Determining Groundwater Recharge Rate with a Distributed Model and Remote Sensing Techniques

M. Babaei () and H. Ketabchi ()
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M. Babaei: Tarbiat Modares University
H. Ketabchi: Tarbiat Modares University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 14, No 4, 5423 pages

Abstract: Abstract Groundwater balance estimation techniques, as important tools for dealing with many hydrological problems, are one of the main issues in water resources management. One of the critical challenges in estimating the groundwater balance components is the uncertainty in the proposed inflow and outflow rates. Groundwater recharge rate varies both spatially and temporally, making direct measurement difficult. In order to reliably estimate the groundwater recharge rate in the groundwater balance equations, the uncertainties in estimation of the other components such as evapotranspiration (ET) should be reduced by estimating them using more accurate techniques such as remote sensing-based methods. The present study applies the WetSpass-M distributed model to the Rafsanjan aquifer in Kerman, Iran. This model has been run for eight years (2009–2016) with monthly time steps. The recorded monthly surface flow data of the hydrometric station is used as the observed data for calibration and validation. ET is also calculated with satellite images of Landsat8 by using SSEB and SEBAL algorithms on a monthly scale in order to evaluate the reliability of the estimated ET by the model. The average rainfall rate during the simulation period is 297.1 MCM/year. The obtained results showed that the average ET and groundwater recharge from rainfall is 185.1 and 102.1 MCM/year, respectively. Although, considering the rainfall rate and irrigation, these numbers are estimated to be 552.3 and 417.2 MCM/year, respectively. Two components of recharge rate and ET constitute large portions of the groundwater balance.

Keywords: Evapotranspiration; Recharge rate; Remote sensing; Reliable estimation; WetSpass-M (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03315-w

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