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Volume 18, issue 2 | Copyright

Special issue: Landslide early warning systems: monitoring systems, rainfall...

Nat. Hazards Earth Syst. Sci., 18, 633-646, 2018
https://doi.org/10.5194/nhess-18-633-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Mar 2018

Research article | 01 Mar 2018

Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

David J. Peres1, Antonino Cancelliere1, Roberto Greco2, and Thom A. Bogaard3 David J. Peres et al.
  • 1Department of Civil Engineering and Architecture, University of Catania, Catania, Italy
  • 2Dipartimento di Ingegneria Civile Design Edilizia e Ambiente, Università degli Studi della Campania Luigi Vanvitelli, Aversa (CE), Italy
  • 3Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide-triggering thresholds. In this paper, we perform a quantitative analysis of the impacts of uncertain knowledge of landslide initiation instants on the assessment of rainfall intensity–duration landslide early warning thresholds. The analysis is based on a synthetic database of rainfall and landslide information, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model, and is therefore error-free in terms of knowledge of triggering instants. This dataset is then perturbed according to hypothetical reporting scenarios that allow simulation of possible errors in landslide-triggering instants as retrieved from historical archives. The impact of these errors is analysed jointly using different criteria to single out rainfall events from a continuous series and two typical temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant, especially when errors exceed 1 day or the actual instants follow the erroneous ones. Errors generally lead to underestimated thresholds, i.e. lower than those that would be obtained from an error-free dataset. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall limits the possibility to set up links between thresholds and physio-geographical factors.

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We investigate the influence of imprecise identification of triggering instants on landslide early warning thresholds by perturbing an error-free synthetic dataset. Combined impacts of uncertainty with respect to temporal discretization of data and criteria for singling out rainfall events are assessed as well. Results show that thresholds can be significantly affected by these uncertainty sources.
We investigate the influence of imprecise identification of triggering instants on landslide...
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