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Title:Accounting for parameter uncertainty and temporal variability in coupled groundwater-surface water models using component and systems reliability analysis
Author(s):Oviedo Salcedo, Diego
Director of Research:Valocchi, Albert J.; Cai, Ximing
Doctoral Committee Chair(s):Valocchi, Albert J.
Doctoral Committee Member(s):Cai, Ximing; Song, Junho; Lin, Yu-Feng
Department / Program:Civil and Environmental Engineering
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):River-aquifer interaction
component and systems reliability analysis
autoregressive–moving-average (ARMA) process
Abstract:The connections between streams and aquifers can be spatially variable and uncertain due to heterogeneity in geology and topography. During drought seasons, farming activities may induce critical peak pumping rates to supply irrigation water needs for crops. This may lead to increased concerns about reduction in baseflow and adverse impacts upon riverine ecosystems. As an example, conflicts have been documented between different water users in Kankakee River during the low-flow seasons in 1987, 1988, and 2005. Quantitative management of the groundwater is a required component in this particular human-nature system to evaluate the trade offs between irrigation agriculture and the ecosystems requirements. Forecast of the impact of pumping on river-aquifer exchange depends upon uncertain and spatially variable hydrogeological parameters, as well as temporally uncertain streamflow. In this study a novel component - systems reliability analysis framework is developed to assess risk. Physical parameters uncertainty is studied in light of the Glover-Balmer and MODFLOW models, while temporal random streamflow is modeled as a Markov process. Reliability methods have been developed in the aerospace industry and extensively applied in structural engineering, but have only seen limited use in water resources. In addition to risk evaluation, the proposed framework will produce sensitivities, importance measures and shares of individual uncertain sources on the overall risk. It naturally accounts for any type of statistical dependence. By means of hypothetical examples, the fundamental aspects of the proposed scheme are introduced. They also open an afresh avenue to address efficiently key issues for managers who frequently deal with risk-informed decisions. The results have been validated with MCS, solely for the risk assessment. With MCS would result computationally demanding to obtain sensitivities and importance measures under transient conditions.
Issue Date:2014-01-16
Rights Information:Copyright 2013 Diego Oviedo Salcedo
Date Available in IDEALS:2014-01-16
Date Deposited:2013-12

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