There is a need to determine the maximum backwater staging possible from ice jam flooding along high flood risk prone sections of northern rivers. Similar to the probable maximum flood PMF, which is primarily estimated for the most extreme open-water floods, probable maximum floods from ice jamming PMFice can provide upper thresholds of water level elevations so essential for infrastructure designed in and along cold-region rivers. However, the processes for maximum ice-jam flooding are quite different from those of extreme open-water floods which requires river ice processes to be incorporated into the calculation approach. This paper presents a novel method for estimating the probable maximum staging from ice-jam floods. The method is based on the implementation of a deterministic hydraulic model that mimics ice jam processes and is nested in a stochastic framework to carry out Monte-Carlo simulations to randomise parameter and boundary condition value inputs for many hundreds of simulations. This stochastic approach provides the frequency distributions of many of the boundary conditions used to force the river ice hydraulic model. The stochastic modelling framework yields ensembles of backwater levels from which the maximum level provides an indication of the probable maximum staging possible, the PMFice.

中文翻译：

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一种确定冰塞洪水可能最大分期的随机水力建模方法

有必要确定沿北部河流高洪水风险段的冰塞洪水可能造成的最大回水分段。与主要针对最极端的开放水域洪水估计的可能最大洪水 PMF 类似，来自冰堵 PMFice 的可能最大洪水可以提供水位升高的上限阈值，这对于在寒冷地区河流中和沿线设计的基础设施至关重要。然而，最大冰塞洪水的过程与极端开放水域洪水的过程有很大不同，后者需要将河冰过程纳入计算方法。本文提出了一种估计冰塞洪水可能最大分期的新方法。该方法基于模拟冰堵塞过程的确定性水力模型的实现，并嵌套在随机框架中以执行蒙特卡罗模拟，以随机化数百个模拟的参数和边界条件值输入。这种随机方法提供了许多用于强制建立河冰水力模型的边界条件的频率分布。随机建模框架产生回水水位的集合，从中最高水位提供了可能的最大分级的指示，即 PMFice。这种随机方法提供了许多用于强制建立河冰水力模型的边界条件的频率分布。随机建模框架产生回水水位的集合，从中最高水位提供了可能的最大分段指示，即 PMFice。这种随机方法提供了许多用于强制建立河冰水力模型的边界条件的频率分布。随机建模框架产生回水水位的集合，从中最高水位提供了可能的最大分级的指示，即 PMFice。