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Testing probabilistic adaptive real-time flood forecasting models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Testing probabilistic adaptive real-time flood forecasting models. / Smith, P. J.; Beven, K. J.; Leedal, D. et al.
In: Journal of Flood Risk Management, Vol. 7, No. 3, 09.2014, p. 265-279.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Smith, PJ, Beven, KJ, Leedal, D, Weerts, AH & Young, PC 2014, 'Testing probabilistic adaptive real-time flood forecasting models', Journal of Flood Risk Management, vol. 7, no. 3, pp. 265-279. https://doi.org/10.1111/jfr3.12055

APA

Smith, P. J., Beven, K. J., Leedal, D., Weerts, A. H., & Young, P. C. (2014). Testing probabilistic adaptive real-time flood forecasting models. Journal of Flood Risk Management, 7(3), 265-279. https://doi.org/10.1111/jfr3.12055

Vancouver

Smith PJ, Beven KJ, Leedal D, Weerts AH, Young PC. Testing probabilistic adaptive real-time flood forecasting models. Journal of Flood Risk Management. 2014 Sept;7(3):265-279. Epub 2013 Jul 3. doi: 10.1111/jfr3.12055

Author

Smith, P. J. ; Beven, K. J. ; Leedal, D. et al. / Testing probabilistic adaptive real-time flood forecasting models. In: Journal of Flood Risk Management. 2014 ; Vol. 7, No. 3. pp. 265-279.

Bibtex

@article{9f2f7bf1e31344b28d5165d07a62ee91,
title = "Testing probabilistic adaptive real-time flood forecasting models",
abstract = "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 National Flood Forecasting System. 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 River Eden at Carlisle and River Severn at Shrewsbury, are presented.",
keywords = "Forecast, Modelling, Uncertainty analysis",
author = "Smith, {P. J.} and Beven, {K. J.} and D. Leedal and Weerts, {A. H.} and Young, {P. C.}",
year = "2014",
month = sep,
doi = "10.1111/jfr3.12055",
language = "English",
volume = "7",
pages = "265--279",
journal = "Journal of Flood Risk Management",
issn = "1753-318X",
publisher = "Wiley/Blackwell (10.1111)",
number = "3",

}

RIS

TY - JOUR

T1 - Testing probabilistic adaptive real-time flood forecasting models

AU - Smith, P. J.

AU - Beven, K. J.

AU - Leedal, D.

AU - Weerts, A. H.

AU - Young, P. C.

PY - 2014/9

Y1 - 2014/9

N2 - 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 National Flood Forecasting System. 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 River Eden at Carlisle and River Severn at Shrewsbury, are presented.

AB - 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 National Flood Forecasting System. 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 River Eden at Carlisle and River Severn at Shrewsbury, are presented.

KW - Forecast

KW - Modelling

KW - Uncertainty analysis

U2 - 10.1111/jfr3.12055

DO - 10.1111/jfr3.12055

M3 - Journal article

AN - SCOPUS:84905680401

VL - 7

SP - 265

EP - 279

JO - Journal of Flood Risk Management

JF - Journal of Flood Risk Management

SN - 1753-318X

IS - 3

ER -