On the correlation between precipitation and potential evapotranspiration climate change signals for hydrological impact analyses

References

• Adler, R.F., et al., 2008. Relationships between global precipitation and surface temperature on interannual and longer timescales (1979–2006). Journal of Geophysical Research, 113, D22104. doi:10.1029/2008JD010536
   Web of Science ®Google Scholar
• Baguis, P., et al., 2010. Climate change scenarios for precipitation and potential evapotranspiration over central Belgium. Theoretical and Applied Climatology, 99, 273–286. doi:10.1007/s00704-009-0146-5
   Web of Science ®Google Scholar
• Birkinshaw, S.J., et al., 2017. Climate change impacts on Yangtze River discharge at the Three Gorges Dam. Hydrology and Earth System Sciences, 21, 1911–1927. doi:10.5194/hess-21-1911-2017
   Web of Science ®Google Scholar
• Bultot, F., Coppens, A., and Dupriez, G.L., 1983. Estimation de l’évapotranspiration potentielle en Belgique (Procédure Révisée). Uccle - Brussels, Belgium: Institut Royal Météorologique.
   Google Scholar
• Bürger, G., et al., 2013. Downscaling extremes: an intercomparison of multiple methods for future climate. Journal of Climate, 26, 3429–3449. doi:10.1175/JCLI-D-12-00249.1
   Web of Science ®Google Scholar
• Ceola, S., et al., 2015. Virtual laboratories: new opportunities for collaborative water science. Hydrology and Earth System Sciences, 19, 2101–2117. doi:10.5194/hess-19-2101-2015
   Web of Science ®Google Scholar
• Chamorro, A., et al., 2017. Effect of (quasi-) optimum model parameter sets and model characteristics on future discharge projection of two basins from Europe and Asia. Climatic Change, 142, 559–573. doi:10.1007/s10584-017-1974-4
   Web of Science ®Google Scholar
• Chen, J., et al., 2011. Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resources Research, 47, 1–16. doi:10.1029/2011WR010602
   Web of Science ®Google Scholar
• Dams, J., et al., 2015. Multi-model approach to assess the impact of climate change on runoff. Journal of Hydrology, 529, 1601–1616. doi:10.1016/j.jhydrol.2015.08.023
   Web of Science ®Google Scholar
• de Boer-Euser, T., et al., 2017. Looking beyond general metrics for model comparison - lessons from an international model intercomparison study. Hydrology and Earth System Sciences, 21, 423–440. doi:10.5194/hess-21-423-2017
   Web of Science ®Google Scholar
• DHI (Danish Hydrological Institute) (2009) Mike 11 software manual. Available form: https://www.mikepoweredbydhi.com/products/mike-11 [14 Mar 2015]
   Google Scholar
• Drouet, L., Bosetti, V., and Tavoni, M., 2015. Selection of climate policies under the uncertainties in the fifth assessment report of the IPCC. Nature Climate Change, 5, 937–940. doi:10.1038/nclimate2721
   Web of Science ®Google Scholar
• Ekström, M., et al., 2016. The method of producing climate change datasets impacts the resulting policy guidance and chance of mal-adaptation. Climate Services, 4, 13–29. doi:10.1016/J.CLISER.2016.09.003
   Google Scholar
• Ekström, M., Grose, M.R., and Whetton, P.H., 2015. An appraisal of downscaling methods used in climate change research. WIREs Climate Change, 6, 301–319. doi:10.1002/wcc.339
   Google Scholar
• Fatichi, S., et al., 2016. Uncertainty partition challenges the predictability of vital details of climate change. Earth’s Future, 4, 240–251. doi:10.1002/2015EF000336
   Web of Science ®Google Scholar
• Fowler, H.J., Blenkinsop, S., and Tebaldi, C., 2007. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27, 1547–1578. doi:10.1002/joc.1556
   Web of Science ®Google Scholar
• Gosling, S.N., et al., 2017. A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C. Climatic Change, 141, 577–595. doi:10.1007/s10584-016-1773-3
   Web of Science ®Google Scholar
• Habets, F., et al., 2013. Impact of climate change on the hydrogeology of two basins in northern France. Climatic Change, 121, 771–785. doi:10.1007/s10584-013-0934-x
   Web of Science ®Google Scholar
• Hartmann, G. and Bárdossy, A., 2005. Investigation of the transferability of hydrological models and a method to improve model calibration. Advances in Geoscience, 5, 83–87. doi:10.5194/adgeo-5-83-2005
   Google Scholar
• Hawkins, E. and Sutton, R., 2009. The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Geological Society, 90, 1095–1107. doi:10.1175/2009BAMS2607.1
   Google Scholar
• Hosseinzadehtalaei, P., Tabari, H., and Willems, P., 2017a. Uncertainty assessment for climate change impact on intense precipitation : how many model runs do we need? International Journal of Climatology, 37, 1105–1117. doi:10.1002/joc.5069
   Web of Science ®Google Scholar
• Hosseinzadehtalaei, P., Tabari, H., and Willems, P., 2017b. Quantification of uncertainty in reference evapotranspiration climate change signals in Belgium. Hydrology Research, 48, 1391–1401. doi:10.2166/nh.2016.243
   Web of Science ®Google Scholar
• Hundecha, Y., et al., 2016. Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe. Journal of Hydrology, 541, 1273–1286. doi:10.1016/j.jhydrol.2016.08.033
   Web of Science ®Google Scholar
• Islam, S.U., Dery, S.J., and Werner, A.T., 2017. Future climate change impacts on snow and water resources of the Fraser River Basin, British Columbia. Journal of Hydrometeorology, 18, 473–496. doi:10.1175/JHM-D-16-0012.1
   Web of Science ®Google Scholar
• Jacob, D., et al., 2014. EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environmental Change, 14, 563–578. doi:10.1007/s10113-013-0499-2
   Web of Science ®Google Scholar
• Katz, R.W., et al., 2013. Uncertainty analysis in climate change assessments. Nature Climate Change, 3, 769–771. doi:10.1038/nclimate1980
   Web of Science ®Google Scholar
• Kingston, D.G., et al., 2009. Uncertainty in the estimation of potential evapotranspiration under climate change. Geophysical Research Letters, 36, 3–8. doi:10.1029/2009GL040267
   Web of Science ®Google Scholar
• KNMI (Koninklijk Nederlands Meteorologisch Instituut), 2015. KNMI’14 climate scenarios for the Netherlands; A guide for professionals in climate adaptation. De Bilt, The Netherlands.
   Google Scholar
• Koch, J., et al., 2016. Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment. Journal of Hydrology, 533, 234–249. doi:10.1016/J.JHYDROL.2015.12.002
   Web of Science ®Google Scholar
• Kunreuther, H., et al., 2014. Integrated risk and uncertainty assessment of climate change response policies coordinating lead authors: integrated risk and uncertainty assessment of climate change response policies. In: O. Edenhofer, et al., eds. Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press, 151–205.
   Google Scholar
• Liu, J., et al., 2017. Local climate change and the impacts on hydrological processes in an arid alpine catchment in Karakoram. Water, 9, 1–17. doi:10.3390/w9050344
   PubMed Web of Science ®Google Scholar
• Liu, T., et al., 2011. Climate change impact on water resource extremes in a headwater region of the Tarim basin in China. Hydrology and Earth System Sciences, 15, 3511–3527. doi:10.5194/hess-15-3511-2011
   Web of Science ®Google Scholar
• Maraun, D., 2016. Bias correcting climate change simulations - a critical review. Current Climate Change Reports, 2, 211–220. doi:10.1007/s40641-016-0050-x
   Web of Science ®Google Scholar
• Maraun, D., et al., 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3). doi:10.1029/2009RG000314
   Web of Science ®Google Scholar
• McMillan, H., et al., 2017. How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications. Water Resources Research, 53, 5220–5228. doi:10.1002/2016WR020328
   Web of Science ®Google Scholar
• Meaurio, M., et al., 2017. Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay). Journal of Hydrology, 548, 46–62. doi:10.1016/j.jhydrol.2017.02.029
   Web of Science ®Google Scholar
• Milly, P.C.D. and Dunne, K.A., 2016. Potential evapotranspiration and continental drying. Nature Climate Change, 6, 946–949. doi:10.1038/nclimate3046
   Web of Science ®Google Scholar
• Milly, P.C.D. and Dunne, K.A., 2017. A hydrologic drying bias in water-resource impact analyses of anthropogenic climate change. Journal of the American Water Resources Assocication JAWRA, 53, 822–838. doi:10.1111/1752-1688.12538
   Web of Science ®Google Scholar
• Monteith, J.L., 1965. Evaporation and the environment. In: The state and movement of water in living organisms, XIXth symposium, Swansea UK. Cambridge, UK: Cambridge University Press, pp 205–234.
   Google Scholar
• Moore, R.J., 1985. The probability-distributed principle and runoff production at point and basin scales. Hydrological Sciences Journal, 30, 273–297. doi:10.1080/02626668509490989
   Web of Science ®Google Scholar
• Moore, R.J., 2007. The PDM rainfall–runoff model. Hydrology and Earth System Sciences, 11, 483–499. doi:10.5194/hess-11-483-2007
   Web of Science ®Google Scholar
• Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society for Agricultural and Biological Engineering (ASABE), 50, 885–900.
   Web of Science ®Google Scholar
• Moss, R.H., et al., 2010. The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756. doi:10.1038/nature08823
   PubMed Web of Science ®Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology, 10, 282–290. doi:10.1016/0022-1694(70)90255-6
   Google Scholar
• Nicolle, P., et al., 2014. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments. Hydrology and Earth System Sciences, 18, 2829–2857. doi:10.5194/hess-18-2829-2014
   Web of Science ®Google Scholar
• Nielsen, S.A. and Hansen, E., 1973. Numerical simulation of the rainfall-runoff process on a daily basis. Nordic Hydrology, 4, 171–190. doi:10.2166/nh.1973.0013
   Google Scholar
• Ntegeka, V., et al., 2014. Developing tailored climate change scenarios for hydrological impact assessments. Journal of Hydrology, 508, 307–321. doi:10.1016/j.jhydrol.2013.11.001
   Web of Science ®Google Scholar
• Paparrizos, S., Maris, F., and Matzarakis, A., 2017. Sensitivity analysis and comparison of various potential evapotranspiration formulae for selected Greek areas with different climate conditions. Theoretical and Applied Climatology, 128 (3–4), 745–759. doi:10.1007/s00704-015-1728-z
   Web of Science ®Google Scholar
• Patt, A.G. and Weber, E.U., 2014. Perceptions and communication strategies for the many uncertainties relevant for climate policy. WIREs Climate Change, 5, 219–232. doi:10.1002/wcc.259
   Web of Science ®Google Scholar
• Penman, H.L., 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society London A, Mathematical, Physical and Engineering Sciences, 193, 120–145.
   PubMed Web of Science ®Google Scholar
• Reshmidevi, T.V., et al., 2017. Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs. Journal of Hydrology. doi:10.1016/j.jhydrol.2017.02.016
   Web of Science ®Google Scholar
• Seiller, G. and Anctil, F., 2016. How do potential evapotranspiration formulas influence hydrological projections? Hydrological Sciences Journal, 61, 2249–2266. doi:10.1080/02626667.2015.1100302
   Web of Science ®Google Scholar
• Seiller, G., Roy, R., and Anctil, F., 2017. Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources. Journal of Hydrology, 547, 280–295. doi:10.1016/j.jhydrol.2017.02.004
   Web of Science ®Google Scholar
• Seong, C., Sridhar, V., and Billah, M.M., 2017. Implications of potential evapotranspiration methods for streamflow estimations under changing climatic conditions. International Journal of Climatology. doi:10.1002/joc.5218
   Web of Science ®Google Scholar
• Sunyer, M.A., et al., 2015. Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrology and Earth System Sciences, 19, 1827–1847. doi:10.5194/hess-19-1827-2015
   Web of Science ®Google Scholar
• Tabari, H., Taye, M.T., and Willems, P., 2015. Water availability change in central Belgium for the late 21st century. Global Planetary Change, 131, 115–123. doi:10.1016/j.gloplacha.2015.05.012
   Web of Science ®Google Scholar
• Tavakoli, M., et al., 2014. Impact of climate change and urban development on extreme flows in the Grote Nete watershed, Belgium. Natural Hazards, 71, 2127–2142. doi:10.1007/s11069-013-1001-7
   Web of Science ®Google Scholar
• Taylor, K.E., Stouffer, R.J., and Meehl, G.A., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Geological Society, 93, 485–498. doi:10.1175/BAMS-D-11-00094.1
   Google Scholar
• Teklesadik, A.D., et al., 2017. Inter-model comparison of hydrological impacts of climate change on the Upper Blue Nile basin using ensemble of hydrological models and global climate models. Climatic Change, 141, 517–532. doi:10.1007/s10584-017-1913-4
   Web of Science ®Google Scholar
• Teng, J., et al., 2011. Estimating the relative uncertainties sourced from gcms and hydrological models in modeling climate change impact on runoff. Journal of Hydrometeorology, 13, 122–139. doi:10.1175/JHM-D-11-058.1
   Web of Science ®Google Scholar
• Teshager, A.D., et al., 2016. Assessment of impacts of agricultural and climate change scenarios on watershed water quantity and quality, and crop production. Hydrology and Earth System Sciences, 20, 3325–3342. doi:10.5194/hess-20-3325-2016
   Web of Science ®Google Scholar
• Thornthwaite, C.W. and Mather, J.R., 1951. The role of evapotranspiration in climate. Archiv für Meteorologie Geophysik und Bioklimatologie Serie B, 3, 16–39. doi:10.1007/BF02242588
   Google Scholar
• Van Hoey, S., et al., 2014. A qualitative model structure sensitivity analysis method to support model selection. Journal of Hydrology, 519, 3426–3435. doi:10.1016/j.jhydrol.2014.09.052
   Web of Science ®Google Scholar
• Van Uytven, E. and Willems, P., 2018. Greenhouse gas scenario sensitivity and uncertainties in precipitation projections for central Belgium. Journal of Hydrology, 558, 9–19. doi:10.1016/j.jhydrol.2018.01.018
   Web of Science ®Google Scholar
• van Vuuren, D.P., et al., 2011. The representative concentration pathways: an overview. Climatic Change, 109, 5–31. doi:10.1007/s10584-011-0148-z
   Web of Science ®Google Scholar
• Vansteenkiste, T., et al., 2012. Climate change impact on river flows and catchment hydrology: A comparison of two spatially distributed models. Hydrological Processes, 3662, 3649–3662. doi:10.1002/hyp.9480
   Google Scholar
• Vansteenkiste, T., et al., 2014a. Intercomparison of hydrological model structures and calibration approaches in climate scenario impact projections. Journal of Hydrology, 519, 743–755. doi:10.1016/j.jhydrol.2014.07.062
   Web of Science ®Google Scholar
• Vansteenkiste, T., et al., 2014b. Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation. Journal of Hydrology, 511, 335–349. doi:10.1016/j.jhydrol.2014.01.050
   Web of Science ®Google Scholar
• Vrebos, D., et al., 2014. Water displacement by sewer infrastructure in the Grote Nete catchment, Belgium, and its hydrological regime effects. Hydrology and Earth System Sciences, 18, 1119–1136. doi:10.5194/hess-18-1119-2014
   Web of Science ®Google Scholar
• Vrugt, J.A., et al., 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
• Willems, P., 2009. A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models. Environmental Modelling and Software, 24, 311–321. doi:10.1016/j.envsoft.2008.09.005
   Web of Science ®Google Scholar
• Willems, P., 2014. Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes - Part 1: step-wise model-structure identification and calibration approach. Journal of Hydrology, 510, 578–590. doi:10.1016/j.jhydrol.2014.01.017
   Web of Science ®Google Scholar
• Willems, P., et al., 2014. Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes - Part 2: intercomparison of models and calibration approaches. Journal of Hydrology, 510, 591–609. doi:10.1016/j.jhydrol.2014.01.028
   Web of Science ®Google Scholar