Statistical downscaling of precipitation on a spatially dependent network using a regional climate model

  • R. J. Erhardt
  • L. E. Band
  • R. L. Smith
  • B. J. Lopes
Original Paper


We present a detailed downscaling simulation methodology for generating precipitation events, conditioned on external climate covariates, on a network of meteorological stations. These events can be input to hydrological models. To simulate an event on a future day t, the method uses the K-nearest neighbor algorithm to identify close neighbors from the historical record, and resamples a past event with resampling probabilities determined from external covariates. This preserves the spatial dependence of precipitation on the network, and other important distributional features of precipitation. Large numbers of arbitrary tuning parameters and model assumptions are reduced through the use of a multivariate Gaussian model relating climate covariates to historical precipitation. The approach is demonstrated by simulating daily precipitation, maximum temperature, and minimum temperature on a network of 93 locations in North Carolina, all conditioned on climate model output. The downscaling is based on a regional climate model (RCM) embedded within a global NCEP reanalysis model. The method is demonstrated using precipitation in North Carolina with the Canadian climate RCM as an NCEP driven RCM.


Climate model Downscaling Hydrology K nearest neighbor 



The authors wish to thank the UNC Institute for the Environment for their generous support.


  1. Apipattanavis S, Podest G, Rajagopalan B, Katz RW (2007) A semiparametric multivariate and multisite weather generator. Water Resour Res 43(11):1Google Scholar
  2. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonCrossRefGoogle Scholar
  3. DeChant CM, Moradkhani H (2011) Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation. Hydrol Earth Syst Sci 15(11):3399–3410CrossRefGoogle Scholar
  4. Fowler H, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impact studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(1547):1578Google Scholar
  5. Gangopadhyay S, Clark M, Rajagopalan B (2005) Statistical downscaling using K-nearest neighbors. Water Resour Res 41(2):W02024Google Scholar
  6. Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess 25(4):475–484CrossRefGoogle Scholar
  7. Kannan S, Ghosh S (2011) Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output. Stoch Environ Res Risk Assess 25(4):457–474CrossRefGoogle Scholar
  8. Laio F, Tamea S (2007) Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol Earth Syst Sci 11(4):1267–1277CrossRefGoogle Scholar
  9. Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour Res 2(3):679–693CrossRefGoogle Scholar
  10. Liu W, Fu G, Liu C, Song X, Ouyang R (2013) Projection of future rainfall for the North China Plain using two statistical downscaling models and its hydrological implications. Stoch Environ Res Risk Assess 27(8):1783–1797CrossRefGoogle Scholar
  11. Mahalanobis P (1936) On the generalised distance in statistics. Proc Natl Inst Sci India 2(1):4955Google Scholar
  12. Mehrotra R, Sharma A (2006) Conditional resampling of hydrologic time series using multiple predictor variables: a K-nearest neighbour approach. Adv Water Resour 29:987–999CrossRefGoogle Scholar
  13. Maraun D, Wetterhall F, Ireson A, Chandler R, Kendon E, Widmann M, Brienen S, Rust H, Sauter T, Themesl M, Venema V, Chun K, Goodess C, Jones R, Onof C, Vracv 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: RG3003.Google Scholar
  14. Mearns LO et al (2007) (updated 2012) The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2013–02-17. doi: 10.5065/D6RN35ST
  15. Ouyang F,L H, Zhu Y, Zhang J, Yu Z, Chen X, and Li M (2014) Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology. Stoch Environ Res Risk Assess 28(4):991–1010Google Scholar
  16. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
  17. Sharif M, Burn D (2005) Simulating climate change scenarios using an improved K-nearest neighbor model. J Hydrol 325:179196Google Scholar
  18. Sousa Filho F, Lall U (2003) Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: applications of a multivariate, semiparametric algorithm. Water Resour Res 39(11):1307Google Scholar
  19. Stehlk J, Brdossy A (2002) Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation. J Hydrol 256(1):120–141CrossRefGoogle Scholar
  20. Thyer M, Renard B, Kavetski D, Kuczera G, Franks SW, Srikanthan S (2009) Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: a case study using Bayesian total error analysis. Water Resour Res 45(12):1Google Scholar
  21. Wilby RL, Tomlinson OJ, Dawson CW (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23(3):183–194CrossRefGoogle Scholar
  22. Wilks DS (1999) Multisite downscaling of daily precipitation with a stochastic weather generator. Clim Res 11(2):125–136CrossRefGoogle Scholar
  23. Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Access Online via ElsevierGoogle Scholar
  24. Yates D, Gangopadhyay S, Rajagopalan B, Strzepek K (2003) A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour Res 39(7):1199Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • R. J. Erhardt
    • 1
  • L. E. Band
    • 2
  • R. L. Smith
    • 3
    • 4
  • B. J. Lopes
    • 4
  1. 1.Department of MathematicsWake Forest UniversityWinston-SalemUSA
  2. 2.UNC Institute for the Environment and Department of GeographyUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Statistics and Applied Mathematical Sciences InstituteResearch Triangle ParkDurhamUSA
  4. 4.Department of Statistics and Operations ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

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