An extreme May 2018 debris flood case study in northern Slovenia: analysis, modelling, and mitigation

Bezak, Nejc; Jež, Jernej; Sodnik, Jošt; Auflič, Mateja Jemec; Mikoš, Matjaž

doi:10.1007/s10346-019-01325-1

Cited by 15 publications

(14 citation statements)

References 32 publications

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“…Степента на природната опасност се определя от геоложки (наличие на кластичен материал и фактори, водещи до акумулирането му), геоморфоложки (характер на водосборите) и климатични фактори. Разработени и тествани са методики за оценка на различните фактори (Theule et al, 2012;Kadetova et al, 2016;Mazzorana et al, 2020) в различни обстановки (Wilford et al, 2005;Sepúlveda et al, 2015;Jakob et al, 2016;Bezak et al, 2020). Според дефиницията на NRC (1996) конусът на р. Глазне се характеризира като активен.…”

Section: природни опасностиunclassified

Alluvial fan of Glazne river, Bansko town, SW Bulgaria

Gerdjikov¹,

Dotseva²,

Gikov

et al. 2021

Rev. Bulg. Geol. Soc.

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At the place where Banderitsa and Demyanitsa rivers and their tributaries ceased to be confined to their narrow valleys one of the most impressive alluvial fans in South Bulgaria is formed – the one of Glazne river. The river valley morphology, as well as the evolution and the position of the Glazne fan, are controlled by the active normal faulting in the NE slopes and foot of Pirin Mountain. Тhe Quaternary glaciations produced large volumes of debris in the river valleys. There is an agreement that the processes of sediment transfer from the mountain to the Razlog graben have been highly active at the time and immediately after the Pleistocene glaciations. As a result, in the Bulgarian geological and geomorphological reports and scientific papers, the age of the alluvial fans at the NE foot of Pirin Mountain is assumed to be Late Pleistocene–Early Holocene. The results of our studies require a re-evaluation of these ideas. Using widely accepted methods for natural hazard assessment, our field and historical research and analysis allow us to claim that the Glazne fan is active. Parts of Bansko, that are situated in the upper-middle parts of the alluvial fan, have been affected by at least two significant debris floods during the XX century. The conducted measures to control river behavior have an important effect on risk reduction, yet they lead to significant modification of the zones of active aggradation and this means that new actions against future events must be taken.

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Section: природни опасностиunclassified

Alluvial fan of Glazne river, Bansko town, SW Bulgaria

Gerdjikov¹,

Dotseva²,

Gikov

et al. 2021

Rev. Bulg. Geol. Soc.

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“…The RAMMS model was calibrated using the extreme May 2018 debris flood in Brezovški and Lukenjski graben torrents below the Krvavec ski area close to the City of Ljubljana (Bezak et al, 2020). The estimated magnitude of the May 2018 flood was 4,000 m 3 .…”

Section: One-and Two-dimensional Numerical Modelling Of Debris Flowsmentioning

confidence: 99%

After 2000 Stože Landslide: Part I – Development in landslide research in Slovenia

Mikoš

2020

Acta Hydrotechnica

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The 2000 Stože Landslide with the consecutive wet debris flow with a volume close to 1 million m3 that hit the village of Log pod Mangartom in November 2000 had many consequences. It triggered vast efforts not only for the mitigation of the devastated area but also sparked many studies in several scientific disciplines. Various studies were performed in order to better understand this type of natural hazard and its consequences for the natural and built environment, and for society as a whole. After two decades since the disastrous event in 2000, this paper provides an aggregate survey different studies on this event performed by researchers and engineers, summarising this event’s impact on in-depth landslide research in Slovenia. The advancement in this field in Slovenia since 2000 is obvious.

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“…Check dams are an effective soil and water conservation engineering measure for trapping sediment and mitigating soil erosion effects and have been used worldwide (Abbasi et al, 2019). Check dams are constructed in gullied channels to mitigate flood damage (Bezak et al, 2020), control sediment transport (Shi et al, 2019), stabilize slopes and torrential channels (Piton & Recking, 2017), enhance carbon storage and sequestration (Mongil-Manso et al, 2019) and serve as high-quality croplands when filled with sediment (Jin et al, 2012). Wang et al (2011) reported that >100 000 check dams had been built on the Loess Plateau in the last 50 years.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for check dam area extraction with optical images and digital elevation model: A case study in the hilly and gully regions of the Loess Plateau, China

Sun

Guo

Wang

et al. 2023

Earth Surf Processes Landf

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Check dams are widely used on the Loess Plateau of China to control soil and water loss, develop agricultural land and improve watershed ecology. Detailed information on the spatial distribution of check dams and the area of dam land is critical for quantitatively evaluating hydrological and ecological effects, planning the construction of new dams and repairing damaged dams. Therefore, this research presents a method that integrates deep learning and geospatial analysis to facilitate the extraction of check dam areas in broad areas from high‐resolution Gaofen‐2 (GF‐2) multispectral imageries, including red (R), green (G), blue (B) and near‐infrared (NIR) bands and digital elevation model (DEM). First, we generated three datasets with different band combinations (RGB, RGB + NIR and RGB + DEM) using GF‐2 remote sensing images combined with Advanced Land Observing Satellite—the Phased Array type L‐band Synthetic Aperture Radar DEM data to determine the optimal data combination for dam area extraction. Next, four widely used semantic segmentation networks—Fully Convolutional Network (FCN), U‐Net, PSPNet and DeepLabv3+—were modified to support the arbitrary number of input channels and evaluated for check dam area extraction. Finally, the check dam candidate areas in the Yan River basin were extracted from DEM using geospatial analysis to optimize the dam area extraction results. The results showed that all deep learning (DL) models could extract dam areas quickly and accurately with mean intersection over union and overall accuracy values >85% and >98%, respectively. PSPNet had the best performance for testing datasets with different band combinations. We also found that the DL models in the RGB + DEM images had the best remote sensing image segmentation results, avoiding many miss‐classified pixels. The F1 scores in the RGB + DEM test dataset for FCN U‐Net, PSPNet and DeepLabv3+ reached up to 92%, or 3.3%, 2.3%, 1.9% and 3.1% higher than the corresponding values for RGB images. Potential check dam candidate regions (~3969 km2) were obtained for application analysis, which reduced the original area by 50%. The DL models for the RGB + DEM images were applied in the dam candidate regions, extracting 91.9 km2 of agricultural production dam land and 10.6 km2 of runoff and sediment silted dam land. The extraction results will facilitate quantitative analyses of check dams, improve the management of these structures and promote the efficiency of controlling soil losses.

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An extreme May 2018 debris flood case study in northern Slovenia: analysis, modelling, and mitigation

Cited by 15 publications

References 32 publications

Alluvial fan of Glazne river, Bansko town, SW Bulgaria

Alluvial fan of Glazne river, Bansko town, SW Bulgaria

After 2000 Stože Landslide: Part I – Development in landslide research in Slovenia

Deep learning for check dam area extraction with optical images and digital elevation model: A case study in the hilly and gully regions of the Loess Plateau, China

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