Skip to main content

Advertisement

SpringerLink
Log in

Station-scale bias correction and uncertainty analysis for the estimation of irrigation water requirements in the Swiss Rhone catchment under climate change

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Irrigation water requirements (IWR) are expected to be influenced by changes in the climate variables driving water availability in the soil-plant system. Most of the agricultural surface areas of the heterogeneous Swiss Rhone catchment are already exposed to drought. Aiming at investigating future pressures on the water resources to fill the growing gap between rain-fed and optimum water supply for cultivation, we downscaled and bias corrected 16 regional climate scenarios from the ENSEMBLES dataset for the period 1951–2050 using a Quantile Mapping methodology calibrated with daily observations from 5 contrasting weather stations. The data reveal an increased evaporative demand over the growing season for almost all stations and scenarios (2021–2049 vs. 1981–2009). The picture is less clear for precipitation, with a projected decrease or increase depending on the scenario, station and month. The main results indicate that bias correction of climate scenarios not only reduces the remaining error between baseline and observations but also enhances the change signal in seasonal IWR estimates. This is due to a higher and more realistic sensitivity of IWR to the atmospheric water budget, the slope of this relationship being steeper in the observations than in the uncorrected data. The seasonal cycle of the IWR change signal shows different sensitivities and climate drivers across crops (grassland and maize) and stations, but a consistent trend towards an increase despite uncertainty. This increased water demand will have to be reconciled with possibly decreased or shifted future water availability from glacier and snow melt.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements; FAO Irrigation and Drainage Paper 56; Food and Agriculture Organization of the United Nations (FAO). Rome, Italy

    Google Scholar 

  • Beniston M, Stoffel M, Hill M (2011) Impacts of climatic change on water and natural hazards in the Alps: can current water governance cope with future challenges? Examples from the European “ACQWA” project. Environ Sci Policy 14:734–43

    Article  Google Scholar 

  • Boberg F, Berg P, Thejll P, Gutowski WJ, Christensen JH (2010) Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Clim Dyn 35(7–8):1509–1520. doi:10.1007/s00382-009-0683-8

    Article  Google Scholar 

  • Calanca P, Roesch A, Jasper K, Wild M (2006) Global warming and the summertime evapotranspiration regime of the Alpine region. Clim Chang 79:65–78. doi:10.1007/s10584-006-9103-9

    Article  Google Scholar 

  • Denis B, Côté J, Laprise R (2002) Spectral Decomposition of two-dimensional atmospheric fields on limited-area domains using the Discrete Cosine Transform (DCT). Mon Weather Rev 130:1812–1829. doi:10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2

    Article  Google Scholar 

  • Dobler A, Ahrens B (2008) Precipitation by a regional climate model and bias correction in Europe and South Asia. Meteorol Z 17:499–509. doi:10.1127/0941-2948/2008/0306

    Article  Google Scholar 

  • Deque M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change 57(1–2):16–26. doi:10.1016/j.gloplacha.2006.11.030

    Google Scholar 

  • Dosio A, Paruolo P, Rojas R (2012) Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal. J Geophys Res-Atmos 117, D17110. doi:10.1029/2012JD017968

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Finger D, Heinrich G, Gobiet A, Bauder A (2012) Projections of future water resources and their uncertainty in a glacierized catchment in the Swiss Alps and the subsequent effects on hydropower production during the 21st century. Water Resources Research 48. doi: 10.1029/2011WR010733

  • Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps. Journal of Geophysical Research 108, no. D3, ACL9–1–19

  • 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(12):1547–1578. doi:10.1002/joc.1556

    Article  Google Scholar 

  • Fuhrer J (2012) Bewässerungsbedarf und Wasserdargebot unter heutigen und künftigen Klimabedingungen. Zürich, Forschungsanstalt Agroscope Reckenholz-Tänikon ART, http://www.agroscope.admin.ch/publikationen/05362/index.html?lang = de, 48 pp (in German)

  • Fuhrer J, Smith PC and Gobiet A (2013) Implications of climate change scenarios for agriculture in alpine regions — A case study in the Swiss Rhone catchment. Science of The Total Environment, Available online 5 July 2013, ISSN 0048-9697, http://dx.doi.org/10.1016/j.scitotenv.2013.06.038 (http://www.sciencedirect.com/science/article/pii/S0048969713006906)

  • Gobiet A, Kotlarski S, Beniston M, Heinrich G, Rajczak J and Stoffel M (2013) 21st century climate change in the European Alps—A review, Science of The Total Environment, Available online 15 August 2013, ISSN 0048-9697, http://dx.doi.org/10.1016/j.scitotenv.2013.07.050 (http://www.sciencedirect.com/science/article/pii/S0048969713008188)

  • Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrol Earth Syst Sci 16(9):3383–3390. doi:10.5194/hess-16-3383-2012

    Article  Google Scholar 

  • Haerter JO, Hageman S, Moseley C, Piani C (2011) Climate model bias correction and the role of timescales. Hydrol Earth Syst Sci 15:1065–1079

    Article  Google Scholar 

  • Hagemann S, Machenhauer B, Jones R, Christensen OB, Déqué M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Clim Dyn 23:547–567. doi:10.1007/s00382-004-0444-7

    Article  Google Scholar 

  • Hallegatte S (2009) Strategies to adapt to an uncertain climate change. Glob Environ Chang 19:240–247

    Article  Google Scholar 

  • Heinrich G, Gobiet A (2012) The future of dry and wet spells in Europe: a comprehensive study based on the ENSEMBLES regional climate models. Int J Climatol 32:1951–1970. doi:10.1002/joc.2421

    Article  Google Scholar 

  • Heinrich G, Gobiet A, Mendlik T (2013) Extended regional climate model projections for Europe until the mid-21st century: combining ENSEMBLES and CMIP3. Clim Dyn. doi:10.1007/s00382-013-1840-7

    Google Scholar 

  • IPCC (2007) The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA

  • IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp

  • Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer L, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, and Yiou P (2013) EURO-CORDEX: new high-resolution climate change projections for European impact research, Regional Environmental Change, 1-16, doi:10.1007/s10113-013-0499-2

  • Maraun D (2013) Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue. J Clim 26(6):2137–2143. doi:10.1175/JCLI-D-12-00821.1

    Article  Google Scholar 

  • Meehl GA, Covey C, Taylor KE et al (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 

  • Nakicenovic N, Alcamo J, Davis G, et al. (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA

  • Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. doi:10.1007/s00704-009-0134-9

    Article  Google Scholar 

  • Prein AF, Gobiet A, Truhetz H (2011) Analysis of uncertainty in large scale climate change projections over Europe. Meteorol Z 20:383–395. doi:10.1127/0941-2948/2011/0286

    Article  Google Scholar 

  • Smith PC, Calanca P, Fuhrer J (2012) A simple scheme for modelling irrigation water requirements at the regional scale applied to an Alpine river catchment. Water 4:869–886

    Article  Google Scholar 

  • Steininger KW, Weck-Hannemann H (eds) (2002) Global Environmental Change in Alpine Regions. Recognition, Impact, Adaptation and Mitigation. Edward Elgar, Cheltenham

    Google Scholar 

  • Suklitsch M, Gobiet A, Leuprecht A, Frei C (2008) High resolution sensitivity studies with the regional climate model CCLM in the Alpine region. Meterol Z 17(4):467–476. doi:10.1127/0941-2948/2008/0308

    Article  Google Scholar 

  • Suklitsch M, Gobiet A, Truhetz H, Awan NK, Göttel H, Jacob D (2011) Error characteristics of high resolution regional climate models over the Alpine area. Clim Dyn 37(1):377–390. doi:10.1007/s00382-010-0848-5

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An Overview of Cmip5 and the Experiment Design. Bull Am Meteorol Soc 93(4):485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  • Themessl MJ, Gobiet A, Heinrich G (2011a) Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim Chang 112:449–468. doi:10.1007/s10584-011-0224-4

    Article  Google Scholar 

  • Themessl MJ, Gobiet A, Leuprecht A (2011b) 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 

  • van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview, Clim. Change 109(1–2):5–31. doi:10.1007/s10584-011-0148-z

    Google Scholar 

  • Viviroli D, Weingartner R (2008) Water towers - A global view on the hydrological importance of mountains. In: Wiegandt E, editor. Mountains: Sources of Water, Sources of Knowledge. Adv Global Chang Res 31:15–20, Dordrecht; Springer-Verlag

    Google Scholar 

  • Wilcke R, Mendlik T, and Gobiet A (2013) Performance and Consistency of Multi-Variable Downscaling and Error-Correction of Regional Climate Models, Clim. Change, doi:10.1007/s10584-013-0845-x

  • Yang W, Andréasson J, Graham LP, Olsson J, Rosberg J, Wetterhall F (2010) Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impact studies. Hydrol Res 41(3/4):211–229

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the EU-FP7 Project ACQWA (Assessing Climate impacts on the Quantity and quality of WAter, agreement number 212,250). We thank the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) and the EU-FP6 Project ENSEMBLES project (contract number 505,539) for granting access to their databases. Thomas Mendlik (Wegener Center, University of Graz) is gratefully acknowledged for helping with scenario data processing.

Author information

Authors and Affiliations

  1. Agroscope, Air pollution/Climate Group, Reckenholzstrasse 191, 8046, Zürich, Switzerland

    Pascalle C. Smith & Jürg Fuhrer

  2. Institute for Environmental Sciences, University of Geneva, 7 route de Drize, 1227, Carouge, Switzerland

    Pascalle C. Smith & Markus Stoffel

  3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010, Graz, Austria

    Pascalle C. Smith, Georg Heinrich, Martin Suklitsch & Andreas Gobiet

  4. Central Institute for Meteorology and Geodynamics, Hohe Warte 38, 1190, Vienna, Austria

    Martin Suklitsch

Authors
  1. Pascalle C. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Georg Heinrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Suklitsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Gobiet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Markus Stoffel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jürg Fuhrer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pascalle C. Smith.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smith, P.C., Heinrich, G., Suklitsch, M. et al. Station-scale bias correction and uncertainty analysis for the estimation of irrigation water requirements in the Swiss Rhone catchment under climate change. Climatic Change 127, 521–534 (2014). https://doi.org/10.1007/s10584-014-1263-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-014-1263-4

Keywords

Search

Navigation