Theoretical and Applied Climatology

, Volume 132, Issue 1–2, pp 287–300 | Cite as

Application of physical scaling towards downscaling climate model precipitation data

Original Paper

Abstract

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.

Notes

Acknowledgements

The work presented in this paper is supported by the Natural Sciences and Engineering Research Council grant to the second author.

References

  1. Bergant K, Kajfez-Bogataj L (2005) N-PLS regression as empirical downscaling tool in climate change studies. Theor Appl Climatol 81:11–23CrossRefGoogle Scholar
  2. Coulibaly P (2004) Downscaling daily extreme temperatures with genetic programming. Geophys Res Lett 31:L16203CrossRefGoogle Scholar
  3. Coulibaly P, Dibike YB, Anctil F (2005) Downscaling precipitation and temperature with temporal neural networks. J Hydrometeorol 6:483–496CrossRefGoogle Scholar
  4. Encyclopedia of Saskatchewan (2016) Climate. University of Regina, Accessed March 7 2016. [Available online at http://esask.uregina.ca/entry/climate.html]
  5. Fealy R, Sweeney J (2007) Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalized linear modeling approach. Int J Climatol 27:2083–2094CrossRefGoogle Scholar
  6. Friederichs P, Hense A (2007) Statistical downscaling of extreme precipitation events using censored quantile regression. Mon Weather Rev 135:2365–2378CrossRefGoogle Scholar
  7. Gagnon S, Singh B, Rousselle J, Roy L (2005) An application of the Statistical DownScaling Model (SDSM) to simulate climatic data for streamflow modelling in Québec. Canadian Water Resources Journal 30(4):297–314Google Scholar
  8. Gaur A, Simonovic SP (2016a) A scaling method for physically representative downscaling of climate model data. Under review in Climate DynamicsGoogle Scholar
  9. Gaur A, Simonovic SP (2016b) Extension of SP method and its application towards downscaling climate model based near surface air temperature. Under review in Journal of Applied Meteorology and ClimatologyGoogle Scholar
  10. Ghosh S, Mujumdar PP (2008) Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv Water Resour 31:132–146CrossRefGoogle Scholar
  11. Gutmann E, Pruitt T, Clark MP, Brekke L, Arnold JR, Raff DA, Rasmussen RM (2014) An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour Res 50:7167–7186CrossRefGoogle Scholar
  12. Hayhoe K et al (2008) Regional climate change projections for the Northeast USA. Mitig Adapt Strateg Glob Chang 13(5):425–436CrossRefGoogle Scholar
  13. Hertig E, Jacobeit J (2008) Downscaling future climate change: temperature scenarios for the Mediterranean area. Glob Planet Chang 63:127–131CrossRefGoogle Scholar
  14. Hurtt GC et al (2011) Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim Chang 109(1–2):117–161CrossRefGoogle Scholar
  15. Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org)
  16. Land Processes Distributed Active Archive Center (LP DAAC) (2001) Land Cover Type Yearly L3 Global 500 m SIN Grid. Version 051. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov), accessed March 14, 2016, at doi: 10.5067/ASTER/AST_L1B.003.
  17. Maraun D et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003CrossRefGoogle Scholar
  18. Maurer EP, Hidalgo GP, Das T, Dettinger MD, Cayan DR (2010) The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol Earth Syst Sci 14:1125–1138CrossRefGoogle Scholar
  19. Mesinger F, DiMego G, Kalnay E et al (2006) North American regional reanalysis. Bull Amer Meteor Soc 87:343–360CrossRefGoogle Scholar
  20. Salathé EP (2003) Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin. Int J Climatol 23:887–901CrossRefGoogle Scholar
  21. Schmidli J, Frei C, Vidale PL (2006) Downscaling from GCM predictors: a benchmark for dynamical and statistical downscaling methods. Int J Climatol 26:679–689CrossRefGoogle Scholar
  22. Schoof JT (2013) Statistical downscaling in climatology. Geogr Compass 7:249–265CrossRefGoogle Scholar
  23. Svoboda J, Chladova Z, Pop L, Hosek J (2012) Statistical-dynamical downscaling of wind roses over the Czech Republic. Theor Appl Climatol 112(3):713–722Google Scholar
  24. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experimental design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  25. Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640CrossRefGoogle Scholar
  26. Verburg PH, de Nijs TCM, van Eck JR, Visser H, de Jong K (2004) A method to analyse neighbourhood characteristics of land use patterns. Comput Environ Urban Syst 28:667–690CrossRefGoogle Scholar
  27. Widmann M, Bretherton CS, Salathé EP (2003) Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor. J Clim 16:799–816CrossRefGoogle Scholar
  28. Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17(2):145–157CrossRefGoogle Scholar
  29. Wood SN (2000) Modelling and smoothing parameter estimation with multiple quadratic penalties. JR Statist Soc B 62(2):413–428CrossRefGoogle Scholar
  30. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62:189–216CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Facility for Intelligent Decision Support, Department of Civil and Environmental EngineeringWestern UniversityLondonCanada

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