Environmental Earth Sciences

, Volume 72, Issue 12, pp 4787–4799 | Cite as

Climate change in the Western Bug river basin and the impact on future hydro-climatic conditions

  • Dirk Pavlik
  • Dennis Söhl
  • Thomas Pluntke
  • Christian Bernhofer
Thematic Issue


Within the IWRM research project IWAS, we generated regional climate projections via dynamic downscaling for the catchment area of the Western Bug river in the border area of Poland, Belarus, and Ukraine. Data sources used are WMO meteorological station data and global climate ERA40 reanalysis data for an evaluation run, as well as global simulations from the ECHAM5 model for a twentieth-century control run and for the IPCC emission scenarios A2 and B1. We evaluated the performance of the regional climate model CCLM on the basis of simulated 2m temperature and precipitation. Furthermore, we analyzed the hydro-climatic conditions of the past and their projected future changes in the catchment based on 2m temperature, precipitation, potential evaporation and climatic water balance. The latter is discussed as an indicator for potential water availability in the region. Our evaluation, like many other studies, attests that the CCLM performs well for 2m temperature. Precipitation is not modelled adequately for the Western Bug basin. Remarkably, the precipitation bias is five times higher in the ECHAM5-driven control run than in the ERA40-driven evaluation run. Despite all the uncertainties, the significance of the modelled changes clearly suggests robust model results for the last three decades of this century. Up to the end of the century, both scenarios A2 and B1 lead to highly significant warming for each month in the long-term mean, with highest warming rates in winter. Instead, precipitation does not change significantly in the long-term yearly mean, but the intra-annual distribution of monthly precipitation sums shifts, with an increase in winter and a strong decrease in summer. Combined, this leads to a changed climatic water balance with a stronger deficit in summer and a higher gain in winter. Particular in the south-eastern part, the summer deficit cannot be compensated within the annual cycle. This might have serious implications for many socio-economic sectors.


Climate change Climatic water balance Hydro-climatic conditions Regional climate modelling Eastern Europe IWAS-project 



This work was supported by main funding from the Federal Ministry for Education and Research (BMBF) in the framework of the project “IWAS—International Water Research Alliance Saxony” (grant 02WM1028) and partially by the Helmholtz Association with HIGRADE. The authors would like to thank the Centre for Information Services and High Performance Computing in Dresden (ZIH) for providing the high-performance computer resources and for support, the German High Performance Computing Centre for Climate and Earth System Research (DKRZ) for providing the ERA40 and ECHAM5 data sets, the State Environment Agency Rheinland-Pfalz, Germany, for providing the software package InterMet and the CLM-Community for providing access to and support for the CCLM as well as for scientific discussions and valuable advice. Special thanks to all colleagues and partners of the project IWAS for successful cooperation and support.


  1. Bachner S, Kapala A, Simmer C (2008) Evaluation of daily precipitation characteristics in the CLM and their sensitivity to parameterizations. Meteorol Z 17:407–419CrossRefGoogle Scholar
  2. Blumensaat F, Pavlik D, Schubert K, Tönnies A, Bernhofer C, Krebs P (2012) Temporal rainfall disaggregation under minimum data requirements. In: Molnar P, Burlando P, Einfalt T (eds) 9th International Workshop on precipitation in urban areas. ETH Zürich, St. Moritz, pp 16–21Google Scholar
  3. 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 CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon WT, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. Climate Change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York, pp 847–940Google Scholar
  6. Conradt T, Kaltofen M, Hentschel M, Hattermann FF, Wechsung F (2007) Impacts of global change on water-related sectors and society in a trans-boundary central European river basin—part 2: from eco-hydrology to water demand management. Adv Geosci 11:93–99. doi: 10.5194/adgeo-11-93-2007 CrossRefGoogle Scholar
  7. Déqué M, Rowell DP, Lüthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellström E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change 81:53–70. doi: 10.1007/s10584-006-9228-x CrossRefGoogle Scholar
  8. Dierer S, Arpagaus M, Seifert A, Avgoustoglou E, Dumitrache R, Grazzini F, Mercogliano P, Milelli M, Starosta K (2009) Deficiencies in quantitative precipitation forecasts: sensitivity studies using the COSMO model. Meteorol Z 18:631–645CrossRefGoogle Scholar
  9. Dietrich O, Steidl J, Pavlik D (2012) The impact of global change on the water balance of large wetlands in the Elbe Lowland. Reg Environ Change 12:701–713. doi: 10.1007/s10113-012-0286-5 CrossRefGoogle Scholar
  10. DVWK (1996) Ermittlung der Verdunstung von Land- und Wasserflächen. Bonn (in German)Google Scholar
  11. Feldmann H, Früh B, Schädler G, Panitz HJ, Keuler K, Jacob D, Lorenz P (2008) Evaluation of the precipitation for South-western Germany from high resolution simulations with regional climate models. Meteorol Z 17:455–465CrossRefGoogle Scholar
  12. Fischer S, Pluntke T, Pavlik D, Bernhofer C (2014) Impact of climate change on various socio-economic sectors in a sub basin of Western Bug. Environ Earth Sci (this issue)Google Scholar
  13. 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 CrossRefGoogle Scholar
  14. Frei C, Christensen JH, Déqué M, Jacob D (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res. doi: 10.1029/2002JD002287 Google Scholar
  15. Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL (2006) Future change of precipitation extremes in Europe: intercomparison of scenarios from regional climate models. J Geophys Res. doi: 10.1029/2005JD005965 Google Scholar
  16. Giorgi F, Bi X, Pal J (2004) Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071–2100). Clim Dyn 23:839–858. doi: 10.1007/s00382-004-0467-0 CrossRefGoogle Scholar
  17. Graham LP, Andréasson J, 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. Clim Change 81:293–307. doi: 10.1007/s10584-006-9215-2 CrossRefGoogle Scholar
  18. Haslinger K, Anders I, Hofstätter M (2012) Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the Greater Alpine Region. Clim Dyn. doi: 10.1007/s00382-012-1425-7 Google Scholar
  19. Hattermann FF, Post J, Krysanova V, Conradt T, Wechsung F (2008) Assessment of water availability in a Central-European River Basin (Elbe) under climate change. Adv Clim Change Res 4:42–50Google Scholar
  20. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107. doi: 10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  21. Helm B, Tavares Wahren F, Pluntke T (2014) Integrated water quantity and quality modelling of river basins: strategy for a transdisciplinary approach under data scarce conditions. Environ Earth Sci (this issue)Google Scholar
  22. Jacob D, Bärring L, Christensen OB, de Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, van Ulden A, van den Hurk B (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Change 81:31–52. doi: 10.1007/s10584-006-9213-4 CrossRefGoogle Scholar
  23. Jaeger EB, Anders I, Lüthi D, Rockel B, Schär C, Seneviratne SI (2008) Analysis of ERA40-driven CLM simulations for Europe. Meteorol Z 17:349–367. doi: 10.1127/0941-2948/2008/0301 CrossRefGoogle Scholar
  24. Kalbus E, Kalbacher T, Kolditz O, Krüger E, Seegert J, Teutsch G, Borchardt D, Krebs P (2012) Integrated water resources management under different hydrological, climatic and socio-economic conditions. Environ Earth Sci 65:1363–1366. doi: 10.1007/s12665-011-1330-3 CrossRefGoogle Scholar
  25. Kjellström E, Bärring L, Jacob D, Richard J, Lenderink G, Schär C (2007) Modelling daily temperature extremes: recent climate and future changes over Europe. Clim Change 81:249–265. doi: 10.1007/s10584-006-9220-5 CrossRefGoogle Scholar
  26. Knote C, Heinemann G, Rockel B (2010) Changes in weather extremes: assessment of return values using high resolution climate simulations at convection-resolving scale. Meteorol Z 19:11–23. doi: 10.1127/0941-2948/2010/0424 CrossRefGoogle Scholar
  27. Koch H, Vögele S (2009) Dynamic modelling of water demand, water availability and adaptation strategies for power plants to global change. Ecol Econ 68:2031–2039. doi: 10.1016/j.ecolecon.2009.02.015 CrossRefGoogle Scholar
  28. Koch H, Kaltofen M, Schramm M, Grünewald U (2006) Adaptation strategies to global change for water resources management in the Spree river Catchment, Germany. Int J River Basin Manag 4:273–281. doi: 10.1080/15715124.2006.9635296 CrossRefGoogle Scholar
  29. Leidel M, Niemann S, Hagemann N (2012) Capacity development as a key factor for integrated water resources management (IWRM): improving water management in the Western Bug River Basin, Ukraine. Environ Earth Sci 65:1415–1426. doi: 10.1007/s12665-011-1223-5 CrossRefGoogle Scholar
  30. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res 115:D10101. doi: 10.1029/2009JD012882 CrossRefGoogle Scholar
  31. Lipinskyy VM, Dyachuk VA, Babichenko VM (2003) Climate of Ukraine. Rayevskyy Publishing, Kyiv, p 343 (in Ukraine)Google Scholar
  32. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:34. doi: 10.1029/2009RG000314 CrossRefGoogle Scholar
  33. Middelkoop H, Daamen K, Gellens D, Grabs W, Kwadijk JCJ, Lang H, Parmet BWAH, Schädler B, Schulla H, Wilke K (2001) Impact of climate change on hydrological regimes and water resources management in the Rhine Basin. Clim Change 49:105–128. doi: 10.1023/A:1010784727448 CrossRefGoogle Scholar
  34. Nakicenovic N, Swart R (2000) IPCC special report on emission scenarios. Cambridge University Press, CambridgeGoogle Scholar
  35. Olesen JE, Carter TR, Díaz-Ambrona CH, Fronzek S, Heidmann T, Hickler T, Holt T, Minguez MI, Morales P, Palutikof JP, Quemada M, Ruiz-Ramos M, Rubæk 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 CrossRefGoogle Scholar
  36. Pavlik D, Söhl D, Pluntke T, Mykhnovych A, Bernhofer C (2012) Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km. Environ Earth Sci 65:1475–1482. doi: 10.1007/s12665-011-1081-1 CrossRefGoogle Scholar
  37. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc Royal Soc Lond Ser A 193:120–145CrossRefGoogle Scholar
  38. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192CrossRefGoogle Scholar
  39. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Z 17:347–348CrossRefGoogle Scholar
  40. Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L (2003) The atmospheric general circulation model ECHAM5. MPI-report No. 349, p 127Google Scholar
  41. Roesch A, Jaeger EB, Lüthi D, Seneviratne SI (2008) Analysis of CCLM model biases in relation to intra-ensemble model variability. Meteorol Z 17:369–382CrossRefGoogle Scholar
  42. Rowell DP (2005) A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability. Clim Dyn 25:837–849. doi: 10.1007/s00382-005-0068-6 CrossRefGoogle Scholar
  43. Rummukainen M (2010) State-of-the-art with regional climate models. Wiley Interdiscip Rev: Clim Change 1:82–96. doi: 10.1002/wcc.8 Google Scholar
  44. Schanze J, Trümper J, Burmeister C, Pavlik D, Kruhlov I (2012) A methodology for dealing with regional change in integrated water resources management. Environ Earth Sci 65:1405–1414. doi: 10.1007/s12665-011-1311-6 CrossRefGoogle Scholar
  45. Steppeler J, Doms G, Schättler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96CrossRefGoogle Scholar
  46. Suklitsch M, Gobiet A, Leuprecht A, Frei C (2008) High resolution sensitivity studies with the regional climate model CCLM in the Alpine Region. Meteorol Z 17:467–476CrossRefGoogle Scholar
  47. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Vande Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof F, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hòlm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf JF, Morcrette JJ, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  48. Von Storch H, Zwiers F (2012) Testing ensembles of climate change scenarios for “statistical significance”. Clim Change 117:1–9. doi: 10.1007/s10584-012-0551-0 CrossRefGoogle Scholar
  49. Vörösmarty CJ, Green P, Salisbury J, Lammers RB (2000) Global water resources: vulnerability from climate change and population growth. Science 289:284–288. doi: 10.1126/science.289.5477.284 CrossRefGoogle Scholar
  50. Wendling U, Müller J, Schwede K (1991) Bereitstellung von täglichen Informationen zum Wasserhaushalt des Bodens für die Zwecke der agrarmeteorologischen Beratung. Meteorol Z 41:468–475 (in German)Google Scholar
  51. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, LondonGoogle Scholar
  52. Yokohata T, Annan JD, Collins M, Jackson CS, Tobis M, Webb MJ, Hargreaves JC (2011) Reliability of multi-model and structurally different single-model ensembles. Clim Dyn 39:599–616. doi: 10.1007/s00382-011-1203-1 CrossRefGoogle Scholar
  53. Zebisch M, Grothmann T, Schröter D, Hasse C, Fritsch U, Cramer W (2005) Climate change in Germany—vulnerability and adaptation of climate sensitive sectors. Umweltbundesamt, DessauGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Dirk Pavlik
    • 1
  • Dennis Söhl
    • 1
  • Thomas Pluntke
    • 1
  • Christian Bernhofer
    • 1
  1. 1.Institute of Hydrology and Meteorology, Chair of MeteorologyTechnische Universität DresdenDresdenGermany

Personalised recommendations