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Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium

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Abstract

The impact of mid-century climatic changes on crop productivity of winter wheat, maize, potato and sugar beet was assessed for a temperate maritime climate in the Flemish Region, Belgium. Climatic projections of multiple regional and global climate models (RCMs from the EU-ENSEMBLES project and GCMs from the Coupled Model Intercomparison Project phase 3) were stochastically downscaled by the LARS-WG weather generator for use in the crop models AquaCrop and Sirius. Primarily positive effects on mean yield were simulated. Crops benefitted from elevated CO2, and from more radiation interception if the cropping period was adapted in response to higher temperatures. However, increased productivity was linked with increased susceptibility to water stress and greater inter-annual yield variability, particularly with adapted management. Impacts differed among and within ensembles of climate models, and among crops and environments. Although RCMs may be more suitable for local impact assessments than GCMs, inter-ensemble differences and contingent wider ranges of impacts with GCM projections found in this study indicate that applying RCMs driven by a limited number of GCMs alone would not give the full range of possible impacts. Further, this study suggests that the simulated intermodel variation can be larger than spatial variation within the region. These findings advocate the use of both GCM and RCM ensembles in assessments where temperature and precipitation are central, such as for crop production.

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References

  • Abrha B, Delbecque N, Raes D, Tsegay A, Todorovic M, Heng L, Vanuytrecht E, Geerts S, Garcia-Vila M, Deckers S (2012) Sowing strategies for barley (Hordeum vulgare L.) based on modelled yield response to water with AquaCrop. Exp Agric 48:252–271. doi:10.1017/S0014479711001190

    Article  Google Scholar 

  • Ainsworth EA, Rogers A (2007) The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ 30:258–270. doi:10.1111/j.1365-3040.2007.01641.x

    Article  CAS  Google Scholar 

  • Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rotter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Muller C, Naresh Kumar S, Nendel C, O/’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stockle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Change 3:827–832. doi:10.1038/nclimate1916

    Article  CAS  Google Scholar 

  • Audsley E, Pearn KR, Simota C, Cojocaru G, Koutsidou E, Rounsevell MDA, Trnka M, Alexandrov V (2006) What can scenario modelling tell us about future European scale agricultural land use, and what not? Environ Sci Policy 9:148–162. doi:10.1016/j.envsci.2005.11.008

    Article  Google Scholar 

  • Baguis P, Roulin E, Willems P, Ntegeka V (2010) Climate change scenarios for precipitation and potential evapotranspiration over central Belgium. Theor Appl Climatol 99:273–286. doi:10.1007/s00704-009-0146-5

    Article  Google Scholar 

  • Bindi M, Olesen JE (2011) The responses of agriculture in Europe to climate change. Reg Environ Change 11:S151–S158. doi:10.1007/s10113-010-0173-x

    Article  Google Scholar 

  • Bontems P, Thomas A (2006) Regulating nitrogen pollution with risk averse farmers under hidden information and moral hazard. Am J Agric Econ 88:57–72. doi:10.1111/j.1467-8276.2006.00838.x

    Article  Google Scholar 

  • Calanca P, Semenov MA (2013) Local-scale climate scenarios for impact studies and risk assessments: integration of early 21st century ENSEMBLES projections into the ELPIS database. Theor Appl Climatol 113:445–455. doi:10.1007/s00704-012-0799-3

    Article  Google Scholar 

  • Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401:190–202. doi:10.1016/j.jhydrol.2011.02.020

    Article  Google Scholar 

  • Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Change 81:7–30. doi:10.1007/s10584-006-9210-7

    Article  Google Scholar 

  • Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett. doi:10.1029/2008gl035694

    Google Scholar 

  • Daccache A, Keay C, Jones RJA, Weatherhead EK, Stalham MA, Knox JW (2012) Climate change and land suitability for potato production in England and Wales: impacts and adaptation. J Agric Sci 150:161–177. doi:10.1017/s0021859611000839

    Article  Google Scholar 

  • Eckersten H, Blomback K, Katterer T, Nyman P, Modelling CN (2001) Water and heat dynamics in winter wheat under climate change in southern Sweden. Agric Ecosyst Environ 86:221–235. doi:10.1016/s0167-8809(00)00284-x

    Article  CAS  Google Scholar 

  • Economie FOD (2014) Kerncijfers landbouw 2014. Federale overheidsdienst Economie—Algemene Directie Statistiek en Economische Informatie, Brussels

    Google Scholar 

  • Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) Should we apply bias correction to global and regional climate model data? Hydrol Earth Syst Sci 16:3391–3404. doi:10.5194/hess-16-3391-2012

    Article  Google Scholar 

  • Ewert F, Rounsevell MDA, Reginster I, Metzger MJ, Leemans R (2005) Future scenarios of European agricultural land use I. Estimating changes in crop productivity. Agric Ecosyst Environ 107:101–116. doi:10.1016/j.agee.2004.12.003

    Article  Google Scholar 

  • Feser F, Rockel B, von Storch H, Winterfeldt J, Zahn M (2011) Regional climate models add value to global model data. A review and selected examples. Bull Am Meteorol Soc 92:1181–1192. doi:10.1175/2011bams3061.1

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578. doi:10.1002/joc.1556

    Article  Google Scholar 

  • Fronzek S, Carter TR (2007) Assessing uncertainties in climate change impacts on resource potential for Europe based on projections from RCMs and GCMs. Clim Change 81:357–371. doi:10.1007/s10584-006-9214-3

    Article  Google Scholar 

  • Giannakopoulos C, Le Sager P, Bindi M, Moriondo M, Kostopoulou E, Goodess CM (2009) Climatic changes and associated impacts in the Mediterranean resulting from a 2 °C global warming. Glob Planet Change 68:209–224. doi:10.1016/j.gloplacha.2009.06.001

    Article  Google Scholar 

  • Gobin A (2010) Modelling climate impacts on crop yields in Belgium. Clim Res 44:55–68. doi:10.3354/cr00925

    Article  Google Scholar 

  • Groom B, Koundouri P, Nauges C, Thomas A (2008) The story of the moment: risk averse cypriot farmers respond to drought management. Appl Econ 40:315–326. doi:10.1080/00036840600592916

    Article  Google Scholar 

  • Guereña A, Ruiz-Ramos M, Diaz-Ambrona CH, Conde JR, Minguez MI (2001) Assessment of climate change and agriculture in Spain using climate models. Agron J 93:237–249

    Article  Google Scholar 

  • Himanen SJ, Hakala K, Kahiluoto H (2013) Crop responses to climate and socioeconomic change in northern regions. Reg Environ Change 13:17–32. doi:10.1007/s10113-012-0308-3

    Article  Google Scholar 

  • Holden NM, Brereton AJ, Fealy R, Sweeney J (2003) Possible change in Irish climate and its impact on barley and potato yields. Agric For Meteorol 116:181–196. doi:10.1016/s0168-1923(03)00002-9

    Article  Google Scholar 

  • IPCC (2013) Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. 1535

  • IPCC (2014) Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: Impacts, adaptations, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. 1132

  • Jamieson PD, Semenov MA (2000) Modelling nitrogen uptake and redistribution in wheat. F Crops Res 68:21–29. doi:10.1016/s0378-4290(00)00103-9

    Article  Google Scholar 

  • Jamieson PD, Porter JR, Wilson DR (1991) A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. F Crops Res 27:337–350. doi:10.1016/0378-4290(91)90040-3

    Article  Google Scholar 

  • Jamieson PD, Semenov MA, Brooking IR, Francis GS (1998) Sirius: a mechanistic model of wheat response to environmental variation. Eur J Agron 8:161–179

    Article  Google Scholar 

  • Jamieson PD, Berntsen J, Ewert F, Kimball BA, Olesen JE, Pinter PJ, Porter JR, Semenov MA (2000) Modelling CO2 effects on wheat with varying nitrogen supplies. Agric Ecosyst Environ 82:27–37. doi:10.1016/S0167-8809(00)00214-0

    Article  CAS  Google Scholar 

  • Kant S, Seneweera S, Rodin J, Materne M, Burch D, Rothstein SJ, Spangenberg G (2012) Improving yield potential in crops under elevated CO2: integrating the photosynthetic and nitrogen utilization efficiencies. Front Plant Sci. doi:10.3389/fpls.2012.00162

    Google Scholar 

  • Kaukoranta T, Hakala K (2008) Impact of spring warming on sowing times of cereal, potato and sugar beet in Finland. Agric Food Sci 17:165–176

    Article  Google Scholar 

  • Kendon EJ, Rowell DP, Jones RG, Buonomo E (2008) Robustness of future changes in local precipitation extremes. J Clim 21:4280–4297. doi:10.1175/2008JCLI2082.1

    Article  Google Scholar 

  • Kim Y, Katz RW, Rajagopalan B, Podesta GP, Furrer EM (2012) Reduced overdispersion in stochastic weather generator using a generalized linear modeling approach. Clim Res 53:13–24

    Article  Google Scholar 

  • Knoke T, Steinbeis O-E, Bösch M, Román-Cuesta RM, Burkhardt T (2011) Cost-effective compensation to avoid carbon emissions from forest loss: an approach to consider price-quantity effects and risk-aversion. Ecol Econ 70:1139–1153. doi:10.1016/j.ecolecon.2011.01.007

    Article  Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758. doi:10.1175/2009jcli3361.1

    Article  Google Scholar 

  • Leakey ADB, Ainsworth EA, Bernacchi CJ, Rogers A, Long SP, Ort DR (2009) Elevated CO2 effects on plant carbon, nitrogen, and water relations: six important lessons from face. J Exp Bot 60:2859–2876

    Article  CAS  Google Scholar 

  • Maracchi G, Sirotenko O, Bindi M (2005) Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Clim Change 70:117–135. doi:10.1007/s10584-005-5939-7

    Article  CAS  Google Scholar 

  • Mearns LO, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396

    Article  Google Scholar 

  • Mearns LO, Easterling W, Hays C, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: part I. The uncertainty due to spatial scale. Clim Change 51:131–172. doi:10.1023/a:1012297314857

    Article  Google Scholar 

  • Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel dataset—A new era in climate change research. Bull Am Meteorol Soc 88:1383–1394. doi:10.1175/bams-88-9-1383

    Article  Google Scholar 

  • Mhizha T, Geerts S, Vanuytrecht E, Makarau A, Raes D (2014) Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe. Water SA 40:233–244. doi:10.4314/wsa.v40i2.5

    Article  Google Scholar 

  • Mkhabela MS, Bullock PR (2012) Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agric Water Manag. doi:10.1016/j.agwat.2012.03.009

    Google Scholar 

  • Moriondo M, Giannakopoulos C, Bindi M (2011) Climate change impact assessment: the role of climate extremes in crop yield simulation. Clim Change 104:679–701. doi:10.1007/s10584-010-9871-0

    Article  Google Scholar 

  • Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung T, Kram T, La Rovere E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) IPCC Special Report on emissions scenarios. Cambridge University Press, Cambridge

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–290. doi:10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. Eur J Agron 16:239–262. doi:10.1016/s1161-0301(02)00004-7

    Article  Google Scholar 

  • Olesen JE, Carter TR, Diaz-Ambrona CH, Fronzek S, Heidmann T, Hickler T, Holt T, Minguez MI, Morales P, Palutikof JP, Quemada M, Ruiz-Ramos M, Rubaek GH, Sau F, Smith B, Sykes MT (2007) Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim Change 81:123–143. doi:10.1007/s10584-006-9216-1

    Article  Google Scholar 

  • Olesen JE, Trnka M, Kersebaum KC, Skjelvag AO, Seguin B, Peltonen-Sainio P, Rossi F, Kozyra J, Micale F (2011) Impacts and adaptation of European crop production systems to climate change. Eur J Agron 34:96–112. doi:10.1016/j.eja.2010.11.003

    Article  Google Scholar 

  • Olesen JE, Borgesen CD, Elsgaard L, Palosuo T, Rotter RP, Skjelvag AO, Peltonen-Sainio P, Borjesson T, Trnka M, Ewert F, Siebert S, Brisson N, Eitzinger J, van Asselt ED, Oberforster M, van der Fels-Klerx HJ (2012) Changes in time of sowing, flowering and maturity of cereals in Europe under climate change. Food Addit Contam Part Chem Anal Control Expo Risk Assess 29:1527–1542. doi:10.1080/19440049.2012.712060

    Article  CAS  Google Scholar 

  • Poorter H (1993) Interspecific variation in the growth-response of plant to an elevated ambient CO2 concentration. Vegetatio 104:77–97

    Article  Google Scholar 

  • Porter J, Semenov M (2005) Crop responses to climatic variation. Philos Trans R Soc B Biol Sci 360:2021–2035

    Article  Google Scholar 

  • Prudhomme C, Wilby RL, Crooks S, Kay AL, Reynard NS (2010) Scenario-neutral approach to climate change impact studies: application to flood risk. J Hydrol 390:198–209. doi:10.1016/j.jhydrol.2010.06.043

    Article  Google Scholar 

  • Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41. doi:10.1016/0304-3800(91)90053-4

    Article  Google Scholar 

  • Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop-the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101:438–447. doi:10.2134/agronj2008.0140s

    Article  Google Scholar 

  • Richter GM, Qi A, Semenov MA, Jaggard KW (2006) Modelling the variability of UK sugar beet yields under climate change and husbandry adaptations. Soil Use Manag 22:39–47. doi:10.1111/j.1475-2743.2006.00018.x

    Article  Google Scholar 

  • Sacks W, Kucharik C (2011) Crop management and phenology trends in the US Corn Belt: impacts on yields, evapotranspiration and energy balance. Agric For Meteorol 151:882–894

    Article  Google Scholar 

  • Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci Rep 1:1–5. doi:10.1038/srep00066

    Article  Google Scholar 

  • Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41:1–14. doi:10.3354/cr00836

    Article  Google Scholar 

  • Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107

    Article  Google Scholar 

  • Semenov MA, Donatelli M, Stratonovitch P, Chatzidaki E, Baruth B (2010) ELPIS: a dataset of local-scale daily climate scenarios for Europe. Clim Res 44:3–15. doi:10.3354/cr00865

    Article  Google Scholar 

  • Serra T, Zilberman D, Goodwin BK, Featherstone A (2006) Effects of decoupling on the mean and variability of output. Eur Rev Agric Econ 33:269–288. doi:10.1093/erae/jbl014

    Article  Google Scholar 

  • Shrestha N, Raes D, Vanuytrecht E, Sah SK (2013) Cereal yield stabilization in Terai (Nepal) by water and soil fertility management modeling. Agric Water Manag 122:53–62. doi:10.1016/j.agwat.2013.03.003

    Article  Google Scholar 

  • Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—The FAO crop model to simulate yield response to water: i. Concepts and underlying principles. Agron J 101:426–437. doi:10.2134/agronj2008.0139s

    Article  Google Scholar 

  • Stitt M, Krapp A (1999) The interaction between elevated carbon dioxide and nitrogen nutrition: the physiological and molecular background. Plant Cell Environ 22:583–621

    Article  CAS  Google Scholar 

  • Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L (2011) Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agric Water Manag 98:1615–1621. doi:10.1016/j.agwat.2011.05.011

    Article  Google Scholar 

  • Sunyer MA, Madsen H, Ang PH (2012) A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change. Atmos Res 103:119–128. doi:10.1016/j.atmosres.2011.06.011

    Article  Google Scholar 

  • Supit I, van Diepen CA, de Wit AJW, Kabat P, Baruth B, Ludwig F (2010) Recent changes in the climatic yield potential of various crops in Europe. Agric Syst 103:683–694. doi:10.1016/j.agsy.2010.08.009

    Article  Google Scholar 

  • Supit I, van Diepen CA, de Wit AJW, Wolf J, Kabat P, Baruth B, Ludwig F (2012) Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agric For Meteorol 164:96–111. doi:10.1016/j.agrformet.2012.05.005

    Article  Google Scholar 

  • Taub DR, Miller B, Allen H (2008) Effects of elevated CO2 on the protein concentration of food crops: a meta-analysis. Glob Change Biol 14:565–575. doi:10.1111/j.1365-2486.2007.01511.x

    Article  Google Scholar 

  • Taye M, Willems P (2013) Influence of downscaling methods in projecting climate change impact on hydrological extremes of upper Blue Nile basin. Hydrol Earth Syst Sci Discuss 10:7857–7896

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc Lond Math Phys Eng Sci 365:2053–2075. doi:10.1098/rsta.2007.2076

    Article  Google Scholar 

  • Themessl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544. doi:10.1002/joc.2168

    Article  Google Scholar 

  • Torriani DS, Calanca P, Schmid S, Beniston M, Fuhrer J (2007) Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. Clim Res 34:59–69. doi:10.3354/cr034059

    Article  Google Scholar 

  • Van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter

    Google Scholar 

  • Vanuytrecht E (2013) Crop responses to climate change: impact on agricultural production and the soil water balance in the Flemish Region of Belgium. Dissertation, KU Leuven

  • Vanuytrecht E, Raes D, Willems P (2011) Considering sink strength to model crop production under elevated atmospheric CO2. Agric For Meteorol 151:1753–1762. doi:10.1016/j.agrformet.2011.07.011

    Article  Google Scholar 

  • Vanuytrecht E, Raes D, Geerts S, Willems P (2012) Quantifying field-scale effects of elevated carbon dioxide concentration on crops. Clim Res 54:35–47. doi:10.3354/cr01096

    Article  Google Scholar 

  • Vanuytrecht E, Raes D, Steduto P, Hsiao TC, Fereres E, Heng LK, Garcia Vila M, Mejias Moreno P (2014a) AquaCrop: fAO’S crop water productivity and yield response model. Environ Model Softw 62:351–360. doi:10.1016/j.envsoft.2014.08.005

    Article  Google Scholar 

  • Vanuytrecht E, Raes D, Willems P, Semenov M (2014b) Comparing climate change impacts on cereals based on CMIP3 and EU-ENSEMBLES climate scenarios. Agric For Meteorol 195–196:12–23. doi:10.1016/j.agrformet.2014.04.017

  • Wang YQ, Leung LR, McGregor JL, Lee DK, Wang WC, Ding YH, Kimura F (2004) Regional climate modeling: progress, challenges, and prospects. J Meteorol Soc Jpn 82:1599–1628. doi:10.2151/jmsj.82.1599

    Article  Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548. doi:10.1177/030913339702100403

    Article  Google Scholar 

  • Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34:2995–3008. doi:10.1029/98wr02577

    Article  Google Scholar 

  • Wilks DS, Wilby RL (1999) The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23:329–357. doi:10.1191/030913399666525256

    Article  Google Scholar 

  • Willems P, Arnbjerg-Nielsen K, Olsson J, Nguyen VTV (2012a) Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings. Atmos Res 103:106–118. doi:10.1016/j.atmosres.2011.04.003

    Article  Google Scholar 

  • Willems P, Olsson J, Arnbjerg-Nielsen K, Beecham S, Pathirana A, Gregersen IB, Madsen H, Nguyen V-T-V (eds) (2012b) Impacts of climate change on rainfall extremes and urban drainage systems. IWA Publishing, London

    Google Scholar 

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Acknowledgments

The authors acknowledge the Research Foundation—Flanders (FWO) and KU Leuven for funding E.V. Anonymous reviewers are acknowledged for valuable comments on a previous version of this manuscript. Colleagues in the CSIRO Agriculture Flagship are acknowledged for their advice on writing style.

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  1. Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E-2411, 3001, Leuven, Belgium

    Eline Vanuytrecht & Dirk Raes

  2. Department of Civil Engineering, KU Leuven, Kasteelpark 40-2448, 3001, Leuven, Belgium

    Patrick Willems

  3. Visiting Scientist in CSIRO Agriculture Flagship, Brisbane, Australia

    Eline Vanuytrecht

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  1. Eline Vanuytrecht
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Correspondence to Eline Vanuytrecht.

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Vanuytrecht, E., Raes, D. & Willems, P. Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium. Reg Environ Change 16, 659–672 (2016). https://doi.org/10.1007/s10113-015-0773-6

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