Rainer Bell

Integration of a limit-equilibrium model into a landslide early warning system

Landslides are a significant hazard in many parts of the world and exhibit a high, and often underestimated, damage potential. Deploying landslide early warning systems is one risk management strategy that, amongst others, can be used to protect local communities. In geotechnical applications, slope stability models play an important role in predicting slope behaviour as a result of external influences; however, they are only rarely incorporated into landslide early warning systems. In this study, the physically based slope stability model CHASM (Combined Hydrology and Stability Model) was initially applied to a reactivated landslide in the Swabian Alb to assess stability conditions and was subsequently integrated into a prototype of a semi-automated landslide early warning system. The results of the CHASM application demonstrate that for several potential shear surfaces the Factor of Safety is relatively low, and subsequent rainfall events could cause instability. To integrate and automate CHASM within an early warning system, international geospatial standards were employed to ensure the interoperability of system components and the transferability of the implemented system as a whole. The CHASM algorithm is automatically run as a web processing service, utilising fixed, predetermined input data, and variable input data including hydrological monitoring data and quantitative rainfall forecasts. Once pre-defined modelling or monitoring thresholds are exceeded, a web notification service distributes SMS and email messages to relevant experts, who then determine whether to issue an early warning to local and regional stakeholders, as well as providing appropriate action advice. This study successfully demonstrated the potential of this new approach to landslide early warning. To move from demonstration to active issuance of early warnings demands the future acquisition of high-quality data on mechanical properties and distributed pore water pressure regimes.

Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models

Bell, R., Petschko, H., Röhrs, M. and Dix, A. Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geografiska Annaler: Series A, Physical Geography, 94, 135–156. doi:10.1111/j.1468‐0459.2012.00454.x ABSTRACT Landslides occur worldwide and contribute significantly to sediment budgets as well as to landform evolution. Furthermore, they pose hazards and risks to people and their goods. To assess the role of landslides, information on their age or persistence (i.e. the length of time the morphological characteristics of a landslide remain recognizable in the terrain) is essential. In this study, the potential of airborne laser scanning digital terrain models (ALS DTMs) is analysed for estimating landslide age, landslide persistence and human impact. Therefore, landslides in two study areas, Swabian Alb in Germany and Lower Austria in Austria, are mapped from hillshades of ALS DTMs and combined with historical information on landslide occurrence. It is tested whether the modification of the geomorphological features of landslides can be used to assess landslide age. In the Swabian Alb older landslides might show fresher features than younger ones because of different degrees of human impact, natural erosion and different histories of landslide reactivation. Estimated persistence times range between 27 and 320 years but are minimum values only. In Lower Austria four landslides show estimated minimum persistence times between 4 and 28 years. In Lower Austria 27 landslides disappeared in less than 7 years after occurrence mainly because of planation by farmers. The results show no clear trend in landslide persistence, neither regarding landslide magnitude, nor regarding land use. However, it is evident that human impact plays a major role in landslide persistence.

Evaluating the Effect of Modelling Methods and Landslide Inventories Used for Statistical Susceptibility Modelling

Landslide susceptibility maps can be elaborated using a variety of methodological approaches. This study investigates quantitative and qualitative differences between two statistical modelling methods, taking into account the impact of two different response variables (landslide inventories) for the Rhenodanubian Flysch zone of Lower Austria. Quantitative validation of the four generated susceptibility maps is conducted by calculating conventional accuracy statistics for an independent random landslide subsample. Qualitative geomorphic plausibility is estimated by comparing the final susceptibility maps with hillshades of a high resolution Airborne Laser Scan Digital Terrain Model (ALS-DTM). Spatial variations between the final susceptibility maps are displayed by difference maps and their densities. Although statistical quality criterions reveal similar qualities for all maps, difference maps and geomorphic plausibility expose considerable differences between the maps. Given that, this conclusion could only be drawn by evaluating additionally the geomorphic plausibility and difference maps. Therefore, we indicate that conventional statistical quality assessment should be combined with qualitative validation of the maps.

Relative Age Estimation at Landslide Mapping on LiDAR Derivatives: Revealing the Applicability of Land Cover Data in Statistical Susceptibility Modelling

In statistical landslide susceptibility modelling the identification of appropriate explanatory variables describing the predisposing and preparatory factors for the landslides of a given inventory is important. In this context information on the age and the respective land cover at the time of occurrence is beneficiary. The potential of mapping very old (or prehistoric) landslides using LiDAR derivatives has not been analysed yet. Additionally, performing a visual interpretation of derivatives of a single LiDAR DTM it is not possible to assign the accurate age or date of the occurrence of the event to each mapped landslide. Therefore, commonly no information on the land cover at the time of landslide occurrence for these very old landslides (but also for younger ones) is available. The objective of this study is, to estimate the relative age of landslides during the mapping and to explore differences of the recent land cover distribution in the relative ages of the landslides. This is performed to evaluate the sustainability of including recent land cover data into susceptibility modelling. The relative age of the landslides is estimated for each landslide according to its morphological footprint on the LiDAR DTM derivatives and to its appearance on the orthophoto. The different relative ages assigned are “very old”, “old”, “young” and “very young”. The study area is located in three districts of Lower Austria, namely Amstetten, Baden and Waidhofen/Ybbs. The resulting inventory includes 1834 landslides and shows that the “very old” and “old” landslides (60 % of all mapped landslides) are mainly covered by forest (~60 % of all land cover types). We conclude that using this inventory including recent land cover data in the susceptibility model is not appropriate for Lower Austria. There is a potential of mapping “old” or “very old” landslides on the LiDAR derivatives. The absolute age remains unknown.

Landslide Inventories for Reliable Susceptibility Maps in Lower Austria

Landslide inventories, their accuracy and the stored information are of major importance for landslide susceptibility modelling. Working on the scale of a province (Lower Austria with about 10,000 km2) challenges arise due to data availability and its spatial representation. Furthermore, previous studies on existing landslide inventories showed that only few inventories can be used for statistical susceptibility modelling. In this study two landslide inventories and their resulting susceptibility maps are compared: the Building Ground Register (BGR) of the Geological Survey of Lower Austria and an inventory that was mapped on the basis of a high resolution LiDAR DTM. This analysis was performed to estimate minimum requirements on landslide inventories to allow for deriving reliable susceptibility maps while minimizing mapping efforts. Therefore a consistent landslide inventory once from the BGR and once from the mapping was compiled. Furthermore, a logistic regression model was fitted with randomly selected points of each landslide inventory to compare the resulting maps and validation rates. The resulting landslide susceptibility maps show significant differences regarding their visual and statistical quality. We conclude that the application of randomly selected points in the main scarp of the mapped landslides gives satisfactory results.

The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements

Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors.The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness.The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors.This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling.

Susceptibility maps for landslides using different modelling approaches

This study focuses on the comparison of different approaches for landslide susceptibility modelling and is part of the research project “MoNOE” (Method development for landslide susceptibility modelling in Lower Austria). The main objective of the project is to design a method for landslide susceptibility modelling for a large study area. For other objectives of the project we refer to Bell et al. (Proceedings of the 2nd world landslide forum, Rome, 3–7 Oct 2011, this volume). To reach the main objective, the two different statistical models “Weights of Evidence” and “Logistic Regression” are applied and compared. By using nearly the same input data in test areas it is possible to compare the capabilities of both methods. First results of the comparison indicate that in valleys and on south facing slopes the results are quite similar. In contrast, the analysis on north facing slopes shows differences. In the ongoing work the reasons for these differences will be analysed. Furthermore, attention will be paid to finding adequate validation methods for the two modelling approaches.

Landslide Susceptibility Maps for Spatial Planning in Lower Austria

Landslides threaten most parts of the provincial state of Lower Austria and cause damage to agricultural land, forests, infrastructure, settlements and people. Thus, the project “MoNOE” (Method development for landslide susceptibility modelling in Lower Austria) was initiated by the provincial government to tackle these problems and to reduce further damage by landslides. The main aim is to prepare landslide susceptibility maps for slides and rock falls and to implement these maps into the spatial planning strategies of the provincial state.

Rainfall Threshold Analysis and Landslide Susceptibility Mapping in Wudu County

The main purpose of this study is the analysis of rainfall thresholds and landslide susceptibility mapping in order to assist the prediction, mitigation and management of slope instability in the Wudu county in China. Firstly, the rainfall thresholds were assessed using the Antecedent soil water status (ASWS) model based on landslides induced by multi-temporal rainfall events in the Wudu county. Secondly, three separate susceptibility maps were produced using historic landslide inventories, and inventories reflecting single landslide triggering events, i.e. the 2008 Wenchuan earthquake and heavy rain storms. The separate maps were combined to illustrate the maximum landslide probability of all three landslide susceptibility maps. The results show that rainfall thresholds could be applied to forecast rainfall-induced landslides, and the integrated landslide susceptibility map could be used for planning of spatial development as well as emergency response actions.

Distribution and Susceptibility Assessments of Landslide Triggered by Wencuan Earthquake at Longnan

It is important to calculate these areas prone for future landsiding through landslide susceptibility maps. A landslide causative factor database both before and after datasets was constructed. Data are stored in a resolution of 30×30m and include digital orthophoto maps (DOM), digital elevation model (DEM) and derived topographical parameters (e.g. altitude, slope, aspect, profile curvature, plan curvature), geology, land use, average annual rainfall, peak ground acceleration and further different environmental layers including road network and rivers. The 1334 post WenChuan earthquake landslide inventory is build up by field investigation and by interpretation of remote-sensing imagery (SPOT 5 and ALOS) and by monoscopic manual interpretation. The quality of susceptibility mapping was validated by splitting the study area into a training and validation set. The prediction capability analysis showed that the landslide susceptibility map has the potential to be used for land planning in this region as well as for precautionary emergency planning by local authorities.