Tree-ring reconstruction of snow avalanche activity: Does avalanche path selection matter?

Historical activity of debris flows in the medium-high mountains: Regional reconstruction using dendrogeomorphic approach

Detailed knowledge of the occurrence of debris flows in the past is key to understanding their linkage to changing climatic variables and their occurrence in the future. For a comprehensive understanding of the origin of these processes, regional reconstruction is optimal rather than detailed analysis of isolated catchments. This study presents the results of a dendrogeomorphic reconstruction of debris flows across an entire medium-high mountain range in Central Europe covering more than 500 km². The tree-ring data allowed the reconstruction of 96 debris flow events at 21 sites. The average frequency of events was 6.8 per decade, which is comparable or higher compared to alpine valleys. A detailed analysis of potential precipitation triggers was also performed in the paper, whose magnitude significantly influenced not only the number of debris flows but also their magnitude. Debris flows occur in two forms in the study area, with channelized debris flows showing significantly higher magnitude but lower frequency than fan debris flows. The differences between the two types are probably due to the different source of material that is reactivated during precipitation events of different duration and magnitude.

Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China

Data-driven methods are commonly applied in avalanche hazard evaluation. However, few studies have tapped into the relationship between the explanatory variables and avalanche hazard in arid–frigid areas, and the seasonal dynamics of avalanche hazard and its attribution has not been discussed. Therefore, to fill the gap in the hazard assessment of a dry–cold snow avalanche, quantify the dynamic driving process of seasonal nonlinear explanatory variables on avalanche hazard, and improve the reliability of the assessments, this study used Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms to construct three assessment models; these were used and verified in the western Tianshan Mountains, China. The following results were obtained: The causative factors of avalanches varied based on the season. In winter, terrain and snow depth played a major role, whereas spring was mainly influenced by snow depth and meteorological factors. The dynamic process of avalanche hazard was mainly governed by the seasonality of snow depth and temperature. The seasonal changes in avalanche hazard increased from low to high. The performance of all models was consistent for season and more reliable than the inter-annual evaluations. Among them, the RF model had the best prediction accuracy, with AUC values of 0.88, 0.91 and 0.78 in winter, spring and the control group, respectively. The overall accuracy of the model with multi-source heterogeneous factors was 0.212–0.444 higher than that of exclusive terrain factors. In general, the optimised model could accurately describe the complex nonlinear collaborative relationship between avalanche hazard and its explanatory variables, coupled with a more accurate evaluation. Moreover, free from inter-annual scale, the seasonal avalanche hazard assessment tweaked the model to the best performance.

Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data

Avalanche disasters are extremely destructive and catastrophic, often causing serious casualties, economic losses and surface erosion. However, far too little attention has been paid to utilizing remote sensing mapping avalanches quickly and automatically to mitigate calamity. Such endeavors are limited by formidable natural conditions, human subjective judgement and insufficient understanding of avalanches, so they have been incomplete and inaccurate. This paper presents an objective and widely serviceable method for regional auto-detection using the scattering and interference characteristics of avalanches extracted from Sentinel-1 SLC images. Six indices are established to distinguish avalanches from surrounding undisturbed snow. The active avalanche belts in Kizilkeya and Aktep of the Western TianShan Mountains in China lend urgency to this research. Implementation found that smaller avalanches can be consistently identified more accurately in descending images. Specifically, 281 and 311 avalanches were detected in the ascending and descending of Kizilkeya, respectively. The corresponding numbers on Aktep are 104 and 114, respectively. The resolution area of single avalanche detection can reach 0.09 km2. The performance of the model was excellent in all cases (areas under the curve are 0.831 and 0.940 in descending and ascending of Kizilkeya, respectively; and 0.807 and 0.938 of Aktep, respectively). Overall, the evaluation of statistical indices are POD > 0.75, FAR < 0.34, FOM < 0.13 and TSS > 0.75. The results indicate that the performance of the innovation proposed in this paper, which employs multivariate comprehensive descriptions of avalanche characteristics to actualize regional automatic detection, can be more objective, accurate, applicable and robust to a certain extent. The latest and more complete avalanche inventory generated by this design can effectively assist in addressing the increasingly severe avalanche disasters and improving public awareness of avalanches in alpine areas.

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.