文章基本信息

标题：DEVELOPMENT OF A REAL-TIME RIVER FLOOD FORECASTING TRANSFER FUNCTION-NOISE MODEL WITH A KALMAN FILTER FOR SNOWMELT DRIVEN FLOODS
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作者：Jan Adamowski ; Kaz Adamowski
期刊名称：Journal of Environmental Hydrology (ältere Jahrgänge)
印刷版ISSN：1058-3912
电子版ISSN：1996-7918
出版年度：2008
卷号：16
出版社：IAEH
摘要：A real-time flood forecasting model for the Rideau River in Ottawa, Canada was developed for issuing flood warnings with sufficient lead-time. A Transfer Function-Noise (TFN) stochastic model coupled with recursive parameter estimation via a Kalman prediction algorithm was used to forecast the spring flood at the Ottawa gauging station using an upstream station (at Manotick) and tributary flows (at Jock River) as model inputs. Also, spring snowmelt runoff computed using mean daily temperature, snowfall and areally averaged snowdepth was explicitly represented in the model. The model was calibrated and tested on spring flood data from 2002 and 2004. Comparison of forecast results for a six hour lead-time showed that the new model is better suited to the Rideau River flow than the previously developed Self-Tuning Predictor (STP) model.