Results 2016 from SP 4 FoU Snøskred: Work Package 2 Statistical models

Transcription

1 Results 2016 from SP 4 FoU Snøskred: Work Package 2 Statistical models Project nr : Title : Statistical approach for avalanche hazard zoning and warning Total budget (knok) From Dept. Of Oil and Energy (knok) Costs per (knok) Main objective: Through a cross-disciplinary team effort involving avalanche experts, georisk professionals, mathematicians and statisticians, a probabilistic approach for hazard mapping and avalanche warning should be proposed. The proposed method will be tested and validated in selected mapping and warning case studies. Task 1: Framework for probabilistic analysis of avalanche hazard zoning and warning The corresponding software tool has to fulfill the following requirements: Coupling of probability of condition for triggering a snow avalanche (weather and topography etc.) and the run-out (Tasks 2 4) Framework to be implemented in ArcGIS Task 2: Statistical model for extreme weather Define methodology, criterion and data needed for the purpose of both mapping and forecast Task 3: Statistics model for avalanche release Define methodology, release criteria and data needed for the purposes of both hazard mapping and avalanche forecasting Task 4: Statistics model for avalanche run-out Revise alpha-beta method to include smaller slides and possibly include the effect of width and snow depth Task 5: Validation and example studies (to be carried out in 2017) Har prosjektet oppnådd de oppsatte mål: Ja: X Nei: X Begrunnelse for eventuelle avvik og beskrivelse av korrigerende tiltak: The application of fuzzy-logic methods in module for weather conditions proved to be much more cumbersome and time-consuming than anticipated, due to the excessive number of criterion combinations that had to be assessed manually. This delayed model testing by several months. In order to achieve a practical model, the parameter space was restricted into a number

2 of smaller blocks. Nevertheless, practical model testing must be deferred to the continuation of this work package in the next project period In WP2, the main part of D2.3 has been completed, i.e., the framework program with the prototypes of the various modules. However, further development and extensive testing are still needed and are planned for the project period The first version of the model has been developed for the purpose of snow avalanche forecasting. Hopefully, we will be able to develop the model to be used in hazard mapping also through Dato Prosjektleder Dato Fagleder Sylfest Glimsdal Christian Jaedicke

3 Page: 3 Title: Project Manager: Project Members: WP2 Statistical approach for avalanche hazard zoning and warning Sylfest Glimsdal Peter Gauer, Carl B. Harbitz, Galina Ragulina, Marco Uzielli TASK 1: DRAFT FRAMEWORK FOR PROBABILISTIC ANALYSIS OF AVALANCHE HAZARD ZONING AND WARNING The framework of the probabilistic avalanche model is based on a distribution of probabilities of the factors for triggering (including weather conditions) and run-out. The probabilities for a large set of combinations are combined, using a Bayesian approach. The output of the model is a map showing the probability to be hit by a snow avalanche in the area of interest in the nearest future (forecast) or during a winter (hazard mapping). So far, the model has been developed for forecasting purposes. In the model the area of interest is divided into a given number of cells. From each cell a slide path is determined using the flow direction. Figure 1: Conceptual sequence for the statistical run-out model. The input to the model: DTM (raster) Flow direction (raster) Slope angles (raster) Probability of weather and triggering (one single value for each source cell)

4 Page: 4 The computational steps for each source cell are the following: 1. Determine the slide path (flow direction) 2. Find the β-point along the slide path 3. Calculate the distribution of the run-out (compute the runout for a larger set of α-values) 4. Keep track of influenced cells (number of occupations for each cell downslope) 5. Calculate the frequentist impact probability at each cell by multiplying the probability for weather, triggering and runout Use of the method of steepest descent for determining the slide paths channels the avalanches along the network of streams and rivers, leading to distribution maps with a lot of "fingers", see Figure 2. In the near future, an algorithm for smearing out the probability distribution of hits will be developed in order to obtain more realistic results. Figure 2: The probability of being hit along a slide path starting at the k-th source cell. Slide path follows the flow direction.

5 Page: 5 Figure 3: An example of the probability of being hit, given a probability of being released between 0.2 and 1.0, depending on the slope angle only (not weather). The model is developed outside ArcGIS using freeware/open source software based on Python and GRASS GIS, to avoid forcing users to buy expensive licenses to run the model. However, the model will also be implemented in ArcGIS after first being completed in the Python/GRASS environment. TASK 2: STATISTICAL MODEL FOR EXTREME WEATHER Task 2 is closely related to Task 3, see below for details. TASK 3: STATISTICAL MODEL FOR AVALANCHE RELEASE Rules for weather/triggering factors are implemented through member functions. We make use of a so-called fuzzy inference system (FIS) for the purpose of quantifying avalanche triggering likelihood. The FIS relies on a set of input variables (weather, geometry, etc.) and on a set of expert-defined if-then rules which verbally characterize the likelihood of triggering as a result of other conditions for input factors (e.g., temperature last days, precipitation, wind direction, etc.). The application of fuzzy-logic methods in the module for weather conditions proved to be much more cumbersome and time-consuming than anticipated, due to the excessive number of criterion combinations that had

6 Page: 6 to be assessed manually. In order to achieve a practical model, the parameter space was restricted into a number of smaller blocks. TASK 4: STATISTICAL MODEL FOR AVALANCHE RUN-OUT This task is completed. However. if possible we will also take into account the width of the slide by tracking the slide from each cell in a cluster of cells (release area). REMARKS The work for WP2 is delayed due to problems with the fuzzy-system (see above). Detailed reports on the framework program and on the results of the tests (Deliverables D2.3 D2.6) are postponed to 2017.