@article{GermerZimmermannNeilletal.2012, author = {Germer, Sonja and Zimmermann, Alexander and Neill, Christopher and Krusche, Alex V. and Elsenbeer, Helmut}, title = {Disproportionate single-species contribution to canopy-soil nutrient flux in an Amazonian rainforest}, series = {Forest ecology and management}, volume = {267}, journal = {Forest ecology and management}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0378-1127}, doi = {10.1016/j.foreco.2011.11.041}, pages = {40 -- 49}, year = {2012}, abstract = {Rainfall, throughfall and stemflow were monitored on an event basis in an undisturbed open tropical rainforest with a large number of palm trees located in the southwestern Amazon basin of Brazil. Stemflow samples were collected from 24 trees with a diameter at breast height (DBH) > 5 cm, as well as eight young and four full-grown babassu palms (Attalea speciosa Mart.) for 5 weeks during the peak of the wet season. We calculated rainfall, throughfall and stemflow concentrations and fluxes of Na+, K+, Ca2+, Mg2+,, Cl-, SO42-, NO3- and H+ and stemflow volume-weighted mean concentrations and fluxes for three size classes of broadleaf trees and three size classes of palms. The concentrations of most solutes were higher in stemflow than in rainfall and increased with increasing tree and palm size. Concentration enrichments from rainfall to stemflow and throughfall were particularly high (81-fold) for NO3-. Stemflow fluxes of NO3- and H+ exceeded throughfall fluxes but stemflow fluxes of other solutes were less than throughfall fluxes. Stemflow solute fluxes to the forest soil were dominated by fluxes on babassu palms, which represented only 4\% of total stem number and 10\% of total basal area. For NO3-, stemflow contributed 51\% of the total mass of nitrogen delivered to the forest floor (stemflow + throughfall) and represented more than a 2000-fold increase in NO3- flux compared what would have been delivered by rainfall alone on the equivalent area. Because these highly localized fluxes of both water and NO3- persist in time and space, they have the potential to affect patterns of soil moisture, microbial populations and other features of soil biogeochemistry conducive to the creation of hotspots for nitrogen leaching and denitrification, which could amount to an important fraction of total ecosystem fluxes. Because these hotspots occur over very small areas, they have likely gone undetected in previous studies and need to be considered as an important feature of the biogeochemistry of palm-rich tropical forest.}, language = {en} } @article{BaeseElsenbeerNeilletal.2012, author = {B{\"a}se, Frank and Elsenbeer, Helmut and Neill, Christopher and Krusche, Alex V.}, title = {Differences in throughfall and net precipitation between soybean and transitional tropical forest in the southern Amazon, Brazil}, series = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere}, volume = {159}, journal = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-8809}, doi = {10.1016/j.agee.2012.06.013}, pages = {19 -- 28}, year = {2012}, abstract = {The expansion of soybean cultivation into the Amazon in Brazil has potential hydrological effects at local to regional scales. To determine the impacts of soybean agriculture on hydrology, a comparison of net precipitation (throughfall, stemflow) in undisturbed tropical forest and soybean fields on the southern edge of the Amazon Basin in the state of Mato Grosso is needed. This study measured throughfall with troughs and stemflow with collar collectors during two rainy seasons. The results showed that in forest 91.6\% of rainfall was collected as throughfall and 0.3\% as stemflow, while in soybean fields with two-month old plants, 46.2\% of rainfall was collected as throughfall and 9.0\% as stemflow. Hence, interception of precipitation in soybean fields was far greater than in intact forests. Differences in throughfall, stemflow and net precipitation were found to be mainly associated with differences in plant structure and stem density in transitional forest and soybean cropland. Because rainfall interception in soybean fields is higher than previously believed and because both the area of cropland and the frequency of crop cycles (double cropping) are increasing rapidly, interception needs to be reconsidered in regional water balance models when consequences of land cover changes are analyzed in the Amazon soybean frontier region. Based on the continued expansion of soybean fields across the landscape and the finding that net precipitation is lower in soy agriculture, a reduction in water availability in the long term can be assumed.}, language = {en} } @article{ZimmermannZimmermann2014, author = {Zimmermann, Alexander and Zimmermann, Beate}, title = {Requirements for throughfall monitoring: The roles of temporal scale and canopy complexity}, series = {Agricultural and forest meteorology}, volume = {189}, journal = {Agricultural and forest meteorology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2014.01.014}, pages = {125 -- 139}, year = {2014}, abstract = {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.}, language = {en} } @article{VossZimmermannZimmermann2016, author = {Voss, Sebastian and Zimmermann, Beate and Zimmermann, Alexander}, title = {Detecting spatial structures in throughfall data: The effect of extent, sample size, sampling design, and variogram estimation method}, series = {Journal of hydrology}, volume = {540}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.06.042}, pages = {527 -- 537}, year = {2016}, abstract = {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.}, language = {en} } @article{ZimmermannVossMetzgeretal.2016, author = {Zimmermann, Alexander and Voss, Sebastian and Metzger, Johanna Clara and Hildebrandt, Anke and Zimmermann, Beate}, title = {estimating mean throughfall}, series = {Journal of hydrology}, volume = {542}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.09.047}, pages = {781 -- 789}, year = {2016}, abstract = {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.}, language = {en} }