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Groundwater is vulnerable and more susceptible to contamination from various anthropogenic elements. Various steps are taken to measure the groundwater vulnerability for a sustainable groundwater development. The present study estimates the aquifer vulnerability by applying DRASTIC model in the Geographic Information System (GIS) environment. The DRASTIC model uses seven hydrological parameters which include depth to water level, net recharge, aquifer media, soil media, topography, the impact of vadose zone and hydraulic conductivity. DRASTIC index was calculated from DRASTIC model that ranged from 31 to 154. All these parameters characterize the hydrological setting for evaluating aquifer vulnerability. Sensitivity analyses have also been performed to determine the sensitivity of every individual DRASTIC parameter towards the aquifer vulnerability. Sensitivity analysis indicated that all the parameters have an almost similar influence on vulnerability index. Depth to water parameter inflicts larger impact on aquifer vulnerability followed by recharge, topography and soil Media. The whole of Kodaganar basin is classified into very low, low, moderate and high vulnerable zones. Nearly threefourth of the basin has very low and low vulnerability. Incorporating DRASTIC model in the GIS environment has proved efficient in handling large volumes of data and in determining the groundwater vulnerability.El agua subterránea es vulnerable y más susceptible a la contaminación de varios elementos antropogénicos. Se midió la vulnerabilidad del agua subterránea en varias etapas para establecer el desarrollo sustentable de la fuente acuífera. Este trabajo estima la vulnerabilidad del agua subterránea por la aplicación del método DRASTIC en el entorno del Sistema de Información Geográfica (GIS, en inglés). El método DRASTIC utiliza siete parámetros hidrológicos: profundidad del agua subterránea, recarga neta, litología del acuífero, tipo de suelo, topografía naturaleza de la zona no saturada y conductividad hidráulica del acuífero. El índice DRASTIC fue calculado a través de este método y que oscila entre 31 y 154 unidades. Estos parámetros caracterizan la configuración hidrológica para la evaluación de vulnerabilidad del acuífero. También se realizaron los análisis de susceptibilidad para determinar la respuesta de cada parámetro DRASTIC frente a la vulnerabilidad del agua subterránea. El análisis de susceptibilidad indicó que todos los parámetros tienen una influencia similar en el índice de vulnerabilidad. El parámetro de profundidad ocasiona un mayor impacto en el índice de vulnerabilidad, seguido por la recarga, la topografía y el tipo de suelo. Toda la cuenca de Kodaganar se clasifica en zonas de vulnerabilidad muy baja, baja, moderada y alta. La incorporación del método DRASTIC en el entorno GIS prueba la eficiencia en el manejo de grandes volúmenes de información y en la evaluación de vulnerabilidad de aguas subterráneas. ABSTRACT RESUMEN

Groundwater is a dynamic and replenishable natural resource. The numerical modeling techniques serve as a tool to assess the effect of artificial recharge from the water conservation structures and its response with the aquifers under different recharge conditions. The objective of the present study is to identify the suitable sites for artificial recharge structures to augment groundwater resources and assess its performance through the integrated approach of Geographic Information System (GIS) and numerical groundwater modeling techniques using MODFLOW software for the watershed located in the Kodaganar river basin, Dindigul district, Tamil Nadu. Thematic layers such as geology, geomorphology, soil, runoff, land use and slope were integrated to prepare the groundwater prospect and recharge site map. These potential zones were categorized as good (23%), moderate (54%), and poor (23%) zones with respect to the assigned weightage of different thematic layers. The major artificial recharge structures like percolation ponds and check dams were recommended based on the drainage morphology in the watershed. Finally, a three-layer groundwater flow model was developed. The model was calibrated in two stages, which involved steady and transient state condition. The transient calibration was carried out for the time period from January 1989 to December 2008. The groundwater model was validated after model calibration. The prediction scenario was carried out after the transient calibration for the time period of year up to 2013. The results show that there is 15 to 38% increase in groundwater quantity due to artificial recharge. The present study is useful to assess the effect of artificial recharge from the proposed artificial structures by integrating GIS and groundwater model together to arrive at reasonable results.

Vegetation is an intricate event with large amount of intrinsic spectral, spatial and temporal inconsistency and it is naturally characterized by strapping assimilation in the red wavelengths and towering reflectance in the near infra-red (NIR) wavelengths of the electromagnetic spectrum. The image descriptions generating from various vegetation index like NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index) etc., from multispectral imagery be able to provide exclusive vegetation information about an area. Soil environment circumstances are considerable influence on partial canopy spectra and vegetation index. Consequently, it is significant to monitor the vegetation vitality changes with reverence to the soil background circumstances. The present study an appropriate remote sensing based algorithm, i.e. soil adjusted vegetation index (SAVI) was selected. The investigation of vegetation vigor variations was done for dissimilar time sequence in the part of Andhra Pradesh State, India. The MODIS vegetation index images of 250m resolution are used. NDVI and NDWI images are derivative for red and black soil types and SAVI model was fashioned and executed in ERDAS IMAGINE platform. In SAVI equation, the soil accustomed factor ‘L’ was personalized with dissimilar values and multivariate SAVI images are derived for both red and black soil regions. In the an assortment of red soil regions, the SAVI with different ‘L’ values of 0.25, 0.3, 0.4, 0.5 and black soil region, the vegetation envelop is medium and SAVI with ‘L’ values of 0.3 and 0.4 fashioned fair result on variations of soil and vegetation reflectance over the crop period. The present study was done with the two types of soil regions and with accessible datasets. The psychoanalysis fraction of the study can be extended with multiple data sets and dissimilar seasons.