of large-scale groundwater storage variability over the karstic regions in Southwest China

Cited by 42 publications

References 56 publications

Developing observational methods to drive future hydrological science: Can we make a start as a community?

Developing observational methods to drive future hydrological science: Can we make a start as a community?

Sensitivity Analysis of Leakage Correction of GRACE Data in Southwest China Using A-Priori Model Simulations: Inter-Comparison of Spherical Harmonics, Mass Concentration and In Situ Observations

Multisource remote sensing data facilitate ecohydrological simulations without runoff calibration

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