Dynamic real-time prediction of flood inundation probabilities
作者:
RENATA ROMANOWICZ,
KEITH BEVEN,
期刊:
Hydrological Sciences Journal
(Taylor Available online 1998)
卷期:
Volume 43,
issue 2
页码: 181-196
ISSN:0262-6667
年代: 1998
DOI:10.1080/02626669809492117
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
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