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.

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 three- fourth 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. Evaluación basada en el Sistema de Información Geográ ca a la vulnerabilidad de un acuífero a partir del método DRASTIC: caso de estudio en la cuenca Kodaganar ResumenEl 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 e ciencia en el manejo de grandes volúmenes de información y en la evaluación de vulnerabilidad de aguas subterráneas.

COVID-19 pandemic situation has highly restricted the human movements including public transport, commercial and industrial activities in India since the end of March 2020. With the necessity to demonstrate the effectiveness of local emissions, the study aims in analysing the relative variation of ambient air quality before and during COVID-19 with the aid of in situ measurement and Sentinel 5P space technologies. ESA Sentinel 5P Level 2 data \sets including Carbon monoxide (CO), nitrogen dioxide (NO₂), Sulphur dioxide (SO₂), and Ozone (O₃)were pre-processed in SNAP and QGIS platform for the extraction of air quality parameters. Results concluded that the concentration of air quality parameters CO, NO₂, SO₂, and O₃reduced 50.52%, 85.81%, 71.61% and 313.4%, respectively, between March and June 2020. Accuracy of air quality parameters CO, NO₂, SO₂, and O₃obtained from Sentinel 5P datasets are found to be 89.5%, 83.54%, 86% and 94%, respectively, when compared with the in situ measurements. As a favourable outcome of the COVID 19 lockdown, better air quality is recorded over the vulnerable locations due to the absence of significant activities and with the improvement in the air quality the study illustrates the importance of temporary pollution source control measures in a suitable time interval. Integration of in situ measurements and satellite datasets will remove the conventional barriers and provide spatially distributed observations that can assist the decision-makers in formulating the best air pollution control strategy.