ABSTRACT
We introduce and compare two approaches to consistently combine release and runout in GIS-based landslide susceptibility modeling. The computational experiments are conducted on data from the Schnepfau investigation area in western Austria, which include a high-quality landslide inventory and a landslide release susceptibility map. The two proposed methods use a constrained random walk approach for downslope routing of mass points and employ the probability density function (PDF) and the cumulative density function (CDF) of the angles of reach and the travel distances of the observed landslides. The bottom-up approach (A) produces a quantitative spatial probability at the cost of losing the signal of the release susceptibility, whereas the top-down approach (B) retains the signal and performs better, but results in a semi-quantitative score. Approach B also reproduces the observed impact area much better than a pure analysis of landslide release susceptibility. The levels of performance and conservativeness of the model results also strongly depend on the choice of the PDF and CDF (angle of reach, maximum travel distance, or a combination of both).
ABSTRACT
The Bostanlik district, Uzbekistan, is characterized by mountainous terrain susceptible to landslides. The present study aims at creating a statistically derived landslide susceptibility map - the first of its type for Uzbekistan - for part of the area in order to inform risk management. Statistical index (SI), frequency ratio (FR) and certainty factor (CF) are employed and compared for this purpose. Ten predictor layers are used for the analysis, including geology, soil, land use and land cover, slope, aspect, elevation, distance to lineaments, distance to faults, distance to roads, and distance to streams. 170 landslide polygons are mapped based on GeoEye-1 and Google Earth imagery. 119 (70%) out of them are randomly selected and used for the training of the methods, whereas 51 (30%) are retained for the evaluation of the results. The three landslide susceptibility maps are split into five classes, i.e. very low, low, moderate, high, and very high. The evaluation of the results obtained builds on the area under the success rate and prediction rate curves (AUC). The training accuracies are 82.1%, 74.3% and 74%, while the prediction accuracies are 80%, 70% and 71%, for the SI, FR and CF methods, respectively. The spatial relationships between the landslides and the predictor layers confirmed the results of previous studies conducted in other areas, whereas model performance was slightly higher than in some earlier studies - possibly a benefit of the polygon-based landslide inventory.
ABSTRACT
A wide variety of issues are now being addressed using the concept of connectivity, which has initiated the development of various methods to assess a river's relationship to its catchment. This study tests two well-established methods, the Effective Catchment Area (ECA) and the Index of Connectivity (IC) in the study area of the Fella River in northeastern Italy, to gain an idea of their potentials, limitations and ability to represent connectivity patterns observable in the field. The results show that both methods provide largely agreeing outputs, which widely match field observations. Disagreement is mainly found where human-induced features, especially roads, encroach the rivers system. Focusing on a natural hazard background, the study furthermore approaches the issue of events of different frequencies and magnitudes and their representation in terms of connectivity. This is done by correlating debris flows at varying return periods with the IC, which seemed more fitting for this comparison due to the differentiation between different intensities of connectivity. Over the entire catchment, patterns of debris flow intensities (DFI) only agree weakly with the patterns of the IC, however, debris flows reaching the main channel show strong correlations with IC values. This can be traced back to the fact that connectivity focuses on a catchment's relationship with the river and does not include processes that happen in those parts of the catchment not directly linked to the main channel network. The IC is therefore able to represent patterns of processes reaching the main valley very well but cannot be used to explain or even predict the occurrence of processes that have no direct spatial connection to the river.
ABSTRACT
Changing high-mountain environments are characterized by destabilizing ice, rock or debris slopes connected to evolving glacial lakes. Such configurations may lead to potentially devastating sequences of mass movements (process chains or cascades). Computer simulations are supposed to assist in anticipating the possible consequences of such phenomena in order to reduce the losses. The present study explores the potential of the novel computational tool r.avaflow for simulating complex process chains. r.avaflow employs an enhanced version of the Pudasaini (2012) general two-phase mass flow model, allowing consideration of the interactions between solid and fluid components of the flow. We back-calculate an event that occurred in 2012 when a landslide from a moraine slope triggered a multi-lake outburst flood in the Artizón and Santa Cruz valleys, Cordillera Blanca, Peru, involving four lakes and a substantial amount of entrained debris along the path. The documented and reconstructed flow patterns are reproduced in a largely satisfactory way in the sense of empirical adequacy. However, small variations in the uncertain parameters can fundamentally influence the behaviour of the process chain through threshold effects and positive feedbacks. Forward simulations of possible future cascading events will rely on more comprehensive case and parameter studies, but particularly on the development of appropriate strategies for decision-making based on uncertain simulation results. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
