A comparison of methods to estimate groundwater recharge from bare soil based on data observed by a large‐scale lysimeter

Abstract

Effectively estimating groundwater recharge is critical to manage water resources, especially in arid and semi‐arid regions as impacted by intensive human activities and climate changes. Rare insights have been gained into groundwater recharge since direct observation is hard to carry out. Although several methods are currently available to estimate groundwater recharge, the estimated results may cover noticeable bias. The behaviours of different methods based on different conceptual frameworks and exhibiting different levels of complexity should be examined to estimate actual groundwater recharge. This study aims to assess the performance of four common methods to estimate groundwater recharge. For this end, large‐scale lysimeters equipped with soil water content sensors and water table sensors were set up at a research site established in Guanzhong Basin of China. The data achieved by 1‐year observation were employed to compare four estimation methods. As revealed from the results, the following findings are drawn. (a) Groundwater level fluctuation (GLF) method is simple, whereas its accuracy is determined by specific yield, and adopting a water balance method to estimate specific yield can considerably enhance the accuracy of GLF. (b) The calibrated numerical model can obtain the optimal result compared with the other methods, whereas long‐term observation data are required for parameter calibration. (c) In the water balance method, the maximum entropy production (MEP) model and a practical method (estimating evaporation between two rainfall events) were used to calculate evaporation. As indicated by the results, water balance method combined with MEP is capable of obtaining more reliable results of groundwater recharge compared with the practical method. (d) With an analytical model based on linearized Richards' equation, accurate results can be achieved. What is more, the analytical model only needs the measurement of soil moisture near the surface. The limitation of this method is that it is difficult to determine the maximal water flux. The mentioned findings are of critical implications to the management and sustainable development of groundwater.

On Groundwater Recharge in Variably Saturated Subsurface Flow Models

“…(2005), and Zhang et al. (2020), this simplification can result in a significant overestimation of recharge. Conversely, the addition of a small amount of water can cause a significant rise of the water table if the soil above the water table is close to saturation (Gillham, 1984).…”

“…However, this expression for specific yield ignores the initial soil water content profile. As pointed out by multiple authors, e.g., Allison et al (1990), Healy and Cook (2002), Crosbie et al (2005), and Zhang et al (2020), this simplification can result in a significant overestimation of recharge. Conversely, the addition of a small amount of water can cause a significant rise of the water table if the soil above the water table is close to saturation (Gillham, 1984).…”

Groundwater recharge estimation using water table fluctuation and empirical methods

“…The methods for estimating groundwater recharge range from simple to complicated. Recharge has been determined using a water table fluctuation (WTF) method (Delottier et al 2018), empirical methods (Falalakis & Gemitzi 2020;Andualem et al 2021), an integrated surface water-groundwater modeling approach (Chemingui et al 2015), baseflow separation (Coes et al 2007), soil moisture budget (Noorduijn et al 2018), water balance method (Dhungel & Fiedler 2016), lysimeter (Zhang et al 2020;Gong et al 2021), seepage meter (Michael et al 2003), Darcy's method (Yin et al 2011), chloride mass balance (Yin et al 2011;Crosbie et al 2018), stable isotopes (Jesiya et al 2021), modeling approach (Ebrahimi et al 2016;Mogaji & Lim 2020), GIS-based approach and satellite imageries (Batelaan & De Smedt 2007), etc. According to Healy & Cook (2002), the application of multiple recharge estimation methods increases the accuracy of recharge estimates.…”