Rainfall redistribution in a tropical forest: Spatial and temporal patterns
Rainfall redistribution in a tropical forest: Spatial and temporal patterns
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DOI：
10.1029/2008wr007470
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发表时间：
2009-11
影响因子：
5.4
通讯作者：
A. Zimmermann;B. Zimmermann;H. Elsenbeer
中科院分区：
文献类型：
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作者：
A. Zimmermann;B. Zimmermann;H. Elsenbeer
在过去的几十年里，穿透模式的研究受到了极大的关注。然而，以前相关研究及其数据分析方法和途径的地理偏差使人们对有关穿透降雨时空模式的说法的总体有效性产生了怀疑。我们在巴拿马一块1公顷的半落叶热带雨林中雇佣了220名采集员，在14个月的时间里采集了穿透雨的样本。我们对空间模式的分析基于60个数据集，而时间分析包括91个事件。这两个数据集都显示了扭曲的频率分布。当大的离群值引起偏度时，经典的非稳健变差函数估计器高估了窗口方差，在某些情况下，甚至会导致虚假的自相关结构。在这些情况下，稳健的变异函数估计技术提供了一个解决方案。通过我们的地块，通常没有或仅显示出微弱的空间自相关。相比之下，时间相关性很强，也就是说，潮湿和干燥的地点持续了连续的雨季。有趣的是，季节性和落叶性对时空格局没有影响。我们认为，如果穿透模式要对近地表过程的模式有任何解释能力，必须排除数据分析伪迹，以免虚假关联与因果关系混淆；此外，感兴趣领域的时间稳定性是必不可少的。
The investigation of throughfall patterns has received considerable interest over the last decades. And yet, the geographical bias of pertinent previous studies and their methodologies and approaches to data analysis cast a doubt on the general validity of claims regarding spatial and temporal patterns of throughfall. We employed 220 collectors in a 1‐ha plot of semideciduous tropical rain forest in Panama and sampled throughfall during a period of 14 months. Our analysis of spatial patterns is based on 60 data sets, whereas the temporal analysis comprises 91 events. Both data sets show skewed frequency distributions. When skewness arises from large outliers, the classical, nonrobust variogram estimator overestimates the sill variance and, in some cases, even induces spurious autocorrelation structures. In these situations, robust variogram estimation techniques offer a solution. Throughfall in our plot typically displayed no or only weak spatial autocorrelations. In contrast, temporal correlations were strong, that is, wet and dry locations persisted over consecutive wet seasons. Interestingly, seasonality and hence deciduousness had no influence on spatial and temporal patterns. We argue that if throughfall patterns are to have any explanatory power with respect to patterns of near‐surface processes, data analytical artifacts must be ruled out lest spurious correlation be confounded with causality; furthermore, temporal stability over the domain of interest is essential.