Articles | Volume 26, issue 4
https://doi.org/10.5194/hess-26-1001-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1001-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Feb 2022
Research article |
| 22 Feb 2022
| 22 Feb 2022
Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II
Jing Xu
CORRESPONDING AUTHOR
Department of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Quebec, QC, Canada
François Anctil
Department of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Quebec, QC, Canada
Marie-Amélie Boucher
Department of Civil and Building Engineering, Université de Sherbrooke, 2500 Boul. de l’Université, Sherbrooke, QC, Canada
Related authors
No articles found.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
Hydrol. Earth Syst. Sci., 29, 2445–2465, https://doi.org/10.5194/hess-29-2445-2025, https://doi.org/10.5194/hess-29-2445-2025, 2025
Short summary
Short summary
Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
Short summary
Short summary
Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-116, https://doi.org/10.5194/gmd-2024-116, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Valérie Jean, Marie-Amélie Boucher, Anissa Frini, and Dominic Roussel
Hydrol. Earth Syst. Sci., 27, 3351–3373, https://doi.org/10.5194/hess-27-3351-2023, https://doi.org/10.5194/hess-27-3351-2023, 2023
Short summary
Short summary
Flood forecasts are only useful if they are understood correctly. They are also uncertain, and it is difficult to present all of the information about the forecast and its uncertainty on a map, as it is three dimensional (water depth and extent, in all directions). To overcome this, we interviewed 139 people to understand their preferences in terms of forecast visualization. We propose simple and effective ways of presenting flood forecast maps so that they can be understood and useful.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
Short summary
Short summary
A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Jean Odry, Marie-Amélie Boucher, Simon Lachance-Cloutier, Richard Turcotte, and Pierre-Yves St-Louis
The Cryosphere, 16, 3489–3506, https://doi.org/10.5194/tc-16-3489-2022, https://doi.org/10.5194/tc-16-3489-2022, 2022
Short summary
Short summary
The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
Short summary
Short summary
We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
Short summary
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Simon Ricard, Philippe Lucas-Picher, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-451, https://doi.org/10.5194/hess-2021-451, 2021
Revised manuscript not accepted
Short summary
Short summary
We propose a simplified hydroclimatic modelling workflow for producing hydrologic scenarios without resorting to meteorological observations. This innovative approach preserves trends and physical consistency between simulated climate variables, allows the implementation of modelling cascades despite observation scarcity, and supports the participation of end-users in producing and interpreting climate change impacts on water resources.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
Short summary
Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Konstantin F. F. Ntokas, Jean Odry, Marie-Amélie Boucher, and Camille Garnaud
Hydrol. Earth Syst. Sci., 25, 3017–3040, https://doi.org/10.5194/hess-25-3017-2021, https://doi.org/10.5194/hess-25-3017-2021, 2021
Short summary
Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
Cited articles
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting, Mon. Weather Rev., 141, 3462–3476, https://doi.org/10.1175/MWR-D-12-00206.1, 2013. a
Abaza, M., Anctil, F., Fortin, V., and Perreault, L.: Hydrological Evaluation of the Canadian Meteorological Ensemble Reforecast Product, Atmos. Ocean., 55, 195–211, https://doi.org/10.1080/07055900.2017.1341384, 2017. a
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water. Resour. Res., 43, 1–19, https://doi.org/10.1029/2005WR004745, 2007. a, b
Bergström, S. and Forsman, A.: Development of a conceptual deterministic rainfall-runoff model, Nord. Hydrol., 4, 147–170, https://doi.org/10.2166/nh.1973.0012, 1973. a
Beven, K. and Binley, A.: GLUE: 20 years on, Hydrol. Process., 28, 5897–5918, https://doi.org/10.1002/hyp.10082, 2014. a
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. a
Bougeault, P., Toth, Z., Bishop, C., Brown, B., Burridge, D., Chen, D. H., Ebert, B., Fuentes, M., Hamill, T. M., Mylne, K., Nicolau, J., Paccagnella, T., Park, Y.-Y., Parsons, D., Raoult, B., Schuster, D., Silva Dias, P., Swinbank, R., Takeuchi, Y., Tennant, W., Wilson, L., and Worley, S.: The THORPEX interactive grand global ensemble, B. Am. Meteorol. Soc., 91, 1059–1072, https://doi.org/10.1175/2010BAMS2853.1, 2010 (data available at: https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/, last access: 6 February 2022). a
Buizza, R., Asensio, H., Balint, G., Bartholmes, J., Bliefernicht, J., Bogner, K., Chavaux, F., de Roo, A., Donnadille, J., Ducrocq, V., Edlund, C., Kotroni, V., Krahe, P., Kunz, M., Lacire, K., Lelay, M., Marsigli, C., Milelli, M., Montani, A., Pappenberger, F., Rabufetti, D., Ramos, M. -H., Ritter, B., Schipper, J, W., Steiner, P., J. Del Pozzo, T., and Vincendon, B.: EURORISK/PREVIEW report on the technical quality, functional quality and forecast value of meteorological and hydrological forecasts, ECMWF Technical Memorandum, ECMWF Research Department: Shinfield Park, Reading, United Kingdom, 516, 1–21, available at: http://www.ecmwf.int/publications/ (last access: 6 February 2022), 2007. a
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A generalized streamflow simulation system: conceptual modeling for digital computers, Technical Report, Joint Federal and State River Forecast Center, US National Weather Service and California Department of Water Resources, Sacramento, available at: https://books.google.ca/books?hl=en&lr=&id=aQJDAAAAIAAJ&oi=fnd&pg=PR2&dq=A+generalized+streamflow+simulation+system:+conceptual+modeling+for+digital+computers,+Technical+Report,+Joint+Federal+and+State+River+Forecast+Center&ots=4tSaTg69cs&sig=sVb7nFZmMBgqy2p3oPmJYuztR6c&redir_esc=y#v=onepage&q&f=false, (last access: 6 February 2022) 204 pp., 1973. a
Brochero, D., Gagné, C., and Anctil, F.: Evolutionary multiobjective optimization for selecting members of an ensemble streamflow forecasting model. Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference-GECCO, New York, United States, 6 July 2013, 13, 1221–1228, https://doi.org/10.1145/2463372.2463538, 2013. a, b
Boucher, M.-A., Anctil, F., Perreault, L., and Tremblay, D.: A comparison between ensemble and deterministic hydrological forecasts in an operational context, Adv. Geosci., 29, 85–94, https://doi.org/10.5194/adgeo-29-85-2011, 2011. a
Boucher, M. A., Tremblay, D., Delorme, L., Perreault, L., and Anctil, F.: Hydro-economic assessment of hydrological forecasting systems, J. Hydrol., 416, 133–144, https://doi.org/10.1016/j.jhydrol.2011.11.042, 2012. a
Cheng, C. T. and Chau, K. W.: Flood control management system for reservoirs, Environ. Modell. Softw., 19, 1141–1150, https://doi.org/10.1016/j.envsoft.2003.12.004, 2004. a
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. a, b, c
ConfesorJr., R. B. and Whittaker, G. W.: Automatic Calibration of Hydrologic Models With Multi‐Objective Evolutionary Algorithm and Pareto Optimization 1, J. Am. Water Resour. As., 43, 981–989, https://doi.org/10.1111/j.1752-1688.2007.00080.x, 2007. a
Coulibaly, P., Anctil, F., and Bobée, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 230, 244–257, https://doi.org/10.1016/S0022-1694(00)00214-6, 2000. a
Crochemore, L., Ramos, M.-H., Pappenberger, F., and Perrin, C.: Seasonal streamflow forecasting by conditioning climatology with precipitation indices, Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, 2017. a
Datta, B. and Burges, S. J.: Short-term, single, multiple-purpose reservoir operation: importance of loss functions and forecast errors, Water. Resour. Res., 20, 1167–1176, https://doi.org/10.1029/WR020i009p01167, 1984. a
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A. M. T.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE. T. Evolut. Comput., 6, 182–197, https://doi.org/10.1109/4235.996017, 2002. a, b
De Vos, N. J. and Rientjes, T. H. M.: Multi-objective performance comparison of an artificial neural network and a conceptual rainfall–runoff model, Hydrolog. Sci. J., 52, 397–413, https://doi.org/10.1029/2007WR006734, 2007. a
Duan, Q., Ajami, N. K., Gao, X., and Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water. Resour., 30, 1371–1386, https://doi.org/10.1016/j.advwatres.2006.11.014, 2007. a
Evensen, G.: Inverse Methods and Data Assimilation in Nonlinear Ocean Models, Physica D, 77, 108–129, https://doi.org/10.1016/0167-2789(94)90130-9, 1994. a
Fraley, C., Raftery, A. E., and Gneiting, T.: Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging, Mon. Weather Rev., 138, 190–202, https://doi.org/10.1175/2009MWR3046.1, 2010. a
Fisher, J. B., Tu, K. P., and Baldocchi, D. D.: Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ., 112, 901–919, https://doi.org/10.1016/j.rse.2007.06.025, 2008. a
Fortin, V., Favre, A. C., and Saïd, M.: Probabilistic forecasting from ensemble prediction systems: Improving upon the best-member method by using a different weight and dressing kernel for each member, Q. J. Roy. Meteor. Soc., 132, 1349–1369, https://doi.org/10.1256/qj.05.167, 2006. a
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R.: Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?, J. Hydrometeorol., 15, 1708–1713, https://doi.org/10.1175/JHM-D-14-0008.1, 2014. a, b
Gaborit, É., Anctil, F., Fortin V., and Pelletier, G.: On the reliability of spatially disaggregated global ensemble rainfall forecasts, Hydrol. Process., 27, 45–56, https://doi.org/10.1002/hyp.9509, 2013. a
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc., 102, 359–378. https://doi.org/10.1198/016214506000001437, 2007. a
Gneiting, T., Raftery, A., Westveld III, A. H., and Goldmann, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev. 133, 1098–1118, https://doi.org/10.1175/MWR2904.1, 2005. a
Groşan, C., Mihai, O., and Mihaela, O.: The role of elitism in multiobjective optimization with evolutionary algorithms, Acta Univ. Apulensis Math. Inform., 83–90, available at: https://www.researchgate.net/publication/265834177_The_role_of_elitism_in_multiobjective_optimization_with_evolutionary_algorithms (last access: 6 February 2022), 2003. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a, b
Houtekamer, P. L., Lefaivre, L., Derome, J., and Ritchie, H.: A system simulation approach to ensemble prediction, Mon. Weater. Rev., 124, 1225–1242, https://doi.org/10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2, 1996. a
Jakeman, A. J., Littlewood, I. G., and Whitehead, P. G.: Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments, J. Hydrol., 117, 275–300, https://doi.org/10.1016/0022-1694(90)90097-H, 1990. a
Jewson, S.: Comparing the ensemble mean and the ensemble standard deviation as inputs for probabilistic medium-range temperature forecasts, Cornell University, available at: https://arxiv.org/pdf/physics/0310059.pdf (last access: 16 February 2022), 2003. a
Jing, X.: Code for hess-2020-238 (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.6113443, 2022. a
Klemeš, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012 a
Kulturel-Konak, S., Smith, A. E., and Norman, B. A.: Multi-objective search using a multinomial probability mass function, Eur. J. Oper. Res., 169, 918–931, https://doi.org/10.1016/j.ejor.2004.08.026, 2006. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on statistical postprocessing methods for hydrometeorological ensemble forecasting, Wires Water, 4, e1246, https://doi.org/10.1002/wat2.1246, 2017. a
Liong, S. Y., Khu, S. T. and Chan, W. T.: Derivation of Pareto front with genetic algorithm and neural network, J. Hydrol. Eng., 6, 52–61, https://doi.org/10.1061/(ASCE)1084-0699(2001)6:1(52), 2001. a
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water. Resour. Res., 43, 1–18, https://doi.org/10.1029/2006WR005756, 2007. a
Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved post-processing of hydrologic forecast ensembles, Hydrol. Process., 28, 104–122, https://doi.org/10.1002/hyp.9562, 2014. a
Marty, R., Fortin, V., Kuswanto, H., Favre, A. C., and Parent, E.: Combining the bayesian processor of output with bayesian model averaging for reliable ensemble forecasting, J. R. Stat. Soc. C-Appl., 64, 75–92, https://doi.org/10.1111/rssc.12062, 2015. a
Matheson, J. E. and Winkler, R. L.: Scoring Rules for Continuous Probability Distributions, Manage. Sci., 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976. a
McMillan, H. K., Hreinsson, E. Ö., Clark, M. P., Singh, S. K., Zammit, C., and Uddstrom, M. J.: Operational hydrological data assimilation with the recursive ensemble Kalman filter, Hydrol. Earth Syst. Sci., 17, 21–38, https://doi.org/10.5194/hess-17-21-2013, 2013. a
Mockler, E. M., O’Loughlin, F. E., and Bruen, M.: Understanding hydrological flow paths in conceptual catchment models using uncertainty and sensitivity analysis, Comput. Geosci., 90, 66–77, https://doi.org/10.1016/j.cageo.2015.08.015, 2016. a
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R.: Dual state-parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water. Resour., 28, 135–147, https://doi.org/10.1016/j.advwatres.2004.09.002, 2005. a
Moradkhani, H., Dechant, C. M., and Sorooshian, S.: Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method, Water Resour. Res., 48, 121–134, https://doi.org/10.1029/2012WR012144, 2012. a
Movahedinia, F.: Assessing hydro-climatic uncertainties on hydropower generation, Université Laval, Québec city, 7 pp., available at: https://corpus.ulaval.ca/jspui/handle/20.500.11794/25294 (last access: 6 February 2022), 2014. a
Najafi, M. R., Moradkhani, H., and Jung, I. W.: Assessing the uncertainties of hydrologic model selection in climate change impact studies, Hydrol. Process., 25, 2814–2826, https://doi.org/10.1002/hyp.8043, 2011. a
Nash, J. E. and Sutcliffe, I.: River flow forecasting through conceptual models. Part 1 – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Oudin, L., Michel, C., and Anctil, F.: Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 1 – Can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs?, J. Hydrol., 303, 275–289, https://doi.org/10.1016/j.jhydrol.2004.08.025, 2005. a
Palmer, T. N.: Extended-Range Atmospheric Prediction and the Lorenz Model, B. Am. Meteorol. Soc., 74, 49–65, https://doi.org/10.1175/1520-0477(1993)074<0049:ERAPAT>2.0.CO;2, 1993. a
Palmer, T. N.: The economic value of ensemble forecasts as a tool for risk assessment: From days to decades, Q. J. Roy. Meteor. Soc., 128, 747–774, https://doi.org/10.1256/0035900021643593, 2002. a
Ramos, M. H., Mathevet, T., Thielen, J., and Pappenberger, F.: Communicating uncertainty in hydro‐meteorological forecasts: mission impossible?, Meteorol. Appl., 17, 223–235, https://doi.org/10.1002/met.202, 2010. a, b
Perrin, C.: Vers une amélioration d'un modèle global pluie-débit au travers d'une approche comparative, Houille Blanche, 6–7, 84–91, https://doi.org/10.1051/lhb/2002089, 2002. a
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003. a
Reichle, R., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103–114, https://doi.org/10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2, 2002. a
Roulston, M. S. and Smith, L. A.: Combining dynamical and statistical ensembles, Tellus A, 55, 16–30, https://doi.org/10.3402/tellusa.v55i1.12082, 2003. a, b
Salamon, P. and Feyen, L.: Disentangling uncertainties in distributed hydrological modeling using multiplicative error models and sequential data assimilation, Water Resour. Res., 46, 1–20, https://doi.org/10.1029/2009WR009022, 2010. a
Schaake, J., Demargne, J., Hartman, R., Mullusky, M., Welles, E., Wu, L., Herr, H., Fan, X., and Seo, D. J.: Precipitation and temperature ensemble forecasts from single-value forecasts, Hydrol. Earth Syst. Sci. Discuss., 4, 655–717, https://doi.org/10.5194/hessd-4-655-2007, 2007. a
Schaffer, J.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Proceedings of the First International Conference on Genetic Algortithms, Lawrence Erlbaum Associates. Inc., 93–100, https://www.researchgate.net/publication/216301392_Multiple_Objective_Optimization_with_Vector_Evaluated_Genetic_Algorithms (last access: 16 February 2022), 1985. a
Seiller, G., Anctil, F., and Perrin, C.: Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrol. Earth Syst. Sci., 16, 1171–1189, https://doi.org/10.5194/hess-16-1171-2012, 2012. a, b
Seiller, G., Roy, R., and Anctil, F.: Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources, J. Hydrol., 547, 280–295, https://doi.org/10.1016/j.jhydrol.2017.02.004, 2017. a
Seo, D.-J., Herr, H. D., and Schaake, J. C.: A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrol. Earth Syst. Sci. Discuss., 3, 1987–2035, https://doi.org/10.5194/hessd-3-1987-2006, 2006. a
Shim, K. C., Fontane, D. G., and Labadie, J. W.: Spatial Decision Support System for Integrated River Basin Flood Control, J. Water. Res. Pl.-ASCE, 128, 190–201, https://doi.org/10.1061/(ASCE)0733-9496(2002)128:3(190), 2002. a
Silverman, B. W.: Density estimation for statistics and data analysis, Published in Monographs on Statistics and Applied Probability, CRC Press, Chapman and Hal, London, 26, https://doi.org/10.1201/9781315140919, 1986. a, b, c
Sloughter, J. M. L., Raftery, A. E., Gneiting, T., and Fraley, C.: Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging, Mon. Weather Rev., 135, 3209–3220, https://doi.org/10.1175/MWR3441.1, 2007. a
Stanski, H. R., Wilson, L. J., and Burrows, W. R.: Survey of common verification methods in meteorology, World Weather Watch Tech. Report 8, WMO/TD, 358, 114 pp., https://doi.org/10.13140/RG.2.2.26947.71208, 1989. a
Thiboult, A. and Anctil, F.: On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments, J. Hydrol., 529, 1147–1160, https://doi.org/10.1016/j.jhydrol.2015.09.036, 2015. a
Thiboult, A., Anctil, F., and Boucher, M.-A.: Accounting for three sources of uncertainty in ensemble hydrological forecasting, Hydrol. Earth Syst. Sci., 20, 1809–1825, https://doi.org/10.5194/hess-20-1809-2016, 2016. a, b, c
Thiboult, A., Seiller, G., and Anctil, F.: HOOPLA, GitHub [code], available at: https://github.com/AntoineThiboult/HOOPLA (last access: 16 February 2022), 2019. a
Thiboult, A., Seiller, G., Poncelet, C., and Anctil, F.: The HOOPLA toolbox: a HydrOlOgical Prediction LAboratory to explore ensemble rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-6, 2020. a
Thielen, J., Ramos, M. H., Bartholmes, J., De Roo, A., Cloke, H., Pappenberger, F., and Demeritt, D.: Summary report of the 1st EFAS workshop on the use of Ensemble Prediction System in flood forecasting, European Report EUR, Ispra, 22118, available at: https://www.preventionweb.net/files/2610_EUR22118EN.pdf (last access: 16 February 2022), 2005. a
Thirel, G., Salamon, P., Burek, P., and Kalas, M.: Assimilation of MODIS snow cover area data in a distributed hydrological model using the particle filter, Remote. Sens., 5, 5825–5850, https://doi.org/10.3390/rs5115825, 2013. a
Thornthwaite, C. W. and Mather, J. R.: The water balance, Centerton, New Jersey: Drexel Institute of Technology, Laboratory of Climatology, 8, 1–104, available at: https://oregondigital.org/downloads/oregondigital:df70pr001 (last access: 16 February 2022), 1955. a
Toth, Z. and Kalnay, E.: Ensemble Forecasting at NCEP and the Breeding Method, Mon. Weather Rev., 125, 3297–3319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2, 1997. a
Valéry, A., Andréassian, V., and Perrin, C.: 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, J. Hydrol., 517, 1176–1187, https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014. a
Velázquez, J. A., Anctil, F., Ramos, M. H., and Perrin, C.: Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures, Adv. Geosci., 29, 33–42, https://doi.org/10.5194/adgeo-29-33-2011, 2011. a
Velázquez, J. A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V., and Anctil, F.: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrol. Earth Syst. Sci., 13, 2221–2231, https://doi.org/10.5194/hess-13-2221-2009, 2009. a, b
Vrugt, J. A. and Robinson, B. A.: Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging, Water Resour. Res., 43, 1–15, https://doi.org/10.1029/2005WR004838, 2007. a
Wand, M. P. and Jones, M. C.: Kernel smoothing, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 60, 1–15, available at: http://matt-wand.utsacademics.info/webWJbook/KernelSmoothingSample.pdf (last access: 16 February 2022), 1994. a
Wang, X. and Bishop, C. H.: Improvement of ensemble reliability with a new dressing kernel, Applied Meteorology and Physical Oceanography 131, 965–986, https://doi.org/10.1256/qj.04.120, 2005. a
Weigel, A. P., Liniger, M., and Appenzeller, C.: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?, Q. J. Roy. Meteorol. Soc., 134, 241–260, https://doi.org/10.1002/qj.210, 2008. a
Wetterhall, F., Pappenberger, F., Alfieri, L., Cloke, H. L., Thielen-del Pozo, J., Balabanova, S., Daňhelka, J., Vogelbacher, A., Salamon, P., Carrasco, I., Cabrera-Tordera, A. J., Corzo-Toscano, M., Garcia-Padilla, M., Garcia-Sanchez, R. J., Ardilouze, C., Jurela, S., Terek, B., Csik, A., Casey, J., Stankūnavičius, G., Ceres, V., Sprokkereef, E., Stam, J., Anghel, E., Vladikovic, D., Alionte Eklund, C., Hjerdt, N., Djerv, H., Holmberg, F., Nilsson, J., Nyström, K., Sušnik, M., Hazlinger, M., and Holubecka, M.: HESS Opinions ”Forecaster priorities for improving probabilistic flood forecasts”, Hydrol. Earth Syst. Sci., 17, 4389–4399, https://doi.org/10.5194/hess-17-4389-2013, 2013. a
Wilks, D. S.: Smoothing forecast ensembles with fitted probability distributions, Q. J. Roy. Meteor. Soc., 128, 2821–2836, https://doi.org/10.1256/qj.01.215, 2002. a
Wilks, D. S.: On the Reliability of the Rank Histogram, Mon. Weather Rev., 139, 311–316, https://doi.org/10.1175/2010MWR3446.1, 2011. a
Short summary
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a...
