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Statistical downscaling of meteorological time series and climatic projections in a watershed in Turkey

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Abstract

In this study, meteorological time series from five meteorological stations in and around a watershed in Turkey were used in the statistical downscaling of global climate model results to be used for future projections. Two general circulation models (GCMs), Canadian Climate Center (CGCM3.1(T63)) and Met Office Hadley Centre (2012) (HadCM3) models, were used with three Special Report Emission Scenarios, A1B, A2, and B2. The statistical downscaling model SDSM was used for the downscaling. The downscaled ensembles were put to validation with GCM predictors against observations using nonparametric statistical tests. The two most important meteorological variables, temperature and precipitation, passed validation statistics, and partial validation was achieved with other time series relevant in hydrological studies, namely, cloudiness, relative humidity, and wind velocity. Heat waves, number of dry days, length of dry and wet spells, and maximum precipitation were derived from the primary time series as annual series. The change in monthly predictor sets used in constructing the multiple regression equations for downscaling was examined over the watershed and over the months in a year. Projections between 1962 and 2100 showed that temperatures and dryness indicators show increasing trends while precipitation, relative humidity, and cloudiness tend to decrease. The spatial changes over the watershed and monthly temporal changes revealed that the western parts of the watershed where water is produced for subsequent downstream use will get drier than the rest and the precipitation distribution over the year will shift. Temperatures showed increasing trends over the whole watershed unparalleled with another period in history. The results emphasize the necessity of mitigation efforts to combat climate change on local and global scales and the introduction of adaptation strategies for the region under study which was shown to be vulnerable to climate change.

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References

  • Albek E, Albek M, Göncü S (2011) Investigation of the effects of climate change on the hydrology and water quality of the Lower Porsuk Stream Watershed using HSPF and determination of best water management strategies. In., vol Scientific and technological research project. The Scientific and Technological Research Council of Turkey, Turkey

  • Apak G, Ubay B (2007) First national communication of Turkey on climate change. The Ministry of Environment and Forestry, Ankara

    Google Scholar 

  • Arnell NW, Livermore MJL, Kovats S, Levy PE, Nicholls R, Parry ML, Gaffin SR (2004) Climate and socio-economic scenarios for global-scale climate change impacts assessments: characterising the SRES storylines. Glob Environ Chang 14:3–20. doi:10.1016/j.gloenvcha.2003.10.004

    Article  Google Scholar 

  • Benestad RE, Hanssen-Bauer I, Chen D (2008) Empirical-statistical downscaling. World Scientific Publishing Company Incorporated, Singapore

    Book  Google Scholar 

  • Brands S, Taboada JJ, Cofiño AS, Sauter T, Schneider C (2011) Statistical downscaling of daily temperatures in the NW Iberian Peninsula from global climate models: validation and future scenarios. Clim Res 48:163–176. doi:10.3354/cr00906

    Article  Google Scholar 

  • Canadian Centre for Climate Modelling and Analysis (2012) University of Victoria. http://www.cccma.ec.gc.ca/data/cgcm3/cgcm3.shtml. Accessed 2008

  • Conover WJ (1971) Practical nonparametric statistics. John Wiley & Sons

  • Conover WJ, Iman RL (1976) On some alternative procedures using ranks for the analysis of experimental designs. Commun Stat A:1348–1368

  • Demir İ, Kılıç G, Coşkun M PRECIS Bölgesel İklim Modeli ile Türkiye İçin İklim Öngörüleri: HadAMP3 SRES A2 Senaryosu. In: , IV. Atmosfer Bilimleri Sempozyumu, İstanbul, 25–28 Mart 2008 2008. İTÜ Uçak ve Uzay Bilimleri Fakültesi Meteoroloji Mühendisliği Bölümü, pp. 365–373

  • Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307:145–163. doi:10.1016/j.hydrol.2004.10.012

    Article  Google Scholar 

  • Easterling DR (1999) Development of regional climate scenarios using a downscaling approach. Clim Chang 41:615–634. doi:10.1023/A:1005425613593

    Article  Google Scholar 

  • ECMWF ERA-40 data (2012) ECMWF data server. http://www.ecmwf.int/research/era/do/get/era-40. Accessed 2008

  • Esterby SR (1996) Review of methods for the detection and estimation of trends with emphasis on water quality applications. Hydrol Process 10:127–149

    Article  Google Scholar 

  • Gungor O, Goncu S (2013) Application of the soil and water assessment tool model on the Lower Porsuk Stream Watershed. Hydrol Process 27:453–466. doi:10.1002/Hyp.9228

    Article  Google Scholar 

  • Hamlet AFS, Salathé EP, Carrasco P (2010) Statistical downscaling techniques for GCM simulations of temperature and precipitation with application to water resources planning studies. In: Chapter 4 in Final Project Report for the Columbia Basin Climate Change Scenarios Project

  • Helsel DR (1987) Advantages of nonparametric procedures for analysis of water-quality data. Hydrol Sci J 32:179–190. doi:10.1080/02626668709491176

    Article  Google Scholar 

  • Helsel DR, Hirsch RM (2002) Statistical methods in water resources techniques of water resources investigations. Book 4, chapter A3. U.S. Geological Survey

  • Hessami M, Gachon P, Ouarda TBMJ, St-Hilaire A (2008) Automated regression-based statistical downscaling tool. Environ Model Softw 23:813–834. doi:10.1016/j.envsoft.2007.10.004

    Article  Google Scholar 

  • Hu YR, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theor Appl Climatol 112:447–460. doi:10.1007/s00704-012-0745-4

    Article  Google Scholar 

  • Jones PG, Thornton PK (2013) Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agric Syst 114:1–5. doi:10.1016/j.agsy.2012.08.002

    Article  Google Scholar 

  • Kalnay E et al. (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. doi:10.1175/1520-0477(1996)077<0437:Tnyrp>2.0.Co;2

    Article  Google Scholar 

  • Khan MS, Coulibaly P, Dibike Y (2006a) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382. doi:10.1016/j.jhydrol.2005.06.035

    Article  Google Scholar 

  • Khan MS, Coulibaly P, Dibike Y (2006b) Uncertainty analysis of statistical downscaling methods using Canadian global climate model predictors. Hydrol Process 20:3085–3104. doi:10.1002/Hyp.6084

    Article  Google Scholar 

  • Kilsby CG et al. (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22:1705–1719. doi:10.1016/j.envsoft.2007.02.005

    Article  Google Scholar 

  • Kistler R et al. (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–267. doi:10.1175/1520-0477(2001)082<0247:Tnnyrm>2.3.Co;2

    Article  Google Scholar 

  • Liu ZF, Xu ZX, Charles SP, Fu GB, Liu L (2011) Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. Int J Climatol 31:2006–2020. doi:10.1002/Joc.2211

    Article  Google Scholar 

  • Met Office Hadley Centre (2012). Climate Impacts LINK Project, NCAS British Atmospheric Data Centre. http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_linkdata. Accessed 2008

  • Miller GE (1991) Use of the squared ranks test to test for the equality of the coefficients of variation. Commun Stat Simul Comput 20:743–750. doi:10.1080/03610919108812981

    Article  Google Scholar 

  • Nakicenovic N et al. (2001) Emissions scenarios. IPCC, Geneva, Switzerland, Cambridge University Press, UK

  • NCAR/NCEP Reanalysis (2012a) National Center for Atmospheric Research. http://ncar.ucar.edu. Accessed 2008

  • NCAR/NCEP Reanalysis (2012b) National Center for Environmental Prediction. http://www.nco.ncep.noaa.gov/. Accessed 2008

  • NRC (2010) Advancing the science of climate change. The National Academies Press

  • Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Process 16:1137–1150. doi:10.1002/Hyp.1054

    Article  Google Scholar 

  • Segui PQ, Ribes A, Martin E, Habets F, Boe J (2010) Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. J Hydrol 383:111–124. doi:10.1016/j.jhydrol.2009.09.050

    Article  Google Scholar 

  • Shapiro SS, Wilk MB (1965) An analysis of variance test for normality complete samples. Biometrika 52:591–611

    Article  Google Scholar 

  • Shapiro SS, Wilk MB, Chen HJ (1968) A comparative study of various tests for normality. J Am Stat Assoc 63:1343–1372

    Article  Google Scholar 

  • Solomon S, Change IPoC, I. IPoCCWG (2007) Climate change 2007—the physical science basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge University Press

  • 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 

  • Tanizaki H (1997) Power comparison of non-parametric tests: small-sample properties from Monte Carlo experiments. J Appl Stat 24:603–632. doi:10.1080/02664769723576

    Article  Google Scholar 

  • Timbal B, Fernandez E, Li Z (2009) Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environ Model Softw 24:341–358. doi:10.1016/j.envsoft.2008.07.007

    Article  Google Scholar 

  • TUMAS (2012) Meteorological database management service of Turkey. http://tumas.mgm.gov.tr/. Accessed 2008

  • Uppala SM et al. (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. doi:10.1256/Qj.04.176

    Article  Google Scholar 

  • Vernon C, Thompson E, Cornell S (2011) Carbon dioxide emission scenarios: limitations of the fossil fuel resource. Procedia Environ Sci 6:206–215. doi:10.1016/j.proenv.2011.05.022

    Article  Google Scholar 

  • von Storch H (1999) On the use of “Inflation” in statistical downscaling. J Clim 12:3505–3506

    Article  Google Scholar 

  • Wetterhall F, Bardossy A, Chen DL, Halldin S, Xu CY (2009) Statistical downscaling of daily precipitation over Sweden using GCM output. Theor Appl Climatol 96:95–103. doi:10.1007/s00704-008-0038-0

    Article  Google Scholar 

  • Wilby RL, Dawson CW (2007) Statistical downscaling model SDSM user manual, Version 4.2. In. Loughborough University

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

    Article  Google Scholar 

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:147–159

    Article  Google Scholar 

  • Wilby RLC, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. In: Supporting material of the IPCC. pp 1–27

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83. doi:10.2307/3001968

    Article  Google Scholar 

Download references

Acknowledgments

This study has been funded by TÜBİTAK (The Scientific and Technological Research Council of Turkey) under project no. 108Y091.

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  1. Environmental Engineering Department, Anadolu University, Iki Eylül Campus, Eskişehir, Turkey

    S. Göncü & E. Albek

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  1. S. Göncü
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  2. E. Albek
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Correspondence to E. Albek.

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Göncü, S., Albek, E. Statistical downscaling of meteorological time series and climatic projections in a watershed in Turkey. Theor Appl Climatol 126, 191–211 (2016). https://doi.org/10.1007/s00704-015-1563-2

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