Hauser Juerg, Wellmann Florian, Trefry Mike
Aachen Institute for Advanced Study in Computation Engineering Science (AICES), RWTH Aachen University, Schinkelstr. 2., Aachen 52062, Germany.
CSIRO Land and Water, Underwood Avenue, Floreat WA 6014, Western Australia, Australia.
Ground Water. 2018 Mar;56(2):251-265. doi: 10.1111/gwat.12577. Epub 2017 Aug 29.
We consider two sources of geology-related uncertainty in making predictions of the steady-state water table elevation for an unconfined aquifer. That is the uncertainty in the depth to base of the aquifer and in the hydraulic conductivity distribution within the aquifer. Stochastic approaches to hydrological modeling commonly use geostatistical techniques to account for hydraulic conductivity uncertainty within the aquifer. In the absence of well data allowing derivation of a relationship between geophysical and hydrological parameters, the use of geophysical data is often limited to constraining the structural boundaries. If we recover the base of an unconfined aquifer from an analysis of geophysical data, then the associated uncertainties are a consequence of the geophysical inversion process. In this study, we illustrate this by quantifying water table uncertainties for the unconfined aquifer formed by the paleochannel network around the Kintyre Uranium deposit in Western Australia. The focus of the Bayesian parametric bootstrap approach employed for the inversion of the available airborne electromagnetic data is the recovery of the base of the paleochannel network and the associated uncertainties. This allows us to then quantify the associated influences on the water table in a conceptualized groundwater usage scenario and compare the resulting uncertainties with uncertainties due to an uncertain hydraulic conductivity distribution within the aquifer. Our modeling shows that neither uncertainties in the depth to the base of the aquifer nor hydraulic conductivity uncertainties alone can capture the patterns of uncertainty in the water table that emerge when the two are combined.
在对无压含水层的稳态地下水位高程进行预测时，我们考虑了与地质相关的两个不确定性来源。即含水层底部深度的不确定性以及含水层内部水力传导率分布的不确定性。水文建模的随机方法通常使用地质统计学技术来处理含水层内部的水力传导率不确定性。在缺乏能推导地球物理参数与水文参数之间关系的井数据的情况下，地球物理数据的使用通常仅限于约束构造边界。如果我们通过地球物理数据分析确定无压含水层的底部，那么相关的不确定性就是地球物理反演过程的结果。在本研究中，我们通过量化西澳大利亚金蒂尔铀矿周围古河道网络形成的无压含水层的地下水位不确定性来说明这一点。用于反演现有航空电磁数据的贝叶斯参数自助法的重点是恢复古河道网络的底部及其相关不确定性。这使我们能够在一个概念化的地下水使用情景中量化对地下水位的相关影响，并将由此产生的不确定性与含水层内部水力传导率分布不确定导致的不确定性进行比较。我们的建模表明，含水层底部深度的不确定性和水力传导率的不确定性单独都无法捕捉到两者结合时出现的地下水位不确定性模式。
