TY - THES A1 - Vorpahl, Peter T1 - Spatiotemporal dynamics of shallow translational landslides in tropical montane forests Y1 - 2013 CY - Potsdam ER - TY - JOUR A1 - Vorpahl, Peter A1 - Dislich, Claudia A1 - Elsenbeer, Helmut A1 - Märker, Michael A1 - Schröder-Esselbach, Boris T1 - Biotic controls on shallow translational landslides JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - In undisturbed tropical montane rainforests massive organic layers accommodate the majority of roots and only a small fraction of roots penetrate the mineral soil. We investigated the contribution of vegetation to slope stability in such environments by modifying a standard model for slope stability to include an organic layer with distinct mechanical properties. The importance of individual model parameters was evaluated using detailed measurements of soil and vegetation properties to reproduce the observed depth of 11 shallow landslides in the Andes of southern Ecuador. By distinguishing mineral soil, organic layer and above-ground biomass, it is shown that in this environment vegetation provides a destabilizing effect mainly due to its contribution to the mass of the organic layer (up to 973 t ha-1 under wet conditions). Sensitivity analysis shows that the destabilizing effect of the mass of soil and vegetation can only be effective on slopes steeper than 37.9 degrees. This situation applies to 36% of the study area. Thus, on the steep slopes of this megadiverse ecosystem, the mass of the growing forest promotes landsliding, which in turn promotes a new cycle of succession. This feedback mechanism is worth consideration in further investigations of the impact of landslides on plant diversity in similar environments. KW - shallow translational landslides KW - tropical montane forest KW - biomass KW - organic layer Y1 - 2013 U6 - https://doi.org/10.1002/esp.3320 SN - 0197-9337 VL - 38 IS - 2 SP - 198 EP - 212 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Vorpahl, Peter A1 - Elsenbeer, Helmut A1 - Märker, Michael A1 - Schröder-Esselbach, Boris T1 - How can statistical models help to determine driving factors of landslides? JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion. Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the 'black box' of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency. With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes. KW - Landslides KW - Tropical montane forests KW - Statistical modeling KW - Model comparison KW - Artificial neuronal network KW - Classification trees KW - Random forests KW - Boosted regression trees KW - Generalized linear models KW - Multivariate adaptive regression splines KW - Maximum entropy method KW - Weighted model ensembles Y1 - 2012 U6 - https://doi.org/10.1016/j.ecolmodel.2011.12.007 SN - 0304-3800 SN - 1872-7026 VL - 239 IS - 7 SP - 27 EP - 39 PB - Elsevier CY - Amsterdam ER -