Effect of urbanisation and population density on groundwater in India using satellite remote sensing data

Abstract

For the selected study regions in India, this work investigates the relationship of urbanization and population density along with meteorological variables (temperature and precipitation) with groundwater during 2003-2017. newlineIn order to understand variations in groundwater, Gravity Recovery and Climate Experiment (GRACE) derived variations of Terrestrial Water Storage ( TWS) have been widely used in literature. Thus, in the newlinefirst part of this work, we ve examined and established the application of GRACE TWS for prediction of GWL for selected study sites across different geographic regions in India. In order to model and predict GWL with the help of TWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). It has been observed in our study that TWS newlineis highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of TWS varies from 36% to 74%..We have found that for the model and#119866;and#119882; and#119871; and#8764; TWS, performances of SVR and ANN are better than that of LRM. It also has been found in our study that with the inclusion of meteorological variables along with TWS as input parameters to newlinemodel GWL, the performance of SVR improves and it performs better than ANN. newlineNext, we ve studied changes in TWS across India from January newlineto January and have found evidence of significant declining trend (and#8722; . ± . cm/year) of the same in northern part of India encompassing Ganga-Brahmaputra river basin and North-West India during this time. As TWS newlineserves as a strong indicator for groundwater, the observed declining trend of the same imply significant depletion of groundwater in this belt during this period. Interestingly, for the same time period, this particular belt with declining TWS, has observed significant positive trend in precipitation and no significant trend for temperature.