Debris floods can cause large economic damage and endanger human lives. This paper presents an extreme May 2018 debris flood that occurred in northern Slovenia near the Krvavec ski resort and caused large economic damage. The debris flood was initiated by an extreme rainfall event with a return period of over 50 years. There were large differences in the measured rainfall amounts using different equipment. The estimated volume of the debris material during the event was 4000 m3/km2 for the Brezovški graben. In order to mitigate the risk due to future debris flood and debris flows, a check dam is planned to be constructed. The part of the design process is presented in this paper. Additionally, RAMMS model was used to validate the empirical equations that were used in the process of the check dam stability design. The model was calibrated using information about the deposition area. Two adjacent torrents were modelled, and we were not able to find a common RAMMS parameter set that would yield adequate simulation performance in both cases.
COBISS.SI-ID: 9010529
Debris flows with different magnitudes can have a large impact on debris fan characteristics such as height or slope. Moreover, knowledge about the impact of random sequences of debris flows of different magnitudes on debris fan properties is sparse in the literature and can be improved using numerical simulations of debris fan formation. Therefore, in this paper we present the results of numerical simulations wherein we investigated the impact of a random sequence of debris flows on torrential fan formation, where the total volume of transported debris was kept constant, but different rheological properties were used. Overall, 62 debris flow events with different magnitudes from 100 m3 to 20,000 m3 were selected, and the total volume was approximately 225,000 m3. The sequence of these debris flows was randomly generated, and selected debris fan characteristics after the 62 events were compared. For modelling purposes, we applied the Rapid Mass Movement Simulations (RAMMS) software and its debris flow module (RAMMS-DF). The modelling was carried out using: (a) real fan topography from an alpine environment (i.e., an actual debris fan in north-west (NW) Slovenia formed by the Suhelj torrent), and (b) an artificial flat surface with a constant slope. Several RAMMS model parameters were tested. The simulation results confirm that the random sequence of debris flow events has only some minor effects on the fan formation (e.g., slope, maximum height), even when changing debris flow rheological properties in a wide range. After the 62 events, independent of the selected sequence of debris flows, the final fan characteristics were not significantly different from each other. Mann–Whitney (MW) tests and t-tests were used for this purpose, and the selected significance level was 0.05. Moreover, this conclusion applies for artificial and real terrain and for a wide range of tested RAMMS model rheological parameters. Further testing of the RAMMS-DF model in real situations is proposed in order to better understand its applicability and limitations under real conditions for debris flow hazard assessment or the planning of mitigation measures.
COBISS.SI-ID: 8679265
The concept of "surface modelling" generally describes the process of representing a physical or artificial surface by a geometric model, namely a mathematical expression. Among the existing techniques applied for the characterization of a surface, terrain modelling relates to the representation of the natural surface of the Earth. Cartographic terrain or relief models as three-dimensional representations of a part of the Earth's surface convey an immediate and direct impression of a landscape and are much easier to understand than two-dimensional models. This paper addresses a major problem in complex surface modelling and evaluation consisting in the characterization of their topography and comparison among different textures, which can be relevant in different areas of research. A new algorithm is presented that allows calculating the fractal dimension of images of complex surfaces. The method is used to characterize different surfaces and compare their characteristics. The proposed new mathematical method computes the fractal dimension of the 3D space with the average space component of Hurst exponent H, while the estimated fractal dimension is used to evaluate, compare and characterize complex surfaces that are relevant in different areas of research. Various surfaces with both methods were analysed and the results were compared. The study confirms that with known coordinates of a surface, it is possible to describe its complex structure. The estimated fractal dimension is proved to be an ideal tool for measuring the complexity of the various surfaces considered.
COBISS.SI-ID: 9010785
In some rainfall-triggered landslides, intensity-duration thresholds can have limited prediction ability; therefore, investigation of alternative approaches that can be used for temporal prediction of rainfall-induced landslides is needed. This paper presents a methodology for predicting rainfall-induced shallow landslides based on a lumped conceptual hydrological model. The production storage level during the rainfall event and the rainfall sum during the event are used for landslide prediction. Based on these two hydro-meteorological variables a threshold is defined that could be used for rainfall-induced landslides prediction as part of an early warning system. The presented methodology is tested using the meso-scale Selška Sora River catchment case study in western Slovenia where 20 active landslides from the Slovenian National Landslide Database are used to calibrate and evaluate the methodology performance. The results are compared to three different (i.e. local, regional, and global) intensity-duration thresholds. The results of the presented approach are superior in terms of several goodness-of-fit criteria compared to tested local and global ID thresholds. Because only daily rainfall, evapotranspiration, and discharge data are needed to calibrate the selected hydrological model and only daily rainfall and evapotranspiration to run the model, the presented approach could also be useful for data-scarce areas where detailed physically based landslide prediction models that require many data cannot be constructed. Moreover, we have also derived the probabilistic version of the proposed threshold for triggering of shallow landslides using copula functions.
COBISS.SI-ID: 8770401
Alluvial (torrential) fans, especially those created from debris flow activity often endanger built environment and human life. It is well known that this kind of territories where human activities are favoured are characterized by increasing instability and related hydrological risk, therefore treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyse and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. Digital elevation model (DEM) was adopted for the geomorphometric acquisitions and a new hybrid Euler graph method of machine learning was developed to analyse the geomorphological parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans compared with data of the empirical model proposed by Bertrand et al. (2013), in order to classify torrential fans, confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.
COBISS.SI-ID: 61449731