Figure 2 Bankura district with in grids
By using panoply datasets could be understood properly and the data files of NC and NC4 could be properly read. In this study datasets of CSR, GFZ and JPL of RL05 versions were used. We selected the 4 pixels covering the Bankura district during a study period from November 2007 to January 2017 which are shown in figure 2. It means the data of pixel 1, pixel 2, pixel 3 and pixel 4 represented by the locations with coordinates of 86.50E; 23.50N, 87.50E; 23.50N, 87.50E; 22.50N and 86.50E; 22.50N respectively. Each pixel has different values of terrestrial water storing data and soil moisture data. After using the equations 1 and 2, we found the Groundwater storing variations of each pixel which are shown in below figures.
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Figure 3 GRACE-GLDAS results for pixel 1
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Figure 4 GRACE-GLDAS results for pixel 2
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Figure 5 GRACE-GLDAS results for pixel 3
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Figure 6 GRACE-GLDAS results for pixel 4
GRACE and GLDAS datasets are analysed and groundwater storage changes are estimated as liquid equivalent of 4 pixels. The groundwater storage changes and its trend lines of each pixel are shown in above graphs. All 4 pixels of Bankura district are showing decreasing trend with different slopes during the period of November 2007 to January 2017. None of the above slopes denoted the groundwater storage changes of the Bankura district because some pixels were covering more area and some covering less. Hence it becomes difficult to analyse groundwater changes of the study area.
From the above results we observed that the groundwater storing variations trend line decreasing in all 4 pixels with different slopes during study period from November 2007 to January 2017. Trend line equations of each pixel are mentioned in the above figures. None of the above trend line represents the Groundwater variations in the Bankura district. Because some pixels are covering more area of the district and some pixels are covering less area. To know the groundwater storing variations in the district, we use spatial interpolation over the Bankura district through obtained GWS variations of 4 pixels. For the spatial interpolation, here we used Inverse Distance Weighting (IDW) technique in ArcGIS software. The obtained image of ArcGIS represents the variation of GWS variations over the district and also gives mean value of GWS variations over the study period. Mean value as GWS changes of whole Bankura district are considered. After the interpolation we got the time variant images of Bankura district as shown in below.