The identification of ground water potential zones in the Chinhat block, district Lucknow, U.P. has been carried out based on the scientific investigation of drainage, gydro-geomorphology, lithology, soil, land use and land cover, geo-resistivity data and their inter-relationship. Thematic layers of drainage, canal, surface water body, geomorphology, lithology, lineament, soil, and land use and land cover were prepared with the help of satellite images. Field traverses along litho-stratigraphic units was made to check ground truth. Dug well data were collected at different locations in the district. digital elevation model (DEM) of the area was generated using SRTM data. Slope map was prepared from DEM. To generate these layers a step-approach, i.e., digitization, editing, building topological structure and finally, polygonization for GIS overlay analysis was followed.
Every class in the thematic layers were placed into one of the following categories viz. (i) excellent (ii) good (iii) moderate (iv) poor, and (v) very poor depending on their level of groundwater potential. Considering their behavior with respect to groundwater control, the different classes were given suitable values, according to their importance relative to other classes in the same thematic layer.
To find out the more realistic ground water potentiality map of the area, the relevant layers which include geomorphology, lithology, lineament, slope, drainage density, soil type and landuse/landcover were integrated in Arc/Info grid environment. Criteria for GIS analysis have been defined on the basis of ground water conditions and appropriate weightage were assigned to each information layer according to relative contribution towards the desired output.
The inferred results have also been validated by the yield data collected from the existing sources. The findings based on the drilling data confirms that the higher yield categories fall in the excellent ground water potential zones while the area marked as very poor ground water potential zone fall under low yield categories.
