Statistical downscaling of precipitation using quantile regression

Journal of Hydrology

Volumn 487, Issue , 2013, Pages 122-135

  Tareghian, Reza ^a   Rasmussen, Peter F ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Precipitation; Quantile regression; Statistical downscaling; Variable selection

Indexed keywords

CONDITIONAL DISTRIBUTION; LINEAR REGRESSION MODELS; PREDICTOR VARIABLES; QUANTILE REGRESSION; STATISTICAL DOWNSCALING; SUMMER PRECIPITATION; VARIABLE SELECTION; WINTER PRECIPITATION;

BAYESIAN NETWORKS; CLIMATE CHANGE; LASERS; NORMAL DISTRIBUTION; PRECIPITATION (CHEMICAL);

REGRESSION ANALYSIS;

CLIMATE CHANGE; DOWNSCALING; PRECIPITATION (CLIMATOLOGY); REGRESSION; SUMMER; WEATHER STATION; WINTER;

CANADA;

EID: 84875663443     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.02.029     Document Type: Article

References (53)

1. Alhamzawi R., Yu K. Variable selection in quantile regression via Gibbs sampling. J. Appl. Stat. 2012, 39(4):799-813.
2. Auld M.C., Powell L.M. Economics of food energy density and adolescent body weight. Economica 2009, 76(304):719-740.
3. Barbieri M.M., Berger J.O. Optimal predictive model selection. Ann. Stat. 2004, 32(3):870-897.
4. Barbosa S.M. Quantile trends in Baltic Sea level. Geophys. Res. Lett. 2008, 35(22):L22704.
5. Baur D., Saisana M., Schulze N. Modeling the effects of meteorological variables on ozone concentration - a quantile regression approach. Atmos. Environ. 2004, 38(28):4689-4699.
6. Breiman L. Heuristics of instability and stabilization in model selection. Ann. Stat. 1996, 24(6):2350-2383.
7. Bremnes J.B. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Mon. Weather Rev. 2004, 132(1):338-347.
8. Buishand T.A., Shabalova M.V., Brandsma T. On the choice of the temporal aggregation level for statistical downscaling of precipitation. J. Clim. 2004, 17(9):1816-1827.
9. Cade B.S., Noon B.R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 2003, 1(8):412-420.
10. Cade B.S., Terrell J.W., Porath M.T. Estimating fish body condition with quantile regression. North Am. J. Fish. Manag. 2008, 28(2):349-359.
11. Cannon A.J. Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Comput. Geosci. 2011, 37(9):1277-1284.
12. Chen S.-T., Tseng H.-W., Lin C.-Y., Yang T.-C., Yu P.-S. Hydrological drought in Tseng-Wen reservoir basin under climate change scenarios. J. Taiwan Agric. Eng. 2011, 57(3):44-60.
13. Dunham J.B. Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles. Trans. Am. Fish. Soc. 2002, 131(1):86-98.
14. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. Ann. Stat. 2004, 32(2):407-499.
15. Elsner J.B., Kossin J.P., Jagger T.H. The increasing intensity of the strongest tropical cyclones. Nature 2008, 455(7209):92-95.
16. Fan J., Li R. Variable selection via non-concave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 2001, 96(456):1348-1360.
17. Fernández C., Ley E., Steel M.F.J. Model uncertainty in cross-country growth regressions. J. Appl. Economet. 2001, 16(5):563-576.
18. Fowler H.J., Blenkinsop S., Tebaldi C. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol. 2007, 27(12):1547-1578.
19. Friederichs P. Statistical downscaling of extreme precipitation events using extreme value theory. Extremes 2010, 13(2):109-132.
20. Friederichs P., Hense A. Statistical downscaling of extreme precipitation events using censored quantile regression. Mon. Weather Rev. 2007, 135(6):2365-2378.
21. George E.I., McCulloch R.E. Variable selection via Gibbs sampling. J. Am. Stat. Assoc. 1993, 88:881-889.
22. Goodess C.M. Statistical and regional dynamical downscaling of extremes for European regions: STARDEX. Eggs 2003, 6:25-29.
23. Hao L., Naiman D.Q. Quantile Regression 2007, SAGE, Thousand Oaks, California.
24. Harnack R.P., Apffel K., Cermak J.R. Heavy precipitation events in New Jersey: attendant upper-air conditions. Weather Forecast. 1999, 14(6):933-954.
25. Harrell F.E. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis 2001, Springer, New York.
26. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2009, Springer, New York.
27. Hellström C. Atmospheric conditions during extreme and non-extreme precipitation events in Sweden. Int. J. Climatol. 2005, 25(5):631-648.
28. Hessami M., Gachon P., Ouarda T.B.M.J., St-Hilaire A. Automated regression-based statistical downscaling tool. Environ. Model. Softw. 2008, 23(6):813-834.
29. Jeong D.I., St-Hilaire A., Ouarda T.B.M.J., Gachon P. Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada. Stoch. Environ. Res. Risk Assess. 2012, 26(5):633-653.
30. Koenker R. Quantile Regression 2005, Cambridge University Press, New York.
31. Koenker R., Bassett G. Regression quantiles. Econometrica 1978, 46(1):33-50.
32. Koenker R., Hallock K.F. Quantile regression. J. Econ. Perspect. 2001, 15(4):143-156.
33. Koenker R., Schorfheide F. Quantile spline models for global temperature change. Clim. Change 1994, 28(4):395-404.
34. Li Y., Smith I. A statistical downscaling model for Southern Australia winter rainfall. J. Clim. 2009, 22(5):1142-1158.
35. Li Q., Xiy R., Lin N. Bayesian regularized quantile regression. Bayesian Anal. 2010, 5(3):533-556.
36. Liu Z., Xu Z., Charles S.P., Fu G., Liu L. Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. Int. J. Climatol. 2011, 31(13):2006-2020.
37. Luce C.H., Holden Z.A. Declining annual streamflow distributions in the Pacific Northwest United States, 1948-2006. Geophys. Res. Lett. 2009, 36(16):L16401.
38. Madigan, D., Raftery, A.E., Volinsky, C., Hoeting, J., 1996. Bayesian model averaging. In: Proceedings of the AAAI Workshop on Integrating Multiple Learned Models, Portland, OR, pp. 77-83.
39. Mazvimavi D. Investigating changes over time of annual rainfall in Zimbabwe. Hydrol. Earth Syst. Sci. 2010, 14(12):2671-2679.
40. Palatella L., Miglietta M.M., Paradisi P., Lionello P. Climate change assessment for Mediterranean agricultural areas by statistical downscaling. Nat. Hazard. Earth Syst. Sci. 2010, 10(7):1647-1661.
41. Reed, C., Dunson, D., Yu, K., 2009. Bayesian Variable Selection in Quantile Regression. Technical Report. Department of Mathematical Sciences, Brunel University.
42. Tareghian R., Rasmussen P. Analysis of Arctic and Antarctic sea ice extent using quantile regression. Int. J. Climatol. 2012, 10.1002/joc.3491.
43. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 1996, 58(1):267-288.
44. Tolika K., Anagnostopoulou C., Maheras P., Vafiadis M. Simulation of future changes in extreme rainfall and temperature conditions over the Greek area: a comparison of two statistical downscaling approaches. Global Planet. Change 2008, 63(2-3):132-151.
45. Vickers, G., Buzza, S., Schmidt, D., Mullock, J., 2000. The Weather of the Canadian Prairies. NAV CANADA Report.
46. Villarini G., Smith J.A., Napolitano F., Baeck M.L. Hydrometeorological analysis of the December 2008 flood in Rome. Hydrol. Sci. J. 2011, 56(7):1150-1165.
47. Weerts A.H., Winsemius H.C., Verkade J.S. Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrol. Earth Syst. Sci. 2011, 15(1):255-265.
48. Wetterhall F., Halldin S., Xu C.-Y. Seasonality properties of four statistical-downscaling methods in central Sweden. Theoret. Appl. Climatol. 2007, 87(1-4):123-137.
49. Wilby R.L. Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices. Clim. Res. 1998, 10(3):163-178.
50. Wilby R.L., Wigley T.M.L. Downscaling general circulation model output: a review of methods and limitations. Prog. Phys. Geogr. 1997, 21(4):530-548.
51. Wilby R.L., Hay L.E., Leavesley G.H. A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. J. Hydrol. 1999, 225(1-2):67-91.
52. Wilby R.L., Dawson C.W., Barrow E.M. SDSM - a decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw. 2002, 17(2):147-159.
53. Yu K., Moyeed R.A. Bayesian quantile regression. Stat. Probab. Lett. 2001, 54(4):437-447.