On the spatial scaling of soil moisture
On the spatial scaling of soil moisture
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DOI：
10.1016/s0022-1694(98)00232-7
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发表时间：
1999-04
影响因子：
6.4
通讯作者：
A. Western;G. Blöschl
中科院分区：
文献类型：
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作者：
A. Western;G. Blöschl
土壤湿度测量的空间尺度往往与土壤湿度预测所需的尺度不一致。因此，需要从测量值到预测值或模型值的尺度变化（放大或缩小）。测量或模型比例可以定义为一个比例三元组，包括间距、范围和支持。“间距”是指样品之间的距离;“范围”是指总体覆盖范围;“支持度”是指每个样品所整合的面积。数据中出现的统计特性，即表观方差和表观相关长度，由于测量尺度引入的偏差，通常与其真实值不同。在本文中，高分辨率的土壤水分数据从10.5公顷Tarrakala集水区在澳大利亚东南部进行了分析，以评估这种偏见定量。每次调查使用空间中多达1536个数据点。这允许两个数量级的尺度变化。在响应分析中计算表观方差和表观相关长度。表观相关长度总是随着间距、范围或支持度的增加而增加。表观方差随范围的增加而增加，随支持度的增加而减小，不随间距的变化而变化。所有这些偏差来源都是测量尺度（在间距、范围和支持方面）与自然变异尺度（即土壤水分的真实相关长度或过程尺度）之比的函数。在第二步中，本文探讨是否可以预测的标准地质统计技术的正则化和变异函数分析的偏差，由于间距，范围和支持。这样做是因为土壤水分模式的属性，如连通性，违反了这些地质统计技术的标准假设。因此，有必要通过应用于观测数据来测试这些技术的稳健性。比较表明，这些技术确实适用于有组织的土壤水分场和预测的偏差同样有组织的和随机的土壤水分模式。给出了一些例子来证明这些结果的影响水文建模和采样设计。
The spatial scale of soil moisture measurements is often inconsistent with the scale at which soil moisture predictions are needed. Consequently a change of scale (upscaling or downscaling) from the measurements to the predictions or model values is needed. The measurement or model scale can be defined as a scale triplet, consisting of spacing, extent and support. ‘Spacing’ refers to the distance between samples; ‘extent’ refers to the overall coverage; and ‘support’ refers to the area integrated by each sample. The statistical properties that appear in the data, the apparent variance and the apparent correlation length, are as a rule different from their true values because of bias introduced by the measurement scale. In this paper, high-resolution soil moisture data from the 10.5ha Tarrawarra catchment in south-eastern Australia are analysed to assess this bias quantitatively. For each survey up to 1536 data points in space are used. This allows a change of scale of two orders of magnitude. Apparent variances and apparent correlation lengths are calculated in a resampling analysis. Apparent correlation lengths always increase with increasing spacing, extent or support. The apparent variance increases with increasing extent, decreases with increasing support, and does not change with spacing. All of these sources of bias are a function of the ratio of measurement scale (in terms of spacing, extent and support) and the scale of the natural variability (i.e. the true correlation length or process scale of soil moisture). In a second step this paper examines whether the bias due to spacing, extent and support can be predicted by standard geostatistical techniques of regularisation and variogram analysis. This is done because soil moisture patterns have properties, such as connectivity, that violate the standard assumptions underlying these geostatistical techniques. Therefore, it is necessary to test the robustness of these techniques by application to observed data. The comparison indicates that these techniques are indeed applicable to organised soil moisture fields and that the bias is predicted equally well for organised and random soil moisture patterns. A number of examples are given to demonstrate the implications of these results for hydrologic modelling and sampling design.