Abstract
Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space–time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes.
Similar content being viewed by others
References
Bretherton CS, Smith C, Wallace JM (1992) An intercomparison of methods for finding coupled patterns in climate data. J Clim 5:541–560
Brissette FP, Khalili M, Leconte R (2007) Efficient stochastic generation of multi-site synthetic precipitation data. J Hydrol 345:121–133
Buerger G, Chen Y (2005) Regression-based downscaling of spatial variability for hydrologic applications. J Hydrol 311:299–317
Cannon AJ, Lord ER (2000) Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: An ensemble neural network approach. J Air Waste Manag Assoc 50:322–339
Cavadias G S (1985) A multivariate seasonal model for streamflow simulation. Paper presented at the symposium on the research of hydrology in Quebec, 23 May 1985, Montreal, Quebec, Canada
Cavazos T, Hewitson BC (2005) Performance of NCEP variables in statistical downscaling of daily precipitation. Clim Res 28:95–107
Chaleeraktrakoon C (1995) Stochastic modelling and simulation of streamflow processes. Ph.D. thesis, Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, Quebec, Canada
Chandler RE, Wheater HS (2002) Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland. Water Resour Res 38(10):1192–1202
Charles SP, Bates BC, Smith IN, Hughes JP (2004) Statistical downscaling of daily precipitation from observed and modelled atmospheric fields. Hydrol Process 18(8):1373–1394
Chu J, Kang H, Tam C, Park C, Chen C (2008) Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling. J Geophys Res Atmos. doi:10.1029/2007JD009424
DAI CGCM3 Predictors (2008) Sets of predictor variables derived from CGCM3 T47 and NCEP/NCAR reanalysis, version 1.1, Nov 2009, Montreal, QC, Canada
Dibike Y, Gachon P, St-Hilaire A, Ouarda TBMJ, Nguyen VTV (2008) Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada. Theor Appl Climatol 91(1–4):149–170
Feddersen H, Andersen U (2005) A method for statistical downscaling of seasonal ensemble predictions. Tellus Ser A Dyn Meteorol Oceanogr 57(3):398–408. doi:10.1111/j.1600-0870.2005.00102.x
Feuerverger A (1979) On some methods of analysis for weather experiments. Biometrika 66:655–658
Fistikoglu O, Okkan U (2011) Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River Basin in Turkey. J Hydrol Eng 16(2):157–164
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27:1547–1578
Frauke 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(9):1181–1192. doi:10.1175/2011BAMS3061.1
Gachon P, Dibike Y (2007) Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs. Int J Climatol 27:1623–1641
Gachon P, St-Hilaire A, Ouarda TBMJ, Nguyen VTV, Lin C, Milton J, Chaumont D, Goldstein J, Hessami M, Nguyen TD, Selva F, Nadeau M, Roy P, Parishkura D, Major N, Choux M, Bourque A (2005) A first evaluation of the strength and weaknesses of statistical downscaling methods for simulating extremes over various regions of Eastern Canada. Final (sub-component) report submitted to Canadian Climate Change Action Fund, Natural Resources Canada, Environment Canada, Montreal, Quebec
Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16(2–3):147–168. doi:10.1007/s003820050010
Green PE (1978) Analyzing multivariate data. The Dryden Press, Hinsdale
Greene AM, Robertson AW, Smyth P, Triglia S (2011) Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model. Q J R Meteorol Soc 137(655):347–359
Griffith DA (2003) Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization. Advances in spatial science. Springer, Berlin, p 247
Harpham C, Wilby RL (2005) Multi-site downscaling of heavy daily precipitation occurrence and amounts. J Hydrol 312:235–255
Harun S (1999) Forecasting and simulation of net inflows for reservoir operation and management. Ph.D. thesis, Faculty of Civil Engineering, Universiti Teknologi Malaysia
Hellevik O (2009) Linear versus logistic regression when the dependent variable is a dichotomy. Qual Quant 43(1):59–74. doi:10.1007/s11135-007-9077-3
Hessami M, Gachon P, Ouarda TBMJ, St-Hilaire A (2008) Automated regression-based statistical downscaling tool. Environ Model Softw 23:813–834
Hewitson BC, Crane RG (1992) Large-scale atmospheric controls on local precipitation in tropical Mexico. Geophys Res Lett 19:1835–1838
Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95
Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 31:91–101
Huth R (2004) Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J Clim 17:640–652
Jeong DI, St-Hilaire A, Ouarda TBMJ, Gachon P (2012) Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator. Clim Change 114:567–591. doi:10.1007/s10584-012-0451-3
Joreskog KG, Klovan JE, Rayment RA (1978) Geological factor analysis. Elsevier, Amsterdam
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471
Kao CYJ, Langley DL, Reisner JM, Smith WS (1998) Development of the first nonhydrostatic nested-grid grid-point global atmospheric modeling system on parallel machines. Tech. rept., LA-UR-98-2231, Los Alamos National Lab., NM
Khalili M, Leconte R, Brissette F (2006) Efficient watershed modeling using a multi-site weather generator for meteorological data. Geo-environment and landscape evolution II, transaction of Wessex Institute of Technology, UK, D., vol 89, pp 273–281
Khalili M, Leconte R, Brissette F (2007) Stochastic multi-site generation of daily precipitation data using spatial autocorrelation. J Hydrometeorol 8(3):396–412
Khalili M, Brissette F, Leconte R (2009) Stochastic multi-site generation of daily weather data. Stoch Environ Res Risk Assess 23(6):837–849
Khalili M, Brissette F, Leconte R (2011) Effectiveness of multi-site weather generator for hydrological modeling. J Am Water Resour Assoc 47(2):303–314. doi:10.1111/j.1752-1688.2010.00514.x
Khalili M, Nguyen VTV, Gachon P (2013) A statistical approach to multi-site multivariate downscaling of daily extreme temperature series. Int J Climatol 33(1):15–32. doi:10.1002/joc.3402
Kim M, Kang I, Park C, Kim K (2004) Superensemble prediction of regional precipitation over Korea. Int J Climatol 24(6):777–790
Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, Dool H, Jenne R, Fiorino M (2001) The NCEP/NCAR 50-year reanalysis. Bull Am Meteorol Soc 82(2):247–267
Kutner MH, Nachtsheim C, Neter J (2004) Applied linear regression models. McGraw-Hill/Irwin, New York, p 701
Laprise R (2008) Regional climate modelling. J Comput Phys 227(7):3641–3666
Liu Y, Ren HL (2015) A hybrid statistical downscaling model for prediction of winter precipitation in China. Int J Climatol 35(7):1309–1321. doi:10.1002/joc.4058
Machenhauer B, Windelband M, Botzet M, Christensen JH, Déqué M, Jones RG, Ruti PM, Visconti G (1998) Validation and analysis of regional present-day climate and climate change simulations over Europe. Tech. rept. 275, Max Planck-Institute für Meteorologie
Mahadevan A, Archer D (1998) Modeling a limited region of the ocean. J Comput Phys 145(2):555–574
McCuen RH (2003) Modeling hydrologic change. CRC Press, Boca Raton, pp 261–263
Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23
Nguyen VTV (2002) A state-of-the-art review of downscaling methods for evaluating climate change impacts on the hydrologic cycle. Water resources management and engineering series. Research Report. No. WRME03/1, McGill University, Montreal, Quebec, Canada
Nguyen VTV, Chaleeraktrakoon C (1995) Multisite multiseason streamflow simulation using singular value decomposition approach. In: Proceedings of second international conference on hydroscience and engineering, Beijing, China, 22–26 Mar 1995, pp 681–687
Nguyen VTV, Nguyen TD (2008) Statistical downscaling of daily precipitation process for climate-related impact studies. In: Singh VP (ed) Hydrology and hydraulics. Water Resources Publications, Littleton, pp 587–604
Odland J (1988) Spatial autocorrelation. Sage Publications, Newbury Park, p 87
Qian B, Corte-Real J, Xu H (2002) Multi-site stochastic weather models for impact studies. Int J Climatol 22:1377–1397
Richardson C (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190
Semenov MA, Barrow E (1997) Use of stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414
Skaugen TE, Hanssen-Bauer I, Forland EJ (2002) Adjustment of dynamically downscaled temperature and precipitation data in Norway. KLIMA 20/02. The Norwegian Meteorological Institute, PO Box 43 Blindern, 0313 Oslo, Norway (www.met.no)
Srikanthan R, McMahon TA (2001) Stochastic generation of annual, monthly and daily climate data: a review. Hydrol Earth Syst Sci 5:653–670
Wilby RL (1997) Non-stationarity in daily precipitation series: Implications for GCM down-scaling using atmospheric circulation indices. Int J Climatol 17:439–454
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
Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225:67–91
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
Wilby RL, Tomlinson OJ, Dawson CW (2003) Multisite simulation of precipitation by conditional resampling. Clim Res 23:183–194
Wilks DS (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydrol 210:178–191
Wilks DS (1999) Multisite downscaling of daily precipitation with a stochastic weather generator. Clim Res 11:125–136
Xu CY (1999) From GCMs to river flow: a review of downscaling methods and hydrologic modeling approaches. Prog Phys Geogr 23(2):229–249
Yang T, Kuo C, Liu C, Tseng H, Yu P (2013) Effects of domain selection on singular-value-decomposition based statistical downscaling of monthly rainfall accumulation in Southern Taiwan. Terr Atmos Ocean Sci 24(3):369–381. doi:10.3319/TAO.2013.01.09.01(A)
Yarnal B, Comrie AC, Frakes B, Brown DP (2001) Developments and prospects in synoptic climatology. Int J Climatol 21:1923–1950
Acknowledgements
The authors acknowledge the Data Access Integration (DAI, see http://loki.qc.ec.gc.ca/DAI/) Team for providing the data. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division (AIRD) of Environment Canada, and the Drought Research Initiative (DRI). Also, the authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (Special Research Opportunity Program) for the project entitled “Probabilistic assessment of regional changes in climate variability and extremes”, and the “Fond Québécois de Recherche sur la Nature et les Technologies”.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Khalili, M., Van Nguyen, V.T. An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change. Clim Dyn 49, 2261–2278 (2017). https://doi.org/10.1007/s00382-016-3443-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-016-3443-6