Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-3105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-3105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal
Melanie Fischer
CORRESPONDING AUTHOR
Institute of Environmental Science and Geography, University of
Potsdam, Potsdam, Germany
Jana Brettin
Institute of Environmental Science and Geography, University of
Potsdam, Potsdam, Germany
Sigrid Roessner
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
Ariane Walz
Institute of Environmental Science and Geography, University of
Potsdam, Potsdam, Germany
Monique Fort
Département de Géographie, Université Paris Cité,
Paris, France
Oliver Korup
Institute of Environmental Science and Geography, University of
Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Z. Xiong, D. Stober, M. Krstić, O. Korup, M. I. Arango, H. Li, and M. Werner
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Glacial lake outburst floods (GLOFs) in the greater Himalayan region threaten local communities and infrastructure. We assess this hazard objectively using fully data-driven models. We find that lake and catchment area, as well as regional glacier-mass balance, credibly raised the susceptibility of a glacial lake in our study area to produce a sudden outburst. However, our models hardly support the widely held notion that rapid lake growth increases GLOF susceptibility.
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We explored differences in the damaging process across different flood types, regions within Germany, and six flood events through a numerical model in which the groups can learn from each other. Differences were found mostly across flood types, indicating the importance of identifying them, but there is great overlap across regions and flood events, indicating either that socioeconomic or temporal information was not well represented or that they are in fact less different within our cases.
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Short summary
Nepal’s second-largest city has been rapidly growing since the 1970s, although its valley has been affected by rare, catastrophic floods in recent and historic times. We analyse potential impacts of such floods on urban areas and infrastructure by modelling 10 physically plausible flood scenarios along Pokhara’s main river. We find that hydraulic effects would largely affect a number of squatter settlements, which have expanded rapidly towards the river by a factor of up to 20 since 2008.
Nepal’s second-largest city has been rapidly growing since the 1970s, although its valley has...
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