Operational forecast uncertainty assessment for better information to stakeholders and crisis managers

Lionel Berthet; Olivier Piotte; Éric Gaume; Renaud Marty; Constantin Ardilouze

doi:10.1051/e3sconf/20160718005

All issues

Volume 7 (2016)

E3S Web Conf., 7 (2016) 18005

References

Open Access

Issue		E3S Web Conf. Volume 7, 2016 3^rd European Conference on Flood Risk Management (FLOODrisk 2016)


Article Number		18005
Number of page(s)		10
Section		Forecasting and warning
DOI		https://doi.org/10.1051/e3sconf/20160718005
Published online		20 October 2016

Gaume E., Bain V., Bernardara P., Newinger O., Barbuc M., Bateman A., Blaskovicová L, Blöschl G., Borga M., Dumitrescu A, Daliakopoulos I, Garcia J., Irimescu A., Kohnova S., Koutroulis A., Marchi L., Matreata S., Medina V., Preciso E., Sempere-Torres D., Stancalie G., Szolgay J., Tsanis I., Velasco D. and Viglione A.(2009). A compilation of data on European flash floods. Journal of Hydrology, 367, 70–78 [Google Scholar]
Baubion C. (2015). Losing memory – the risk of a major flood in the Paris region: Improving prevention policies. Water policy, 17, 156–179, doi:10.2166/wp.2015.008 [CrossRef] [Google Scholar]
Demeritt D., Cloke H., Pappenberger F., Thielen J., Bartholmes J. and Ramos M.-H. (2007). Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting. Environmental Hazards, 7, 115–127 [CrossRef] [Google Scholar]
Marty R., Zin I., Obled C., Bontron G. and Djerboua A. (2012). Toward real-time daily pqpf by an analog sorting approach: application to flash-flood catchments. J. Appl. Meteor. Climatol., 51, 505–520, doi: http://dw.doi.org/10.1175/JAMC-D-11-011.1 [CrossRef] [Google Scholar]
Madsen H. and Skotner C. (2005). Adaptive state updating in real-time river flow forecasting – A combined filtering and error forecasting procedure. Journal of Hydrology, 308, 302–312 [CrossRef] [Google Scholar]
Moradkhani H., Sorooshian S., Gupta H. and Houser P. (2005). Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28, 135–147 [CrossRef] [Google Scholar]
Liu Y., Weerts A. H., Clark M., Hendricks Franssen H.-J., Kumar S., Moradkhani H., Seo D.-J., Schwanenberg D., Smith P., van Dijk A. I. J. M., van Velzen N., He M., Lee H., Noh S. J., Rakovec O. and Restrepo P. (2012). Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16, 3863–3887 [CrossRef] [Google Scholar]
Lang M., Pobanz K., Renard B., Renouf E. and Sauquet E. (2010). Extrapolation of rating curves by hydraulic modelling, with application to flood frequency analysis. Hydrological Sciences Journal, 55: 6, 883–898, DOI: 10.1080/02626667.2010.504186 [CrossRef] [Google Scholar]
National Research Council (2006).Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts. National Academic Press, 112 pages. [Google Scholar]
Nobert S., Demeritt D. and Cloke H. (2010). Informing operational flood management with ensemble predictions: lessons from Sweden. Journal of Flood Risk Management, 3, 72–79 [CrossRef] [Google Scholar]
Uccellini L. (2015). Press conference call (January, the 27th, 2015). http://allensalkin.com/post/109330021597/here-is-apress-conference-call-with-louis [Google Scholar]
Berthet L. and Piotte O. (2014). International survey for good practices in forecasting uncertainty assessment and communication. Proceedings of the EGU General Assembly, EGU 2014–8579. [Google Scholar]
Pagano T. (2013). International review of the role of automation in river forecasting systems. Technical. report, Bureau of Meteorology, Melbourne, VIC, Australia, 69 pages. [Google Scholar]
Georgakakos K. P., Seo D.-J., Gupta H., Schaake J. and Butts M. B. (2004). Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 298, 222–241 [CrossRef] [Google Scholar]
Velázquez J. A., Anctil F., Ramos M. H. and Perrin C. (2011). Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures. Advances in Geosciences,29, 33–42 [CrossRef] [Google Scholar]
Beven K. and Binley A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6, 279–298 [Google Scholar]
Thielen J., Bartholmes J., Ramos M.-H. and de Roo A. (2009). The European Flood Alert System - Part 1: Concept and development. Hydrology and Earth System Sciences, 13, 125–140 [CrossRef] [Google Scholar]
Zalachori I., Ramos M.-H., Garçon R., Mathevet T. and Gailhard J.(2012). Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies. Advances in Science and Research, 8, 135–141 [CrossRef] [Google Scholar]
Kuczera G. and Parent E. (1998). Monte-Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm. Journal of Hydrology, 211, 69–85 [CrossRef] [Google Scholar]
Kavetski D., Kuczera G. and Franks S. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42, W03407 [Google Scholar]
Schoups G. and Vrugt J. A. (2010). A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors. Water Resour. Res., 46, W10531 [Google Scholar]
Krzysztofowicz R. and Maranzano C. (2004). Hydrologic uncertainty processor for probabilistic stage transition forecasting. Journal of Hydrology, 293, 57–73 [CrossRef] [Google Scholar]
Montanari A. and Brath A. (2004). A stochastic approach for assessing the uncertainty of rainfallrunoff simulations. Water Resour. Res., 40, W01106, doi:10.1029/2003WR002540. [Google Scholar]
Weerts A. H., Winsemius J.S., Verkade J. S. (2011). Estimation of predictive hydrological uncertainty using quantile regression: Examples from the National Flood Forecasting System (England and Wales). Hydrological Earth Syst. Sci., 15, 255–265, doi:10.5194/hess-15-255-2011. [CrossRef] [Google Scholar]
Bourgin F. (2014). Comment quantifier l’incertitude prédictive en modélisation hydrologique ? Travail exploratoire sur un grand échantillon de basins versants. Ph. D. Thesis, AgroParisTech and IRSTEA, 208 pp. [Google Scholar]
Bourgin F., Ramos M.-H., Thirel G., Andréassian V. (2014). Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting. Journal of Hydrology, 519, 2775–2784, DOI: 10.1016/j.jhydrol.2014.07.054. [Google Scholar]
Houdant B. (2004). Contribution à l’amélioration de la prévision hydrométéorologique opérationnelle. Pour l’usage des probabilités dans la communication entre acteurs. Ph.D. Thesis, École Nationale du Génie Rural, des Eaux et Forêts [Google Scholar]
Marty R. and Faucard Y. (2015). Forecasts and uncertainty: first evaluation, proceedings of the annual forecasters’ team meeting, unpublished. [Google Scholar]
Berthet L., Andréassian V. Perrin C. and Javelle P. (2009). How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments. Hydrol. EarthSyst. Sci.13, 819–831. [CrossRef] [Google Scholar]
Morss R.E., Demuth J. L. and Lazo J. K. (2008). Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public. Weather and Forecasting, 23, 974–991, doi: 10.1175/2008WAF2007088.1 [CrossRef] [Google Scholar]

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[1] Gaume E., Bain V., Bernardara P., Newinger O., Barbuc M., Bateman A., Blaskovicová L, Blöschl G., Borga M., Dumitrescu A, Daliakopoulos I, Garcia J., Irimescu A., Kohnova S., Koutroulis A., Marchi L., Matreata S., Medina V., Preciso E., Sempere-Torres D., Stancalie G., Szolgay J., Tsanis I., Velasco D. and Viglione A.(2009). A compilation of data on European flash floods. Journal of Hydrology, 367, 70–78 [Google Scholar]

[2] Baubion C. (2015). Losing memory – the risk of a major flood in the Paris region: Improving prevention policies. Water policy, 17, 156–179, doi:10.2166/wp.2015.008 [CrossRef] [Google Scholar]

[3] Demeritt D., Cloke H., Pappenberger F., Thielen J., Bartholmes J. and Ramos M.-H. (2007). Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting. Environmental Hazards, 7, 115–127 [CrossRef] [Google Scholar]

[4] Marty R., Zin I., Obled C., Bontron G. and Djerboua A. (2012). Toward real-time daily pqpf by an analog sorting approach: application to flash-flood catchments. J. Appl. Meteor. Climatol., 51, 505–520, doi: http://dw.doi.org/10.1175/JAMC-D-11-011.1 [CrossRef] [Google Scholar]

[5] Madsen H. and Skotner C. (2005). Adaptive state updating in real-time river flow forecasting – A combined filtering and error forecasting procedure. Journal of Hydrology, 308, 302–312 [CrossRef] [Google Scholar]

[6] Moradkhani H., Sorooshian S., Gupta H. and Houser P. (2005). Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28, 135–147 [CrossRef] [Google Scholar]

[7] Liu Y., Weerts A. H., Clark M., Hendricks Franssen H.-J., Kumar S., Moradkhani H., Seo D.-J., Schwanenberg D., Smith P., van Dijk A. I. J. M., van Velzen N., He M., Lee H., Noh S. J., Rakovec O. and Restrepo P. (2012). Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16, 3863–3887 [CrossRef] [Google Scholar]

[8] Lang M., Pobanz K., Renard B., Renouf E. and Sauquet E. (2010). Extrapolation of rating curves by hydraulic modelling, with application to flood frequency analysis. Hydrological Sciences Journal, 55: 6, 883–898, DOI: 10.1080/02626667.2010.504186 [CrossRef] [Google Scholar]

[9] National Research Council (2006).Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts. National Academic Press, 112 pages. [Google Scholar]

[10] Nobert S., Demeritt D. and Cloke H. (2010). Informing operational flood management with ensemble predictions: lessons from Sweden. Journal of Flood Risk Management, 3, 72–79 [CrossRef] [Google Scholar]

[11] Uccellini L. (2015). Press conference call (January, the 27th, 2015). http://allensalkin.com/post/109330021597/here-is-apress-conference-call-with-louis [Google Scholar]

[12] Berthet L. and Piotte O. (2014). International survey for good practices in forecasting uncertainty assessment and communication. Proceedings of the EGU General Assembly, EGU 2014–8579. [Google Scholar]

[13] Pagano T. (2013). International review of the role of automation in river forecasting systems. Technical. report, Bureau of Meteorology, Melbourne, VIC, Australia, 69 pages. [Google Scholar]

[14] Georgakakos K. P., Seo D.-J., Gupta H., Schaake J. and Butts M. B. (2004). Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 298, 222–241 [CrossRef] [Google Scholar]

[15] Velázquez J. A., Anctil F., Ramos M. H. and Perrin C. (2011). Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures. Advances in Geosciences,29, 33–42 [CrossRef] [Google Scholar]

[16] Beven K. and Binley A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6, 279–298 [Google Scholar]

[17] Thielen J., Bartholmes J., Ramos M.-H. and de Roo A. (2009). The European Flood Alert System - Part 1: Concept and development. Hydrology and Earth System Sciences, 13, 125–140 [CrossRef] [Google Scholar]

[18] Zalachori I., Ramos M.-H., Garçon R., Mathevet T. and Gailhard J.(2012). Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies. Advances in Science and Research, 8, 135–141 [CrossRef] [Google Scholar]

[19] Kuczera G. and Parent E. (1998). Monte-Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm. Journal of Hydrology, 211, 69–85 [CrossRef] [Google Scholar]

[20] Kavetski D., Kuczera G. and Franks S. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42, W03407 [Google Scholar]

[21] Schoups G. and Vrugt J. A. (2010). A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors. Water Resour. Res., 46, W10531 [Google Scholar]

[22] Krzysztofowicz R. and Maranzano C. (2004). Hydrologic uncertainty processor for probabilistic stage transition forecasting. Journal of Hydrology, 293, 57–73 [CrossRef] [Google Scholar]

[23] Montanari A. and Brath A. (2004). A stochastic approach for assessing the uncertainty of rainfallrunoff simulations. Water Resour. Res., 40, W01106, doi:10.1029/2003WR002540. [Google Scholar]

[24] Weerts A. H., Winsemius J.S., Verkade J. S. (2011). Estimation of predictive hydrological uncertainty using quantile regression: Examples from the National Flood Forecasting System (England and Wales). Hydrological Earth Syst. Sci., 15, 255–265, doi:10.5194/hess-15-255-2011. [CrossRef] [Google Scholar]

[25] Bourgin F. (2014). Comment quantifier l’incertitude prédictive en modélisation hydrologique ? Travail exploratoire sur un grand échantillon de basins versants. Ph. D. Thesis, AgroParisTech and IRSTEA, 208 pp. [Google Scholar]

[26] Bourgin F., Ramos M.-H., Thirel G., Andréassian V. (2014). Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting. Journal of Hydrology, 519, 2775–2784, DOI: 10.1016/j.jhydrol.2014.07.054. [Google Scholar]

[27] Houdant B. (2004). Contribution à l’amélioration de la prévision hydrométéorologique opérationnelle. Pour l’usage des probabilités dans la communication entre acteurs. Ph.D. Thesis, École Nationale du Génie Rural, des Eaux et Forêts [Google Scholar]

[28] Marty R. and Faucard Y. (2015). Forecasts and uncertainty: first evaluation, proceedings of the annual forecasters’ team meeting, unpublished. [Google Scholar]

[29] Berthet L., Andréassian V. Perrin C. and Javelle P. (2009). How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments. Hydrol. EarthSyst. Sci.13, 819–831. [CrossRef] [Google Scholar]

[30] Morss R.E., Demuth J. L. and Lazo J. K. (2008). Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public. Weather and Forecasting, 23, 974–991, doi: 10.1175/2008WAF2007088.1 [CrossRef] [Google Scholar]