Young Peter C
Centre for Research on Environmental Systems and Statistics, University of Lancaster, Lancaster LA1 4YQ, UK.
Philos Trans A Math Phys Eng Sci. 2002 Jul 15;360(1796):1433-50. doi: 10.1098/rsta.2002.1008.
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.
本文探讨了在实时洪水预报背景下，河流系统中降雨-径流（降雨-流量）和水流路径过程的建模。有人认为，确定性的、还原论的（或“自下而上”的）模型不适用于实时预报，因为河流集水区动态具有内在的不确定性，以及模型过度参数化的问题。文中讨论了使用统计方法识别和估计的基于数据的替代有效参数化机理模型的优点。结果表明，此类模型非常适合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）的实时自适应预报系统。基于对英格兰西北部霍德河一组有限的每小时降雨-流量数据的分析给出的一个示例，证明了该方法在困难情况下的实用性，并说明了纳入实时状态和参数自适应的优点。
