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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.
Changes in rainfall interception along a secondary forest succession gradient in lowland Panama
(2013)
Secondary forests are rapidly expanding in tropical regions. Yet, despite the importance of understanding the hydrological consequences of land-cover dynamics, the relationship between forest succession and canopy interception is poorly understood. This lack of knowledge is unfortunate because rainfall interception plays an important role in regional water cycles and needs to be quantified for many modeling purposes. To help close this knowledge gap, we designed a throughfall monitoring study along a secondary succession gradient in a tropical forest region of Panama. The investigated gradient comprised 20 forest patches 3 to 130 yr old. We sampled each patch with a minimum of 20 funnel-type throughfall collectors over a continuous 2month period that had nearly 900 mm of rain. During the same period, we acquired forest inventory data and derived several forest structural attributes. We then applied simple and multiple regression models (Bayesian model averaging, BMA) and identified those vegetation parameters that had the strongest influence on the variation of canopy interception. Our analyses yielded three main findings. First, canopy interception changed rapidly during forest succession. After only a decade, throughfall volumes approached levels that are typical for mature forests. Second, a parsimonious (simple linear regression) model based on the ratio of the basal area of small stems to the total basal area outperformed more complex multivariate models (BMA approach). Third, based on complementary forest inventory data, we show that the influence of young secondary forests on interception in realworld fragmented landscapes might be detectable only in regions with a substantial fraction of young forests. Our re-sults suggest that where entire catchments undergo forest regrowth, initial stages of succession may be associated with a substantial decrease of streamflow generation. Our results further highlight the need to study hydrological processes in all forest succession stages, including early ones.
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.
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.
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.
In the last decades, an increasing number of studies analyzed spatial patterns in throughfall by means of variograms. The estimation of the variogram from sample data requires an appropriate sampling scheme: most importantly, a large sample and a layout of sampling locations that often has to serve both variogram estimation and geostatistical prediction. While some recommendations on these aspects exist, they focus on Gaussian data and high ratios of the variogram range to the extent of the study area. However, many hydrological data, and throughfall data in particular, do not follow a Gaussian distribution. In this study, we examined the effect of extent, sample size, sampling design, and calculation method on variogram estimation of throughfall data. For our investigation, we first generated non Gaussian random fields based on throughfall data with large outliers. Subsequently, we sampled the fields with three extents (plots with edge lengths of 25 m, 50 m, and 100 m), four common sampling designs (two grid-based layouts, transect and random sampling) and five sample sizes (50, 100, 150, 200, 400). We then estimated the variogram parameters by method-of-moments (non-robust and robust estimators) and residual maximum likelihood. Our key findings are threefold. First, the choice of the extent has a substantial influence on the estimation of the variogram. A comparatively small ratio of the extent to the correlation length is beneficial for variogram estimation. Second, a combination of a minimum sample size of 150, a design that ensures the sampling of small distances and variogram estimation by residual maximum likelihood offers a good compromise between accuracy and efficiency. Third, studies relying on method-of-moments based variogram estimation may have to employ at least 200 sampling points for reliable variogram estimates. These suggested sample sizes exceed the number recommended by studies dealing with Gaussian data by up to 100 %. Given that most previous through fall studies relied on method-of-moments variogram estimation and sample sizes <<200, currently available data are prone to large uncertainties. (C) 2016 Elsevier B.V. All rights reserved.
estimating mean throughfall
(2016)
The selection of an appropriate spatial extent of a sampling plot is one among several important decisions involved in planning a throughfall sampling scheme. In fact, the choice of the extent may determine whether or not a study can adequately characterize the hydrological fluxes of the studied ecosystem. Previous attempts to optimize throughfall sampling schemes focused on the selection of an appropriate sample size, support, and sampling design, while comparatively little attention has been given to the role of the extent. In this contribution, we investigated the influence of the extent on the representativeness of mean throughfall estimates for three forest ecosystems of varying stand structure. Our study is based on virtual sampling of simulated throughfall fields. We derived these fields from throughfall data sampled in a simply structured forest (young tropical forest) and two heterogeneous forests (old tropical forest, unmanaged mixed European beech forest). We then sampled the simulated throughfall fields with three common extents and various sample sizes for a range of events and for accumulated data. Our findings suggest that the size of the study area should be carefully adapted to the complexity of the system under study and to the required temporal resolution of the throughfall data (i.e. event-based versus accumulated). Generally, event-based sampling in complex structured forests (conditions that favor comparatively long autocorrelations in throughfall) requires the largest extents. For event-based sampling, the choice of an appropriate extent can be as important as using an adequate sample size. (C) 2016 Elsevier B.V. All rights reserved.
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.
Soils in various places of the Panama Canal Watershed feature a low saturated hydraulic conductivity (K-s) at shallow depth, which promotes overland-flow generation and associated flashy catchment responses. In undisturbed forests of these areas, overland flow is concentrated in flow lines that extend the channel network and provide hydrological connectivity between hillslopes and streams. To understand the dynamics of overland-flow connectivity, as well as the impact of connectivity on catchment response, we studied an undisturbed headwater catchment by monitoring overland-flow occurrence in all flow lines and discharge, suspended sediment, and total phosphorus at the catchment outlet. We find that connectivity is strongly influenced by seasonal variation in antecedent wetness and can develop even under light rainfall conditions. Connectivity increased rapidly as rainfall frequency increased, eventually leading to full connectivity and surficial drainage of entire hillslopes. Connectivity was nonlinearly related to catchment response. However, additional information on factors such as overland-flow volume would be required to constrain relationships between connectivity, stormflow, and the export of suspended sediment and phosphorus. The effort to monitor those factors would be substantial, so we advocate applying the established links between rain event characteristics, drainage network expansion by flow lines, and catchment response for predictive modeling and catchment classification in forests of the Panama Canal Watershed and in similar regions elsewhere.
Physical hydrogels with tunable stress-relaxation and excellent stress recovery are formed from anionic polyurethanes via addition of acids, monovalent salts, or divalent salts. Gel properties can be widely adjusted through pH, salt valence, salt concentration, and monomer composition. We propose and investigate a novel gelation mechanism based on a colloidal system interacting through charge repulsion and chrage shielding, allowing a broad use of the material, from acidic (pH 4–5.5) to pH-neutral hydrogels with Young's moduli ranging from 10 to 140 kPa.