Probabilistic online flood forecasting for flash flood prone catchments in lower mountain ranges

低山区山洪易发流域的概率在线洪水预报

• 批准号: 36862337
• 负责人: Professor Dr. Clemens Simmer
• 金额: --
• 依托单位: Institut für Geowissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2007
• 资助国家: 德国
• 起止时间: 2006-12-31 至 2009-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/36862337?language=en
• 关键词: Probabilistic; online; flood; forecasting; flash

The questionable reliability and brief validity of flood forecasting for fast responding catchments remains one of the most challenging problems in hydrometeorology. We tackle this problem by considering the hydrological and meteorological model uncertainties using rigorous meteorological/hydrological/hydraulic modelling together with computationally highly efficient artificial intelligence techniques. The meteorological part develops a suite of ensemble precipitation forecasts from days to minutes with increasing accuracy by (a) a novel approach to improve operational ensembles from the national weather service for lead times from 18 hours down to 6 hours, (b) extending the validity of best ensemble members using physical initialisation for lead times down to 3 hours and (c) nowcasting based on ensemble forecasts from radar feature tracking. Quantifying the uncertainties from the ensembles is a major task of the meteorological project part. The hydrologic part processes the meteorological forecast uncertainty basically by Monte Carlo rainfall-runoff and flood routing simulations. The inclusion of the hydrologic model uncertainties employs a perturbation approach for setting up a physically based stochastic catchment model. This is subsequently fully portrayed by a quasi-stochastic artificial neural network (ANN-S), which originates from an extensive training on the basis of uncertain soil data together with simulations of all flood relevant rainstorm scenarios using the stochastic catchment model. Combining the resulting hydrologic model uncertainty with the meteorological uncertainty allows coupling the ANN-S to a flood routing ANN (ANN-F), which itself is trained by a hydrodynamic model. The Monte Carlo simulations of the coupled ANN-S and ANN-F thus allow a relatively simple and fast prediction of the probability of exceeding critical water levels also in rivers with backwater effects.

快速响应流域洪水预报的可靠性和短期有效性是水文气象学中最具挑战性的问题之一。我们通过考虑水文和气象模型的不确定性，使用严格的气象/水文/水力建模与计算高效的人工智能技术来解决这个问题。气象部分开发了一套集合降水预报，从几天到几分钟，精度不断提高，方法是：（a）采用一种新的方法，改进国家气象局的业务集合，将提前时间从18小时降到6小时，（B）将使用物理初始化的最佳集合成员的有效性延长至3小时，以及（c）基于雷达特征跟踪的集合预报的临近预报。对集合的不确定性进行量化是气象项目部分的主要任务。水文部分主要通过蒙特卡罗模拟径流和洪水演进来处理气象预报的不确定性。水文模型的不确定性采用摄动方法建立一个基于物理的随机流域模型。这是随后充分描绘的准随机人工神经网络（ANN-S），它起源于一个广泛的训练的基础上，不确定的土壤数据与所有洪水相关的暴雨情景模拟使用随机集水模型。结合所得到的水文模型的不确定性与气象的不确定性，允许耦合的ANN-S的洪水路由人工神经网络（ANN-F），这本身是由一个水动力学模型训练。因此，耦合的ANN-S和ANN-F的Monte Carlo模拟允许一个相对简单和快速的预测超过临界水位的概率，也在河流回水效应。