Inverse unsaturated-zone flow modeling for groundwater recharge estimation: a regional spatial nonstationary approach View Full Text


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Article Info

DATE

2022-06-18

AUTHORS

Mohammad Karamouz, Hadi Meidani, Davood Mahmoodzadeh

ABSTRACT

Groundwater recharge estimation (GRE), particularly at a regional scale, is an important challenge in hydrogeology. Unsaturated zone flow (UZF) modeling is now being used increasingly for GRE, though its validity relies on the accurate estimation of the soil hydraulic parameters (SHPs). In this study, a regional spatial-nonstationarity-based inverse-UZF-modeling framework is developed for GRE. The regional-scale investigation is achieved using a multiple column approach and by applying the software package HYDRUS-1D. Considering the inverse modeling, the SHPs are calibrated against observed data from the stations of a large-scale monitoring network. Moreover, a nonstationary kriging technique is implemented to provide a regional map of recharge from the calculated values. Additionally, to report probability maps of recharge, a probabilistic approach through the sequential Gaussian simulation algorithm is incorporated. The proposed methodology has been tested at 100 stations of the Oklahoma Mesonet network across Oklahoma (USA) for the period 2014–2019. The comparison between the simulated and observed pressure head data endorses the performance of the regional-scale inverse UZF modeling. The distribution of recharge in the produced map increases from northwest to southeast, following the similar pattern of rainfall. Finally, the probabilistic approach results in an e-type (mean) map yielding an expected value of 166 mm/year statewide mean recharge, and 90% confidence interval maps that provide a workable range of 139–194 mm/year for planning purposes. With the rapid expansion of large-scale monitoring networks, this study can be applied to other areas where such observed data exist. More... »

PAGES

1529-1549

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10040-022-02502-8

DOI

http://dx.doi.org/10.1007/s10040-022-02502-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148785841


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