Assessment of a multimodel ensemble against an operational hydrological forecasting system

References

• Abaza, M., F. Anctil, V. Fortin, and R. Turcotte. 2014. A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting. Monthly Weather Review 142: 2561–2562.
   Web of Science ®Google Scholar
• Ajami, N. K., Q. Duan, X. Gao, and S. Sorooshian. 2006. Multimodel combination techniques for analysis of hydrological simulations: Application to Distributed Model Intercomparison Project results. Journal of Hydrometeorology 7: 755–768.
   Web of Science ®Google Scholar
• Ajami, N. K., Q. Duan, and S. Sorooshian. 2007. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research 43: doi:10.1029/2005WR004745.
   PubMed Web of Science ®Google Scholar
• Andreassian, V., C. Perrin, L. Berthet, N. Le Moine, J. Lerat, C. Loumagne, L. Oudin, et al. 2009. HESS Opinions: Crash tests for a standardized evaluation of hydrological models. Hydrology and Earth System Sciences 13: 1757–1764.
   Web of Science ®Google Scholar
• Bergström, S., and A. Forsman. 1973. Development of a conceptual deterministic rainfall-runoff model. Nordic Hydrology 4: 147–170.
   Google Scholar
• Beven, K., and A. Binley. 1992. The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes 6: 279–298.
   Web of Science ®Google Scholar
• Beven, K., and J. Freer. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology 249: 11–29.
   Web of Science ®Google Scholar
• Beven, K., M. J. Kirkby, N. Schoeld, and A. F. Tagg. 1984. Testing a physically based flood forecasting model (TOPMODEL) for 3 UK catchments. Journal of Hydrology 69: 119–143.
   Web of Science ®Google Scholar
• Block, P. J., F. A. Souza, L. Q. Sun, and H. H. Kwon. 2009. A streamflow forecasting framework using multiple climate and hydrological models. Journal of the American Water Resources Association 45: 828–843.
   Web of Science ®Google Scholar
• Boucher, M. A., D. Tremblay, L. Delorme, L. Perreault, and F. Anctil. 2012. Hydro-economic assessment of hydrological forecasting systems. Journal of Hydrology 416: 133–144.
   Web of Science ®Google Scholar
• Breuer, L., J. A. Huisman, P. Willems, H. Bormann, A. Bronstert, B. F. W. Croke, H. G. Frede, et al. 2009. Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use. Advances in Water Resources 32: 129–146.
   Web of Science ®Google Scholar
• Burnash, R. J. C., R. L. Ferral, and R. A. McGuire. 1973. A generalized streamflow simulation system – Conceptual modelling for digital computers. Sacramento, CA: US Department of Commerce, National Weather Service and State of California.
   Google Scholar
• Chiew, F. H. S., M. C. Peel, and A. W. Western. 2002. Mathematical models of small watershed hydrology and applications. Littleton, CO: Water Resources Publication.
   Google Scholar
• Clark, M. P., D. E. Rupp, R. A. Woods, X. Zheng, R. P. Ibbitt, A. G. Slater, J. Schmidt, and M. J. Uddstrom. 2008a. Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources 31: 1309–1324.
   Web of Science ®Google Scholar
• Clark, M. P., A. G. Slater, D. E. Rupp, R. A. Woods, J. A. Vrugt, H. V. Gupta, T. Wagener, and L. E. Hay. 2008b. Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resources Research 44: 1–14.
   Web of Science ®Google Scholar
• Cloke, H. L., and F. Pappenberger. 2009. Ensemble flood forecasting: A review. Journal of Hydrology 375: 613–626.
   Web of Science ®Google Scholar
• Cormary, Y., and A. Guilbot. 1973. Étude des relations pluie-débit sur trois bassins versants d’investigation. In Design of water resources projects with inadequate data. Proceedings of the International Association of Hydrological Sciences (IAHS) Madrid Symposium. Madrid: IAHS Publication, 265–279.
   Google Scholar
• DeChant, C. M., and H. Moradkhani. 2011. Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation. Hydrology and Earth System Sciences 15: 3399–3410.
   Web of Science ®Google Scholar
• Dietrich, J., A. H. Schumann, M. Redetzky, J. Walther, M. Denhard, Y. Wang, B. Pfuetzner, and U. Buettner. 2009. Assessing uncertainties in flood forecasts for decision making: Prototype of an operational flood management system integrating ensemble predictions. Natural Hazards and Earth System Sciences 9: 1529–1540.
   Web of Science ®Google Scholar
• Duan, Q. Y., N. K. Ajami, X. G. Gao, and S. Sorooshian. 2007. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Advances in Water Resources 30: 1371–1386.
   Web of Science ®Google Scholar
• Duan, Q. Y., S. Sorooshian, and V. Gupta. 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28: 1015–1031.
   Web of Science ®Google Scholar
• Feyen, L., M. Kalas, and J. A. Vrugt. 2008. Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization. Hydrological Sciences Journal–Journal Des Sciences Hydrologiques 53: 293–308.
   Web of Science ®Google Scholar
• Fortin, J. P., R. Moussa, C. Bocquillon, and J. P. Villeneuve. 1995. HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique. Revue des sciences de l'eau/Journal of Water Science 8(1): 97–124.
   Google Scholar
• Fortin, V., and R. Turcotte. 2007. Le modèle hydrologique MOHYSE, Note de cours pour SCA7420. Département des sciences de la Terre et de l'atmosphère: Université du Québec à Montreal.
   Google Scholar
• Gaborit, É., F. Anctil, V. Fortin, and G. Pelletier. 2013. On the reliability of spatially disaggregated global ensemble rainfall forecasts. Hydrological Processes 27: 45–56.
   Web of Science ®Google Scholar
• Garçon, R. 1999. Modèle global Pluie–Débit pour la prévision et la prédétermination des crues. La Houille Blanche 7(8): 88–95.
   Google Scholar
• Georgakakos, K. P., D. J. Seo, H. Gupta, J. Schaake, and M. B. Butts. 2004. Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology 298: 222–241.
   Web of Science ®Google Scholar
• Girard, G., G. Morin, and R. Charbonneau. 1972. Modèle précipitations-débits à discrétisation spatiale. Cahiers ORSTOM, Série Hydrologie 9(4): 35–52.
   Google Scholar
• Gneiting, T., and A. E. Raftery. 2007. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102: 359–378.
   Web of Science ®Google Scholar
• Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman. 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133: 1098–1118.
   Web of Science ®Google Scholar
• Gourley, J. J., and B. E. Vieux. 2006. A method for identifying sources of model uncertainty in rainfall-runoff simulations. Journal of Hydrology 327: 68–80.
   Web of Science ®Google Scholar
• Gupta, H. V., S. Sorooshian, and P. O. Yapo. 1998. Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resources Research 34: 751–763.
   Web of Science ®Google Scholar
• Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept. Dynamic Meteorology and Oceanography 57: 219–233.
   Web of Science ®Google Scholar
• Hamill, T. M. 2001. Interpretation of rank histograms for verifying ensemble forecasts. Monthly Weather Review 129: 550–560.
   Web of Science ®Google Scholar
• Hersbach, H. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting 15: 559–570.
   Web of Science ®Google Scholar
• Jakeman, A. J., I. G. Littlewood, and P. G. Whitehead. 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology 117: 275–300.
   Web of Science ®Google Scholar
• Krzysztofowicz, R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research 35: 2739–2750.
   Web of Science ®Google Scholar
• Krzysztofowicz, R., and C. J. Maranzano. 2004. Hydrologic uncertainty processor for probabilistic stage transition forecasting. Journal of Hydrology 293: 57–73.
   Web of Science ®Google Scholar
• Liu, Y. Q., and H. V. Gupta. 2007. Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resources Research 43: doi:10.1029/2006WR005756.
   PubMed Web of Science ®Google Scholar
• Matheson, J. E., and R. L. Winkler. 1976. Scoring rules for continuous probability distributions. Management Science 22: 1087–1096.
   Web of Science ®Google Scholar
• Mazenc, B., M. Sanchez, and D. Thiery. 1984. Analyse de l'influence de la physiographie d'un bassin versant sur les paramètres d'un modèle hydrologique global et sur les débits caractéristiques à l'exutoire. Journal of Hydrology 69: 97–188.
   Web of Science ®Google Scholar
• Moore, R. J., and R. T. Clarke. 1981. A distribution function approach to rainfall runoff modeling. Water Resources Research 17(5): 1367–1382.
   Web of Science ®Google Scholar
• Moradkhani, H., S. Sorooshian, H. V. Gupta, and P. R. Houser. 2005. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources 28: 135–147.
   Web of Science ®Google Scholar
• Nielsen, S. A., and E. Hansen. 1973. Numerical simulation of the rainfall-runoff process on a daily basis. Nordic Hydrology 4: 171–190.
   Google Scholar
• O'Connell, P. E., J. E. Nash, and J. P. Farrell. 1970. River flow forecasting through conceptual models, Part II – The Brosna catchment at Ferbane. Journal of Hydrology 10: 317–329.
   Google Scholar
• Oudin, L., F. Hervieu, C. Michel, C. Perrin, V. Andreassian, F. Anctil, and C. Loumagne. 2005. Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 2 – Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling. Journal of Hydrology 303: 290–306.
   Web of Science ®Google Scholar
• Perrin, C. 2000. Vers une amélioration d'un modèle global pluie-débit. Hydrology. PhD thesis, Institut National Polytechnique de Grenoble – INPG.
   Google Scholar
• Perrin, C., C. Michel, and V. Andreassian. 2003. Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology 279: 275–289.
   Web of Science ®Google Scholar
• Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133: 1155–1174.
   Web of Science ®Google Scholar
• Ramos, M.-H., T. Mathevet, J. Thielen, and F. Pappenberger. 2010. Communicating uncertainty in hydro-meteorological forecasts: mission impossible? Meteorological Applications 17: 223–235.
   Web of Science ®Google Scholar
• Refsgaard, J. C. 1997. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydrology 28: 65–84.
   Google Scholar
• Roulston, M. S., and L. A. Smith. 2003. Combining dynamical and statistical ensembles. Tellus Series A – Dynamic Meteorology and Oceanography 55: 16–30.
   Web of Science ®Google Scholar
• Seiller, G., F. Anctil, and C. Perrin. 2012. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions. Hydrology and Earth System Sciences 16: 1171–1189.
   Web of Science ®Google Scholar
• Seo, D. J., L. Cajina, R. Corby, and T. Howieson. 2009. Automatic state updating for operational streamflow forecasting via variational data assimilation. Journal of Hydrology 367: 255–275.
   Web of Science ®Google Scholar
• Stanski, H. R., L. J. Wilson, and W. R. Burrows. 1989. Survey of common verification methods in meteorology. WMO World Weather Watch Tech Report 8. Downsview, ON: Atmospheric Environment Service Publication.
   Google Scholar
• Sugawara, M. 1979. Automatic calibration of the tank model. Hydrological Sciences 24: 375–388.
   Google Scholar
• Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian. 2001. Bayesian recursive parameter estimation for hydrologic models. Water Resources Research 37: 2521–2535.
   Web of Science ®Google Scholar
• Thiery, D. 1982. Utilisation d'un modèle global pour identifier sur un niveau piézométrique des influences multiples dues à diverses activités humaines. IAHS Publications 136: 71–77.
   Google Scholar
• Thompson, P. D. 1977. How to improve accuracy by combining independent forecasts. Monthly Weather Review 105: 228–229.
   Web of Science ®Google Scholar
• Thornthwaite, C. W., and J. R. Mather. 1955. The water balance. Report, Drexel Institute of Climatology, Centerton, New Jersey, USA.
   Google Scholar
• Turcotte, R., P. Lacombe, C. Dimnik, and J. P. Villeneuve. 2004. Prévision hydrologique distribuée pour la gestion des barrages publics du Québec. Canadian Journal of Civil Engineering 31(2): 308–320.
   Web of Science ®Google Scholar
• Valery, A., V. Andreassian, and C. Perrin. 2014. “As simple as possible but not simpler”: What is useful in a temperature-based snow accounting routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments. Journal of Hydrology 517: 1176–1187.
   Web of Science ®Google Scholar
• Velázquez, J. A., F. Anctil, and C. Perrin. 2010. Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments. Hydrology and Earth System Sciences 14: 2303–2317.
   Web of Science ®Google Scholar
• Velázquez, J. A., T. Petit, A. Lavoie, M. A. Boucher, R. Turcotte, V. Fortin, and F. Anctil. 2009. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting. Hydrology and Earth System Sciences 13: 2221–2231.
   Web of Science ®Google Scholar
• Viney, N. R., H. Bormann, L. Breuer, A. Bronstert, B. F. W. Croke, H. Frede, T. Grae, et al. 2009. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions. Advances in Water Resources 32: 147–158.
   Web of Science ®Google Scholar
• Vrugt, J. A., C. G. H. Diks, H. V. Gupta, W, Bouten, and J.M. Verstraten. 2005. Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation. Water Resources Research 41: doi:10.1029/2004wr003059.
   PubMed Web of Science ®Google Scholar
• Vrugt, J. A., H. V. Gupta, W. Bouten, and S. Sorooshian. 2003. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research 39: doi:10.1029/2002wr001642.
   Web of Science ®Google Scholar
• Wagener, T., D. P. Boyle, M. J. Lees, H. S. Wheater, H. V. Gupta, and S. Sorooshian. 2001. A framework for development and application of hydrological models. Hydrology and Earth System Sciences 5: 13–26.
   Web of Science ®Google Scholar
• Walker, W. E., P. Harremoës, J. Rotmans, J. P. Van Der Sluijs, M. B. A. Van Asselt, P. Janssen, and M. P. Krayer von Krauss. 2003. Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment 4(1): 5–17.
   Google Scholar
• Warmerdam, P. M., J. Kole, and J. Chormanski. 1997. Modelling rainfall-runoff processes in the Hupselse Beek research basin. In Ecohydrological processes in small basins. Proceedings of the Strasbourg Conference, ed. D. Viville and I. G. Littlewood, 155–161.
   Google Scholar
• Yapo, P. O., H. V. Gupta, and S. Sorooshian. 1998. Multi-objective global optimization for hydrologic models. Journal of Hydrology 204: 83–97.
   Web of Science ®Google Scholar
• Zhao, R. J., Y. L. Zuang, L. R. Fang, X. R. Liu, and Q. Zhang. 1980. The Xinanjiang model. IAHS Publications 129: 351–356.
   Google Scholar