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首页> 外文期刊>Australian journal of water resources >Application Of Ensemble Kalman Filter For Flood Forecasting In Australian Rivers
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Application Of Ensemble Kalman Filter For Flood Forecasting In Australian Rivers

机译：集合卡尔曼滤波在澳大利亚河流洪水预报中的应用

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摘要

There is a growing interest in understanding the uncertainty in flood forecasting and the resulting flood warnings. This is borne out of the fact that the processes involved in flood forecasting have inherent uncertainties in them. The procedure used in flood forecasting consists of a number of steps. The first step is rainfall measurement and forecasting rainfall during a flood event. The rainfall is then transformed into flow using a combined water balance and runoff-routing model. There are uncertainties associated with rainfall measurement/forecasting, model (conceptualisation and parameters) and flow measurements. All these uncertainties contribute to the uncertainty in the resulting flood forecasts. The Ensemble Kalman Filter (EnKF) enables all these uncertainties to be combined in a systematic way and it has been used by a number of researchers in the past. In this paper, the EnKF with state and parameter updating is used with the Probability Distributed Moisture model to forecast flood events in six rivers located in different parts of Australia. The results showed that the quality of forecasts deteriorated with lead time greater than 6 hours and the peak discharge magnitudes were underestimated. Of the two variations used, state updating performed better than parameter updating.
机译：人们越来越了解洪水预测的不确定性以及由此产生的洪水预警。这是因为以下事实：洪水预报所涉及的过程具有内在的不确定性。洪水预报中使用的过程包括许多步骤。第一步是降雨测量和洪水事件期间的降雨量预报。然后使用组合的水平衡和径流路由模型将降雨转化为流量。降雨测量/预报，模型（概念和参数）和流量测量存在不确定性。所有这些不确定性都会导致洪水预报的不确定性。 Ensemble Kalman滤波器（EnKF）可以系统地组合所有这些不确定性，并且过去已被许多研究人员使用。在本文中，带有状态和参数更新的EnKF与概率分布水分模型一起用于预测位于澳大利亚不同地区的六条河流中的洪水事件。结果表明，预测质量随着交付时间超过6小时而变差，并且峰值放电幅度被低估了。在使用的两个变体中，状态更新的性能优于参数更新。

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