@misc{OzturkPittoreBehlingetal.2020,
  author    = {Ozturk, Ugur and Pittore, Massimiliano and Behling, Robert and R{\"o}ßner, Sigrid and Andreani, Louis and Korup, Oliver},
  title     = {How robust are landslide susceptibility estimates?},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {2},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54198},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-541980},
  pages     = {17},
  year      = {2020},
  abstract  = {Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10\% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.},
  language  = {en}
}
@article{VehKorupvonSpechtetal.2019,
  author    = {Veh, Georg and Korup, Oliver and von Specht, Sebastian and R{\"o}ßner, Sigrid and Walz, Ariane},
  title     = {Unchanged frequency of moraine-dammed glacial lake outburst floods in the Himalaya},
  series = {Nature climate change},
  volume    = {9},
  journal   = {Nature climate change},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {1758-678X},
  doi       = {10.1038/s41558-019-0437-5},
  pages     = {379 -- 383},
  year      = {2019},
  abstract  = {Shrinking glaciers in the Hindu Kush-Karakoram-Himalaya-Nyainqentanglha (HKKHN) region have formed several thousand moraine-dammed glacial lakes(1-3), some of these having grown rapidly in past decades(3,4). This growth may promote more frequent and potentially destructive glacial lake outburst floods (GLOFs)(5-7). Testing this hypothesis, however, is confounded by incomplete databases of the few reliable, though selective, case studies. Here we present a consistent Himalayan GLOF inventory derived automatically from all available Landsat imagery since the late 1980s. We more than double the known GLOF count and identify the southern Himalayas as a hotspot region, compared to the more rarely affected Hindu Kush-Karakoram ranges. Nevertheless, the average annual frequency of 1.3 GLOFs has no credible posterior trend despite reported increases in glacial lake areas in most of the HKKHN3,8, so that GLOF activity per unit lake area has decreased since the late 1980s. We conclude that learning more about the frequency and magnitude of outburst triggers, rather than focusing solely on rapidly growing glacial lakes, might improve the appraisal of GLOF hazards.},
  language  = {en}
}
@article{OzturkPittoreBehlingetal.2021,
  author    = {Ozturk, Ugur and Pittore, Massimiliano and Behling, Robert and R{\"o}ßner, Sigrid and Andreani, Louis and Korup, Oliver},
  title     = {How robust are landslide susceptibility estimates?},
  series = {Landslides},
  volume    = {18},
  journal   = {Landslides},
  number    = {2},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1612-510X},
  doi       = {10.1007/s10346-020-01485-5},
  pages     = {681 -- 695},
  year      = {2021},
  abstract  = {Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10\% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.},
  language  = {en}
}