Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach

Hasan, Emad; Tarhule, Aondover

doi:10.1016/j.hydroa.2021.100108

Cited by 6 publications

(2 citation statements)

References 62 publications

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“…A number of separate releases (RL)-RL04, RL05, and RL06-have been produced as a result of the numerous revisions and improvements that each of these solutions have been through. With each new release, GRACE products have become better than their predecessors [33]. The climatological variability of TWSAs is also analyzed by a globally accepted nonparametric trend test to identify the nature and magnitude of the trend using the Mann-Kendall (MK) test, the modified Mann-Kendall (MMK) test, Sen's slope estimator (SS), and the parametric test by simple linear regression analysis (SLRA) [26,29,34].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

et al. 2023

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This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km2 with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

et al. 2023

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“…In literature, existing methods describe variability only in the spatial domain (Haining et al, 2010, Khaki et al, 2017 or only in the temporal domain, based on time series analysis (Fu, 2011, Sprott and Sprott, 2003, Vishwakarma et al, 2021. Several methods can be found in the literature that analyzes ∆TWS and SM space and time domains together such as temporal stability analysis (TSA) (Martínez-Fernández andCeballos, 2005, Wang et al, 2018), triple collocation (TC) (Crow et al, 2015, Gruber et al, 2017, Hasan and Tarhule, 2021, Yin and Park, 2021, and empirical orthogonal functions (EOFs) (Eom et al, 2017, Lei et al, 2012, Navarra and Simoncini, 2010, Yoo and Kim, 2004. Whether the analysis is temporal or spatiotemporal, researchers in previous work have indicated the importance of SM as a component of the ∆TWS signal.…”

Section: Introductionmentioning

confidence: 99%

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

Szabó

Osińska-Skotak

2023

Artificial Satellites

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The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed comparison. Similarities and differences in signal convergence results were compared with land cover data describing soil conditions, vegetation cover, urbanization status, and cultivated land. Convergence was determined using Pearson correlation coefficients and cross-correlation. In order to compare ΔTWS and SM in individual seasons, ΔTWS observations were normalized. Results show that naturally forested areas and large open spaces used for agriculture support the compatibility between GRACE and AMSRE observations and are characterized by a good Pearson correlation coefficient >0.8. Subpolar regions with permafrost present constraints for AMSR-E observations and have little convergence with GRACE observations.

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Integration of Three Standardized Drought Indices utilizing Modified Triple Collocation and Scaled Triple Collocation relative to Triple Collocation

Badar,

Almazah,

Hussain

et al. 2023

Water Resour Manage

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Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach

Cited by 6 publications

References 62 publications

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

Integration of Three Standardized Drought Indices utilizing Modified Triple Collocation and Scaled Triple Collocation relative to Triple Collocation

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