Abimbola Olufemi P, Mittelstet Aaron R, Gilmore Troy E, Korus Jesse T
Department of Biological Systems Engineering, University of Nebraska-Lincoln, 223 L. W. Chase Hall, Lincoln, NE, 68583-0726, United States.
Conservation and Survey Division, School of Natural Resources, University of Nebraska-Lincoln, 101 Hardin Hall, 3310 Holdrege Street, Lincoln, NE, 68583-0996, United States.
Sci Rep. 2020 Feb 28;10(1):3696. doi: 10.1038/s41598-020-60658-3.
Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (K) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized K estimates from column experiments or grain-size distribution, but K may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting K spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on K variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of K and drainage area as a response variable reduces the fuzziness in selecting the "best" PTF. We propose that the PTF can be used in predicting the ranges of K values across multiple stream orders.
其物理性质调节着含水层与溪流之间通量的速率、时间和位置。河床垂直水力传导率（K）是流域模型中的一个关键参数，因此了解其空间变异性和不确定性对于准确预测压力和环境信号如何在水文系统中传播至关重要。大多数分布式建模研究使用来自柱实验或粒度分布的广义K估计值，但对于给定的粒度组，K可能涵盖广泛的数量级范围。因此，在空间上精确预测K仍然停留在概念、实验阶段，或者约束较差。这通常会导致建模结果的不确定性增加。有必要通过提出一种能够应用于整个流域同时纳入复杂水文过程空间变异性的新方法，将重点从扩大孔隙尺度的柱实验到流域尺度上转移。在此，我们提出一种新方法——多茎嵌套漏斗法（MSNF），来开发能够模拟复杂沉积物输移对美国法国人溪流域多个河流阶次K变异性影响的土壤传递函数（PTF）。我们发现，将K与流域面积的乘积作为响应变量可减少选择“最佳”PTF时的模糊性。我们建议PTF可用于预测多个河流阶次的K值范围。
