Spatial variability of throughfall and stemflow measurements in Amazonian rainforest
Abstract The spatial variability of throughfall and stemflow in Amazonian tropical rainforest is analysed. Statistical tests are used to assess the minimum sampling size and number of gauges required. Random relocation of throughfall gauges is shown to be better than fixed position gauges and random relocation of gauges on a transect line is better than over an area. During the study period, throughfall and stemflow, calculated as percentages of gross rainfall, were 91(± 2) and 1.8(± 1)%, respectively. No attempt was made in this study to investigate storm size dependency of these figures.
The problem of mining for outliers in sequential datasets is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. However, most of them use a sample of known typical sequences to build the model. Besides, they remain greedy in terms of memory usage. In this paper we propose an extension of one such approach, based on a probabilistic suffix tree and on a measure of similarity. We add a pruning criterion which reduces the size of the tree while improving the model, and a sharp inequality for the concentration of the measure of similarity, to better sort the outliers. We prove the feasibility of our approach through a set of experiments over a protein database.