@article{HunzikerSigurdssonHalldorssonetal.2014,
  author    = {Hunziker, Matthias and Sigurdsson, Bjarni D. and Halldorsson, Gudmundur and Schwanghart, Wolfgang and Kuhn, Nikolaus},
  title     = {Biomass allometries and coarse root biomass distribution of mountain birch in southern Iceland},
  series = {Icelandic agricultural sciences},
  volume    = {27},
  journal   = {Icelandic agricultural sciences},
  publisher = {Agricultural University of Iceland},
  address   = {Reykjavik},
  issn      = {1670-567X},
  pages     = {111 -- 125},
  year      = {2014},
  abstract  = {Root systems are an important pool of biomass and carbon in forest ecosystems. However, most allometric studies on forest trees focus only on the aboveground components. When estimated, root biomass has most often been calculated by using a fixed conversion factor from aboveground biomass. In order to study the size-related development of the root system of native mountain birch (Betula pubescens Ehrh. ssp. czerepanovii), we collected the coarse root system of 25 different aged birch trees (stem diameter at 50 cm length between 0.2 and 14.1 cm) and characterized them by penetration depth (< 1 m) and root thickness. Based on this dataset, allometric functions for coarse roots (> 5 mm and > 2 mm), root stock, total belowground biomass and aboveground biomass components were calculated by a nonlinear and a linear fitting approach. The study showed that coarse root biomass of mountain birch was almost exclusively (> 95 weight-\%) located in the top 30 cm, even in a natural old-growth woodland. By using a cross-validation approach, we found that the nonlinear fitting procedure performed better than the linear approach with respect to predictive power. In addition, our results underscore that general assumptions of fixed conversion factors lead to an underestimation of the belowground biomass. Thus, our results provide allometric functions for a more accurate root biomass estimation to be utilized in inventory reports and ecological studies.},
  language  = {en}
}
@article{MeyerSchwanghartKorupetal.2014,
  author    = {Meyer, Nele Kristin and Schwanghart, Wolfgang and Korup, Oliver and Romstad, Bard and Etzelmuller, Bernd},
  title     = {Estimating the topographic predictability of debris flows},
  series = {Geomorphology : an international journal on pure and applied geomorphology},
  volume    = {207},
  journal   = {Geomorphology : an international journal on pure and applied geomorphology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0169-555X},
  doi       = {10.1016/j.geomorph.2013.10.030},
  pages     = {114 -- 125},
  year      = {2014},
  abstract  = {The Norwegian traffic network is impacted by about 2000 landslides, avalanches, and debris flows each year that incur high economic losses. Despite the urgent need to mitigate future losses, efforts to locate potential debris flow source areas have been rare at the regional scale. We tackle this research gap by exploring a minimal set of possible topographic predictors of debris flow initiation that we input to a Weights-of-Evidence (WofE) model for mapping the regional susceptibility to debris flows in western Norway. We use an inventory of 429 debris flows that were recorded between 1979 and 2008, and use the terrain variables of slope, total curvature, and contributing area (flow accumulation) to compute the posterior probabilities of local debris flow occurrence. The novelty of our approach is that we quantify the uncertainties in the WofE approach arising from different predictor classification schemes and data input, while estimating model accuracy and predictive performance from independent test data. Our results show that a percentile-based classification scheme excels over a manual classification of the predictor variables because differing abundances in manually defined bins reduce the reliability of the conditional independence tests, a key, and often neglected, prerequisite for the WofE method. The conditional dependence between total curvature and flow accumulation precludes their joint use in the model. Slope gradient has the highest true positive rate (88\%), although the fraction of area classified as susceptible is very large (37\%). The predictive performance, i.e. the reduction of false positives, is improved when combined with either total curvature or flow accumulation. Bootstrapping shows that the combination of slope and flow accumulation provides more reliable predictions than the combination of slope and total curvature, and helps refining the use of slope-area plots for identifying morphometric fingerprints of debris flow source areas, an approach used outside the field of landslide susceptibility assessments.},
  language  = {en}
}
@article{SchwanghartScherler2014,
  author    = {Schwanghart, Wolfgang and Scherler, Dirk},
  title     = {Short Communication: TopoToolbox 2-MATLAB-based software for topographic analysis and modeling in Earth surface sciences},
  series = {Earth surface dynamics},
  volume    = {2},
  journal   = {Earth surface dynamics},
  number    = {1},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {2196-6311},
  doi       = {10.5194/esurf-2-1-2014},
  pages     = {1 -- 7},
  year      = {2014},
  abstract  = {TopoToolbox is a MATLAB program for the analysis of digital elevation models (DEMs). With the release of version 2, the software adopts an object-oriented programming (OOP) approach to work with gridded DEMs and derived data such as flow directions and stream networks. The introduction of a novel technique to store flow directions as topologically ordered vectors of indices enables calculation of flow-related attributes such as flow accumulation similar to 20 times faster than conventional algorithms while at the same time reducing memory overhead to 33\% of that required by the previous version. Graphical user interfaces (GUIs) enable visual exploration and interaction with DEMs and derivatives and provide access to tools targeted at fluvial and tectonic geomorphologists. With its new release, TopoToolbox has become a more memory-efficient and faster tool for basic and advanced digital terrain analysis that can be used as a framework for building hydrological and geomorphological models in MATLAB.},
  language  = {en}
}