Cees J. van Westen

Multivariate Regression Analysis for Landslide Hazard Zonation

Based on several layers of spatial map patterns, multivariate regression methods have been developed for the construction of landslide hazard maps. The method proposed in this paper assumes that future landslides can be predicted by the statistical relationships established between the past landslides and the spatial data set of map patterns. The application of multivariate regression techniques for delineating landslide hazard areas runs into two critical problems using GIS (geographic information systems): (i) the need to handle thematic data; and (ii) the sample unit for the observations. To overcome the first problem related to the thematic data, favourability function approaches or dummy variable techniques can be used.

The modelling of landslide hazards using gis

Slope instability hazard assessment is based on theanalysis of the terrain conditions at sites whereslope failures occurred in the past. For the analysisof the causative factors the application of geographicinformation systems (GIS) is an essential tool in thedata analysis and the subsequent hazard assessment.Three scale levels of hazard mapping are defined – adirect experience-driven mapping at reconnaissancelevel, a statistical approach to determine thecausative factors in a quantitative susceptibilitymapping, and a methodology at large scale making use ofdeterministic models.Slope instability hazard assessment is based on theanalysis of the terrain conditions at sites whereslope failures occurred in the past. For the analysisof the causative factors the application of geographicinformation systems (GIS) is an essential tool in thedata analysis and the subsequent hazard assessment.Three scale levels of hazard mapping are defined – adirect experience-driven mapping at reconnaissancelevel, a statistical approach to determine thecausative factors in a quantitative susceptibilitymapping, and a methodology at large scale making use ofdeterministic models.

Deterministic Modelling in Gis-Based Landslide Hazard Assessment

Deterministic models are based on physical laws of conservation of mass, energy or momentum. In the case of deterministic landslide hazard zonation, distributed hydrological and slope stability programs are used to calculate the spatial distribution of groundwater levels, pore pressures and safety factors. This paper is concentrated on the integration of two-dimensional, raster-based, geographic information systems (GIS) and deterministic models, with emphasis on deterministic hydrological models. Three examples of deterministic landslide hazard zonation are presented; one from Costa Rica and two from Colombia. In the example from Costa Rica, a one- dimensional external hydrological model is used to calculate the height of perched water tables in the upper metre of the soil for different soil types and different rainstorms. In the first example from Colombia, an external two-dimensional hydrological model is used to calculate the maximum groundwater level, for a 20 year period, in different slopes with a sequence of volcanic ashes overlying impermeable residual soils. In the second example from Colombia, a three-dimensional hydrologic model is used in a GIS to simulate groundwater fluctuations during one rainy season. In examples 1 and 2 the results of the hydrologic calculations are used in stability calculations to obtain maps which give the spatial distribution of safety factors and the probability of failure, with the use of distribution functions of the input parameters. In example 3 the calculated groundwater levels are exported to an external slope stability model to calculate the safety factor along slope profiles.

Assessing landslide exposure in areas with limited landslide information

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.

Co-seismic landslide inventory and susceptibility mapping in the 2008 Wenchuan earthquake disaster area, China

The Ms 8.0 May 12, 2008 Wenchuan earthquake triggered tens of thousands of landslides. The widespread landslides have caused serious casualties and property losses, and posed a great threat to post-earthquake reconstruction. A spatial database, inventoried 43,842 landslides with a total area of 632 km2, was developed by interpretation of multi-resolution remote sensing images. The landslides can be classified into three categories: swallow, disrupted slides and falls; deep-seated slides and falls, and rock avalanches. The correlation between landslides distribution and the influencing parameters including distance from co-seismic fault, lithology, slope gradient, elevation, peak ground acceleration (PGA) and distance from drainage were analyzed. The distance from co-seismic fault was the most significant parameter followed by slope gradient and PGA was the least significant one. A logistic regression model combined with bivariate statistical analysis (BSA) was adopted for landslide susceptibility mapping. The study area was classified into five categories of landslide susceptibility: very low, low, medium, high and very high. 92.0% of the study area belongs to low and very low categories with corresponding 9.0% of the total inventoried landslides. Medium susceptible zones make up 4.2% of the area with 17.7% of the total landslides. The rest of the area was classified into high and very high categories, which makes up 3.9% of the area with corresponding 73.3% of the total landslides. Although the susceptibility map can reveal the likelihood of future landslides and debris flows, and it is helpful for the rebuilding process and future zoning issues.

Effect of Sun Elevation Angle on DSMs Derived from Cartosat-1 Data

Along-track stereoscopic satellite data are increasingly used for automatic extraction of digital surface models (DSM) due to the reduced radiometric variation between the images. Problems remain with the quality of such DSMs, especially in steep terrain. This paper explores the accuracy of DSMs extracted from Cartosat-1 data acquired under high and low sun elevation angle conditions in High Himalayan terrain. The metric accuracy of the DSM was estimated by comparing it with check points obtained with a differential GPS . Additionally, we used spatial discrepancy of drainage lines to estimate errors in the DSM due to spatial auto- correlation. For valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data showed 45 percent higher spatial accuracy than the DSM extracted from high sun elevation angle data. The results indicate that the sun elevation angle and valley orientation affect the spatial accuracy of the DSM, though metric accuracy remains comparable.

Use of quantitative landslide hazard and risk information for local disaster risk reduction along a transportation corridor: a case study from Nilgiri district, India

The objective of analyzing hazard and risk in an area is to utilize the result in selecting appropriate landslide risk reduction strategies. However, this does not happen always, and most often results of the hazard and risk analysis remain at an academic level. The under or non-utilization of results in pre-disaster planning could be due to several reasons, including difficulties in understanding the scientific content/meaning of the models, and lack of information on the practical significance and utility of the models. In this study, an attempt is made to highlight the uses of hazard and risk information in different landslide risk reduction strategies along a transportation corridor in Nilgiri, India. At first, a quantitative analysis of landslide hazard and risk was made. The obtained information was then incorporated in risk reduction options such as land use zoning, engineering solutions, and emergency preparedness. For emergency preparedness, the perception of the local Nilgiri communities toward landslide risk was evaluated and simplified maps were generated for the benefit and understanding of end users. A rainfall threshold-based early warning system was presented, which could be used in risk awareness programs involving public participation. The use of quantitative risk information in the cost-benefit analysis for the planning of structural measures to protect the road and railway alignments was also highlighted, and examples were shown how the transport organizations could implement these measures. Finally, the study provided examples of the utility of hazard and risk information for spatial planning and zoning, indicating areas where the landslide hazard is too high for planning future developments.

Physically based dynamic run-out modelling for quantitative debris flow risk assessment: a case study in Tresenda, northern Italy

Quantitative landslide risk assessment requires information about the temporal, spatial and intensity probability of hazardous processes both regarding their initiation as well as their run-out. This is followed by an estimation of the physical consequences inflicted by the hazard, preferentially quantified in monetary values. For that purpose, deterministic hazard modelling has to be coupled with information about the value of the elements at risk and their vulnerability. Dynamic run-out models for debris flows are able to determine physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk can suffer an impact. These results can then be applied for vulnerability and risk calculations. Debris flow risk has been assessed in the area of Tresenda in the Valtellina Valley (Lombardy Region, northern Italy). Three quantitative hazard scenarios for different return periods were prepared using available rainfall and geotechnical data. The numerical model FLO-2D was applied for the simulation of the debris flow propagation. The modelled hazard scenarios were consequently overlaid with the elements at risk, represented as building footprints. The expected physical damage to the buildings was estimated using vulnerability functions based on flow depth and impact pressure. A qualitative correlation between physical vulnerability and human losses was also proposed. To assess the uncertainties inherent in the analysis, six risk curves were obtained based on the maximum, average and minimum values and direct economic losses to the buildings were estimated, in the range of 0.25–7.7 million €, depending on the hazard scenario and vulnerability curve used.

Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.

Generation of a national landslide hazard and risk map for the country of Georgia

AbstractLandslide risk assessment for large areas at a country level requires a different approach and data than what is standard practice at large scales. The main goal of this research was to design a methodology for a nationwide landslide risk assessment for Georgia taking into account the limitations in data availability and detail, which do not allow the use of physically based models or statistical methods. Given these limitations, we decided to generate a qualitative landslide risk index using spatial multicriteria evaluation (SMCE). An attempt was made to compile a national landslide inventory, using old and partly destroyed archives from the Soviet period, combined with information from annual field surveys. A web-based interface for the reporting of landslide events was developed to improve the updating of the inventory in future. Relevant factor maps were prepared for the entire country, partly based on remote sensing data. As the available landslide inventory was not sufficient to use statistical methods, the factor maps were weighted using the expert-based SMCE method, and the resulting susceptibility map was validated using the available landslide inventory. The inventory was also used to make an estimation of the spatial probability of landslide occurrence within the various susceptibility classes. The resulting map was used in combination with element-at-risk maps to calculate exposure maps and to make a tentative assessment of the expected landslide losses in a 50-year time period .