Stochastic and deterministic groundwater modeling of a heterogeneous aquifer
Date
2018-01-29
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Abstract
Groundwater models estimate head and fluxes into and out of an aquifer. They are the most important tools that we have, to predict aquifer response to natural and human-induced stresses; this makes them an inevitable part of any resource and environmental management study. Developing groundwater model is not always a straightforward task. It is especially challenging in heterogeneous and complex aquifers. Paskapoo Formation is a significant bedrock aquifer in Alberta, Canada. This heterogeneous aquifer is comprised mainly of relatively permeable sandstone embedded in relatively low permeable shale. There are several challenges in developing groundwater model in such formations including estimating groundwater recharge and characterizing heterogeneity. Groundwater recharge replenishes aquifers. Estimating spatial distribution of recharge is essential for water supply as well as contamination studies. A new method for estimating the spatial distribution of recharge is presented in this thesis. The method uses a combination of the baseflow method and the chloride mass balance method. In the method, total recharge over the entire watershed is estimated using the baseflow method, and then the spatial variability of recharge is approximated using groundwater chloride concentration. It will be shown that the proposed method does not need an estimation of the atmospheric deposition rate of chloride as long as the groundwater contribution to baseflow is estimated with a high degree of confidence. The efficacy of the method is demonstrated using data from a rural watershed in Alberta, Canada using a groundwater model. I showed that the difference between measured and modeled heads in the model that was calibrated using the spatially distributed recharge was lower than the model that was calibrated applying uniform recharge. Sufficient deterministic data are not usually available to map the heterogeneity of aquifers. Therefore, geostatistical simulations are utilized to build probable geological models of aquifers. I presented a Markov chain method to generate and condition a suite of stochastic representations (hereafter geomodels) of a highly heterogeneous and non-stationary fluvial bedrock aquifer using all available information, including paleo-current statistics, the proportion of each rock category, and lithologs. I also evaluated the capability of a well-known upscaling algorithm to estimate the effective hydraulic conductivity of the generated geomodels. Our numerical experiment showed that the upscaled hydraulic conductivity could not adequately capture the complex behaviours of the formation. In addition, we introduced a novel approach to condition geostatistical simulations of Paskapoo Formation using hydraulic data. The method creates a local geomodel that honors both the lithologic model and pumping test data. The proposed method uses probability perturbation method to locally search through an ensemble of possible geomodels which are generated using a random function, Transition Probability Markov-Chain, and conditioned by lithological data. Then the method rejects the geomodels which fail to reproduce the pumping test drawdown data. Consequently, the additional conditioning data (pumping test data) reduce the uncertainty space of the stochastic geomodel.
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Keywords
Groundwater modeling, Paskapoo Formation, Transition Probability Markov Chain, Probability Perturbation Method, Recharge, Heterogeneity, Numerical models
Citation
Niazi, A. (2018). Stochastic and Deterministic Groundwater Modeling of a Heterogeneous Aquifer (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/5469