Nat. Hazards Earth Syst. Sci., 14, 27112713, 2014www.nat-hazards-earth-syst-sci.net/14/2711/2014/doi:10.5194/nhess-14-2711-2014 Author(s) 2014. CC Attribution 3.0 License.

PrefaceProgress in landslide hazard and risk evaluationP. Reichenbach1 and A. Gnther21Consiglio Nazionale delle Ricerche, IRPI, Perugia, Italy2Federal Institute for Geosciences and Natural Resources, Hanover, Germany

Correspondence to: P. Reichenbach (paola.reichenbach@irpi.cnr.it)

The special issue of Natural Hazards and Earth SystemSciences entitled Progress in landslide hazard and risk eval-uation contains 9 out of more than 30 oral and poster con-tributions originally presented in the NH3.11 Landslide haz-ard and risk assessment, and landslide management sessionheld at the General Assembly of the European GeosciencesUnion, in Vienna (Austria), on 2227 April 2012.

The session was aimed at comparing qualitative or quanti-tative landslide hazard and risk estimates in different physio-graphic and geographical settings for different kinds of pro-cesses affecting the environment at different spatial scales.Presentations focusing on the adoption and integration ofdifferent modeling approaches for quantitative assessmentswere welcomed, and papers providing information on thequality, reliability and limitations of process-oriented or sta-tistical models were encouraged.

The meeting proved to be a valuable opportunity to discussand compare methods, techniques and tools for the recog-nition, evaluation and mitigation of landslide hazards andthe related risks. In particular, the quality, the reliability andthe limitation of models, input variables and output mapshave been discussed for presented case studies at differentgeographical scales and in different physiographic environ-ments. This is important information for filling the gap be-tween academic research and application of the results inenvironmental planning and management. The special issuecontains the following selected papers presented and dis-cussed at the session.

Voumard et al. (2013) have calculated risk along roadsusing a dynamic traffic approach that simulates the durationof the presence of vehicles inside hazardous areas during agiven time interval. The risk is analyzed along three roadsections threatened by different natural hazards (active land-slide, debris flows and dolines) along a mountain road: Aigle

Col du Pillon (western Switzerland). Two different scenar-ios were simulated: (1) a road without obstacles and (2) aroad regulated by traffic lights. Results of the dynamic riskassessment were compared with the static methodology thatconsiders an average number of vehicles per time unit and aconstant vehicle speed. The main advantage of the dynamicapproach is a better representation of the real traffic regardingthe interaction between different vehicles of different types(cars, trucks, coaches). The dynamic approach with a micro-scopic traffic simulator was well designed to analyze the riskon relatively short road sections (up to a few kilometers) indetail. At the regional scale, the risk estimations would beaveraged over the entire network (with large parts at no risk),and differences between static and dynamic risks may not beso pronounced. Thus, the interest of this method is to analyzehotspots, i.e., strongly hazardous short road sections, and tosee for example how the location of traffic lights can increaseor reduce the risk.

Catani et al. (2013) have performed different tests to un-derstand how model tuning and model parameters can af-fect landslide susceptibility mapping. In the paper, the au-thors have adopted the random forest (RF) technique to pro-duce an ensemble of landslide susceptibility maps for a setof different models, input data types and observation scales.The RF model was initially applied using the complete set ofinput variables, then an iterative process was implemented,and progressively smaller subsets of the parameter spacewere considered to estimate the relative importance of sin-gle input parameters and to select the optimal configurationof the classification model. The main results are that (i) theoptimal number of parameters varies with scale and resolu-tion, (ii) the importance of each conditioning variable is in-fluenced by the model settings and the available data, and

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2712 P. Reichenbach and A. Gnther: Preface Progress in landslide hazard and risk evaluation

(iii) the choice of the training set (both for dimension andlocation) is an important issue in obtaining accurate results.

Nicolet et al. (2013) propose a model to assess the riskdue to shallow landslides for a large region, using infor-mation on a rainfall event in Switzerland. In the paper, theauthors describe the event of August 2005 both from a me-teorological and a lithological viewpoint. They introduce amethodology to assess the landslide probability as a functionof rainfall accumulation and lithological context, and presentthe risk analysis results in terms of the expected number oflandslides, the number of affected buildings and the associ-ated cost based on the precipitation amounts and lithologi-cal units. Using stochastic geometry, the authors prepare amodel to evaluate the probability of a landslide reaching abuilding and the damage cost from the estimated mean dam-age cost using an exponential distribution to account for thevariability. Although the model reproduces well the numberof landslides, the number of affected buildings is underesti-mated. This is interpreted as resulting from human influenceon landslide occurrence.

In the paper presented by Marques et al. (2013), the land-slide susceptibility along a sea cliff is evaluated by ex-ploiting bivariate information value and multivariate logis-tic regression statistical methods, using a set of predispos-ing factors, mainly related to geology (lithology, beddingdip, faults) and geomorphology (maximum and mean slope,height, aspect, plan curvature, toe protection). For this study,the cliff failure inventory was compiled using multi-temporalaerial digital photogrammetric methods, using aerial pho-tographs of 1952, 2002 and 2007. The susceptibility models,validated against an inventory data using standard successrate and reciever Operating Characteristic (ROC) curves,provided encouraging results, indicating that the proposedapproaches are effective for susceptibility assessment of seacliff failures for planning purposes, which is a step towardsobjective sea cliff hazard assessment.

Margarint et al. (2013) prepared landslide susceptibilitymaps using binary logistic regression in study areas in Ro-mania situated along the hilly areas of the TransylvanianPlateau and Moldavian Plateau and in the lower mountainregion (Sub-Carpathians). The study focused on identifyingand analyzing the spatial variability and range of variation forthe regression coefficients of landslide predictors under fourdifferent geographical conditions. Landslide susceptibility ismainly controlled by slope angle, land use, slope height, andlithology, while other predictors (profile curvature, plan cur-vature, elevation, and distance from the drainage network)play a secondary role. The least relevant predictors are themean curvature and aspect. For all the study areas, high val-ues of predictor coefficients were obtained for slope angleand land use. In one mountainous sector, the model confirmsthat, under high geological diversity conditions, lithologicalvariables have a significant relevance for the landslide sus-ceptibility. The results of the study may help to improve theaccuracy of landslide susceptibility and hazard mapping in

Romania, by taking into account new landslide predisposalfactors and the differentiation of their weights within majorgeographical units.

Petschko et al. (2014) present an evaluation of the qualityof landslide susceptibility maps by addressing uncertaintyissues associated with statistical modeling regarding inputdata (parametric uncertainty), model performance (modelform uncertainty) and the final susceptibility map. In thestudy area, located in Lower Austria, they focus on the modelform uncertainty to assess the quality of a flexible statisticalmodeling technique, the generalized additive model (GAM).The study area was divided into 16 modeling domains basedon lithology classes, and a model representing the entirestudy area was constructed by combining these models. Theperformances of the models were assessed using repeated k-fold cross-validation with spatial and random subsampling.This reflects the variability of performance estimates arisingfrom sampling variation. Measures of spatial transferabilityand thematic consistency are applied to assess model qual-ity empirically. The authors also analyze and visualize theimplications of spatially varying prediction uncertainties re-garding the susceptibility map classes by taking into accountthe confidence intervals of model predictions. The map re-sulting from their study was planned for implementation bymunicipal authorities. For this reason, the authors pointed outthe need to assess, minimize and communicate uncertaintiesinvolved in susceptibility modeling, to be communicated inan understandable manner to the stakeholder to allow for in-formed decisions instead of giving an impression of certainty.

Heckmann et al. (2014) investigated the effect of samplesize on a logistic regression model with a parameter selec-tion procedure that is based on an information criterion. Thecase study aims at predicting the spatial distribution of de-bris flow release zones in two small catchments located inthe Austrian Central Alps. The study had two main objec-tives: first explore the sensitivity of stepwise model selectionto sample size by investigating if an optimal sampling sizecan be found as a compromise between samples too smalland too large. Second, the uncertainty inherent in a stepwisemodeling approach is quantified with respect to (i) the selec-tion of geofactors, (ii) model parameters, and (iii) the spa-tial pattern of uncertainty in the resulting susceptibility map.Using stepwise model selection with 1000 random samples,the authors investigated the inclusion and exclusion of geo-factors and the diversity of the resulting models as a func-tion of sample size. The diversity of models as a function ofsample size was determined using diversity indices (Shan-non entropy and Simpson diversity index), and the predictivepower of the models was measured using ROC curves (AUC,area under the curve). Sample size apparently did not influ-ence the average predictive power of the model ensemble, butsmaller samples increased the range of AUC and hence alsothe proportion of comparatively poor models.

Fressard et al. (2014) present a study aiming at as-sessing the impact of the data set quality for landslide

Nat. Hazards Earth Syst. Sci., 14, 27112713, 2014 www.nat-hazards-earth-syst-sci.net/14/2711/2014/

P. Reichenbach and A. Gnther: Preface Progress in landslide hazard and risk evaluation 2713

susceptibility mapping using multivariate statistical mod-eling methods. The research was conducted on the PaysdAuge plateau (Normandy, France) at a 1 : 10 000 scale, inorder to fit the French guidelines on risk assessment. Fivesets of data of increasing quality (considering accuracy, scalefitting, and geomorphological significance) and cost of ac-quisition are used to map landslide susceptibility using lo-gistic regression. The best maps obtained with each set ofdata are compared on the basis of different statistical accu-racy indicators (ROC curves and relative error calculation),linear cross-correlation and expert opinion. The results high-light the fact that only high-quality sets of data supplied withdetailed geomorphological variables (i.e., field inventory andsurficial formation maps) can predict a satisfying proportionof landslides.

Yuan et al. (2014) analyzed the huge earthquake-inducedDonghekou landslide triggered by the Wenchuan earth-quake in 2008. They used a two-dimensional granular dis-crete element method to characterize the kinematic behaviorand the mechanics of this ejection landslide. The simulatedresults show that the large local seismic acceleration and afree face under the sliding body caused by the dip differ-ence between the upper sliding face and the natural slopewere responsible for the ejection phenomenon (e.g., thejumping of the landslide body over some slope distance)of the landslide. This study indicates that numerical model-ing has the potential to impose tight constraints on landslidebehavior, using information based on available observationsand measurements, such as geological and geomorphologicalconditions, and rock mechanics. Two-dimensional modelingbased on field observations can explain the kinematic pro-cesses of slope failure well, but to understand the geometryof the motion and the lateral spreading of landslide materialsbetter, three-dimensional modeling should be exploited.

Acknowledgements. We thank all the authors for their contribu-tions, and we are grateful to the referees for their careful commentson the individual papers. Lastly, we acknowledge the professionaleffort of the Copernicus editorial and production offices.

References

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Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.:Assessing the quality of landslide susceptibility maps casestudy Lower Austria, Nat. Hazards Earth Syst. Sci., 14, 95118,doi:10.5194/nhess-14-95-2014, 2014.

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