Modeling and prediction of soil hydrologic processes require identifying soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for combining spatially- and temporally-extensive soil moisture datasets obtained in the Shale Hills Critical Zone Observatory (CZO) from 2004 to 2010. The soil moisture was investigated based on Empirical Orthogonal Function (EOF) analysis. The dominant soil moisture patterns were derived and further correlated with the soil-terrain attributes in the study area. The EOF analyses indicated that one or two EOFs of soil moisture could explain 76–89% of data variation. The primary EOF pattern had high values clustered in the valley region and, conversely, low values located in the sloping hills, with a depth-dependent correlation to which curvature, depth to bedrock, and topographic wetness index at the intermediate depths (0.4 m) exhibited the highest contributions. We suggest a novel approach to integrating the spatially-extensive manually measured datasets with the temporally-extensive automatically monitored datasets. Given the data accessibility, the current data merging framework has provided the methodology for the coupling of the mapped and monitored soil moisture datasets, as well as the conceptual coupling of slow and fast pedologic and hydrologic functions. This successful coupling implies that a combination of diverse and extensive moisture data has provided a solution of data use efficiency and, thus, exciting insights into the understanding of hydrological processes at multiple scales.

中文翻译：

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基于经验正交函数的空间和时间范围广泛的土壤水分数据组合方法

土壤水文过程的建模和预测需要确定土壤水分的时空模式和有效的方法，允许在不同的时空尺度上使用数据观测。这项工作提出了一种将 2004 年至 2010 年在页岩山临界区观测站 (CZO) 中获得的时空范围广泛的土壤水分数据集相结合的方法。基于经验正交函数 (EOF) 分析对土壤水分进行了调查。推导出了主要的土壤水分模式，并与研究区的土壤-地形属性进一步相关。EOF 分析表明，土壤水分的一两个 EOF 可以解释 76-89% 的数据变化。主要的 EOF 模式具有高值聚集在山谷地区，相反，低值位于倾斜的山丘，与深度相关的曲率、基岩深度和中间深度 (0.4 m) 的地形湿度指数表现出最高的贡献。我们建议采用一种新方法将空间广泛的手动测量数据集与时间范围广泛的自动监测数据集相结合。鉴于数据的可访问性，当前的数据合并框架为映射和监测的土壤水分数据集的耦合以及慢速和快速土壤学和水文功能的概念耦合提供了方法。这种成功的耦合意味着多样化和广泛的水分数据的组合提供了数据使用效率的解决方案，从而为理解多个尺度的水文过程提供了令人兴奋的见解。