Rainfall redistribution in a tropical forest: Spatial and temporal patterns

Abstract

[1] 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.

Requirements for throughfall monitoring: The roles of temporal scale and canopy complexity

“…First, due to the decreasing coefficient of variation with increasing event size (Fig. 5a) (Brauman et al, 2010;Carlyle-Moses et al, 2004;Holwerda et al, 2006;Loustau et al, 1992;Price and Carlyle-Moses, 2003;Staelens et al, 2006;Vrugt et al, 2003;Zhang et al, 2006;Zimmermann et al, 2009), the sampling effort necessary to reach a pre-specified error limit of mean throughfall estimates decreases alike (Kimmins, 1973;Price and Carlyle-Moses, 2003;Rodrigo and Àvila, 2001;Zimmermann et al, 2010). The results of our study are in line with these findings (Figs.…”

“…5c and d). The detected decrease of correlation lengths with increasing event size confirms results of previous studies which indicated a particularly pronounced influence of the canopy structure on throughfall patterns for small events (Carlyle-Moses et al, 2004;Loustau et al, 1992;Zimmermann et al, 2009). The consequence in terms of sampling is the poor performance of troughs when low nugget-to-sill ratios coincide with effective ranges that exceed a couple of meters (Fig.…”

“…A comparison with other investigations of throughfall spatial structure from mature forest sites, however, reveals no consistent patterns. That is to say, some studies detected strong spatial dependence (Keim et al, 2005), whereas others did not (Zimmermann et al, 2009(Zimmermann et al, , 2010. At this point it is important to remember that a variety of processes such as the redistribution of rainfall in the understory influences the characteristics of throughfall spatial correlations (Zimmermann et al, 2009).…”

“…The main problem is that throughfall data often contain large outliers which originate from drip points (Lloyd and Marques, 1988;Zimmermann et al, 2009). From a statistical point of view, throughfall data can be regarded as the superposition of two processes (Zimmermann et al, 2009): a dominating process that forms the underlying distribution and a contaminating process which is reflected in a few large outlying values.…”

“…From a statistical point of view, throughfall data can be regarded as the superposition of two processes (Zimmermann et al, 2009): a dominating process that forms the underlying distribution and a contaminating process which is reflected in a few large outlying values. In order to obtain simulated throughfall fields we need to model the underlying distribution and the contaminating process separately because some outlying values cannot be forced to the center of the distribution even after transformation (Zimmermann et al, 2009). In the next sections we describe, step-by-step, the modeling of simulated throughfall fields.…”