A numerical model for river ice dynamics based on discrete element method

Zhai, Biyao; Liu, Lu; Shen, Hung Tao; Ji, Shunying

doi:10.1080/00221686.2021.2004254

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…To convert 𝑑𝑑𝑋𝑋 and 𝑑𝑑𝑋𝑋 to 𝑑𝑑𝑥𝑥 and 𝑑𝑑𝑥𝑥 , respectively, we bring Equation (4) into Equation (7). The conversion calculation process is given from Equations ( 8)- (11). Equation ( 11) is brought into Equation ( 6), getting Equation (12).…”

Section: Pixel Point Scale Methodsmentioning

confidence: 99%

“…For example, the CRISSP2D model [7,8] and the RIVICE model [9] can simulate dynamic transport (ice concertation and drift velocity) of surface ice and ice jam evolution [10] during the freezing period and mechanical breakup conditions during the thawing period. Moreover, the measured true drift ice size and shape can also be used as input for upstream boundary conditions [11] for the river ice model. Previous simulations of river ice transport used the hypothetical drift ice of one size and shape and have not considered the true distribution of drift ice in nature.…”

Section: Introductionmentioning

confidence: 99%

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A Survey Method for Drift Ice Characteristics of the Yellow River Based on Shore-Based Oblique Images

Zhang

et al. 2023

Water

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Acquisition of continuous drift ice characteristic parameters such as ice size, shape, concentration, and drift velocity are of great importance for understanding river freezing and thawing processes. This study acquired hourly oblique images captured by a shore-based camera in the winter of 2021–2022 on the Yellow River, China. The pixel point scale method for correcting oblique images is provided. The 61 lines were measured at the calibration site and the absolute error between the measured value and the calculated value was in the range of 0.009–0.850 m, with a mean error of 0.236 m. After the correction of oblique images, the true equivalent diameter of drift ice during the freezing period ranged from 0.52–13.10 m with a mean size of 3.36 m, which was larger than that of 2.30 m during the thawing period which ranged from 0.20–12.54 m. It was found that the size of drift ice increased with time during the freezing period and decreased with time during the thawing period. The fractal dimension and roundness were used to represent drift ice shape. The fractal dimension ranged from 1.0–1.3 and the roundness ranged from 0.1–1.0. A Gaussian distribution was used to estimate drift ice size and shape distributions. There is a nonlinear relationship between ice concentration and drift velocity, which can be well expressed by the logistic function. In the future, drift ice parameters for more years and hydrometeorological data for the same time need to be accumulated, which helps to analyze the freezing and thawing patterns of river ice.

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Section: Pixel Point Scale Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey Method for Drift Ice Characteristics of the Yellow River Based on Shore-Based Oblique Images

Zhang

et al. 2023

Water

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“…In order to quantify the ice thickness variation of the concave and convex bank in the bend, the ice thickness comparison parameter (𝑀) was introduced. The calculation formula is shown in Equation (5). The 𝑀 values of CS0-CS6 sections were calculated, respectively.…”

Section: Comparison Of the Ice Thickness On Concave And Convex Banksmentioning

confidence: 99%

“…Due to the combined effect of low air temperature (less than 0 • C) and turbulent water flow, the existence of open water will cause an increase in frazil ice, which will block the downstream river and form ice jams in serious cases. Monitoring changes in the ice thickness and open water in the bend is important for modern society in many respects, for example, in relation to simulation of ice cover in the bend [5], analyses of ice-induced scour and erosion in ice engineering [6], and ice flood forecasting [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Morphology Dynamics of Ice Cover in a River Bend Revealed by the UAV-GPR and Sentinel-2

Huang

et al. 2023

Remote Sensing

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After the formation of the bend ice cover, the ice thickness of the bend is not uniformly distributed, and an open-water area is usually formed downstream of the bend. The spatial and temporal variation of the ice thickness in seven cross sections was determined via Unmanned Aerial Vehicle Ground Penetrating Radar (UAV-GPR) technology and traditional borehole measurements. The plane morphology change of the open water was observed by Sentinel-2. The results show that the average dielectric permittivity of GPR was 3.231, 3.249, and 3.317 on three surveys (5 January 2022, 16 February 2022, and 25 February 2022) of the Yellow River ice growing period, respectively. The average ice thickness of the three surveys was 0.402 m, 0.509 m, and 0.633 m, respectively. The ice thickness of the concave bank was larger than that of the convex bank. The plane morphology of the open water first shrinks rapidly longitudinally and then shrinks slowly transversely. The vertical boundary of the open water was composed of two arcs, in which the slope of Arc I (close to the water surface) was steeper than that of Arc II, and the hazardous distance of the open-water boundary was 10.3 m. The increased flow mostly affected the slope change of Arc I. Finally, we discuss the variation of hummocky ice and flat ice in GPR images and the physical factors affecting GPR detection accuracy, as well as the ice-thickness variation of concave and convex banks in relation to channel curvature.

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