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.