A mathematical model of border-ice formation in rivers

Svensson, Urban; Billfalk, L.; Hammar, Lars

doi:10.1016/0165-232x(89)90019-0

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…A negative heat flux for several consecutive days will result in an ice generation phase and therefore corresponds to the beginning of the freeze-up period (Svensson et al, 1989). Conversely, a positive heat flux for several consecutive days will result in an ice deterioration phase and therefore corresponds to the beginning of the breakup period.…”

Section: Development Of Quantitative Criteria For Freeze-up Breakupmentioning

confidence: 99%

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Taha¹,

Bonneau-Lefebvre²,

Bergner³

et al. 2019

Front. Earth Sci.

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Fast ice is often used by coastal communities in the James Bay area for transportation in winter using snowmobiles. Therefore, the extent of fast ice along the James Bay coastline is important for land use and any changes to these extents may have significant impacts on the lifestyles of local communities. The eastern coastline has experienced changes in recent decades that might have affected the ice processes, namely hydrologic modifications due to hydroelectric development by Hydro-Québec along with climatic changes that have been observed worldwide. A statistical analysis in the form of summarized ice charts of the ice extents in the middle of winter have been compiled for the past four decades to highlight any recent changes in ice coverage using data from satellite imagery and ice charts produced by the Canadian Ice Service (CIS). A statistical analysis has also been carried out on the freeze-up and breakup dates using historical data. Moreover, a statistical analysis of hydrological data and climatic data has been carried out to determine the long-term and short-term trends in the parameters influencing the ice processes. After testing sensitivity of the past ice regime to climatic and hydrological parameters, the trends detected in the extents of fast ice, as well as freeze-up and breakup conditions, have been correlated with climatic parameters. Using the dominant parameters, a simplified model of the extents of fast ice has been developed, as well as criteria to determine freeze-up and breakup dates. Air temperature projections have also been obtained for two greenhouse gas emissions scenarios using global climate model results to establish future climatic conditions. Finally, projections of the fast ice regime around the year 2050 have been developed to determine a longterm trend covering both historical changes and future conditions. We expect around the year 2050 a recession of the landfast ice coverage of several kilometers, a delay of 1-3 weeks of the freeze-up dates and an advance of 2-10 days of the breakup dates in comparison to the period of 1998-2016 in the James Bay area.

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Section: Development Of Quantitative Criteria For Freeze-up Breakupmentioning

confidence: 99%

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Taha¹,

Bonneau-Lefebvre²,

Bergner³

et al. 2019

Front. Earth Sci.

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“…All numerical ice growth models use a more or less simplified version of energy budget. Some of these models apply to a specific aspect of ice development such as the ice cover initiation [ Schulyakovskii , 1966], border ice formation [ Matousek , 1984; Svensson et al , 1989], frazil ice formation [ Omstedt , 1985a, 1985b; Svensson and Omstedt , 1994], and ice cover growth [e.g., Beltaos , 1995; Schulyakovskii , 1966; Lock , 1990]. Other models are more complete and may simulate ice formation, transport, growth and decay [ Shen and Chiang , 1984; Shen and Ho , 1986; Shen et al , 1990, 1995].…”

Section: Ice Growth Models Based On Thermodynamic Principlesmentioning

confidence: 99%

Modeling ice growth on Canadian lakes using artificial neural networks

Seidou

Ouarda

Bilodeau

et al. 2006

Water Resources Research

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[1] This paper presents artificial neural network (ANN) models designed to predict ice in Canadian lakes and reservoirs during the early winter ice thickness growth period. The models fit ice thickness measurements at one or more monitored lakes and predict ice thickness during the growth period either at the same locations for dates without measurements (local ANN models) or at any site in the region (regional ANN model), provided that the required meteorological input variables are available. The input variables were selected after preliminary assessments and were adapted from time series of daily mean air temperature, rainfall, cloud cover, solar radiation, and average snow depth. The results of the ANN models compared well with those of the deterministic physics-driven Canadian Lake Ice Model (CLIMO) in terms of root-mean-square error and in terms of relative root-mean-square errors. The ANN models predictions were also marginally more precise than a revised version of Stefan's law (RSL), presented herein. They reproduced some intrawinter and interannual growth rate fluctuations that were not accounted for by RSL. The performance of the models results in good part from a careful choice of input variables, inspired from the work on deterministic models such as CLIMO. ANN models of ice thickness show good potential for the use in contexts where ad hoc adjustments are desirable because of the limited availability of measurements and where poor data nature, availability, and quality precludes using deterministic physics-driven models.

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“…Shen & Ho (1986) and Hirayama (1986) developed one-dimensional extension models, which provided the description for two-dimensional models. Svensson et al (1989) developed a twodimensional river ice model of the thermal growth of border ice, which was based on the conservation equations for mass, momentum, and thermal energy. Although the river ice models are mature and applied in many regions, only few studies have regarded river ice as a whole system to investigate.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the ice thickness of the Heilongjiang River and application of SD models to a river ice model

Xing

Simonoviæ

et al. 2021

Hydrology Research

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The Heilongjiang River is a transboundary river between China and Russia, which often experiences ice dams that can trigger spring floods and significant damages in the region. Owing to insufficient data, no river ice model is applicable for the Heilongjiang River. Therefore, a river ice thickness model based on continuous meteorological data and river ice data at the Mohe Station located in the upper reach of the Heilongjiang River was proposed. Specifically, the proposed model was based on physical river ice processes and the Russian empirical theory. System dynamic models were applied to assess the proposed model. The performance of the river ice model was evaluated using root-mean-square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Subsequently, sensitivity analyses of the model parameters through Latin hypercube sampling and uncertainty analyses of input variables were conducted. Results show that the formation of ice starts 10 days after the air temperature reaches below 0 °C. The maximum ice thickness occurs 10 days after the atmospheric temperature reaches the minimum. Ice starts to melt after the highest temperature is greater than 0 °C. The R2 of ice thickness in the middle of river (ITMR) and ice thickness at the riverside (ITRS) are 0.67 and 0.69, respectively; the RMSEs of ITMR and ITRS are 6.50 and 6.84, respectively; and the NSEs of ITMR and ITRS are 0.72 and 0.70, respectively. Sensitivity analyses show that ice growth and ice melt are sensitive to the air temperature characterizing the thermal state. Uncertainty analyses show temperature has the greatest effect on river ice.

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A mathematical model of border-ice formation in rivers

Cited by 14 publications

References 5 publications

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Modeling ice growth on Canadian lakes using artificial neural networks

Simulation of the ice thickness of the Heilongjiang River and application of SD models to a river ice model

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