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Conventional and futuristic approaches for the computation of groundwater recharge: A comprehensive review
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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.
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Key words:
- Groundwater recharge /
- Groundwater balance /
- Groundwater flow /
- Machine learning /
- Deep learning
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Table 1. Empirical equations for recharge estimation
Formula Name Equation (s) Parameter definition Chaturvedi Formula (Chaturvedi, 1973) Ganga-Yamuna doab region R= 2(P−15)0.4 Modified Chaturvedi Formula - Ganga-Yamuna doab region R = 1.35(P−14)0.5 Sehgal Formula (1973)- Punjab region R = 2.5 (P−16)0.5 P = rainfall (inch) Kumar and Seethapathi (2002) - Upper Ganga Canal command area R = 0.63 (P−15.28)0.76 R = recharge (inch) Mohan and Abraham (2010)- Cuddalore basin , Tamil Nadu R = 3.55 (P−40)0.42 P and R (cm) 
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Table 2. Details of machine learning algorithms utilized in recent studies for groundwater level and recharge predictions.
References Machine learning models Predictive evaluation metrics Input data Target prediction Emamgholizadeh et al. 2014 Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Root-Mean-Square-Error (RMSE) and determination coefficient (R2) Rainfall recharge, irrigation returned flow and pumping rates from water wells Groundwater level (GWL) Pandey et al. 2020 ANN optimized with a Genetic Algorithm (GA-ANN) Coefficient of determination (R2), coefficient of efficiency (CE), correlation coefficient (r), Mean Absolute Deviation (MAD), RMSE, Coefficient of Variation of Error Residuals (CVER), Absolute Prediction Error (APE) and Performance Index (PI) Groundwater recharge, groundwater discharge and previous groundwater level data Seasonal groundwater table depth Derbela and Nouiri, 2020 ANN RMSE, R2, Nash–Sutcliffe (NASH) efficiency coefficient Monthly rainfall, evapotranspiration and initial water table level Monthly water table levels Dadhich et al. 2021 Time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and ANN Root-Mean-Square-Error (RMSE) and determination coefficient (R2) Groundwater data GWL and quality parameters Pham et al. 2022 Random Tree (RT), Random Forest (RF), decision stump, M5P regression algorithm, Support Vector Machine (SVM), locally weighted linear regression (LWLR), and reduce error pruning tree (REP Tree) RMSE, Mean Absolute Error (MAE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Correlation Coefficient (CC), and Taylor diagram Historical GWL, mean temperature, rainfall, and relative humidity datasets Groundwater level (GWL) Huang et al. 2023 Top-down deep learning model (s-LSTM), bottom-up machine learning models (m-Linear, m-MLP, and m-LSTM) Root-Mean-Square-Error (RMSE), absolute errors between calibrated and predicted data Groundwater extraction, mean number of wet days per year, seasonal minimum temperature, seasonal rainfall, and seasonal actual evaporation Groundwater recharge Banerjee et al. 2024 Linear Regression model to the intricate Extreme Gradient Booting (xgboost) Inversive correlation and k-fold cross-validation Precipitation, Land Use Land Cover (LULC), soil type, land slope, temperature, potential evapotranspiration, and aridity index Groundwater recharge pattern under different climate change scenarios Ramadan and Boubaker, 2024 SVM, RF, Linear Regression (LR), and Gradient Boosting (GB) Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (medae) Weather data Water consumption, groundwater recharge Fahim et al. 2024 Multiple Linear Regression (MLR), regression trees, SVM, Gaussian Process Regression (GPR), and ANN Overall correlation coefficient (R) and MSE Groundwater storage (GWS) gridded data from the Global Land Data Assimilation System (GLDAS) and other data sources such as population, rainfall, temperature, irrigation, and elevation Groundwater level (GWL)
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Table 3. Brief outline of the various methods of estimation of groundwater recharge with their advantages, disadvantages and scope of application
Zones Methods Climatic regions Advantages Disadvantages Scope of application Physical methods Surface water zone Channel Water Balance All Climatic Regions Analyzes recharge Rate based on transmission losses;Provides potential recharge values. Uncertainty issues due to inherent fluctuations in hydrologic cycle and related measurement mistakes;Overestimation due to bank storage/evapotranspiration/perched aquifer effects. Represent average recharge values over the reach between gauging stations;Temporal scales range from event scale to long-term summation of individual events. Seepage Meters All Climatic Regions Direct, Fast Measurement;Simple computation;Affordable;Rational on-site implementation. Point estimates of fluxes;Requires multiple measurements. Localized Recharge Estimation providing actual recharge values;Time scales range from individual events to days;Wide application range. Hydrograph Separation Method Humid Simple recharge estimator;No sophisticated instrument required;Estimates recharge over longer times by summing shorter time estimates. Not suitable for large basins with high pumpage, evapotranspiration, deep aquifer underflow and losing stream; Difficulty in separating flow components from bank storage effects. Watershed/catchment/regional level estimation providing net recharge values;Time scales range from months to years;Best for shallow water-table regions with gaining streams. Unsaturated zone techniques Lysimeter Method All Climatic Regions Percolate gathered by lysimeters closely approximates the recharge reaching the water table. High costs and impracticability in non-identical soils, drainage areas, deep rooted vegetation condition and sidewall flow.Overestimation due to changes in surface and subsurface flow routes;Point-estimate of recharge. Measures aquifer renewal rate;Used for local estimation at point scales;Temporal scale ranges from minutes to years, depending on drainage accuracy and lysimeter surface area;Wide application range. Zero Flux Plane Method All Climatic Regions Direct point estimation of potential groundwater recharge. Costly requires expensive devices and data;Fails with sufficient infiltration due to a positive hydraulic gradient;ZFP depth is not fixed and fluctuates throughout the year, ranging from a few centimeters to a few meters below the soil surface; Accurate determination requires special care and sensitive instruments, making it difficult to measure;Not applicable in wet areas. Applicable in areas with FP and deep water table;Cannot be used when water fluxes are downward or when water storage grows;Downward movement of a wetting front can obscure the zero-flux plane. Unstaurated Zone Flux Estimation Using Richards Eqution Arid/Semiarid Water draining below the root zone (or passing through unsaturated zone) contributes to recharge. Difficulties in measuring soil-water potential gradient at deeper layer/profile;Variabilities in hydraulic properties of field soil, field measured data of hydraulic properties, etc; Point estimate of recharge over a wide range of time;Does not indicate total recharge as it only accounts for diffuse or matrix flow. The minimum recharge rate that can be estimated using Richards equation depends on the accuracy of hydraulic conductivity and head gradient measurement. Saturated Zone Techniques Water Table Fluctuation Method All Climatic Regions Widely used method for estimating groundwater recharge based on groundwater levels; Applicable in arid and semiarid regions with shallow WT; Most promising and attractive approaches due to its accuracy, ease of use and low application cost in semiarid areas;Effective for analyzing short-term fluctuations in water levels in shallow water tables and for determining long-term recharge changes induced by climate or land-use change. Not suitable for deep aquifers due to the delayed rise in WT; Time intervals for recording/ measurement should consider wet/dry spell length, aquifer depth, and recharge estimation objective;Accumulated errors from other fluxes can lead to significant mistakes. Applicable to unconfined aquifer only; Used for local to catchment/regional level estimation providing actual recharge values; Rates for recharge range from tens to hundreds or thousands of meters;Time spans range from event scale to hydrographic record length. Chemical methods Surface Water Zone Heat Tracer Arid/Semiarid Measures surface water infiltration and flow through ephemeral rivers; Alternative to flow measurements in semi-arid regions prone to erosion. Point estimate of recharge. Uses a variably saturated flow model to estimate sediment hydraulic conductivity and percolation rates based on temperature fluctuations and matric potential from heat dissipation sensors. Isotopic Tracer All Climatic Regions Direct method for field surveys;Accurate results without absorption or tracer loss;Requires only one-time sampling, allowing for smaller flux estimates;Doesn't require frequent field visits. Radioactive material may not be permitted in all areas due to environmental protection laws;Requires costly instruments for reading samples and technical operation;Point estimates of recharge require multiple measurements;Difficulties in soil sampling at greater depths and locating tracer peak;Water content within root zone is underestimated due to evapotranspiration. Application of tracer at multiple sites and appropriate averaging of the results can give more realistic value of recharge;Understanding groundwater flow patterns, age, recharge zones, losses, and interactions with surface waters. Unsaturated Zone Techniques Environmental Tracer Arid/Semiarid Chloride Mass Balance (CMB) Model for Recharge Rate Estimation;Cost-effective and environmentally friendly;Accurately estimates recharge rates;Conserves atmospheric inputs;Provides integrated value. Ambiguity in determining chloride concentration in wet/dry deposition;Extreme rainfall affects concentration;CMB method relies on runoff for Cl concentration causes errors in humid regions; Provides precise recharge rate approximation for a few years to longer periods;Used for local to catchment/regional level estimation providing potential estimates if withdrawals greater than recharge. Historical Tracer Arid/Semiarid No extra hazard;No extra cost of tracer;Historical tracers provide point estimates of water flux over the last 50 years. Uncertainties regarding tracer location and concentration; Difficulties of soil sampling at greater depths and locating tracer peaks in areas with higher recharge rate;Water fluxes estimated from tracers within the root zone can overestimate water fluxes below the root zone due to evapotranspiration. Historical tracers or event markers such as bomb-pulse tritium (3H) has been widely used in the past in both unsaturated and saturated zones to estimate recharge. Applied Tracer Humid No environmental hazard;Easy to apply and sampling;Low cost; Visual observation is possible for visible dyes;Provides precise recharge estimations as they are unaffected by surface runoff and other water balance component and driven only by recharge component. Observed recharge rate will be higher than actual due to preferential pathways;Negligible concentration towards greater depth with insufficient initial concentration; Tracers don't directly measure water flow, leading to over- or under-estimation;Issues with secondary tracer inputs, mixing, and dual flow mechanisms;Technique yields point estimates of recharge through soil matrix only;Low recharge rate calculation due to the slow movement of tracer through root zone. Calculated recharge rates represent the time between application and sampling;Used for local to catchment/regional level estimation providing potential estimates if withdrawals exceed recharge. Saturated Zone Techniques Ground Water Dating/Aging All Climatic Regions Easy to implement if the instrument for reading the sample is available;No additional field setting/experiment is needed. Costly instrument;Variation of isotopic signature with depth may occur due to various reasons; Multiple sampling throughout the depth up to aquifer is needed;Neoconservative nature;Lack of mass balance research;Affected by contamination;High cost, and specialized personnel requirements. Used for local to catchment/regional level estimation giving actual recharge values; Range is not limited; The temporal scales represented by the recharge values range from years to long term average. Numerical models All Hydrological Zones Numerical Modelling All Climatic Regions Requires less data;Can model large areas and complex conditions; Can provide the missing information;Calibrated models can assess spatial and temporal distribution and Scenarios;Can provide a predictive tool to quantify impacts on the system; Higher generalization ability than AI models. Computationally intensive due to iterative techniques; Simulation models may display errors in parameter estimation, measurement errors, and application scale due to inherent assumptions and validation processes;Complexity in model preparation, realistic problem description and result evaluation. Numerical relationship between basiccomponents in the water budget method is used; Provides the recharge estimate as a residual term;Used for catchment to regional level estimation; Range is medium to large basins; Temporal scales represented by the recharge values range from months to years. Machine learning algorithms All Hydrological Zones Machine Learning/Deep Learning All Climatic Regions Improves calibration of numerical models;Requires fewer input parameters, reducing computational times without sacrificing accuracy of detail;Easy to use with reasonable accuracy without needing to understand the system's physics;Deep learning models are robust, relying on significant predictors, so eliminating any predictor doesn't affect the system. Lack of understanding the underlying physical process; Lower generalization ability due to overtraining; Require a high number of models runs for optimization, sensitivity / uncertainty analysis;Lengthy calibration and prediction time;Spatial recharge dynamics is not covered as it is data intensive;Short forecast time period; Not suitable for large research areas. Effective for groundwater management when used in combination with numerical models;Machine learning models can improve numerical models especially with limited field data, enabling accurate prediction at specific locations using various codes and software.
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