Conventional and futuristic approaches for the computation of groundwater recharge: A comprehensive review

Abstract

Groundwater recharge is a critical hydrologic component that determines groundwater availability and sustainability. Groundwater recharge estimation can be performed in a variety of ways, ranging from direct procedures to simulation models. The optimal strategy for recharge estimation depends on several factors, such as study objectives, climatic zones, hydrogeological conditions, data availability, methodology, and temporal and spatial constraints. Groundwater recharge is influenced by uncertainties in weather and hydrology. This study discusses conventional recharge estimation techniques and their application for optimal recharge calculation, and it also offers an overview of recent advances in recharge estimation methods. Most methods provide direct or indirect estimation of recharge across a small region on a point scale for a shorter time. With recent technological advancements and increased data availability, several advanced computational tools, including numerical, empirical, and artificial intelligence models, have been developed for efficient and reliable computation of groundwater recharge. This review article provides a thorough discussion of the techniques, assumptions, advantages, limitations, and selection procedures for estimating groundwater recharge.

Keywords

• groundwater recharge
• groundwater balance
• groundwater flow
• machine learning
• deep learning