In this study, intrinsic groundwater vulnerability for the shallow aquifer in northeastern Missan governorate, south of Iraq is evaluated using commonly used DRASTIC model in framework of GIS environment. Preparation of DRASTIC parameters is attained through gathering data from different sources including field survey, geological and meteorological data, a digital elevation model DEM of the study area, archival database, and published research. The different data used to build DRASTIC model are arranged in a geospatial database using spatial analyst extension of ArcGIS 10.2 software. The obtained results related to the vulnerability to general contaminants show that the study area is characterized by two vulnerability zones: low and moderate. Ninety-four percentage (94 %) of the study area has a low class of groundwater vulnerability to contamination, whereas a total of (6 %) of the study area has moderate vulnerability. The pesticides DRASTIC index map shows that the study area is also characterized by two zones of vulnerability: low and moderate. The DRASTIC map of this version clearly shows that small percentage (13 %) of the study area has low vulnerability to contamination, and most parts have moderate vulnerability (about 87 %). The final results indicate that the aquifer system in the interested area is relatively protected from contamination on the groundwater surface. To mitigate the contamination risks in the moderate vulnerability zones, a protective measure must be put before exploiting the aquifer and before comprehensive agricultural activities begin in the area.

In recent years, delineation of groundwater productivity zones plays an increasingly important role in sustainable management of groundwater resource throughout the world. In this study, groundwater productivity index of northeastern Wasit Governorate was delineated using probabilistic frequency ratio (FR) and Shannon’s entropy models in framework of GIS. Eight factors believed to influence the groundwater occurrence in the study area were selected and used as the input data. These factors were elevation (m), slope angle (degree), geology, soil, aquifer transmissivity (m²/d), storativity (dimensionless), distance to river (m), and distance to faults (m). In the first step, borehole location inventory map consisting of 68 boreholes with relatively high yield (>8 l/sec) was prepared. 47 boreholes (70 %) were used as training data and the remaining 21 (30 %) were used for validation. The predictive capability of each model was determined using relative operating characteristic technique. The results of the analysis indicate that the FR model with a success rate of 87.4 % and prediction rate 86.9 % performed slightly better than Shannon’s entropy model with success rate of 84.4 % and prediction rate of 82.4 %. The resultant groundwater productivity index was classified into five classes using natural break classification scheme: very low, low, moderate, high, and very high. The high–very high classes for FR and Shannon’s entropy models occurred within 30 % (217 km²) and 31 % (220 km²), respectively indicating low productivity conditions of the aquifer system. From final results, both of the models were capable to prospect GWPI with very good results, but FR was better in terms of success and prediction rates. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans.