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首页> 外文期刊>Journal of Hydrology >Buffer effect on identifying transient streambed hydraulic conductivity with inversion of flood wave responses
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Buffer effect on identifying transient streambed hydraulic conductivity with inversion of flood wave responses

机译：用洪水响应反演识别瞬态流液压导电性的缓冲效应

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Determination of streambed hydraulic conductivity (K-sb) and its variations over time are critical for quantifying exchange fluxes between surface water and groundwater. Previous studies have used flood wave response techniques (FRT) to determine K-sb or its transience, excluding the streambed storage effect, thus the estimated K-sb may not be reliable. In this study, after improving a previous FRT by substituting the gradient descent algorithm with an advanced and robust optimization method, trust region reflective algorithm, we have analyzed the influence of streambed storage effect on the estimation of time-dependent K-sb through successive inversion of flood wave responses. The study shows that the streambed storage effect is not important for identifying the transience in the hydraulic properties of a relatively thin streambed (e.g., several or dozens of centimeters). But significant under-estimation (1-2 orders of magnitude) can be seen because of neglecting the streambed storage effect. It is particularly true for a stream-confined aquifer system if the streambed is composed of fine deposits (e.g., silt, clay) with a thicker thickness greater than 1 m corresponding to low permeability but high storativity, and the aquifer is characterized by relatively low permeability (e.g., roughly 0.1-10 m d(-1)) and relatively low storativity (roughly 1E-5 m(-1)) (e.g., dense fine sands). The new method in which streambed storage effect is incorporated, can provide more reliable estimations of time-dependent K-sb for a stream-confined aquifer system, and it has been successfully applied to estimate the time-dependent K-sb of a reach of the Arkansas River, Kansas, USA.
机译：测定流液压导电性（K-SB）及其随时间变化对于量化地表水和地下水之间的交换通量至关重要。以前的研究使用了泛波响应技术（FRT）来确定K-SB或其瞬态，不包括流储存效果，因此估计的K-SB可能不可靠。在这项研究中，通过用先进和鲁棒优化方法代替梯度下降算法来改善前一个FRT，信任区域反射算法，通过连续反转分析了流储存效应对时间依赖性K-SB估计的影响洪水响应。该研究表明，流储存效果对于识别相对较薄的流（例如，几十厘米）的液压性能中的转诊并不重要。但由于忽略了流储存效果，因此可以看到显着的估计（1-2级）。如果流池含水层系统尤其如此，如果流置于流沉积物（例如，淤泥，粘土），厚度大于1m的细沉积物（例如，淤泥，粘土），则对应于低渗透率但高余量，并且含水层的特征在于相对较低渗透率（例如，大约0.1-10md（-1））和相对较低的余量（大约1E-5M（-1））（例如，致密的细砂）。结合了流储存效果的新方法，可以为流局限性含水层系统提供更可靠的时间依赖K-SB的估计，并且已经成功地应用于估计覆盖范围的时间依赖性K-SB阿肯色州河，堪萨斯州，美国。

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