Avalanche hazard in the Krkonoše mountains and refinement of snow data collection methods

Mgr. Markéta Součková

Doctoral thesis

Abstract:

The impact of climate change on avalanches presents a nuanced picture, with variations depending on local conditions. While lower elevations may experience fewer, wetter, and smaller avalanches due to declining snow cover, higher-altitude regions might witness more frequent and spontaneously triggered snow avalanches. This dissertation theses aims to address several key aspects related to avalanche dynamics and monitoring: 1. Analysis of long-term trends in wet and slab avalanche activity over 59 winter seasons (1962–2021), including examination of frequency, magnitude, and orientation. Additionally, identification of the main meteorological and snow variables of these avalanches from 1979 to 2020 using machine learning techniques, particularly the Random Forest (RF) method. 2. Evaluation of the effectiveness of Unmanned Aerial Systems (UAS) snow depth (SD) monitoring in two distinct environments: the Krkonoše Mountains (Mts) and the Northern Limestone Alps in low altitude mountain ranges. 3. Simulation of selected avalanche path extend in the Krkonoše Mts using the RAMMS model. 4. Introduction to snow monitoring using Cosmic Rays Neutron Sensors (CRNS), providing insights into this advanced technique. 5. Promoting science to the public and schools, emphasizing modern learning methodologies tailored to the 21st century. The analysis reveals significant changes in avalanche dynamics, with an increase in the number and size of wet avalanches during February and March, while the number of slab avalanches has decreased over the past three decades. This rise in wet avalanches correlates with a 1.8°C increase in winter season air temperature since 1979. Machine learning techniques, particularly RF analysis, offer insights into the driving variables of avalanche activity, enhancing decision-making processes for avalanche hazard mitigation. Recommendations include a holistic approach combining expert knowledge, snow profile measurements, and continuous monitoring of meteorological and snow variables to assess avalanche hazards. Close-range sensing techniques, including UAS and CRNS, provide valuable tools for spatial monitoring of avalanche paths and spatial SD distribution. Ongoing research by the Cosmic Sense unit further investigates the CRNS method. The findings can be utilized by public authorities such as the Krkonoše National Park administration, the Mountain Rescue Service of the Czech Republic, and the Institute of Forest Management.

Keywords

Snow, snow avalanche, snow mapping, avalanche path, RAMMS, unmanned aerial systems, photogrammetry, Structure from Motion, snow depth, Krkonoše mountains, release zones, Cosmic Ray Neutron Systems

Language used: English

Date on which the thesis was submitted / produced: 11. 2. 2024

Thesis defence

• Supervisor: prof. Ing. Martin Hanel, Ph.D.
• Reader: Petr Máca, doc. Ing., Ph.D., Michal Jeníček, externi, Josef Krása, Bc., externi

Czech University of Life Sciences Prague

Faculty of Environmental Sciences

Doctoral programme / field:
Landscape Engineering / Environmental Modelling