Python-Driven Mapping of Artificial Groundwater Recharge Zones: Application in Dhar District, Madhya Pradesh, India

In regions with limited rainfall and ephemeral drainage patterns, groundwater is an invaluable resource for sustaining life and supporting agriculture. This study focuses on identifying groundwater potential recharge zones in Madhya Pradesh's tribal-dominated Dhar district, which was declared a 'water scarce' area in 2022 and restricted for groundwater use, even for irrigation. Dhar's geological setting suggests over 95% of impervious rocks (Basalt, Gneiss, and Granite), while aquifer rocks (sandstone and limestone) are less than 4%, poses significant challenges for groundwater recharge. This study leverages Python and open-source tools instead of proprietary software for identifying groundwater recharge zones. The Python integrates various factors influencing the groundwater availability into a composite map and categorizes the Dhar district into six distinct groundwater recharge classes: excellent (about 4.5% area), good (41% area), fine (34%), moderate (10% area), bad (6% area), and unaccepted (about 2.7% area). The Python-generated integrated map has been validated by groundwater level monitoring station data of IWRIS (India Water Resources Information System), confirming the model accuracy. This study substantiate the effectiveness of Python and open-source tools for groundwater management, can provide a scalable solution for water-scarce regions.