Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling

Mustafa, Syed Md Touhidul; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

doi:10.1016/j.envsoft.2020.104654

Cited by 40 publications

(19 citation statements)

References 67 publications

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“…The reliability of groundwater flow models is strongly influenced by different sources of uncertainty, including the uncertainty on input data, model parameters, and model structure [96]. Because these uncertainties also influence 1D estimates, we would like to again highlight that the quality of any model output is strongly dependent on the quality of the input data and the conceptual model assumptions.…”

Section: D Numerical Groundwater Flow Modelmentioning

confidence: 99%

The Significance of Vertical and Lateral Groundwater–Surface Water Exchange Fluxes in Riverbeds and Riverbanks: Comparing 1D Analytical Flux Estimates with 3D Groundwater Modelling

Ghysels

Anibas

Awol

et al. 2021

Water

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Riverbed temperature profiles are frequently used to estimate vertical river–aquifer exchange fluxes. Often in this approach, strictly vertical flow is assumed. However, riverbeds are heterogeneous structures often characterised by complex flow fields, possibly violating this assumption. We characterise the meter-scale variability of river–aquifer interaction at two sections of the Aa River, Belgium, and compare vertical flux estimates obtained with a 1D analytical solution to the heat transport equation with fluxes simulated with a 3D groundwater model (MODFLOW) using spatially distributed fields of riverbed hydraulic conductivity. Based on 115 point-in-time riverbed temperature profiles, vertical flux estimates that are obtained with the 1D solution are found to be higher near the banks than in the center of the river. The total exchange flux estimated with the 3D groundwater model is around twice as high as the estimate based on the 1D solution, while vertical flux estimates from both methods are within a 10% margin. This is due to an important contribution of non-vertical flows, especially through the riverbanks. Quasi-vertical flow is only found near the center of the river. This quantitative underestimation should be considered when interpreting exchange fluxes based on 1D solutions. More research is necessary to assess conditions for which using a 1D analytical approach is justified to more accurately characterise river–aquifer exchange fluxes.

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Section: D Numerical Groundwater Flow Modelmentioning

confidence: 99%

The Significance of Vertical and Lateral Groundwater–Surface Water Exchange Fluxes in Riverbeds and Riverbanks: Comparing 1D Analytical Flux Estimates with 3D Groundwater Modelling

Ghysels

Anibas

Awol

et al. 2021

Water

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“…However, due to subjectivity, an assumed single Bayesian prior may have a potential of an ill-posed posterior model probability (PMP; Kavetski et al 2006) potentially due to drastic changes in posterior probability because of small errors in the observation. Bayesian model averaging (BMA) has been used to quantify groundwater model uncertainty, with model parameters being assumed to follow a uniform prior distribution (Mustafa et al 2020). In a study conducted in the San Joaquin River Basin, BMA was successfully used to estimate the uncertainty of aquifer storage from the machine leaning-based groundwater model (Yin et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of groundwater recharge using multiple climate models in Bayesian frameworks

Achieng

Zhu

2021

Journal of Water and Climate Change

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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.

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“…Accurate simulation of the groundwater flow processes across various spatial and temporal scales is critical to reach efficient, timely, and sustainable groundwater management, particularly in the regions with water scarcity problems such as arid areas (e.g., Krakauer et al 2014). In these regions, groundwater resources are under a constant threat of overdrawn due to irrigation, anthropogenic, and climate pressure among others (Mustafa et al 2020). With these stressors, the lack of perennial sources of groundwater has been a concern for domestic water supply and economic prosperity in these areas.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, very few studies have been conducted on groundwater simulation using MEA. The exceptions are Barzegar et al (2018) and Mustafa et al (2020) who coupled the DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Conductivity) and the PMWIN models with MEA to address aquifer vulnerability. While these studies shed light on the MEA implementation, they largely neglected structural uncertainty during groundwater simulation.…”

Section: Introductionmentioning

confidence: 99%

Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies

Jafarzadeh

Pourreza‐Bilondi

Akbarpour

et al. 2021

Journal of Hydroinformatics

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Growing demands in arid regions have increased groundwater vulnerabilities necessitating appropriate modeling and management strategies to understand and sustain aquifer system behaviors. Sustainable management of aquifer systems, however, requires a proper understanding of groundwater dynamics and accurate estimates of recharge rates which often cause error and uncertainty in simulation. This study aims to quantify the uncertainty and error associated with groundwater simulation using various multi-model ensemble averaging (MEA) techniques such as simple model averaging, weighted averaging model, multi-model super ensemble, and modified MMSE. Two numerical solutions, i.e., finite difference and finite element (FE), were first coupled under three schemes such as explicit scheme (ES), implicit scheme, and Crank-Nicolson Scheme to numerically solve groundwater simulation problems across two case studies, synthetic and real-world (the Birjand aquifer in Iran) case studies. The MEA approach was considerably successful in calibrating a complex arid aquifer in a way that honors complex geological heterogeneity and stress configurations. Specifically, the MEA techniques skillfully reduced the error and uncertainties in simulation, particularly those errors associated with water table variability and fluctuation. Furthermore, a coupled FE-ES method outperformed other approaches and generated the best groundwater-level simulation for the synthetic case study, while stand-alone FE was particularly successful for the Birjand aquifer simulation as a real-world case study.

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Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling

Cited by 40 publications

References 67 publications

The Significance of Vertical and Lateral Groundwater–Surface Water Exchange Fluxes in Riverbeds and Riverbanks: Comparing 1D Analytical Flux Estimates with 3D Groundwater Modelling

The Significance of Vertical and Lateral Groundwater–Surface Water Exchange Fluxes in Riverbeds and Riverbanks: Comparing 1D Analytical Flux Estimates with 3D Groundwater Modelling

Estimation of groundwater recharge using multiple climate models in Bayesian frameworks

Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies

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