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首页> 外文期刊>Stochastic environmental research and risk assessment >Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: linear mixed model approach
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Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: linear mixed model approach

机译：饱和导水率空间变化对自相关陆上流量数据的建模影响：线性混合模型方法

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摘要

The mixed linear model approach was introduced and applied in studying the effects of spatial variation of the saturated hydraulic conductivity (K_s) on the variation of the overland flow. Analysis was carried out with 2,000 rainfall-runoff events, all generated through transformation of real, observed rainfall events and different spatially variable K_s fields in a small (12 ha) agricultural catchment. The parameters accounting for the variation in the generation method were the coefficient of variation (cv) and correlation length (L_xL_y) of K_s both having two levels of values obtained from field measurements of other studies. The analysis showed that the combinations with both parameters having the smaller or bigger value during flow peaks only caused different response in the overland flow. However, the parameters were statistically significant only at the 10% level. Most of the flow variation was explained by the event dynamics. The mixed models were able to model the structure of the data efficiently with less restrictive assumptions than for example the analysis of variance, hence producing more reliable results. The method was able to take into account autocorrelation of the test series, correlation between the factors and unequal variances. The usefulness of the method was supported by the fact that the conclusions drawn by it were confirmed by simple, conventional methods of a previous study, added with statistical criteria and confidence levels for each calculation moment. The findings of the study can be utilized in practise for example when designing the field sampling experiments.
机译：介绍了混合线性模型方法，并将其用于研究饱和导水率（K_s）的空间变化对陆上径流变化的影响。对2,000个降雨径流事件进行了分析，这些事件都是通过转换实际观测到的降雨事件以及在一个小面积（12公顷）农业流域中不同的空间变量K_s场产生的。解释生成方法中的变化的参数是K_s的变化系数（cv）和相关长度（L_xL_y），它们均具有从其他研究的现场测量获得的两个水平的值。分析表明，两个参数在流量峰值期间具有较小或较大值的组合只会在陆上流量中引起不同的响应。但是，这些参数仅在10％的水平上具有统计学意义。大部分流量变化是由事件动力学解释的。混合模型能够以比例如方差分析更少的限制性假设来高效地建模数据的结构，从而产生更可靠的结果。该方法能够考虑测试序列的自相关，因子之间的相关性和不等方差。该方法的有用性得到了以下事实的支持：该方法得出的结论已通过先前研究的简单，常规方法得到证实，并为每个计算时刻添加了统计标准和置信度。研究的结果可以在实践中利用，例如在设计现场采样实验时。

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