During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state‐of‐the‐art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up‐to‐date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods.

中文翻译：

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高分辨率分布式水文模型在美国-加拿大跨界流域中的应用：Richelieu流域多年平均年水文和2011年洪水的模拟

2011年春季，加拿大魁北克南部的黎塞留河沿岸发生了特大洪灾。黎塞留河（Richelieu River）是黎塞留河盆地的最后部分，由位于两个大山之间的山谷中的大型尚普兰湖组成。先前重现黎塞留河水流的尝试都依赖于简化的集总模型的使用，并显示出不同的结果。为了准备一种工具，以准确地评估未来洪水的变化，在黎塞留盆地应用了最先进的分布式水文模型。该模型设置包含多种新颖的方法和数据集，例如超高分辨率河网，考虑尚普兰湖净流域供应的现代标定技术，新的优化算法，并使用最新的气象数据集来强制建立模型。结果表明，与观察到的河流流量和尚普兰湖净流域供应量的估算值相比，该水文模型能够令人满意地再现多年平均年水文图和2011年流量时间序列。受站网密度低，地形陡峭和湖泊蓄水效应影响的许多因素，例如气象强迫数据的质量，对河水流量的模拟提出了挑战。总体而言，对水文模型的令人满意的验证允许进入下一步，包括评估气候变化对黎塞留河洪水再发的影响。结果表明，与观察到的河流流量和尚普兰湖净流域供应量的估算值相比，该水文模型能够令人满意地再现多年平均年水文图和2011年流量时间序列。受站网密度低，地形陡峭和湖泊蓄水效应影响的许多因素，例如气象强迫数据的质量，对河水流量的模拟提出了挑战。总体而言，对水文模型的令人满意的验证允许进入下一步，包括评估气候变化对黎塞留河洪水再发的影响。结果表明，与观察到的河流流量和尚普兰湖净流域供应量的估算值相比，该水文模型能够令人满意地再现多年平均年水文图和2011年流量时间序列。受站网密度低，地形陡峭和湖泊蓄水效应影响的许多因素，例如气象强迫数据的质量，对模拟河流流量提出了挑战。总体而言，对水文模型的令人满意的验证允许进入下一步，包括评估气候变化对黎塞留河洪水再发的影响。诸如受站网密度低，地形陡峭和湖泊蓄水效应影响的气象强迫数据的质量等挑战了河水的模拟。总体而言，对水文模型的令人满意的验证允许进入下一步，包括评估气候变化对黎塞留河洪水再发的影响。诸如受站网密度低，地形陡峭和湖泊蓄水效应影响的气象强迫数据的质量等挑战了河水的模拟。总体而言，对水文模型的令人满意的验证允许进入下一步，包括评估气候变化对黎塞留河洪水再发的影响。