Estimation of groundwater recharge using multiple climate models in Bayesian frameworks

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dc.contributor.author Achieng, Kevin O.
dc.contributor.author Jianting Zhu
dc.date.accessioned 2021-09-14T15:40:56Z
dc.date.available 2021-09-14T15:40:56Z
dc.date.issued 2021-09-15
dc.identifier.citation Kevin O. Achieng, Jianting Zhu; Estimation of groundwater recharge using multiple climate models in Bayesian frameworks. Journal of Water and Climate Change 2021; jwc2021345. doi: https://doi.org/10.2166/wcc.2021.345 en_US
dc.identifier.uri https://doi.org/10.2166/wcc.2021.345
dc.identifier.uri http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/4861
dc.description.abstract Groundwater recharge plays a vital role in replenishing aquifers, sustaining demand, and reducing adverse effects (e.g. land subsidence). In order to manage climate change-induced effects on groundwater dynamics, climate models are increasingly being used to predict current and future recharges. Even though there has been a number of hydrological studies that have averaged climate models’ predictions in a Bayesian framework, few studies have been related to the groundwater recharge. In this study, groundwater recharge estimates from 10 regional climate models (RCMs) are averaged in 12 different Bayesian frameworks with variations of priors. A recession-curve-displacement method was used to compute recharge from measured streamflow data. Two basins of different sizes located in the same water resource region in the USA, the Cedar River Basin and the Rainy River Basin, are selected to illustrate the approach and conduct quantitative analysis. It has been shown that groundwater recharge prediction is affected by the Bayesian priors. The non-Empirical Bayes g-Local-based Bayesian priors result in posterior inclusion probability values that are consistent with the performance of the climate models outside the Bayesian framework. With the proper choice of priors, the Bayesian frameworks can produce good results of groundwater recharge with R2 , percent bias error, and Willmott’s index of agreement of .0.97, ,2%, and .0.97, respectively, in the two basins. The Bayesian framework with an appropriate prior provides opportunity to estimate recharge from multiple climate models. en_US
dc.language.iso en en_US
dc.publisher Journal of Water and Climate Change en_US
dc.title Estimation of groundwater recharge using multiple climate models in Bayesian frameworks en_US
dc.type Article en_US


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