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Saturated hydraulic conductivity (K-s) is an important soil characteristic affecting soil water storage, runoff generation and erosion processes. In some areas where high-intensity rainfall coincides with low K-s values at shallow soil depths, frequent overland flow entails dense drainage networks. Consequently, linear structures such as flowlines alternate with inter-flowline areas. So far, investigations of the spatial variability of K-s mainly relied on isotropic covariance models which are unsuitable to reveal patterns resulting from linear structures. In the present study, we applied two sampling approaches so as to adequately characterize K-s spatial variability in a tropical forest catchment that features a high density of flowlines: A classical nested sampling survey and a purposive sampling strategy adapted to the presence of flowlines. The nested sampling approach revealed the dominance of small-scale variability, which is in line with previous findings. Our purposive sampling, however, detected a strong spatial gradient: surface K-s increased substantially as a function of distance to flowline; 10 m off flowlines, values were similar to the spatial mean of K-s. This deterministic trend can be included as a fixed effect in a linear mixed modeling framework to obtain realistic spatial fields of K-s. In a next step we used probability maps based on those fields and prevailing rainfall intensities to assess the hydrological relevance of the detected pattern. This approach suggests a particularly good agreement between the probability statements of K-s exceedance and observed overland flow occurrence during wet stages of the rainy season.
Spatio-temporal patterns of throughfall and solute deposition in an open tropical rain forest
(2008)
What is the most appropriate sampling scheme to estimate event-based average throughfall? A satisfactory answer to this seemingly simple question has yet to be found, a failure which we attribute to previous efforts' dependence on empirical studies. Here we try to answer this question by simulating stochastic throughfall fields based on parameters for statistical models of large monitoring data sets. We subsequently sampled these fields with different sampling designs and variable sample supports. We evaluated the performance of a particular sampling scheme with respect to the uncertainty of possible estimated means of throughfall volumes. Even for a relative error limit of 20%, an impractically large number of small, funnel-type collectors would be required to estimate mean throughfall, particularly for small events. While stratification of the target area is not superior to simple random sampling, cluster random sampling involves the risk of being less efficient. A larger sample support, e.g., the use of trough-type collectors, considerably reduces the necessary sample sizes and eliminates the sensitivity of the mean to outliers. Since the gain in time associated with the manual handling of troughs versus funnels depends on the local precipitation regime, the employment of automatically recording clusters of long troughs emerges as the most promising sampling scheme. Even so, a relative error of less than 5% appears out of reach for throughfall under heterogeneous canopies. We therefore suspect a considerable uncertainty of input parameters for interception models derived from measured throughfall, in particular, for those requiring data of small throughfall events.
A wide range of basic and applied problems in water resources research requires high-quality estimates of the spatial mean of throughfall. Many throughfall sampling schemes, however, are not optimally adapted to the system under study. The application of inappropriate sampling schemes may partly reflect the lack of generally applicable guidelines on throughfall sampling strategies. In this study we conducted virtual sampling experiments using simulated fields which are based on empirical throughfall data from three structurally distinct forests (a 12-year old teak plantation, a 5-year old young secondary forest, and a 130-year old secondary forest). In the virtual sampling experiments we assessed the relative error of mean throughfall estimates for 38 different throughfall sampling schemes comprising a variety of funnel- and trough-type collectors and a large range of sample sizes. Moreover, we tested the performance of each scheme for both event-based and accumulated throughfall data. The key findings of our study are threefold. First, as errors of mean throughfall estimates vary as a function of throughfall depth, the decision on which temporal scale (i.e. event-based versus accumulated data) to sample strongly influences the required sampling effort. Second, given a chosen temporal scale throughfall estimates can vary considerably as a function of canopy complexity. Accordingly, throughfall sampling in simply structured forests requires a comparatively modest effort, whereas heterogeneous forests can be extreme in terms of sampling requirements, particularly if the focus is on reliable data of small events. Third, the efficiency of trough-type collectors depends on the spatial structure of throughfall. Strong, long-ranging throughfall patterns decrease the efficiency of troughs substantially. Based on the results of our virtual sampling experiments, which we evaluated by applying two contrasting sampling approaches simultaneously, we derive readily applicable guidelines for throughfall monitoring. (C) 2014 Elsevier B.V. All rights reserved.
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.
Motivations and research objectives: During the passage of rain water through a forest canopy two main processes take place. First, water is redistributed; and second, its chemical properties change substantially. The rain water redistribution and the brief contact with plant surfaces results in a large variability of both throughfall and its chemical composition. Since throughfall and its chemistry influence a range of physical, chemical and biological processes at or below the forest floor the understanding of throughfall variability and the prediction of throughfall patterns potentially improves the understanding of near-surface processes in forest ecosystems. This thesis comprises three main research objectives. The first objective is to determine the variability of throughfall and its chemistry, and to investigate some of the controlling factors. Second, I explored throughfall spatial patterns. Finally, I attempted to assess the temporal persistence of throughfall and its chemical composition. Research sites and methods: The thesis is based on investigations in a tropical montane rain forest in Ecuador, and lowland rain forest ecosystems in Brazil and Panama. The first two studies investigate both throughfall and throughfall chemistry following a deterministic approach. The third study investigates throughfall patterns with geostatistical methods, and hence, relies on a stochastic approach. Results and Conclusions: Throughfall is highly variable. The variability of throughfall in tropical forests seems to exceed that of many temperate forests. These differences, however, do not solely reflect ecosystem-inherent characteristics, more likely they also mirror management practices. Apart from biotic factors that influence throughfall variability, rainfall magnitude is an important control. Throughfall solute concentrations and solute deposition are even more variable than throughfall. In contrast to throughfall volumes, the variability of solute deposition shows no clear differences between tropical and temperate forests, hence, biodiversity is not a strong predictor of solute deposition heterogeneity. Many other factors control solute deposition patterns, for instance, solute concentration in rainfall and antecedent dry period. The temporal variability of the latter factors partly accounts for the low temporal persistence of solute deposition. In contrast, measurements of throughfall volume are quite stable over time. Results from the Panamanian research site indicate that wet and dry areas outlast consecutive wet seasons. At this research site, throughfall exhibited only weak or pure nugget autocorrelation structures over the studies lag distances. A close look at the geostatistical tools at hand provided evidence that throughfall datasets, in particular those of large events, require robust variogram estimation if one wants to avoid outlier removal. This finding is important because all geostatistical throughfall studies that have been published so far analyzed their data using the classical, non-robust variogram estimator.
Sediment-discharge hysteresis loops are frequently analyzed to facilitate the understanding of sediment transport processes. Hysteresis patterns, however, are often complex and their interpretation can be complicated. Particularly, quantifying hysteresis patterns remains a problematic issue. Moreover, it is currently unknown how much data is required for analyzing sediment-discharge hysteresis loops in a given area. These open questions and challenges motivated us to develop a new method for quantifying suspended-sediment hysteresis. Subsequently, we applied the new hysteresis index to three suspended-sediment and discharge datasets from a small tropical rainforest catchment. The datasets comprised a different number of events and sampling sites. Our analyses show three main findings: (1) datasets restricted to only few events, which is typical for rapid assessment surveys, were always sufficient to identify the dominating hysteresis pattern in our research area. Furthermore, some of these small datasets contained multiple-peak events that allowed identifying intra-event exhaustion effects and hence, limitations in sediment supply. (2) Datasets comprising complete hydrological years were particularly useful for analyzing seasonal dynamics of hysteresis. These analyses revealed an exhaustion of hysteresis on the inter-event scale which also points to a limited sediment supply. (3) Datasets comprising measurements from two consecutive gauges installed at the catchment outlet and on a slope within that catchment allowed analyzing the change of hysteresis patterns along the flowpath. On the slope, multiple-peak events showed a stronger intra-event exhaustion of hysteresis than at the catchment outlet. Furthermore, exhaustion of hysteresis on the inter-event scale was not evident on the slope but occurred at the catchment outlet. Our results indicate that even small sediment datasets can provide valuable insights into sediment transport processes of small catchments. Furthermore, our results may serve as a first guideline on what to expect from an analysis of hysteresis patterns for datasets of varying quality and quantity. (c) 2014 Elsevier B.V. All rights reserved.
Numerous studies investigated the influence of abiotic (meteorological conditions) and biotic factors (tree characteristics) on stemflow generation. Although these studies identified the variables that influence stemflow volumes in simply structured forests, the combination of tree characteristics that allows a robust prediction of stemflow volumes in species-rich forests is not well known. Many hydrological applications, however, require at least a rough estimate of stemflow volumes based on the characteristics of a forest stand. The need for robust predictions of stemflow motivated us to investigate the relationships between tree characteristics and stemflow volumes in a species-rich tropical forest located in central Panama. Based on a sampling setup consisting of ten rainfall collectors, 300 throughfall samplers and 60 stemflow collectors and cumulated data comprising 26 rain events, we derive three main findings. Firstly, stemflow represents a minor hydrological component in the studied 1-ha forest patch (1.0% of cumulated rainfall). Secondly, in the studied species-rich forest, single tree characteristics are only weakly related to stemflow volumes. The influence of multiple tree parameters (e.g. crown diameter, presence of large epiphytes and inclination of branches) and the dependencies among these parameters require a multivariate approach to understand the generation of stemflow. Thirdly, predicting stemflow in species-rich forests based on tree parameters is a difficult task. Although our best model can capture the variation in stemflow to some degree, a critical validation reveals that the model cannot provide robust predictions of stemflow. A reanalysis of data from previous studies in species-rich forests corroborates this finding. Based on these results and considering that for most hydrological applications, stemflow is only one parameter among others to estimate, we advocate using the base model, i.e. the mean of the stemflow data, to quantify stemflow volumes for a given study area. Studies in species-rich forests that wish to obtain predictions of stemflow based on tree parameters probably need to conduct a much more extensive sampling than currently implemented by most studies. Copyright (c) 2015 John Wiley & Sons, Ltd.
Forests seem to represent low-erosion systems, according to most, but not all, studies of suspended-sediment yield. We surmised that this impression reflects an accidental bias in the selection of monitoring sites towards those with prevailing vertical hydrological flowpaths, rather than a tight causal link between vegetation cover and erosion alone. To evaluate this conjecture, we monitored, over a 2-year period, a 3.3 ha old-growth rainforest catchment prone to frequent and widespread overland flow. We sampled stream flow at two and overland flow at three sites in a nested arrangement on a within-event basis, and monitored the spatial and temporal frequency of overland flow. Suspended-sediment concentrations were modeled with Random Forest and Quantile Regression Forest to be able to estimate the annual yields for the 2 years, which amounted to 1 t ha(-1) and 2 t ha(-1) in a year with below-average and with average precipitation, respectively. These estimates place our monitoring site near the high end of reported suspended-sediment yields and lend credence to the notion that low yields reflect primarily the dominance of vertical flowpaths and not necessarily and exclusively the kind of vegetative cover. Undisturbed forest and surface erosion are certainly no contradiction in terms even in the absence of mass movements.