Advances in real–time flood forecasting
Advances in real–time flood forecasting
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DOI：
10.1098/rsta.2002.1008
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发表时间：
2002-07
期刊：
影响因子：
--
通讯作者：
P. Young
中科院分区：
文献类型：
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作者：
P. Young
本文讨论了在实时洪水预报的背景下，河流系统中降雨-流量（降雨-径流）和水流路径过程的建模。有人认为，确定性、还原论（或“自下而上”）模型不适合实时预测，因为河流流域动态特征固有的不确定性和模型过度参数化的问题。讨论了利用统计方法识别和估计的可选的、有效参数化的基于数据的机制模型的优点。研究表明，这种模型是一种理想的形式，可以纳入基于递归状态空间估计（随机卡尔曼滤波算法的自适应版本）的实时自适应预测系统。一个说白了的例子，基于对来自英格兰西北部霍德河的一组有限的每小时降雨流量数据的分析，证明了这种方法在困难情况下的实用性，并说明了结合实时状态和参数自适应的优势。
This paper discusses the modelling of rainfall–flow (rainfall–run–off) and flow–routeing processes in river systems within the context of real–time flood forecasting. It is argued that deterministic, reductionist (or ‘bottom–up’) models are inappropriate for real–time forecasting because of the inherent uncertainty that characterizes river–catchment dynamics and the problems of model over–parametrization. The advantages of alternative, efficiently parametrized data–based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real–time, adaptive forecasting system based on recursive state–space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall–flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real–time state and parameter adaption.