An integrated approach for investigating geomorphic changes due to flash flooding in two small stream channels (Western Polish Carpathians)

Bucała-Hrabia, Anna; Kijowska-Strugała, Małgorzata; Bryndal, Tomasz; Cebulski, Jarosław; Kiszka, Krzysztof; Kroczak, Rafał

doi:10.1016/j.ejrh.2020.100731

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…The most important information includes rainfall data. Flash floods in small catchments are caused by intense rainfall of short durations, and small streams react to such rainfall very quickly, as already pointed out by Bucała-Hrabia et al [74]. This was confirmed by observations on the analyzed stream, for which the highest stormwater level recorded so far (151 cm) was caused by rainfall with a duration of approximately 40 min and a depth of 41.4 mm.…”

Section: Discussionsupporting

confidence: 80%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

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Section: Discussionsupporting

confidence: 80%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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“…The pattern of geological setting, which we reveal in Figures 2-7 and idealized on the scheme in Figure 8, can be adopted as an environmental framework by hydrotechnical engineers who aim to develop instream river training [73,74]. Such a design practice faces a problem of too high stream power [39,[75][76][77][78][79][80][81][82][83][84][85] in the channel which leads to erosion. There are few concepts of energy dissipation of the stream.…”

Section: Resultsmentioning

confidence: 99%

Rivers Try Harder. Reversed “Differential Erosion” as Geological Control of Flood in the Large Fluvial Systems in Poland

Bihałowicz

Wierzbicki

2021

Water

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We study cross-sections on the Detailed Geological Map of Poland (SMGP) to find a geologic and geomorphic pattern under river valleys in Poland. The pattern was found in 20 reaches of the largest Polish rivers (Odra, Warta, Vistula, Narew, and Bug) located in the European Lowland, in the landscape of old (Pleistocene, Saalian) glacial high plains extending between the Last Glacial Maximum (LGM) moraines on the North and the Upland on the South. The Upland was slightly folded and up-faulted during Alpine orogeny together with the thrust of Carpathian nappes and the uplift of Tatra Mts. and Sudetes. The found pattern is an alluvial river with broad Holocene floodplain and the channel developed atop the protrusion of bedrock (Jurassic, Cretaceous limestones, marlstones, sandstones) or non-alluvial, cohesive, overconsolidated sediments resistant to erosion (glacial tills, lacustrine or “ice-dammed lake” clays) of Cenozoic (Paleogene, Neogene, Quaternary—Elsterian). We regard the sub-alluvial protrusion as the limit of river incision and scour. It cannot be determined why the river flows atop these protrusions, in opposition to “differential erosion”, a geomorphology principle. We assume it is evidence of geological flood control. We propose an environmental and geomorphological framework for the hydrotechnical design of instream river training.

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“…Also, the peaks of unit stream power inferred along the main channel (ω P1-6 = 2487-3865 W m − 2 ) appeared comparable, although at the upper limit, to those reported in literature. In fact, the small Jamne and Jaszcze basins, located in the Western Polish Carpathians, experienced ω between 767 and 2769 W m − 2 as a consequence of flood due to heavy summer precipitation (Bucała-Hrabia et al, 2020). In Fourmille Creek, Wicherski et al (2017) documented a precipitation event of 350 mm in seven days, which generated over-bankfull discharge for 120 h and a maximum ω of about 2500 W m − 2 (Table 4).…”

Section: Hydraulic Forcingmentioning

confidence: 99%

“…All these triggering factors converge in a main effect that is the generation of massive runoff characterized by high water discharges and stream power peaks. An extensive literature found these conditions associated to flash floods, i. e., rapid and severe floods (McEwen and Werritty, 1988;Batalla et al, 1999;Borga et al, 2014;Marchi et al, 2016;Lucía et al, 2018), whereas they were observed less frequently in large floods of long duration (Cassandro et al, 2002;Sholtes et al, 2018;Bucała-Hrabia et al, 2020). The characterization of the hydraulic forcing conditions expressed by high magnitude/low frequency events can be challenging, with the frequent need to rely on indirect methods as post-flood measurements, dendrogeomorphic analysis, paleoflood estimation and hydraulic modelling in order to reconstruct hydrographs and peak discharges (Rico et al, 2001;Stoffel and Bollschweiler, 2008;Victoriano et al, 2018;Wyzga et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hydrological, geomorphic and sedimentological responses of an alpine basin to a severe weather event (Vaia storm)

Rainato

Martini

Pellegrini

et al. 2021

CATENA

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An integrated approach for investigating geomorphic changes due to flash flooding in two small stream channels (Western Polish Carpathians)

Cited by 8 publications

References 36 publications

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Rivers Try Harder. Reversed “Differential Erosion” as Geological Control of Flood in the Large Fluvial Systems in Poland

Hydrological, geomorphic and sedimentological responses of an alpine basin to a severe weather event (Vaia storm)

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