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The value of hydraulic conductivity information for the optimal restoration of an over-exploited aquifer

机译:液压导电性信息的价值,用于过度开发的含水层的最佳恢复

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A stochastic management tool is developed and applied in order to evaluate the worth of hydraulic conductivity data on the optimal restoration and quantitative management of the over-exploited aquifer of Lake Karla watershed in Greece. This tool consists of six models (one geostatistical, four simulation models and one management model) and combines the methodologies of: stochastic simulation-optimization, Bayesian analysis and the value of information analysis. The four simulation models (surface hydrology, reservoir operation, lake-aquifer interaction and hydrogeology) are interlinked in order to satisfy the needs of integrated simulation at the watershed scale. The heterogeneity and the lack of sufficient data of hydraulic conductivity create uncertainty on the hydraulic heads estimation. Monte Carlo realizations of hydraulic conductivity are being performed with the use of geostatistical tools and imported to the groundwater model to give multiple stochastic realizations of the aquifer. A Monte Carlo based optimization problem is then applied for each aquifer realization in order to determine the optimal aquifer's restoration management strategy. Optimal strategy has been defined the one that combines the maximum possible volume of extracted groundwater and the optimal well's position with the least financial cost, under the environmental constraint of restoring the aquifer water table. The hydrogeological uncertainty is being transformed into financial uncertainty through the optimization problem, as certain risks for the decision maker are being introduced. To avoid hydraulic head underestimation, a Bayesian decision analysis for the hydraulic conductivity data collection is being applied on each optimal solution. The worth of the new hydraulic conductivity data can be evaluated by quantifying the reduction of both hydrogeological and financial uncertainties. The results prove that there is a certain number of new hydraulic conductivity measurements up to which the profit by reducing financial uncertainty exceeds the measurement cost.
机译:开发和应用随机管理工具,以评估液压导电性数据的价值,了解希腊湖流域湖流域过度开发的含水层的最佳恢复和定量管理。该工具由六种模型(一个地统计,四种仿真模型和一个管理模式)组成,并结合了:随机仿真优化,贝叶斯分析和信息分析值的方法。四种仿真模型(表面水文,储层操作,湖泊 - 含水层相互作用和水文地质)是相互连接的,以满足流域规模的综合模拟需求。异质性和缺乏液压导电性的足够数据产生了液压头估计的不确定性。蒙特卡罗的液压导电性的实现正在使用地统计工具进行并进口到地下水模型,以提供含水层的多次随机实现。然后应用蒙特卡罗基于Carlo的优化问题,以便确定最佳含水层的恢复管理策略。在恢复含水层水表的环境约束下,已经确定了将提取地下水的最大可能体积与最低财务成本结合的最大可能数量的最佳策略。随着决策者的某些风险,水文地质不确定性正在转化为财务不确定性,因为决策者的某些风险正在介绍。为避免液压头低估,液压导电性数据收集的贝叶斯决策分析正在应用于每个最佳解决方案。通过量化水文地理和财务不确定性的降低,可以评估新的液压导电性数据的价值。结果证明,通过减少财务不确定性的利润,有一定数量的新液压导电性测量超过了测量成本。

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