Utilizing combined deviations of precipitation and GRACE-based terrestrial water storage as a metric for drought characterization: A case study over major Indian river basins

2019 ◽  
Vol 572 ◽  
pp. 294-307 ◽  
Author(s):  
Debanjan Sinha ◽  
Tajdarul H. Syed ◽  
John T. Reager
Keyword(s):  
Water Storage ◽  
River Basins ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Indian River

Evolution of low flows regulation and terrestrial water storage in the Magdalena river basin: Implications for the assessment and monitoring of potential water scarcity in river basins

2020 ◽  
Author(s):  
Juan F. Salazar ◽  
Silvana Bolaños ◽  
Estiven Rodríguez ◽  
Teresita Betancur ◽  
Juan Camilo Villegas ◽  
...  
Keyword(s):  
River Basin ◽  
Water Scarcity ◽  
River Flow ◽  
Water Storage ◽  
River Basins ◽  
Terrestrial Water Storage ◽  
Social Phenomena ◽  
Low Flows ◽  
Terrestrial Water ◽  
Magdalena River

<p>Many natural and social phenomena depend on the regulation of river flow regimes. Regulation is defined here as the capacity of river basins to attenuate extreme flows, which includes the capacity to enhance low flows during dry periods of time. This capacity depends on how basins store and release water through time, which in turn depends on manifold processes that can be highly dynamic and sensitive to global change. Here we focus on the Magdalena river basin in northwestern South America, which is critical for water and energy security in Colombia, and has experienced water scarcity problems in the past, including the collapse of the national hydropower system due to El Niño 1991-1992. In this basin we study the evolution of regulation and related processes from two perspectives. First, we present a widely applicable conceptual framework that is based on the scaling theory and allows assessing the evolution of regulation in river basins, and use this framework to show how the Magdalena basin’s regulation capacity has been changing in recent decades. Second, we use data from the GRACE mission to investigate variations in water storage in the basin, and identify recent decreasing trends in both terrestrial water storage and groundwater storage. Further we show that temporal and spatial patterns of water storage depletion are likely related to the occurrence of ENSO extremes and pronounced differences between the lower and higher parts of the basin, including the presence of major wetland systems in the low lands and Andean mountains in the high lands. Our results provide insights on how to assess and monitor regulation in river basins, as well as on how this regulation relates to the dynamics of low flows and water storage, and therefore to potential water scarcity problems.</p>

scholarly journals Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4144 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Wen ◽  
Zhong ◽  
...  
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Better Than

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

scholarly journals Seasonal Variations in Terrestrial Water Storage for Major Midlatitude River Basins

2006 ◽  
Vol 7 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Martin Hirschi ◽  
Sonia I. Seneviratne ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Cover ◽  
Water Storage ◽  
River Basins ◽  
Interannual Variations ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Seasonal And Interannual Variations

Abstract This paper presents a new diagnostic dataset of monthly variations in terrestrial water storage for 37 midlatitude river basins in Europe, Asia, North America, and Australia. Terrestrial water storage is the sum of all forms of water storage on land surfaces, and its seasonal and interannual variations are in principle determined by soil moisture, groundwater, snow cover, and surface water. The dataset is derived with the combined atmospheric and terrestrial water-balance approach using conventional streamflow measurements and atmospheric moisture convergence data from the ECMWF 40-yr Re-Analysis (ERA-40). A recent study for the Mississippi River basin (Seneviratne et al. 2004) has demonstrated the validity of this diagnostic approach and found that it agreed well with in situ observations in Illinois. The present study extends this previous analysis to other regions of the midlatitudes. A systematic analysis is presented of the slow drift that occurs with the water-balance approach. It is shown that the drift not only depends on the size of the catchment under consideration, but also on the geographical region and the underlying topography. The drift is in general not constant in time, but artificial inhomogeneities may result from changes in the global observing system used in the 44 yr of the reanalysis. To remove this time-dependent drift, a simple high-pass filter is applied. Validation of the results is conducted for several catchments with an appreciable coverage of in situ soil moisture and snow cover depth observations in the former Soviet Union, Mongolia, and China. Although the groundwater component is not accounted for in these observations, encouraging correlations are found between diagnostic and in situ estimates of terrestrial water storage, both for seasonal and interannual variations. Comparisons conducted against simulated ERA-40 terrestrial water storage variations suggest that the reanalysis substantially underestimates the amplitude of the seasonal cycle. The basin-scale water-balance (BSWB) dataset is available for download over the Internet. It constitutes a useful tool for the validation of climate models, large-scale land surface data assimilation systems, and indirect observations of terrestrial water storage variations.

scholarly journals Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India

2017 ◽  
Vol 44 (9) ◽  
pp. 4107-4115 ◽  
Author(s):  
Manuela Girotto ◽  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Matthew Rodell ◽  
Clara Draper ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Changes of Terrestrial Water Storage in River Basins of China Projected by RegCM4

2013 ◽  
Vol 6 (3) ◽  
pp. 154-160 ◽  
Author(s):  
Zou Jing ◽  
Xie Zheng-Hui ◽  
Qin Pei-Hua ◽  
Ma Qian ◽  
Sun Qin
Keyword(s):  
Water Storage ◽  
River Basins ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

scholarly journals Monitoring Terrestrial Water Storage Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of the Chinese Mainland

2021 ◽  
Vol 13 (9) ◽  
pp. 1851
Author(s):  
Zhiwei Chen ◽  
Xingfu Zhang ◽  
Jianhua Chen
Keyword(s):  
River Basin ◽  
Water Storage ◽  
River Basins ◽  
Chinese Mainland ◽  
Terrestrial Water Storage ◽  
The Chinese Mainland ◽  
Terrestrial Water ◽  
Major River ◽  
In Situ Observations

Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an optimized short-arc approach at Tongji University. We analyzed the changes in the TWS and groundwater storage (GWS) in each of the nine major river basins of the Chinese mainland from April 2002 to August 2016, using Tongji-Grace2018, the Global Land Data Assimilation System (GLDAS) hydrological model, in situ observations, and additional auxiliary data (such as precipitation and temperature). Our results indicate that the TWS of the Songliao, Yangtze, Pearl, and Southeastern River Basins are all increasing, with the most drastic TWS growth occurring in the Southeastern River Basin. The TWS of the Yellow, Haihe, Huaihe, and Southwestern River Basins are all decreasing, with the most drastic TWS loss occurring in the Haihe River Basin. The Continental River Basin TWS has remained largely unchanged over time. With the exception of the Songliao and Pearl River Basins, the GWS results produced by the Tongji-Grace2018 model are consistent with the in situ observations of these basins. The correlation coefficients for the Tongji-Grace2018 model results and the in situ observations for the Yellow, Huaihe, Yangtze, Southwestern, and Continental River Basins are higher than 0.710. Overall, the GWS results for the Songliao, Yellow, Haihe, Huaihe, Southwestern, and Continental River Basins all exhibit a downward trend, with the most severe groundwater loss occurring in the Haihe and Huaihe River Basins. However, the Yangtze and Southeastern River Basins both have upward-trending modeled and measured GWS values. This study demonstrates the effectiveness of the Tongji-Grace2018 model for the reliable estimation of TWS and GWS changes on the Chinese mainland, and may contribute to the management of available water resources.

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