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Soil moisture data as a constraint for groundwater recharge estimation

Mathias, Simon A.; Sorensen, James P.R.; Butler, Adrian P.

Soil moisture data as a constraint for groundwater recharge estimation Thumbnail


James P.R. Sorensen

Adrian P. Butler


Estimating groundwater recharge rates is important for water resource management studies. Modeling approaches to forecast groundwater recharge typically require observed historic data to assist calibration. It is generally not possible to observe groundwater recharge rates directly. Therefore, in the past, much effort has been invested to record soil moisture content (SMC) data, which can be used in a water balance calculation to estimate groundwater recharge. In this context, SMC data is measured at different depths and then typically integrated with respect to depth to obtain a single set of aggregated SMC values, which are used as an estimate of the total water stored within a given soil profile. This article seeks to investigate the value of such aggregated SMC data for conditioning groundwater recharge models in this respect. A simple modeling approach is adopted, which utilizes an emulation of Richards’ equation in conjunction with a soil texture pedotransfer function. The only unknown parameters are soil texture. Monte Carlo simulation is performed for four different SMC monitoring sites. The model is used to estimate both aggregated SMC and groundwater recharge. The impact of conditioning the model to the aggregated SMC data is then explored in terms of its ability to reduce the uncertainty associated with recharge estimation. Whilst uncertainty in soil texture can lead to significant uncertainty in groundwater recharge estimation, it is found that aggregated SMC is virtually insensitive to soil texture.


Mathias, S. A., Sorensen, J. P., & Butler, A. P. (2017). Soil moisture data as a constraint for groundwater recharge estimation. Journal of Hydrology, 552, 258-266.

Journal Article Type Article
Acceptance Date Jun 23, 2017
Online Publication Date Jun 29, 2017
Publication Date Jun 29, 2017
Deposit Date Jun 29, 2017
Publicly Available Date Jun 29, 2018
Journal Journal of Hydrology
Print ISSN 0022-1694
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 552
Pages 258-266


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