Uncertainties in the SNOWPACK multilayer snow model for a Canadian avalanche context: sensitivity to climatic forcing data

Abstract

As interest in outdoor activities in remote areas is increasing, there is a strong need for improved avalanche forecasting at the regional scale. Due to important logistical and safety matters, avalanche terrain measurements (avalanche observations, snowpack profiles, and stability tests) are not always possible for practitioners/forecasters. An interesting alternative would be to analyze the snowpack without these challenges by using snow model outputs. The SNOWPACK model is currently used operationally for avalanche forecasting and research in the Swiss Alps. Thus, this paper presents a summary of analyses that have been conducted to assess the potential of using the SNOWPACK model driven with both in-situ and forecasted meteorological data in three different Canadian climate and geomorphological contexts. A comparison of meteorological data from in-situ and predicted datasets for two winters shows that the GEMLAM weather model is the most accurate for the three climatic contexts of this project, but also showed a bias proportional to precipitation intensity/rate. Snow simulations forced with GEMLAM are the closest to field measurements. Finally, predictions of persistent weak layers have been validated using the InfoEx platform from Avalanche Canada. Crust and surface hoar formation dates agree with the information reported in InfoEx.     

Comparing models of debris-flow susceptibility in the alpine environment

Debris-flows are widespread in Val di Fassa (Trento Province, Eastern Italian Alps) where they constitute one of the most dangerous gravity-induced surface processes. From a large set of environmental characteristics and a detailed inventory of debris flows, we developed five models to predict location of debris-flow source areas. The models differ in approach (statistical vs. physically-based) and type of terrain unit of reference (slope unit vs. grid cell). In the statistical models, a mix of several environmental factors classified areas with different debris-flow susceptibility; however, the factors that exert a strong discriminant power reduce to conditions of high slope-gradient, pasture or no vegetation cover, availability of detrital material, and active erosional processes. Since slope and land use are also used in the physically-based approach, all model results are largely controlled by the same leading variables.Overlaying susceptibility maps produced by the different methods (statistical vs. physically-based) for the same terrain unit of reference (grid cell) reveals a large difference, nearly 25% spatial mismatch. The spatial discrepancy exceeds 30% for susceptibility maps generated by the same method (discriminant analysis) but different terrain units (slope unit vs. grid cell). The size of the terrain unit also led to different susceptibility maps (almost 20% spatial mismatch). Maps based on different statistical tools (discriminant analysis vs. logistic regression) differed least (less than 10%). Hence, method and terrain unit proved to be equally important in mapping susceptibility.Model performance was evaluated from the percentages of terrain units that each model correctly classifies, the number of debris-flow falling within the area classified as unstable by each model, and through the metric of ROC curves. Although all techniques implemented yielded results essentially comparable; the discriminant model based on the partition of the study area into small slope units may constitute the most suitable approach to regional debris-flow assessment in the Alpine environment.     

Susceptibility and triggering scenarios at a regional scale for shallow landslides

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an  15,075 km² extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing.Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors.The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps.On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility.Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively.The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.     

国内外雪崩灾害研究综述

伴随着全球气候变暖,冰冻圈雪崩灾害潜在风险逐渐增加,雪崩灾害已受到社会各界的广泛关注.目前,在雪崩形成机制、抛程、动态模拟、风险评价与区划、预防与防治等领域取得了较大进展.雪崩灾害研究正在经历由野外观测到遥感手段与野外观测相结合、由定性半定量到定量、由经验估算到过程模拟、由雪崩机理研究向承灾区适应研究转变的发展过程.然而,以往研究过多集中于气候因子、积雪环境、沟道地形等雪崩形成机制研究与动态模拟,而承灾区承灾体脆弱性、暴露性及其适应性研究却相对较少.只有将雪崩始发区和承灾区作为一个整体进行系统研究,通过早期预警、预测和预报,才能使下游承灾区居民防患于未然,以防止或减小雪崩对承灾区居民和财产带来的损失.     

天山山系的雪崩危险区分布

根据山地生态特征，地形切割深度及河谷横剖面形成和雪崩重视率等，将天山山系雪崩危险区分为：１）低山草原或半荒漠雪崩危险区；２）中山森林－草原雪崩危险区；３）中山草地雪崩危险区；４）高山冰缘带（石质）雪崩危险区；５）高山；极高山冰川带雪崩危险区。     

Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses

Sometimes regional meteorological anomalies trigger different types of mass movements. In May 1998, the western Black Sea region of Turkey experienced such a meteorological anomaly. Numerous residential and agricultural areas and engineering lifelines were buried under the flood waters. Besides the reactivation of many previously delineated landslides, thousands of small-scale landslides (mostly the earthflow type) occurred all over the region. The earthflows were mainly developed in flysch-type units, which have already presented high landslide concentrations. In this study, three different catchments — namely Agustu, Egerci, and Kelemen — were selected because they have the most landslide-prone geological units of the region. The purposes of the present study are to put forward the spatial distributions of the shallow earthflows triggered, to describe the possible factors conditioning the earthflows, and to produce the shallow earthflow susceptibility maps of the three catchments. The unique condition units (UCU) were employed during the production of susceptibility maps and during statistical analyses. The unique condition units numbered 4052 for the Agustu catchment, 13,241 for the Egerci catchment and 12,314 for the Kelemen catchment. The earthflow intensity is the highest in the Agustu catchment (0.038 flow/UCU) and lowest in the Egerci catchment (0.0035 flow/UCU). Logistic regression analyses were also employed. However, during the analyses, some difficulties were encountered. To overcome the difficulties, a series of sensitivity analyses were performed based on some decision rules introduced in the present study. Considering the decision rules, the proper ratios of UCU free from earthflow (0) / UCU including the earthflow (1) for the Agustu, Egerci and Kelemen catchments were obtained as 3, 6, and 5, respectively. Also, a chart for the proper ratio selection was developed. The regression equations from the selected ratios were then applied to the entire catchment and the earthflow susceptibility maps were produced. The landslide susceptibility maps revealed that 15% of the Agustu catchment, 8% of the Egerci catchment, and 7% of the Kelemen catchment have very high earthflow susceptibility; and most of the earthflows triggered by the May 1998 meteorological event were found in the very high susceptibility zones.     

Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry

A map of extant slope failures is the most basic element of any landslide assessment. Without an accurate inventory of slope instability, it is not possible to analyze the controls on the spatial and temporal patterns of mass movement or the environmental, human, or geomorphic consequences of slides. Landslide inventory maps are tedious to compile, difficult to make in vegetated terrain using conventional techniques, and tend to be subjective. In addition, most landslide inventories simply outline landslide boundaries and do not offer information about landslide mechanics as manifested by internal deformation features. In an alternative approach, we constructed accurate, high-resolution DEMs from airborne laser altimetry (LIDAR) data to characterize a large landslide complex and surrounding terrain near Christchurch, New Zealand. One-dimensional, circular (2-D) and spherical (3-D) statistics are used to map the local topographic roughness in the DEMs over a spatial scale of 1.5 to 10 m. The bedrock landslide is rougher than adjacent unfailed terrain and any of the statistics can be employed to automatically detect and map the overall slide complex. Furthermore, statistics that include a measure of the local variability of aspect successfully delineate four kinematic units within the gently sloping lower half of the slide. Features with a minimum size of surface folds that have a wavelength of about 11 to 12 m and amplitude of about 1 m are readily mapped. Two adjacent earthflows within the landslide complex are distinguished by a contrast in median roughness, and texture and continuity of roughness elements. The less active of the earthflows has a surface morphology that presumably has been smoothed by surface processes. The Laplacian operator also accurately maps the kinematic units and the folds and longitudinal levees within and at the margins of the units. Finally, two-dimensional power spectra analyses are used to quantify how roughness varies with length scale. These results indicate that no dominant length scale of roughness exists for smooth, unfailed terrain. In contrast, zones with different styles of landslide deformation exhibit distinctive spectral peaks that correspond to the scale of deformation features, such as the compression folds. The topographic-based analyses described here may be used to objectively delineate landslide features, generate mechanical inferences about landslide behavior, and evaluate relatively the recent activity of slides.     

不同分辨率DEM提取地面坡度的不确定性模拟:以在黄土高原的试验为例

Slope is one of the crucial terrain variables in spatial analysis and land use planning,especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas,but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

Simulation on slope uncertainty derived from DEMs at different resolution levels： a case study in the Loess Plateau

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as(0.0015S2 0.031S-0.0325)X-0.0045S2-0.155S 0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

DEM提取黄土高原地面坡度的不确定性

选择陕北黄土高原6个典型地貌类型区为试验样区,采用野外实测及高精度的1:1万比例尺DEM为基准数据,研究栅格分辨率及地形粗糙度对DEM所提取地面平均坡度精度的影响。结果显示,对于1:1万比例尺DEM,5 m是保证该地区地形描述精度的理想分辨率尺度;多要素逐步回归模拟的方法进一步揭示了DEM所提取的地面平均坡度误差E与栅格分辨率X以及地形起伏的代表性因子-沟壑密度S之间存在的量化关系为E = (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625,该结果也为确定适用的DEM分辨率提供了理论依据。     

Simulation on slope uncertainty derived from DEMs at different resolution levels: a case study in the Loess Plateau

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong

Steep terrain and high a frequency of tropical rainstorms make landslide occurrence on natural terrain a common phenomenon in Hong Kong. This paper reports on the use of a Geographical Information Systems (GIS) database, compiled primarily from existing digital maps and aerial photographs, to describe the physical characteristics of landslides and the statistical relations of landslide frequency with the physical parameters contributing to the initiation of landslides on Lantau Island in Hong Kong. The horizontal travel length and the angle of reach, defined as the angle of the line connecting the head of the landslide source to the distal margin of the displaced mass, are used to describe runout behavior of landslide mass. For all landslides studied, the horizontal travel length of landslide mass ranges from 5 to 785 m, with a mean value of 43 m, and the average angle of reach is 27.7°. This GIS database is then used to obtain a logistic multiple regression model for predicting slope instability. It is indicated that slope gradient, lithology, elevation, slope aspect, and land-use are statistically significant in predicting slope instability, while slope morphology and proximity to drainage lines are not important and thus excluded from the model. This model is then imported back into the GIS to produce a map of predicted slope instability. The results of this study demonstrate that slope instability can be effectively modeled by using GIS technology and logistic multiple regression analysis.     

基于专家知识的滑坡危险性模糊评估方法

滑坡发生的影响因素众多, 其危险性与各因素之间的关系多呈非线性关系, 同时各因素之 间也存在或强或弱的相关性, 而目前的危险性评价方法难以体现这些要求。本文提出了一种借助 滑坡专家知识并利用模糊推理理论进行滑坡危险性评价的方法。该方法通过建立了①坡度与岩 层倾角之差和坡向与岩层倾向之差、②坡度和岩性、③临空面和岩性、④坡形和岩性等四种环境 因子组合, 以此将不同环境因子之间的相关性融入各组合模型中, 并将四种组合所得的模糊危险 度进行叠加用于滑坡危险度的模糊评价。环境组合模型中的参数利用专家经验给出。将该方法应 用于三峡库区云阳- 巫山段, 得到了滑坡危险性的分级分布图。从滑坡危险性分布图上可清楚发 现, 本方法所计算出的危险性值在滑坡发生的地区明显高于未发生滑坡的地区, 该结果可以用于 城镇建设和重要基础规划设施的参考。     

On the frequency of lake-effect snowfall in the Catskill Mountains

Meltwater from snow that falls in the Catskill/Delaware watershed in the Catskill Mountains in south-central New York contributes to reservoirs that supply drinking water to approximately nine million people in and near New York City (NYC). Using the Interactive Multisensor Snow and Ice Mapping System (IMS) 4-km snow maps from the National Oceanic and Atmospheric Administration’s National Ice Center, we identified and tracked 28 lake-effect (LE) storms that deposited snow in the Catskills (2004–2017). These storms, which generally originated from Lake Ontario, but sometimes from Lake Erie, represent an underestimate of the number of LE storms that contribute snowfall to the Catskills snowpack because snowstorms are not visible on IMS maps when they travel over already-snow-covered terrain. Using satellite, meteorological (including NEXRAD and National Weather Service Cooperative Observer Program), and reanalysis data, we identified conditions that contributed to the LE snowstorms and mapped snow-cover extent (SCE) following the storms. We show that LE snow makes an important contribution to the Catskills snowpack because of the frequency of events, even though the total amount of each event may, on average, be small. The IMS 4-km maps tend to overestimate SCE compared to MODerate-resolution Imaging Spectroradiometer and Landsat snow maps. Climate change could impact LE storms and the distribution of rain versus snow in the Catskills, which may affect future reservoir operations in the NYC Water Supply System and winter recreation in the Catskills.     

Thermal Regime of Rock and its Relation to Snow Cover in Steep Alpine Rock Walls: Gemsstock,Central Swiss Alps

Snow cover influences the thermal regime and stability of frozen rock walls. In this study, we investigate and model the impact of the spatially variable snow cover on the thermal regime of steep permafrost rock walls. This is necessary for a more detailed understanding of the thermal and mechanical processes causing changes in rock temperature and in the ice and water contents of frozen rock, which possibly lead to rock wall instability. To assess the temporal and spatial evolution and influence of the snow, detailed measurements have been carried out at two selected points in steep north‐ and south‐facing rock walls since 2012. In parallel, the one‐dimensional energy balance model SNOWPACK is used to simulate the effects of snow cover on the thermal regime of the rock walls. For this, a multi‐method approach with high temporal resolution is applied, combining meteorological, borehole rock temperature and terrain parameter measurements. To validate the results obtained for the ground thermal regime and the seasonally varying snowpack, the model output is compared with near‐surface rock temperature measurements and remote snow cover observations. No decrease of snow depth at slope angles up to 70° was observed in rough terrain due to micro‐topographic structures. Strong contrasts in rock temperatures between north‐ and south‐facing slopes are due to differences in solar radiation, slope angle and the timing and depth of the snow cover. SNOWPACK proved to be useful for modelling snow cover–rock interactions in smooth, homogenous rock slopes.     

Terrain maps displaying hill-shading with curvature

Many types of maps can be created by neighborhood operations on a continuous surface such as provided by a digital elevation model. These most commonly include first derivatives slope or aspect, and second derivatives planimetric or profile curvature. Such variables are often used in geomorphic analyses of terrain. First derivatives also provide subtle enhancements to hill-shaded maps. For example, some maps combine oblique and vertical illumination, with the latter reflecting variations in slope.This study illustrates how second derivative maps, in conjunction with hill-shading, can cartographically enhance topographic detail. A simple conic model indicates that image-tone edges where slope or aspect varies by less than 0.5° are visible on curvature maps. Hill-shaded images combined with curvature enhance the continuity of naturally occurring tonal edges, especially in strongly illuminated areas. Variations in planimetric and profile curvature seem to be especially effective at highlighting convergent and divergent drainages and variations in erosion rate between or within sedimentary units, respectively. Shading curvature with consideration given to illumination models can add detail to hill-shaded terrain maps in a manner similar to cognitive models employed by map viewers.     

Landslide Representation Strategies in Susceptibility Studies using Weights-of-Evidence Modeling Technique

This paper is focused primarily on how to represent landslide scarp areas, how to analyze results achieved by the application of specific strategies of representation and how to compare the outcomes derived by different tests, within a general framework related to landslide susceptibility assessment. These topics are analyzed taking into account the scale of data survey (1:10,000) and the role of a landslide susceptibility map into projects targeted toward the definition of prediction, prevention, and mitigation measures, in a wider context of civil protection planning. These aims are achieved by using ArcSDM (Arc Spatial Data Modeler), a software extension to ArcView GIS useful for developing spatial prediction models using regional datasets. This extension requires a representation by points of the investigated problems (landslide susceptibility, aquifer vulnerability, detection of mineral deposits, identification of natural habitats of animals, and plants, etc.). Maps of spatial evidence from regional geological and geomorphological datasets were used to generate maps showing susceptibility to slope failures in two different study areas, located in the northern Apennines and in the central Alps (Italy), respectively. The final susceptibility maps for both study areas were derived by the application of the weights-of-evidence (WofE) modeling technique. By this method a series of subjective decisions were required, strongly dependent on an understanding of the natural processes under study, supported by statistical analysis of the spatial associations between known landslides and evidential themes. Except for maps of attitude, permeability, and structure, that were not available for both study areas, the other data were the same and comprised geological, land use, slope, and internal relief maps. The paper illustrates how different representations of scarp areas by points (in terms of different number of points) did not greatly influence the final response map, considering the scale of this work. On the contrary, some differences were observed in the capability of the model to describe the relations between predictor variables and landslides. In effect, a representation of the scarp areas using one point every 50 m led to a more efficient model able to better define relationships of this type. It avoided both problems of redundancy of information, deriving by the use of too many points, and problems related to a random positioning of the centroid. Moreover, it permitted to minimize the uncertainty related with identification and mapping of landslides.     

在降雨侵蚀中黄土地面坡度变化的高分辨研究

运用近景数字摄影测量方法,获得在不同人工降雨时段黄土模拟小流域高精度、高分辨率的DEM数据,并以地面坡度及其组合形态的变化为切入点,通过对比分析和理论验证,探讨黄土小流域降雨侵蚀过程中地面坡度变化特征,在更宏观空间尺度和高分辨的时间尺度上,揭示黄土地貌形成与发育特征。研究结果表明：1）在较好地维持黄土的土壤结构与抗蚀特征,较真实地模拟自然降雨的条件下,模拟流域地面的变化能很好地反映自然地面的发育进程。2）在降雨侵蚀过程中,黄土小流域地面坡度呈现持续性变化规律,平均坡度在地貌发育幼年期呈加速增长趋势,到了壮年期增长幅度呈递减性变化;坡度组合以侵蚀临界角度为轴点呈持续逆转变化,侵蚀临界坡度单元所占面积基本保持稳定。3）黄土地面坡度的变化,一方面受地表物质被侵蚀和迁移程度的影响,另一方面,也是沟间地、沟坡地和沟底地空间面积重新分配的结果。     

Pre-processing algorithms and landslide modelling on remotely sensed DEMs

Terrain analysis applications using remotely sensed Digital Elevation Models (DEMs), nowadays easily available, permit to quantify several river basin morphologic and hydrologic properties (e.g. slope, aspect, curvature, flow path lengths) and indirect hydrogeomorphic indices (e.g. specific upslope area, topographic wetness index) able to characterize the physical processes governing the landscape evolution (e.g. surface saturation, runoff, erosion, deposition). Such DEMs often contain artifacts and the automated hydrogeomorphic characterization of the watershed is influenced by terrain analysis procedures consisting in artificial depression (pit) and flat area treatment approaches combined with flow direction methods.In shallow landslide deterministic models, when applied using topographic dataset at medium scale (e.g. 30 m of resolution), the choice of the most suitable DEM-processing procedure is not trivial and can influence model results. This also affects the selection of most critical areas for further finer resolution studies or for the implementation of countermeasures aiming to landslide risk mitigation.In this paper such issue is investigated using as topographic input the ASTER DEMs and comparing two different combinations of DEM correction and flow routing schemes. The study areas comprise ten catchments in Italy for which hydrogeomorphic processes are significant. Aims of this paper are: 1) to introduce a parameter estimation procedure for the physically-based DEM correction method PEM4PIT (Physical Erosion Model for PIT removal); 2) to investigate the influence of different terrain analysis procedures on results of the slope stability model SHALSTAB (SHAllow Landsliding STABility) using remotely-sensed ASTER DEMs; 3) trying to assess which of terrain analysis methods is more appropriate for describing terrain instability.     

Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis.