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Terrestrial Water Storage Changes In Mainland China Using GPS And GRACE

Posted on:2024-06-04Degree:MasterType:Thesis
Country:ChinaCandidate:D RenFull Text:PDF
GTID:2530307139457074Subject:Surveying the science and technology
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Global warming and environmental degradation leading to sea level rise and frequent regional weather extremes greatly threaten human survival and development,of which the variability of the water cycle is one of the most important issues.Surface water storage,the amount of freshwater stored in rivers/wetlands/floodplains/lakes,and their changes are key components of the water cycle and surface hydrology,with strong feedbacks and links to climate change.Because of the vast area of China mainland region and the large geographical and climatic differences among different regions,accurate estimation of the spatial and temporal variability of terrestrial water storage and its load changes in China mainland region is of great practical and scientific importance for revealing hydrology,understanding nonlinear motion geodynamic processes,and human survival and development.In this paper,we analyze the changes in terrestrial water storage in mainland China jointly with GPS and GRACE,and the primary work is as follows:1.This paper used Mascon data from three agencies(CSR,JPL,and GSFC)to estimate the spatial and temporal variability of terrestrial water storage(TWS)in the China mainland region over the last 20 years.The missing parts of the GRACE data(especially the gap between GRACE and GRACE Follow-On)were filled in using singular spectrum analysis iterative interpolation method.Based on the characteristics of complex topography and variable climate in the mainland China region,we divided mainland China into four sub-regions,namely,northern(N),northwestern(NW),southern(S),and Tibetan plateau(TP)regions,and studied in detail the long-term trends,annual phase and amplitude changes of inland water storage in each region,and roughly estimated the changes of groundwater by combining with hydrological model data(GLDAS).The results show that the terrestrial water storage in S and TP areas is most influenced by climatic factors,while for most of N and NW areas,precipitation is not abundant,so it is more prone to decrease in groundwater storage due to anthropogenic overabstraction.2.The elastic loading displacements of the mainland China region were calculated based on the spherical harmonic coefficients,and were compared with the hydrological elastic loading displacement model and GPS station vertical displacement time series,and the results showed that the three are in good agreement.The results show that the correlation coefficient between GRACE and HYDL is the best at 255 GPS station locations in mainland China,with a mean value of 0.7;while the mean values of the correlation coefficients between GPS and GRACE and HYDL are 0.4 and 0.5,respectively(with the best in the S region,both reaching 0.6).In this paper,we used principal component analysis(PCA)to filter the network of 255 GPS stations in the tectonic environment monitoring network of mainland China to identify the principal component(PC)of each of the four sub-regions,and analyze the potential connection between PC and terrestrial water storage.3.The feedback and relation between land water reserves,hydroelastic loading displacement and meteorological factors such as temperature and precipitation in temporal and spatial distribution are analyzed in detail,and the correlation analysis is made based on the global climate cycle events.The interannual fluctuation signals associated with hydrological transformations in terrestrial water storage and PC were then explored using wavelet time spectrum,Fourier transform and other signal processing methods.Among them,GRACE and GPS within the S region show periodic signals around 0.26 cpy(3.8 years),0.51 cpy(2.0 years)and 0.70 cpy(1.4 years)at the same time.
Keywords/Search Tags:GPS, GRACE and GRACE Follow-On, terrestrial water storage variability, singular spectrum analysis iterative interpolation method, principal component analysis method
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