TY - JOUR A1 - Ozturk, Ugur A1 - Pittore, Massimiliano A1 - Behling, Robert A1 - Rößner, Sigrid A1 - Andreani, Louis A1 - Korup, Oliver T1 - How robust are landslide susceptibility estimates? JF - Landslides N2 - 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. KW - Landslide susceptibility KW - Logistic regression KW - Southern Kyrgyzstan KW - Landslide inventory KW - Remote sensing Y1 - 2020 U6 - https://doi.org/10.1007/s10346-020-01485-5 SN - 1612-510X SN - 1612-5118 VL - 18 IS - 2 SP - 681 EP - 695 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Ozturk, Ugur A1 - Pittore, Massimiliano A1 - Behling, Robert A1 - Rößner, Sigrid A1 - Andreani, Louis A1 - Korup, Oliver T1 - How robust are landslide susceptibility estimates? T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1346 KW - Landslide susceptibility KW - logistic regression KW - Southern Kyrgyzstan KW - Landslide inventory KW - remote sensing Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-541980 SN - 1866-8372 IS - 2 ER - TY - JOUR A1 - Veh, Georg A1 - Korup, Oliver A1 - von Specht, Sebastian A1 - Rößner, Sigrid A1 - Walz, Ariane T1 - Unchanged frequency of moraine-dammed glacial lake outburst floods in the Himalaya JF - Nature climate change N2 - 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. KW - Climate change KW - Cryospheric science KW - Environmental impact KW - Geomorphology Y1 - 2019 U6 - https://doi.org/10.1038/s41558-019-0437-5 SN - 1758-678X SN - 1758-6798 VL - 9 IS - 5 SP - 379 EP - 383 PB - Nature Publ. Group CY - London ER -