Open AccessTesting probabilistic adaptive real‐time flood forecasting models
Author(s) -
Smith P.J.,
Beven K.J.,
Leedal D.,
Weerts A.H.,
Young P.C.
Publication year - 2014
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12055
Subject(s) - probabilistic logic , flood forecasting , probabilistic forecasting , flood myth , computer science , calibration , flooding (psychology) , data assimilation , river flood , operations research , econometrics , data mining , meteorology , artificial intelligence , statistics , geography , economics , engineering , mathematics , archaeology , psychology , psychotherapist
Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data‐based mechanistic ( DBM ) models within the operational UK N ational F lood F orecasting S ystem. The position of DBM models in the forecasting chain is considered along with their offline calibration and validation. The online adaptive implementation with assimilation of water level information as used for forecasting is outlined. Two example applications based upon UK locations where severe flooding has occurred, the R iver E den at C arlisle and R iver S evern at S hrewsbury, are presented.