Articles | Volume 27, issue 12
https://doi.org/10.5194/hess-27-2375-2023
© Author(s) 2023. 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-27-2375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations
Pôle de recherche en protection des ressources, Institut de recherche et de développement en agroenvironnement
(IRDA), Quebec, Canada
Département de génie civil et de génie des eaux,
Université Laval, Quebec, Canada
Philippe Lucas-Picher
Centre pour l'Étude et la Simulation du Climat à l'Échelle Régionale (ESCER), Département des sciences de la Terre et de l'atmosphère, Université du Québec à Montréal, Montréal, Canada
Groupe de Météorologie de Grande Échelle et Climat (GMGEC), Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, Centre National de la Recherche Scientifique (CNRS), Toulouse, France
Antoine Thiboult
Département de génie civil et de génie des eaux,
Université Laval, Quebec, Canada
François Anctil
Département de génie civil et de génie des eaux,
Université Laval, Quebec, Canada
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Jing Xu, François Anctil, and Marie-Amélie Boucher
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Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
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Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
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Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
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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.
Cited articles
Ahn, K. H. and Kim, Y. O.: Incorporating climate model similarities and
hydrologic error models to quantify climate change impacts on future
riverine flood risk, J. Hydrol., 570, 118–131,
https://doi.org/10.1016/j.jhydrol.2018.12.061, 2019.
Alfieri, L., Feyen, L., Dottori, F., and Bianchi, A.: Ensemble flood risk
assessment in Europe under high end climate scenarios, Global Environ.
Chang., 35, 199–212, https://doi.org/10.1016/j.gloenvcha.2015.09.004, 2015a.
Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260, https://doi.org/10.5194/hess-19-2247-2015, 2015b.
Bergeron, O.: Grilles climatiques quotidiennes du Programme de surveillance
du climat du Québec, version 1.2 – Guide d'utilisation, ministère
de l'Environnement et de la Lutte contre les changements climatiques,
Québec, Qc., 33 pp., ISBN 978-2-550-73568-7, 2015.
Bergström, S. and Forsman, A.: Development of a conceptual deterministic
rainfall-runoff model, Nord. Hydrol., 4, 147–170, 1973.
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A generalized
streamflow simulation system – Conceptual modelling for digital computers,
Joint Federal-State River Forecast Center, Sacramento, https://searchworks.stanford.edu/view/753303 (last access: 26 June 2023), 1973.
Cannon, A. J.: Multivariate quantile mapping bias correction: an
N-dimensional probability density function transform for climate model
simulations of multiple variables, Clim. Dynam., 50, 31–49,
https://doi.org/10.1007/s00382-017-3580-6, 2018.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM
Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in
Quantiles and Extremes?, J. Climate, 28, 6938–6959,
https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Charles, S. P., Chiew, F. H. S., Potter, N. J., Zheng, H., Fu, G., and Zhang, L.: Impact of downscaled rainfall biases on projected runoff changes, Hydrol. Earth Syst. Sci., 24, 2981–2997, https://doi.org/10.5194/hess-24-2981-2020, 2020.
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate
bias correction methods in downscaling precipitation for hydrologic impact
studies over North America, Water Resour. Res., 49, 4187–4205,
https://doi.org/10.1002/wrcr.20331, 2013.
Chen, J., Brissette, F. P., Liu, P., and Xia, J.: Using raw regional climate
model outputs for quantifying climate change impacts on hydrology, Hydrol.
Process., 31, 4398–4413, https://doi.org/10.1002/hyp.11368, 2017.
Chen, J., Brissette, F. P., and Chen, H.: Using reanalysis-driven regional
climate model outputs for hydrology modelling, Hydrol. Process., 32,
3019–3031, https://doi.org/10.1002/hyp.13251, 2018.
Chen, J., Arsenault, R., Brissette, F. P., and Zhang, S.: Climate Change
Impact Studies: Should We Bias Correct Climate Model Outputs or Post-Process
Impact Model Outputs?, Water Resour. Res., 57, 1–22, https://doi.org/10.1029/2020WR028638, 2021.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution
approach for effective and efficient global minimization, J. Optimiz. Theory
App., 76, 501–521, https://doi.org/10.1007/BF00939380, 1993.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Ficklin, D. L., Abatzoglou, J. T., Robeson, S. M., and Dufficy, A.: The
Influence of Climate Model Biases on Projections of Aridity and Drought, J.
Climate, 29, 1269–1285, https://doi.org/10.1175/JCLI-D-15-0439.1, 2016.
Garçon, R.: Modèle global pluie-débit pour la prévision et
la prédétermination des crues, Houille Blanche, 7, 88–95, 1999.
Girard, G., Morin, G., and Charbonneau, R.: Modèle
précipitations-débits à discrétisation spatiale, Cahiers
ORSTOM, Série Hydrologie, 9, 35–52, 1972.
Hamilton, S. H., ElSawah, S., Guillaume, J. H. A., Jakeman, A. J., and
Pierce, S. A.: Integrated assessment and modelling: Overview and synthesis
of salient dimensions, Environ. Modell. Softw., 64, 215–229, https://doi.org/10.1016/j.envsoft.2014.12.005, 2015.
Hosseinzadehtalaei, P., Tabari, H., and Willems, P.: Precipitation
intensity–duration–frequency curves for central Belgium with an ensemble
of EURO-CORDEX simulations, and associated uncertainties, Atmos. Res., 200,
1–12, https://doi.org/10.1016/j.atmosres.2017.09.015, 2018.
Huard, D., Chaumont, D., Logan, T., Sottile, M., Brown, R. D., St-Denis, B.
G., Grenier, P., and Braun, M.: A Decade of Climate Scenarios: The Ouranos
Consortium Modus Operandi, B. Am. Meteorol. Soc., 95, 1213–1225, https://doi.org/10.1175/BAMS-D-12-00163.1, 2014.
Hwang, S., Graham, W. D., Geurink, J. S., and Adams, A.: Hydrologic
implications of errors in bias-corrected regional reanalysis data for west
central Florida, J. Hydrol., 510, 513–529,
https://doi.org/10.1016/j.jhydrol.2013.11.042, 2014.
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.
Kotlarski, S., Szabó, P., Herrera, S., Räty, O., Keuler, K., Soares,
P. M., Cardoso, R. M., Bosshard, T., Pagé, C. Boberg, F., Gutiérrez, J. M., Isotta, F. A., Jaczewski, A., Kreienkamp, F., Liniger, M. A., Lussana, C., and Pianko-Kluczynska, K.: Observational uncertainty and regional climate model
evaluation: A pan-European perspective, Int. J. Climatol., 39, 3730–3749,
https://doi.org/10.1002/joc.5249, 2017.
Laux, P., Rötter, R. P., Webber, H., Dieng, D., Rahimi, J., Wei, J., Faye, B., Srivastava, A. K., Bliefernicht, J., Adeyeri O., Arnault, J., and Kunstmann, H.: To bias correct or not to bias correct? An agricultural impact
modelers' perspective on regional climate model data, Agric. For. Meteorol.,
304-305, 108406, https://doi.org/10.1016/j.agrformet.2021.108406, 2021.
Lee, M. H., Lu, M., Im, E. S., and Bae, D. H.: Added value of dynamical
downscaling for hydrological projections in the Chungju Basin, Korea, Int.
J. Climatol., 39, 516–531, https://doi.org/10.1002/joc.5825, 2018.
Lucas-Picher, P., Lachance-Cloutier, S., Arsenault, R., Poulin, A., Ricard,
S. Turcotte, R., and Brissette, F.: Will Evolving Climate Conditions Increase
the Risk of Floods of the Large U.S.-Canada Transboundary Richelieu River
Basin?, Am. Water Resour. Assoc., 57, 32–56, https://doi.org/10.1111/1752-1688.12891,
2021.
Mearns, L. O., et al.: The NA-CORDEX dataset, version 1.0. NCAR Climate Data
Gateway [data set], Boulder CO, https://doi.org/10.5065/D6SJ1JCH, 2017.
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability
distributions, Manage. Sci., 22, 1087–1096, 1976.
MELCC: Québec Hydrometric Network,
https://www.cehq.gouv.qc.ca/hydrometrie/, last access: 15 July 2021.
Meresa, H. K. and Romanowicz, R. J.: The critical role of uncertainty in projections of hydrological extremes, Hydrol. Earth Syst. Sci., 21, 4245–4258, https://doi.org/10.5194/hess-21-4245-2017, 2017.
Moore, R. J. and Clarke, R. T.: A distribution function approach to rainfall
runoff modelling, Water Resour. Res., 17, 1367–1382,
https://doi.org/10.1029/WR017i005p01367, 1981.
Mpelasoka, F. S. and Chiew F. H. S.: Influence of Rainfall Scenario
Construction Methods on Runoff Projections, J. Hydrometeorol., 19,
1168–1183, https://doi.org/10.1175/2009JHM1045.1, 2009.
Mudbhatkal, A. and Mahesha, A.: Bias Correction Methods for Hydrologic
Impact Studies over India's Western Ghat Basins, J. Hydrol. Eng., 23,
05017030, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001598, 2018.
Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid, J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., and Turcotte, R.: On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff, Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, 2013.
Nguyen, H., Mehrotra, R., and Sharma, A.: Assessment of climate change
impacts on reservoir storage reliability, resilience, and vulnerability
using a multivariate frequency bias correction approach, Water Resour. Res.,
56, 1–21, https://doi.org/10.1029/2019WR026022, 2020.
Ntegeka, V., Baguis, P., Roulin, E., and Willems, P.: Developing tailored
climate change scenarios for hydrological impact assessments, J. Hydrol.,
508, 307–321, https://doi.org/10.1016/j.jhydrol.2013.11.001, 2014.
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.,
and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? part 2 – Towards a simple and efficient potential
evapo-transpiration model for rainfall-runoff modelling, J. Hydrol., 303,
290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005.
Pierre, A., Jutras, S., Smith, C., Kochendorfer, J., Fortin, V., and Anctil,
F.: Evaluation of Catch Efficiency Transfer Functions for Unshielded and
Single-Alter-Shielded Solid Precipitation Measurements, J. Atmos. Ocean.
Tech., 36, 865–881, https://doi.org/10.1175/JTECH-D-18-0112.1, 2019.
Poulin, A., Brissette, F., Leconte, R., Arsenault, R., and Malo, J. S.:
Uncertainty of hydrological modelling in climate change impact studies in a
Canadian, snow-dominated river basin, J. Hydrol., 409, 626–636,
https://doi.org/10.1016/j.jhydrol.2011.08.057, 2011.
Ricard, S., Sylvain, J. D., and Anctil, F.: Exploring an Alternative
Configuration of the Hydroclimatic Modeling Chain, Based on the Notion of
Asynchronous Objective Functions, Water, 11, 1–18, https://doi.org/10.3390/w11102012,
2019.
Ricard, S., Sylvain, J. D., and Anctil, F.: Asynchronous Hydroclimatic
Modeling for the Construction of Physically Based Streamflow Projections in
a Context of Observation Scarcity, Front. Earth Sci., 8, 1–16,
https://doi.org/10.3389/feart.2020.556781, 2020.
Rössler, O., Fischer, A. M., Huebener, H., Maraun, D., Benestad, R. E.,
Christodoulides, P., Soares, P. M. M., Cardoso, R. M., Pagé, C., Kanamaru, H., Kreienkamp, F., and Vlachogiannis, D.: Challenges to link climate change data
provision and user needs: Perspective from the COST-action VALUE, Int. J.
Climatol., 39, 3704–3716, https://doi.org/10.1002/joc.5060, 2016.
Schmidli, J., Frei, C., and Vidale, P. L.: Downscaling from GCM
precipitation: A benchmark for dynamical and statistical downscaling
methods, Int. J. Climatol, 26, 679–689, https://doi.org/10.1002/joc.1287, 2006.
Seiller, G. and Anctil, F.: Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures, Hydrol. Earth Syst. Sci., 18, 2033–2047, https://doi.org/10.5194/hess-18-2033-2014, 2014.
Seo, S. B., Sinha, T., Mahinthakumar, G., Sankarasubramanian, A., and Kumar,
M.: Identification of dominant source of errors in developing streamflow and
groundwater projections under near-term climate change, J. Geophys. Res.-Atmos., 121, 7652–7672, https://doi.org/10.1002/2016JD025138, 2016.
Shin, Y., Lee, Y., and Park, J. S.: A Weighting Scheme in A Multi-Model
Ensemble for Bias-Corrected Climate Simulation, Atmosphere, 11, 775,
https://doi.org/10.3390/atmos11080775, 2020.
Sunyer, M. A., Hundecha, Y., Lawrence, D., Madsen, H., Willems, P., Martinkova, M., Vormoor, K., Bürger, G., Hanel, M., Kriaučiūnienė, J., Loukas, A., Osuch, M., and Yücel, I.: Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe, Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, 2015.
Teng, J., Potter, N. J., Chiew, F. H. S., Zhang, L., Wang, B., Vaze, J., and Evans, J. P.: How does bias correction of regional climate model precipitation affect modelled runoff?, Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, 2015.
Thiboult, A.: AntoineThiboult/HOOPLA v1.0.1 (v1.0.1), Zenodo [code],
https://doi.org/10.5281/zenodo.2653969, 2019.
Thiboult, A., Poncelet C., and Anctil F.: User Manual: HOOPLA version 1.0.2, GitHub [code],
https://github.com/AntoineThiboult/HOOPLA
(last access: 30 June 2021), 2019.
Valdez, E. S., Anctil, F., and Ramos, M.-H.: Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems, Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, 2022.
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.
Willems, P.: Revision of urban drainage design rules after assessment of
climate change impacts on precipitation extremes at Uccle, Belgium, J.
Hydrol., 496, 166–177, https://doi.org/10.1016/j.jhydrol.2013.05.037, 2013.
Willems, P. and Vrac, M.: Statistical precipitation downscaling for
small-scale hydrological impact investigations of climate change, J.
Hydrol., 402, 193–205, https://doi.org/10.1016/j.jhydrol.2011.02.030, 2011.
Zhao, R. J., Zuang, Y. L., Fang, L. R., Liu, X. R., and Zhang, Q. S.: The
xinanjiang model, IAHS Publications, 129, 351–356, 1980.
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.
A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate...