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Testing probabilistic adaptive real‐time flood forecasting models
AbstractOperational 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 operationalUK NationalFloodForecastingSystem. The position ofDBMmodels 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 uponUKlocations where severe flooding has occurred, theRiverEden atCarlisle andRiverSevern atShrewsbury, are presented.
Testing probabilistic adaptive real‐time flood forecasting models
AbstractOperational 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 operationalUK NationalFloodForecastingSystem. The position ofDBMmodels 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 uponUKlocations where severe flooding has occurred, theRiverEden atCarlisle andRiverSevern atShrewsbury, are presented.
Testing probabilistic adaptive real‐time flood forecasting models
J Flood Risk Management
Smith, P.J. (Autor:in) / Beven, K.J. (Autor:in) / Leedal, D. (Autor:in) / Weerts, A.H. (Autor:in) / Young, P.C. (Autor:in)
Journal of Flood Risk Management ; 7 ; 265-279
01.09.2014
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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