Uncertainty sources in flood projections over contrasting hydrometeorological regimes

References

• AghaKouchak, A., et al., 2012. Extremes in a changing climate: detection, analysis and uncertainty. Springer Dordrecht: Springer Science & Business Media.
   Google Scholar
• Ajami, N.K., et al., 2004. Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298 (1), 112–135. doi:10.1016/j.jhydrol.2004.03.033
   Web of Science ®Google Scholar
• Arsenault, R., et al., 2014. Comparison of stochastic optimization algorithms in hydrological model calibration. Journal of Hydrologic Engineering, 19 (7), 1374–1384. doi:10.1061/(ASCE)HE.1943-5584.0000938
   Web of Science ®Google Scholar
• Arsenault, R., et al., 2019. Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico. Hydrological Sciences Journal, 64 (11), 1297–1311. doi:10.1080/02626667.2019.1639716
   Web of Science ®Google Scholar
• Arsenault, R., et al., 2020a. NAC2H: the North-American Climate Change and hydroclimatology dataset. Water Resources Research, n/a(n/a), e2020WR027097.
   Web of Science ®Google Scholar
• Arsenault, R., et al., 2020b. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds. Scientific Data, 7 (1), 243. doi:10.1038/s41597-020-00583-2
   PubMed Web of Science ®Google Scholar
• Asadzadeh, M. and Tolson, B.A., 2009. A new multi-objective algorithm, pareto archived DDS. Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. Montreal, Québec, Canada: ACM, 1963–1966.
   Google Scholar
• Beven, K., 2016. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrological Sciences Journal, 61 (9), 1652–1665. doi:10.1080/02626667.2015.1031761
   Web of Science ®Google Scholar
• Blöschl, G. and Montanari, A., 2010. Climate change impacts—throwing the dice? Hydrological processes, 24 (3), 374–381.
   Google Scholar
• Bosshard, T., et al., 2013. Quantifying uncertainty sources in an ensemble of hydrological climate‐impact projections. Water Resources Research, 49 (3), 1523–1536. doi:10.1029/2011WR011533
   Web of Science ®Google Scholar
• Castaneda-Gonzalez, M., et al., 2019. Sensitivity of seasonal flood simulations to regional climate model spatial resolution. Climate Dynamics, 53 (7), 4337–4354. doi:10.1007/s00382-019-04789-y
   Web of Science ®Google Scholar
• Castellarin, A., et al., 2012. Review of applied statistical methods for flood frequency analysis in Europe. NERC/Centre for Ecology & Hydrology.
   Google Scholar
• Chan, W.C.H., et al., 2020. Uncertainty assessment in river flow projections for Ethiopia’s upper awash basin using multiple GCMs and hydrological models. Hydrological Sciences Journal, 65 (10), 1720–1737. doi:10.1080/02626667.2020.1767782
   Web of Science ®Google Scholar
• Chegwidden, O.S., et al., 2019. How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates. Earth’s Future, 7 (6), 623–637. doi:10.1029/2018EF001047
   Web of Science ®Google Scholar
• Chen, J., et al., 2013a. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49 (7), 4187–4205. doi:10.1002/wrcr.20331
   Web of Science ®Google Scholar
• Chen, J., et al., 2013b. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. Journal of Hydrology, 479, 200–214. doi:10.1016/j.jhydrol.2012.11.062
   Web of Science ®Google Scholar
• Chen, J., et al., 2019. Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology. Climatic Change, 153 (3), 361–377. doi:10.1007/s10584-019-02393-x
   Web of Science ®Google Scholar
• Chen, J., Brissette, F.P., and Leconte, R., 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401 (3), 190–202. doi:10.1016/j.jhydrol.2011.02.020
   Web of Science ®Google Scholar
• Collet, L., Beevers, L., and Prudhomme, C., 2017. Assessing the impact of climate change and extreme value uncertainty to extreme flows across Great Britain. Water, 9 (2), 103. doi:10.3390/w9020103
   Web of Science ®Google Scholar
• Coron, L., et al., 2012. Crash testing hydrological models in contrasted climate conditions: an experiment on 216 Australian catchments. Water resources research, 48(5), 1-17.
   Web of Science ®Google Scholar
• Cunnane, C. 1989. Statistical distributions for flood frequency analysis. Operational hydrology report (WMO).
   Google Scholar
• Dallaire, G., et al., 2021. Uncertainty of potential evapotranspiration modelling in climate change impact studies on low flows in North America. Hydrological Sciences Journal, 66 (4), 689–702. doi:10.1080/02626667.2021.1888955
   Web of Science ®Google Scholar
• Das, J. and Umamahesh, N.V., 2018. Assessment of uncertainty in estimating future flood return levels under climate change. Natural Hazards, 93 (1), 109–124. doi:10.1007/s11069-018-3291-2
   Web of Science ®Google Scholar
• Déqué, M., et al., 2007. An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Climatic Change, 81 (1), 53–70. doi:10.1007/s10584-006-9228-x
   Web of Science ®Google Scholar
• Deser, C., et al., 2020. Insights from Earth system model initial-condition large ensembles and future prospects. Nature Climate Change, 10 (4), 277–286. doi:10.1038/s41558-020-0731-2
   Web of Science ®Google Scholar
• Escalante-Sandoval, C. and García-Espinoza, E., 2014. Analysis of annual flood peak records in Mexico. WIT Transactions on Information Communication Technology, 47, 49–60.
   Google Scholar
• Falloon, P., et al., 2014. Ensembles and uncertainty in climate change impacts. Frontiers in Environmental Science, 2, 33. doi:10.3389/fenvs.2014.00033
   Google Scholar
• Fortin, V. and Turcotte, R., 2006. Le modèle hydrologique MOHYSE. Montréal: Département des sciences de la terre et de l’atmosphère. Université du Québec à Montréal, Note de cours pour SCA7420.
   Google Scholar
• Giorgi, F. and Gutowski, W.J., 2015. Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40 (1), 467–490. doi:10.1146/annurev-environ-102014-021217
   Web of Science ®Google Scholar
• Giuntoli, I., et al., 2018. Uncertainties in projected runoff over the conterminous United States. Climatic Change, 150 (3), 149–162. doi:10.1007/s10584-018-2280-5
   Web of Science ®Google Scholar
• Gosling, S.N., et al., 2011. A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrology and Earth System Sciences, 15 (1), 279–294. doi:10.5194/hess-15-279-2011
   Web of Science ®Google Scholar
• Graham, L.P., Andréasson, J., and Carlsson, B., 2007. Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods – a case study on the lule river basin. Climatic Change, 81 (1), 293–307. doi:10.1007/s10584-006-9215-2
   Web of Science ®Google Scholar
• Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology, 377 (1), 80–91. doi:10.1016/j.jhydrol.2009.08.003
   Web of Science ®Google Scholar
• Hailegeorgis, T.T. and Alfredsen, K., 2017. Regional flood frequency analysis and prediction in ungauged basins including estimation of major uncertainties for mid-Norway. Journal of Hydrology: Regional Studies, 9, 104–126.
   Google Scholar
• Hall, J., et al., 2014. Understanding flood regime changes in Europe: a state of the art assessment. Hydrology and Earth System Sciences, 18 (7), 2735–2772. doi:10.5194/hess-18-2735-2014
   Web of Science ®Google Scholar
• Hansen, N. and Ostermeier, A., 1997. Convergence properties of evolution strategies with the derandomized covariance matrix adaptation: the CMA-ES. Eufit, 97, 650–654.
   Google Scholar
• Hattermann, F.F., et al., 2018. Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environmental Research Letters, 13 (1), 015006. doi:10.1088/1748-9326/aa9938
   Web of Science ®Google Scholar
• Henderson, and Sellers, A., 1993. An antipodean climate of uncertainty? Climatic Change, 25 (3–4), 203–224. doi:10.1007/BF01098373
   Web of Science ®Google Scholar
• Her, Y., et al., 2019. Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions. Scientific Reports, 9 (1), 4974. doi:10.1038/s41598-019-41334-7
   PubMed Web of Science ®Google Scholar
• Hingray, B., et al., 2019. Uncertainty component estimates in transient climate projections. Climate Dynamics, 53 (5), 2501–2516. doi:10.1007/s00382-019-04635-1
   Web of Science ®Google Scholar
• Hirabayashi, Y., et al., 2008. Global projections of changing risks of floods and droughts in a changing climate. Hydrological Sciences Journal, 53 (4), 754–772. doi:10.1623/hysj.53.4.754
   Web of Science ®Google Scholar
• Ho, J.T., Thompson, J.R., and Brierley, C., 2016. Projections of hydrology in the Tocantins-Araguaia Basin, Brazil: uncertainty assessment using the CMIP5 ensemble. Hydrological Sciences Journal, 61 (3), 551–567. doi:10.1080/02626667.2015.1057513
   Web of Science ®Google Scholar
• Hu, L., et al., 2019. Sensitivity of flood frequency analysis to data record, statistical model, and parameter estimation methods: an evaluation over the contiguous United States. Journal of Flood Risk Management, 13 (1), e12580.
   Web of Science ®Google Scholar
• Huot, P.-L., et al., 2019. A hybrid optimization approach for efficient calibration of computationally intensive hydrological models. Hydrological Sciences Journal, 64 (10), 1204–1222. doi:10.1080/02626667.2019.1624922
   Web of Science ®Google Scholar
• IPCC, 2018. Global warming of 1.5°C. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Geneva, Switzerland: World Meteorological Organization.
   Google Scholar
• Kay, A.L., et al., 2009. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, 92 (1), 41–63. doi:10.1007/s10584-008-9471-4
   Web of Science ®Google Scholar
• Kling, H., Fuchs, M., and Paulin, M., 2012. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424–425, 264–277. doi:10.1016/j.jhydrol.2012.01.011
   Web of Science ®Google Scholar
• Knoben, W.J., Freer, J.E., and Woods, R.A., 2019. Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrology and Earth System Sciences, 23 (10), 4323–4331. doi:10.5194/hess-23-4323-2019
   Web of Science ®Google Scholar
• Kottek, M., et al., 2006. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15 (3), 259–263. doi:10.1127/0941-2948/2006/0130
   Web of Science ®Google Scholar
• Krysanova, V., et al., 2018. How the performance of hydrological models relates to credibility of projections under climate change. Hydrological Sciences Journal, 63 (5), 696–720. doi:10.1080/02626667.2018.1446214
   Web of Science ®Google Scholar
• Kuczera, G., et al., 2010. There are no hydrological monsters, just models and observations with large uncertainties! Hydrological Sciences Journal, 55 (6), 980–991. doi:10.1080/02626667.2010.504677
   Web of Science ®Google Scholar
• Kundzewicz, Z.W., et al., 2017. Differences in flood hazard projections in Europe – their causes and consequences for decision making. Hydrological Sciences Journal, 62 (1), 1–14.
   Web of Science ®Google Scholar
• Kundzewicz, Z.W., et al., 2018. Uncertainty in climate change impacts on water resources. Environmental Science & Policy, 79, 1–8. doi:10.1016/j.envsci.2017.10.008
   Web of Science ®Google Scholar
• Lemaitre-Basset, T., et al., 2021. Climate change impact and uncertainty analysis on hydrological extremes in a French mediterranean catchment. Hydrological Sciences Journal, 66 (5), 888–903. doi:10.1080/02626667.2021.1895437
   Web of Science ®Google Scholar
• Livneh, B., et al., 2013. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. Journal of Climate, 26 (23), 9384–9392. doi:10.1175/JCLI-D-12-00508.1
   Web of Science ®Google Scholar
• Livneh, B., et al., 2015. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. Scientific Data, 2 (1), 150042. doi:10.1038/sdata.2015.42
   PubMed Web of Science ®Google Scholar
• Lucas-Picher, P., et al., 2020. Application of a high-resolution distributed hydrological model on a U.S.-Canada Transboundary Basin: simulation of the multiyear mean annual hydrograph and 2011 flood of theRichelieu river basin. Journal of Advances in Modeling Earth Systems, 12 (4), e2019MS001709. doi:10.1029/2019MS001709
   Web of Science ®Google Scholar
• Maraun, D., 2016. Bias correcting climate change simulations - a critical review. Current Climate Change Reports, 2 (4), 211–220. doi:10.1007/s40641-016-0050-x
   Web of Science ®Google Scholar
• Maraun, D., et al., 2017. Towards process-informed bias correction of climate change simulations. Nature Climate Change, 7 (11), 764–773. doi:10.1038/nclimate3418
   Web of Science ®Google Scholar
• Mareuil, A., et al., 2007. Impacts of climate change on the frequency and severity of floods in the Châteauguay River basin, Canada. Canadian Journal of Civil Engineering, 34 (9), 1048–1060. doi:10.1139/l07-022
   Web of Science ®Google Scholar
• Martel, J.-L., et al., 2017. HMETS-A simple and efficient hydrology model for teaching hydrological modelling, flow forecasting and climate change impacts. The International Journal of Engineering Education, 33, 1307–1316.
   Web of Science ®Google Scholar
• McGuinness, J. and Bordne, E., 1972. A comparison of lysimeter derived potential evapotranspiration with computed values. Tech. Bull., 1452. Washington, DC: Agricultural Research Service, United States Department of Agriculture.
   Google Scholar
• Meresa, H.K. and Romanowicz, R.J., 2017. The critical role of uncertainty in projections of hydrological extremes. Hydrology and Earth System Sciences, 21 (8), 4245–4258. doi:10.5194/hess-21-4245-2017
   Web of Science ®Google Scholar
• Merz, B., et al., 2012. HESS opinions’ more efforts and scientific rigour are needed to attribute trends in flood time series’. Hydrology and Earth System Sciences, 16 (5), 1379–1387. doi:10.5194/hess-16-1379-2012
   Web of Science ®Google Scholar
• Merz, B. and Thieken, A.H., 2005. Separating natural and epistemic uncertainty in flood frequency analysis. Journal of Hydrology, 309 (1), 114–132. doi:10.1016/j.jhydrol.2004.11.015
   Web of Science ®Google Scholar
• Merz, B. and Thieken, A.H., 2009. Flood risk curves and uncertainty bounds. Natural Hazards, 51 (3), 437–458. doi:10.1007/s11069-009-9452-6
   Web of Science ®Google Scholar
• Meybeck, M., Kummu, M., and Dürr, H.H., 2013. Global hydrobelts and hydroregions: improved reporting scale for water-related issues? Hydrology and Earth System Sciences, 17 (3), 1093–1111. doi:10.5194/hess-17-1093-2013
   Web of Science ®Google Scholar
• Minville, M., et al., 2009. Adaptation to climate change in the management of a Canadian water-resources system exploited for hydropower. Water Resources Management, 23 (14), 2965–2986. doi:10.1007/s11269-009-9418-1
   Web of Science ®Google Scholar
• Mpelasoka, F.S. and Chiew, F.H., 2009. Influence of rainfall scenario construction methods on runoff projections. Journal of Hydrometeorology, 10 (5), 1168–1183. doi:10.1175/2009JHM1045.1
   Web of Science ®Google Scholar
• Oudin, L., et al., 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 (1), 290–306. doi:10.1016/j.jhydrol.2004.08.026
   Web of Science ®Google Scholar
• Pechlivanidis, I., et al., 2011. Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST Journal, 13 (3), 193–214.
   Web of Science ®Google Scholar
• Perrin, C., Michel, C., and Andréassian, V., 2003. Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279 (1–4), 275–289. doi:10.1016/S0022-1694(03)00225-7.
   Web of Science ®Google Scholar
• Poulin, A., et al., 2011. Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin. Journal of Hydrology, 409 (3), 626–636. doi:10.1016/j.jhydrol.2011.08.057
   Web of Science ®Google Scholar
• Prudhomme, C. and Davies, H., 2009. Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Climatic Change, 93 (1), 197–222. doi:10.1007/s10584-008-9461-6
   Web of Science ®Google Scholar
• Reszler, C., Switanek, M.B., and Truhetz, H., 2018. Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps. Natural Hazards and Earth System Sciences, 18 (10), 2653–2674. doi:10.5194/nhess-18-2653-2018
   Web of Science ®Google Scholar
• Riboust, P. and Brissette, F., 2015. Climate change impacts and uncertainties on spring flooding of Lake Champlain and the Richelieu River. JAWRA Journal of the American Water Resources Association, 51 (3), 776–793. doi:10.1111/jawr.12271
   Web of Science ®Google Scholar
• Schmidli, J., Frei, C., and Vidale, P.L., 2006. Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods. International Journal of Climatology, 26 (5), 679–689. doi:10.1002/joc.1287
   Web of Science ®Google Scholar
• Schneider, S.H., 1983. CO2, climate and society: a brief overview. In: Social science research and climate change. Springer, Dordrecht: Springer, 9–15.
   Google Scholar
• Seiller, G., Anctil, F., and Perrin, C., 2012. Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions. Hydrology and Earth System Sciences, 16 (4), 1171–1189 . doi:10.5194/hess-16-1171-2012
   Web of Science ®Google Scholar
• Separovic, L., et al., 2013. Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Climate Dynamics, 41 (11), 3167–3201. doi:10.1007/s00382-013-1737-5
   Web of Science ®Google Scholar
• Shen, M., et al., 2018. Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. Journal of Hydrology, 556, 10–24. doi:10.1016/j.jhydrol.2017.11.004
   Web of Science ®Google Scholar
• Song, Y.H., Chung, E.-S., and Shahid, S., 2021. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models. International Journal of Climatology, 41 (13), 5899–5919. doi:10.1002/joc.7159
   Web of Science ®Google Scholar
• Taye, M.T., et al., 2011. Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin. Hydrology and Earth System Sciences, 15 (1), 209–222. doi:10.5194/hess-15-209-2011
   Web of Science ®Google Scholar
• Taylor, K.E., Stouffer, R.J., and Meehl, G.A., 2011. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 (4), 485–498. doi:10.1175/BAMS-D-11-00094.1
   Web of Science ®Google Scholar
• Teng, J., et al., 2012. Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. Journal of Hydrometeorology, 13 (1), 122–139. doi:10.1175/JHM-D-11-058.1
   Web of Science ®Google Scholar
• Teutschbein, C., et al., 2015. Hydrological response to changing climate conditions: spatial streamflow variability in the boreal region. Water Resources Research, 51 (12), 9425–9446. doi:10.1002/2015WR017337
   Web of Science ®Google Scholar
• Themeßl, M.J., Gobiet, A., and Heinrich, G., 2012. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change, 112 (2), 449–468. doi:10.1007/s10584-011-0224-4
   Web of Science ®Google Scholar
• Themeßl, M.J., Gobiet, A., and Leuprecht, A., 2011. Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology, 31 (10), 1530–1544. doi:10.1002/joc.2168
   Web of Science ®Google Scholar
• Thiboult, A., Anctil, F., and Boucher, M.A., 2016. Accounting for three sources of uncertainty in ensemble hydrological forecasting. Hydrology and Earth System Sciences, 20 (5), 1809–1825. doi:10.5194/hess-20-1809-2016
   Web of Science ®Google Scholar
• Thompson, J.R., et al., 2014. Climate change uncertainty in environmental flows for the mekong river. Hydrological Sciences Journal, 59 (3–4), 935–954. doi:10.1080/02626667.2013.842074
   Web of Science ®Google Scholar
• Troin, M., et al., 2018. Uncertainty of hydrological model components in climate change studies over two Nordic Quebec catchments. Journal of Hydrometeorology, 19 (1), 27–46. doi:10.1175/JHM-D-17-0002.1
   Web of Science ®Google Scholar
• Valéry, A., Andréassian, V., and Perrin, C., 2014. ‘As simple as possible but not simpler’: what is useful in a temperature-based snowaccounting routine? Part 2–sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments. Journal of Hydrology, 517, 1176–1187. doi:10.1016/j.jhydrol.2014.04.058
   Web of Science ®Google Scholar
• Velázquez, J.A., et al., 2013. An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources. Hydrology and Earth System Sciences, 17 (2), 565–578. doi:10.5194/hess-17-565-2013
   Web of Science ®Google Scholar
• Vetter, T., et al., 2015. Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth System Dynamics, 6 (1), 17–43. doi:10.5194/esd-6-17-2015
   Web of Science ®Google Scholar
• Vetter, T., et al., 2017. Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change, 141 (3), 419–433. doi:10.1007/s10584-016-1794-y
   Web of Science ®Google Scholar
• Vidal, J.P., et al., 2016. Hierarchy of climate and hydrological uncertainties in transient low-flow projections. Hydrology and Earth System Sciences, 20 (9), 3651–3672. doi:10.5194/hess-20-3651-2016
   Web of Science ®Google Scholar
• Vormoor, K., et al., 2015. Climate change impacts on the seasonality and generation processes of floods–projections and uncertainties for catchments with mixed snowmelt/rainfall regimes. Hydrology & Earth System Sciences, 19 (2), 913–931. doi:10.5194/hess-19-913-2015
   Web of Science ®Google Scholar
• Vormoor, K., et al., 2016. Evidence for changes in the magnitude and frequency of observed rainfall vs. snowmelt driven floods in Norway. Journal of Hydrology, 538 (Supplement C), 33–48. doi:10.1016/j.jhydrol.2016.03.066
   Web of Science ®Google Scholar
• Wen, S., et al., 2020. Comprehensive evaluation of hydrological models for climate change impact assessment in the Upper Yangtze River Basin, China. Climatic Change, 163 (3), 1207–1226. doi:10.1007/s10584-020-02929-6
   Web of Science ®Google Scholar
• Wilby, R.L. and Dessai, S., 2010. Robust adaptation to climate change. Weather, 65 (7), 180–185. doi:10.1002/wea.543
   Web of Science ®Google Scholar
• Wilby, R.L. and Harris, I., 2006. A framework for assessing uncertainties in climate change impacts: low‐flow scenarios for the River Thames, UK. Water resources research, 42 (2).
   Google Scholar
• Zhang, X., Xu, Y.-P., and Fu, G., 2014. Uncertainties in SWAT extreme flow simulation under climate change. Journal of Hydrology, 515, 205–222. doi:10.1016/j.jhydrol.2014.04.064
   Web of Science ®Google Scholar
• Zhao, C., et al., 2020. Frequency change of future extreme summer meteorological and hydrological droughts over North America. Journal of Hydrology, 584, 124316. doi:10.1016/j.jhydrol.2019.124316
   Web of Science ®Google Scholar
• Zhu, Q., et al., 2016. Investigating the uncertainty and transferability of parameters in SWAT model under climate change. Hydrological Sciences Journal, 61 (5), 914–930.
   Web of Science ®Google Scholar