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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content.
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Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content.

机译：使用顺序高斯模拟来评估土壤含水量的田间尺度空间不确定性。

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摘要

Soil water content (SWC) has a vital role in a variety of hydrological processes such as infiltration, runoff and erosion. Mapping the spatial pattern of SWC is then essential for appropriate addressing of these processes. Geostatistics is often used to characterize the spatial variability of SWC. This information may be used for estimating SWC e.g., by ordinary kriging (OK) or modeling location-specific uncertainty (local uncertainty) of the estimates by indicator kriging (IK). Kriging-based algorithms however smooth out the details and are incapable of detecting multi-location uncertainty (spatial uncertainty) of SWC estimates. Sequential Gaussian simulation (sGs) can model the spatial uncertainty through generation of several equally probable stochastic realizations. In this study sGs is used to map SWC spatial distribution and to provide a quantitative measure of its spatial uncertainty in particular. The SWC measurements were performed on 157 soil samples taken from an 18 ha erosion experiment field in Lower Austria. The results show that the spatial pattern of SWC is well recognized using the sGs as the simulated models reproduce the sample statistics including histogram and semivariogram model reasonably well. The difference among realizations is used to provide a quantitative measure of spatial uncertainty of SWC estimates. Knowledge of spatial uncertainty is helpful to evaluate the delineation of vulnerable areas to erosion.
机译：土壤水分（SWC）在各种水文过程（例如渗透，径流和侵蚀）中起着至关重要的作用。因此，映射SWC的空间模式对于正确处理这些过程至关重要。地统计学常用于表征西南沿海地区的空间变异性。该信息可用于例如通过普通克里金法（OK）估计SWC或通过指示器克里金法（IK）对估计的位置特定不确定性（局部不确定性）建模。但是，基于Kriging的算法可以使细节变得平滑，并且无法检测SWC估计值的多位置不确定性（空间不确定性）。顺序高斯模拟（sGs）可以通过生成几个同样可能的随机实​​现来对空间不确定性进行建模。在这项研究中，sGs用于绘制SWC空间分布图，特别是提供其空间不确定性的定量度量。 SWC测量是对下奥地利州一个18公顷侵蚀实验场的157个土壤样品进行的。结果表明，使用sGs可以很好地识别SWC的空间格局，因为模拟模型可以很好地再现样本统计数据，包括直方图和半变异函数模型。实现之间的差异用于提供SWC估计的空间不确定性的定量度量。对空间不确定性的了解有助于评估易蚀区域对侵蚀的描述。

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