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Publications => Publications from the member of forum => Topic started by: Subir Paul on July 29, 2018, 11:59:55 AM

Title: Analyzing Large-Scale Hydrologic Processes Using GRACE and Hydrometeorological Datasets
Post by: Subir Paul on July 29, 2018, 11:59:55 AM
The work carried out by Chandan Banerjee along with Prof. D Nagesh Kumar titled "Analyzing Large-Scale Hydrologic Processes Using GRACE and Hydrometeorological Datasets" was recently published in the Water Resources Management.

Abstract: Water demand in India is growing due to its increasing population, economic growth and urbanization. Consequently, knowledge of interdependencies of large-scale hydrometeorological processes is crucial for efficient water resources management. Estimates of Groundwater(GW) derived from Terrestrial Water Storage (TWS) data provided by Gravity Recovery andClimate Experiment (GRACE) satellite mission along with various other satellite observations and model estimates now facilitates such investigations. In an attempt which is first of its kind in India, this study proposes Independent Component Analysis (ICA) based spatiotemporal analysis of Precipitation (P), Evapotranspiration (ET), Surface Soil Moisture (SSM), RootZone Soil Moisture (RZSM), TWS and GW to understand such interdependencies at intra- and interannual time scales. Results indicate that 8499% of the total variability is explained by the first 6 ICs of all the variables, analyzed for a period 20022014. The Indian Summer monsoon rainfall (ISMR) is the causative-factor of the first component describing 6182% of the total variability. The phase difference between all seasonal components of P and that of RZSM and ET is a month whereas it is 2 months between the seasonal components of P and that of SSM,TWS and GW. ET is observed to be largely dependent on RZSM while GW appears as themajor component of TWS. GW and TWS trends of opposite nature were observed in northernand southern part of India caused by inter-annual rainfall variability. These findings when incorporated in modelling frameworks would improve forecasts resulting in better water management.