Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers - Kumar R.; Tewari A.; Mishra S.; Singh P.K.; Gaur S. (My University, 2025)
Managed aquifer recharge (MAR) has emerged as a potential solution to resolve water insecurity, globally. However, integrated studies quantifying the surplus source water, suitable recharge sites and safe recharge capacity is limited. In this study, a novel methodology is presented to quantify transient injection rates in unconfined aquifers and generate MAR suitability maps based on estimated surplus water and permissible aquifer recharge capacity (PARC). Subbasin scale monthly surplus surface runoff was estimated at 75% dependability using a SWAT model. A linear regression model based on numerical solution was used to capture the aquifer response to injection and to calculate PARC values at subbasin level. The available surplus runoff and PARC values was then used to determine the suitable site and recharge rate during MAR operation. The developed methodology was applied in the semi-arid region of Lower Betwa River Basin (LBRB), India. The estimated surplus runoff was generally confined to the monsoon months of June to September and exhibited spatial heterogeneity with an average runoff rate of 5000 m3/d in 85% of the LBRB. Analysis of the PARC results revealed that thick alluvial aquifers had large permissible storage capacity and about 50% of the LBRB was capable of storing over 3500 m3/d of water. This study revealed that sufficient surplus runoff was generated in the LBRB, but it lacked the adequate safe aquifer storage capacity to conserve it. A total 65 subbasins was identified as the best suited sites for MAR which had enough surplus water and storage capacity to suffice 20% of the total water demand in the LBRB. The developed methodology was computationally efficient, could augment the field problem of determining scheduled recharge rates and could be used as a decision-making tool in artificial recharge projects. © 2024 Elsevier Ltd