Terrestrial Water Storage Anomalies Associated with Drought in Southwestern USA from GPS Observations

Jin, Shuanggen; Zhang, Tengyu

doi:10.1007/s10712-016-9385-z

Cited by 69 publications

(30 citation statements)

References 71 publications

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“…Analysis of TWS GRACE time series and other relevant time series has been successfully used in many hydrological and climatological studies including management of water resources [5,7,8,[23][24][25][26][27][28][29], and analysis, prediction, and forecasting of climate change and variability in climatic (e.g., rainfall, evapotranspiration, and wind speed), and hydrologic (e.g., stream flow, flood, drought, infiltration, recharge, and ground/surface water quantity, quality, and consumption) parameters [30][31][32][33][34][35][36]. The successful implementation of these analyses heavily relies on the availability of comprehensive, continuous, and uninterrupted time series records [37,38].…”

Section: Introductionmentioning

confidence: 99%

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

et al. 2019

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The GRACE-derived terrestrial water storage (TWS GRACE ) provides measurements of the mass exchange and transport between continents, oceans, and ice sheets. In this study, a statistical approach was used to forecast TWS GRACE data using 10 major African watersheds as test sites. The forecasted TWS GRACE was then used to predict drought events in the examined African watersheds. Using a nonlinear autoregressive with exogenous input (NARX) model, relationships were derived between TWS GRACE data and the controlling and/or related variables (rainfall, temperature, evapotranspiration, and Normalized Difference Vegetation Index). The performance of the model was found to be "very good" (Nash-Sutcliffe (NSE) > 0.75; scaled root mean square error (R*) < 0.5) for 60% of the investigated watersheds, "good" (NSE > 0.65; R* < 0.6) for 10%, and "satisfactory" (NSE > 0.50; R* < 0.7) for the remaining 30% of the watersheds. During the forecasted period, no drought events were predicted over the Niger basin, the termination of the latest (March-October 2015) drought event was observed over the Zambezi basin, and the onset of a drought event (January-March 2016) over the Lake Chad basin was correctly predicted. Adopted methodologies generate continuous and uninterrupted TWS GRACE records, provide predictive tools to address environmental and hydrological problems, and help bridge the current gap between GRACE missions.

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Section: Introductionmentioning

confidence: 99%

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

et al. 2019

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“…Furthermore, Jin and Zhang [17] also used GPS-observed radial displacements to invert TWS fields over the southwestern United States. Zhang et al [18] attempted to estimate TWS at a spatial resolution of approximately 100 km using 29 GPS stations over Yunnan Province (approximate area of 394 × 10 3 km 2 ), China.…”

Section: Introductionmentioning

confidence: 99%

“…There are relatively few published papers on the topic of GPS-imaged TWS for the United States [13][14][15]17], and there is only one such study in China [18]. Thus, this investigation is considered as a second case study outside of the United States.…”

Section: Introductionmentioning

confidence: 99%

Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

et al. 2019

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Few studies have used crustal displacements sensed by the Global Positioning System (GPS) to assess the terrestrial water storage (TWS), which causes loadings. Furthermore, no study has investigated the feasibility of using GPS to image TWS over South America (SA), which contains the world’s driest (Atacama Desert) and wettest (Amazon Basin) regions. This work presents a resolution analysis of an inversion of GPS data over SA. Firstly, synthetic experiments were used to verify the spatial resolutions of GPS-imaged TWS and examine the resolving accuracies of the inversion based on checkerboard tests and closed-loop simulations using “TWS” from the Noah-driven Global Land Data Assimilation System (GLDAS-Noah). Secondly, observed radial displacements were used to image daily TWS. The inverted results of TWS at a resolution of 300 km present negligible errors, as shown by synthetic experiments involving 397 GPS stations across SA. However, as a result of missing daily observations, the actual daily number of available stations varied from 60–353, and only 6% of the daily GPS-imaged TWS agree with GLDAS-Noah TWS, which indicates a root-mean-squared error (RMSE) of less than 100 kg/m 2 . Nevertheless, the inversion shows agreement that is better than 0.50 and 61.58 kg/m 2 in terms of the correlation coefficient (Pearson) and RMSE, respectively, albeit at each GPS site.

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“…Using the GPS Plate Boundary Observatory (PBO) network in California, the resulting GPS-inferred TWS was qualitatively comparable to the GRACE-inferred TWS and hydrological models, such as the Global Land Data Assimilation System (GLDAS) [15,16]. Jin and Zhang (2016) [1] applied the same method to drought monitoring in the entire southwestern region of the United States. In addition, a Helmert variance component estimation (HVCE) re-weighting technique was applied for the inversion, along with a critical quantitative assessment [17].…”

Section: Introductionmentioning

confidence: 78%

“…Terrestrial water storage (TWS), being one of the indispensable hydrological components, consists of surface water storage (e.g., reservoir and lake), groundwater, soil moisture, snow, and ice [1]. Its spatial redistributions cause hydrological loading that deforms the solid Earth, in which vertical crustal displacements (VCD) are of major interest in geophysics and geodesy [2].…”

Section: Introductionmentioning

confidence: 99%

An Improved GPS-Inferred Seasonal Terrestrial Water Storage Using Terrain-Corrected Vertical Crustal Displacements Constrained by GRACE

Fok

Liu

2019

Remote Sensing

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Based on a geophysical model for elastic loading, the application potential of Global Positioning System (GPS) vertical crustal displacements for inverting terrestrial water storage has been demonstrated using the Tikhonov regularization and the Helmert variance component estimation since 2014. However, the GPS-inferred terrestrial water storage has larger resulting amplitudes than those inferred from satellite gravimetry (i.e., Gravity Recovery and Climate Experiment (GRACE)) and those simulated from hydrological models (e.g., Global Land Data Assimilation System (GLDAS)). We speculate that the enlarged amplitudes should be partly due to irregularly distributed GPS stations and the neglect of the terrain effect. Within southwest China, covering part of southeastern Tibet as a study region, a novel GPS-inferred terrestrial water storage approach is proposed via terrain-corrected GPS and supplementary vertical crustal displacements inferred from GRACE, serving as "virtual GPS stations" for constraining the inversion. Compared to the Tikhonov regularization and Helmert variance component estimation, we employ Akaike’s Bayesian Information Criterion as an inverse method to prove the effectiveness of our solution. Our results indicate that the combined application of the terrain-corrected GPS vertical crustal displacements and supplementary GRACE spatial data constraints improves the inversion accuracy of the GPS-inferred terrestrial water storage from the Helmert variance component estimation, Tikhonov regularization, and Akaike’s Bayesian Information Criterion, by 55%, 33%, and 41%, respectively, when compared to that of the GLDAS-modeled terrestrial water storage. The solution inverted with Akaike’s Bayesian Information Criterion exhibits more stability regardless of the constraint conditions, when compared to those of other inferred solutions. The best Akaike’s Bayesian Information Criterion inverted solution agrees well with the GLDAS-modeled one, with a root-mean-square error (RMSE) of 3.75 cm, equivalent to a 15.6% relative error, when compared to 39.4% obtained in previous studies. The remaining discrepancy might be due to the difference between GPS and GRACE in sensing different surface water storage components, the remaining effect of the water storage changes in rivers and reservoirs, and the internal error in the geophysical model for elastic loading.

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Terrestrial Water Storage Anomalies Associated with Drought in Southwestern USA from GPS Observations

Cited by 69 publications

References 71 publications

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

An Improved GPS-Inferred Seasonal Terrestrial Water Storage Using Terrain-Corrected Vertical Crustal Displacements Constrained by GRACE

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