A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment

Remondo, Juan; Bonachea, Jaime; Cendrero, Antonio

doi:10.1007/s10346-005-0016-x

Cited by 122 publications

(80 citation statements)

References 12 publications

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“…As the hydraulic conductivity based on field measurements was not available for the study area, this parameter was estimated for the identified soil types based on the work developed by Rawls et al (1982), which summarized the typical hydraulic conductivities for different soil types starting from the respective textural properties. The national digital soil map at 1 : 25 000 scale (DGADR, 1999) was used to extract the clay, silt and sand, and coarse sand fractions for the different soils types in the study area.…”

Section: Hydraulic Modelmentioning

confidence: 99%

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Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Oliveira

Zêzere²,

Lajas

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km 2 ) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

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Section: Hydraulic Modelmentioning

confidence: 99%

“…Guzzetti et al, 2005;Remondo et al, 2005). The relevance for using this study area limits when assessing rainfall-triggered landslide susceptibility is related with the maintenance of the hydrologic processes continuity, mainly runoff and potential infiltration.…”

Section: Introductionmentioning

confidence: 99%

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Oliveira

Zêzere²,

Lajas

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…Examples of quantitative risk analysis (QRA) at regional scale can be found in Cardinali et al (2002), Remondo et al (2005) and Catani et al (2005). However, these studies provide a mean annual risk with no information on the expected distribution of annual costs.…”

Section: P Nicolet Et Al: Shallow Landslide's Stochastic Risk Modelmentioning

confidence: 99%

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

Nicolet

Foresti

Caspar

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Abstract. Due to their relatively unpredictable characteristics, shallow landslides represent a risk for human infrastructures. Multiple shallow landslides can be triggered by widespread intense precipitation events. The event of August 2005 in Switzerland is used in order to propose a risk model to predict the expected number of landslides based on the precipitation amounts and lithological units. The spatial distribution of rainfall is characterized by merging data coming from operational weather radars and a dense network of rain gauges with an artificial neural network. Lithologies are grouped into four main units, with similar characteristics. Then, from a landslide inventory containing more than 5000 landslides, a probabilistic relation linking the precipitation amount and the lithology to the number of landslides in a 1 km 2 cell, is derived. In a next step, this relation is used to randomly redistribute the landslides using Monte Carlo simulations. The probability for a landslide to reach a building is assessed using stochastic geometry and the damage cost is assessed from the estimated mean damage cost using an exponential distribution to account for the variability. Although the model reproduces well the number of landslides, the number of affected buildings is underestimated. This seems to result from the human influence on landslide occurrence. Such a model might be useful to characterize the risk resulting from shallow landslides and its variability.

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“…The database has been the subject of a number of hazard studies (Remondo [7]; Remondo et al [8], [9]). It has been later expanded with addition of socioeconomic information to enable risk assessment (Remondo et al [6]; Bonachea [10]). It covers approximately 140 km 2 , with elevations ranging between 0 m and 700 m a.s.l., and the main annual rainfall is 1500 mm.…”

Section: Study Area Sub-areas and The Problemmentioning

confidence: 99%

“…Examples can be seen of studies of different area extensions with diverse densities of landslide occurrences (van Westen et al [2], 21 km 2 ; Zêzere et al [3], 17 km 2 ; Coe et al, [4], about 315 km 2 , or Cardinali et al [5], 79 places surrounding known landslides within a 8456 km 2 region; and Remondo et al [6], a 500 km 2 area). Generally, no particular statistical justification is used (put forward) for a study and mainly reasons of geomorphologic or physiographic uniformity, cartographic unit delimitation or sociologic relevance are the arguments used to justify the selection of study area and of its boundaries.…”

Section: Introductionmentioning

confidence: 99%

Occurrence neighbourhoods and risk assessment from landslide hazard in northern Spain

Fabbri¹,

Remondo²,

Ballabio³

et al. 2008

WIT Transactions on Information and Communication

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This contribution analyzes the problem of selecting the desirable characteristics of a study area when using geo-information for natural risk assessment. Shape, boundary, density of detail of the study area and the distribution of hazardous occurrences can be fundamental in conditioning the estimation of values in a map of expected risk. A study area in the Basque Country of northern Spain is used in which previous studies produced maps of risks for linear infrastructures, land uses and buildings, from thousands of shallow translational landslides. The area is reconsidered here in terms of five telescopic sub-areas corresponding to different neighbourhoods of the landslide occurrences. The results of the corresponding hazard predictions are interpreted via prediction-rate tables and curves obtained from blind tests, i.e., prediction maps obtained using only part of the occurrences cross-validated with the distribution of the remaining occurrences. The subsequent introduction of socioeconomic thematic maps and scenarios enables the derivation of risk maps based on the prediction rates, the hazard maps and the socioeconomic indicator values. The comparison of the risk maps from the different study-area datasets is used to assess their impact on risk values and to provide guidance on how to perform the selection maintaining greater significance. A critical issue is the loss of significance when reducing study area neighbourhoods closer or further away from the hazardous locations. The application is an example of a general purpose spatial predictive modelling processing strategy for which dedicated software has been developed.

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A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment

Cited by 122 publications

References 12 publications

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

Occurrence neighbourhoods and risk assessment from landslide hazard in northern Spain

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