Dynamic real-time prediction of flood inundation probabilities
Dynamic real-time prediction of flood inundation probabilities
复制标题
DOI：
10.1080/02626669809492117
复制
发表时间：
1998-04
期刊：
影响因子：
--
通讯作者：
R. Romanowicz;K. Beven
中科院分区：
文献类型：
--
作者：
R. Romanowicz;K. Beven
摘要 贝叶斯广义似然不确定性估计（GLUE）方法以前用于降雨径流建模，应用于预测洪泛平原上所有点的淹没概率的空间和时间变化的分布式问题。概率估计基于使用河段沿线站点的已知水位对分布式准二维洪水演算模型的蒙特卡罗实现进行的条件预测。该方法可以应用于洪水预报环境，其中可以使用水位遥测信息实时更新 N 步洪水概率估计。它还表明，可以使用根据河段规模流入和流出数据校准的自适应传递函数模型获得的河段末端下游水位的（不确定）在线预测来实时调节 Nstep 提前预测。
Abstract The Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology, previously used in rainfall-runoff modelling, is applied to the distributed problem of predicting the space and time varying probabilities of inundation of all points on a flood plain. Probability estimates are based on conditioning predictions of Monte Carlo realizations of a distributed quasi-two-dimensional flood routing model using known levels at sites along the reach. The methodology can be applied in the flood forecasting context for which the N-step ahead inundation probability estimates can be updated in real time using telemetered information on water levels. It is also shown that it is possible to condition the Nstep ahead forecasts in real time using the (uncertain) on-line predictions of the downstream water levels at the end of the reach obtained from an adaptive transfer function model calibrated on reach scale inflow and outflow data.