Analysis of uncertainty in optimal groundwater contaminant capture design
Analysis of uncertainty in optimal groundwater contaminant capture design
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DOI：
10.1029/93wr00546
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发表时间：
1993-07
影响因子：
5.4
通讯作者：
C. Tiedeman;S. Gorelick
中科院分区：
文献类型：
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作者：
C. Tiedeman;S. Gorelick
地下水管理模型是为超级基金站点的一个浅层无承压砂质含水层开发的，在那里，氯乙烯羽流正向密歇根湖迁移。我们将非线性模拟-回归应用于一个地下水流瞬变模型，以估计参数值及其不确定性，并使用稳态流动路径分析来验证该模型与污染物位置的一致性。利用一阶泰勒级数近似将参数不确定性转化为流动模型预测的不确定性。设计了羽流稳态水力安全壳的最优最小抽水策略，并用随机规划法考虑了模型预测的不确定性。仅用两口抽油井是不可能达到60%以上的可靠性水平的。对于10口井的情况，抽油率必须增加约40%，才能将可靠性从50%提高到90%。蒙特卡罗分析表明，对于I0井90%的可靠性公式，不确定性传播的一阶方法得到了具有精确性能可靠性的解。我们发现，水力坡度的变异系数决定了是否遵守概率约束。对克服模型不确定性的概率约束和“安全系数”方法的比较表明，概率约束适应模型预测不确定性的局部变化的能力是非常重要的。优化后的溶质传输研究表明，水力遏制可靠性水平的提高并不一定会转化为更快的羽流清理时间。
groundwater management model is developed for a shallow, unconfined sandy aquifer at a Superfund site at which a vinyl chloride plume is migrating toward Lake Michigan. We use nonlinear simulation-regression applied to a transient groundwater flow model to estimate parameter values and their uncertainties and use steady state flow path analyses to confirm the model's consistency with the location of contaminants. Parameter uncertainty is translated into flow model prediction uncertainty using a first-order Taylor series approximation. Optimal minimum-pumping strategies for steady state hydraulic containment of the plume are designed, and model prediction uncertainty is accounted for with stochastic programming. It is impossible to achieve a reliability level higher than 60% using only two pumping wells. For the 10-well case, pumping rates must increase about 40% to extend reliability from 50 to 90%. Monte Carlo analyses indicate that for the I 0-well 90% reliability formulation, the first-order method of propagating uncertainty results in a solution with accurate performance reliabilities. We find that the coefficient of variation in hydraulic gradient dictates whether the probabilistic constraints are obeyed. Comparison of the probabilistic constraint and "safety factor" approaches to overcoming model uncertainty reveals that the ability of probabilistic constraints to accommodate local variations in model prediction uncertainty is highly important. Postoptimization solute transport studies show that increased reliability levels for hydraulic containment do not necessarily translate into faster plume cleanup times.