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Volume 17, issue 2 | Copyright

Special issue: Risk and uncertainty estimation in natural hazards

Nat. Hazards Earth Syst. Sci., 17, 225-241, 2017
https://doi.org/10.5194/nhess-17-225-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Feb 2017

Research article | 21 Feb 2017

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Susana Almeida1, Elizabeth Ann Holcombe1, Francesca Pianosi1, and Thorsten Wagener1,2 Susana Almeida et al.
  • 1Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
  • 2Cabot Institute, University of Bristol, Bristol, BS8 1TR, UK

Abstract. Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89% of the expected behaviour of the studied slope can be characterized based on only two variables – the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

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Landslides threaten communities globally, yet predicting their occurrence is challenged by uncertainty about slope properties and climate change. We present an approach to identify the dominant drivers of slope instability and the critical thresholds at which slope failure may occur. This information helps decision makers to target data acquisition to improve landslide predictability, and supports policy development to reduce landslide occurrence and impacts in highly uncertain environments.
Landslides threaten communities globally, yet predicting their occurrence is challenged by...
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