Modeling Ice-Jam Floods in the Frameworks of an Intelligent System for River Monitoring

Krylenko, Inna; Alabyan, A. M.; Aleksyuk, A. I.; Беликов, В. В.; Sazonov, A. A.; Zavyalova, E. V.; Pimanov, Ilya; Potryasaev, Semyon A.; Zelentsov, V. A.

doi:10.1134/s0097807820030069

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…The main equations and the descriptions of model computational schemes can be found in [69]. STREAM_2D is based on hybrid triangularquadrangular meshes with a variable grid size and has a sediment transport and ice block [70,71]. STREAM_2D software is widely used in Russia for river valleys of different scale [70,71].…”

Section: Hydrodynamic Modeling Of the Outburst Floods From The Lakes Near The Bashkara Glaciermentioning

confidence: 99%

“…STREAM_2D is based on hybrid triangularquadrangular meshes with a variable grid size and has a sediment transport and ice block [70,71]. STREAM_2D software is widely used in Russia for river valleys of different scale [70,71]. Previous studies of potential GLOF from Bashkara Lake were performed using the same model [33,72], and we chose this software for better comparison of results.…”

Section: Hydrodynamic Modeling Of the Outburst Floods From The Lakes Near The Bashkara Glaciermentioning

confidence: 99%

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Modeling of Extreme Hydrological Events in the Baksan River Basin, the Central Caucasus, Russia

et al. 2021

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High mountain areas are prone to extreme hydrological events, and their study is especially important in the context of ongoing intensive deglaciation. In this research, a model “chain” consisting of a hydrodynamic model and a runoff formation model is adopted to simulate a glacier lake outburst flood (GLOF) from Bashkara Lake (the Central Caucasus, Russia) and its effect on downstream. In addition to an actual GLOF event that occurred on 1 September 2017 and led to casualties and significant destruction in the Adylsu and Baksan Rivers valleys, possible scenarios for the re-outburst of the lake are considered. The hydrographs of the outburst and the downstream movement of the flood wave along the Adylsu River valley are estimated using STREAM_2D two-dimensional hydrodynamic model. The water discharges in the entire river network of the Baksan River are assessed using the ECOMAG (ECOlogical Model for Applied Geophysics) runoff formation model. The output flood hydrograph from the hydrodynamic model is set as additional input into the Baksan River runoff formation model in the upper reaches of the Adylsu River. As a result of the simulations, estimates for the contribution of GLOFs and precipitation to an increase in peak discharge along the Baksan River were obtained. The actual outburst flood contributed 45% and precipitation 30% to the peak flow in the Baksan River at the mouth of the Adylsu River (10 km from the outburst site). In Tyrnyauz (40 km from the outburst site), the contributions of the outburst flood and precipitation were equal and, in Zayukovo (70 km from the outburst site), the outburst flood contributed only 20% to the peak flow, whereas precipitation contributed 44%. Similar calculations were made for future potential re-outburst flood, taking into account climatic changes with an increase in air temperatures of 2 °C, an increase in precipitation of 10% in winter and a decrease of 10% in summer. The maximum discharge of the re-outburst flood in the Adylsu River mouth, according to model estimations, will be approximately three times less than the discharge of the actual outburst on 1 September 2017 and can contribute up to 18% of the peak discharge in the Baksan River at the confluence.

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Section: Hydrodynamic Modeling Of the Outburst Floods From The Lakes Near The Bashkara Glaciermentioning

confidence: 99%

Section: Hydrodynamic Modeling Of the Outburst Floods From The Lakes Near The Bashkara Glaciermentioning

confidence: 99%

Modeling of Extreme Hydrological Events in the Baksan River Basin, the Central Caucasus, Russia

et al. 2021

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Algorithm for Edge Detection of Floodable Areas Based on Heightmap Data

Zakharov

Saveliev

2021

Electromechanics and Robotics

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Path planning for outdoor operation of ground-based robots, with respect to surface features of the terrain involves recognition of various obstacles. Besides the areas with complicated surface features (elevations, ravines), buildings, and other human-made objects, floodable areas can be potentially dangerous for operation of outdoor robots in irregular terrain. This paper presents the algorithm for floodable areas edge detection (FAED) through analysis of surface features on heightmap. The algorithm respects height values from any points of the map, as well precipitation data in the area of interest, depending on which the wetness of certain areas can increase or decrease. Experiments were performed to compare number of cells in areas, outlined manually and detected with FAED. The experimental results showed that the total number of cells, revealed with FAED, is 17% less at average, than in areas, outlined manually. Hence, the cell array in areas, whose edges were detected with FAED, is more accurate, than with manual edge detection, what enables more efficient heightmap utilization in path planning for ground-based robotic vehicles.

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