New Approach to Multisite Downscaling of Precipitation by Identifying Different Set of Atmospheric Predictor Variables
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 5
Abstract
Estimating reliable projections of precipitation considering climate change scenarios is important for hydrological studies. General circulation models provide future climate simulations at large scale in terms of large-scale atmospheric variables (LSAVs). Those LSAVs can be downscaled to finer special resolution using several downscaling approaches. This paper presents a support vector regression (SVR)-based downscaling approach to downscale rainfall at several locations in a study area. Because the rainfall generation mechanisms cannot be the same for all the sites in a study area, conventional multisite downscaling approaches that assume the same rainfall generation mechanism should not be used. Therefore, a new downscaling approach is proposed that (1) divides the study area in several climatological regions, and (2) develops different downscaling models for each of the climatological regions to obtain future projections of rainfall. The new approach was implemented on rainfall data obtained for Republic of Ireland to demonstrate the effectiveness of the approach compared with existing approaches. Future projections of rainfall were obtained for the period 2012–2050 corresponding to four Representative Concentration Pathway climate change scenarios. The performance of the SVR approach was compared with that of relevance vector machine– and deep learning–based downscaling approaches.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors gratefully acknowledge the support of the Risk Analysis of Infrastructure Networks (RAIN) Project, Grant No. 608166, which is funded by European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration Activities.
References
Ahmed, K. F., G. Wang, J. Silander, A. M. Wilson, J. M. Allen, R. Horton, and R. Anyah. 2013. “Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the US northeast.” Global Planet. Change 100 (Jan): 320–332. https://doi.org/10.1016/j.gloplacha.2012.11.003.
Basu, B., and V. V. Srinivas. 2016. “Regional flood frequency analysis using entropy-based clustering approach.” J. Hydrol. Eng. 21 (8): 04016020. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001351.
Bharath, R., V. V. Srinivas, and B. Basu. 2016. “Delineation of homogeneous temperature regions: A two-stage clustering approach.” Int. J. Climatol. 36 (1): 165–187. https://doi.org/10.1002/joc.4335.
Brown, C., A. Greene, P. Block, and A. Giannini. 2008. Review of downscaling methodologies for Africa climate applications.. New York: Columbia Univ.
Chandler, R. E., and H. S. Wheater. 2002. “Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland.” Water Resour. Res. 38 (10): 10-1. https://doi.org/10.1029/2001WR000906.
Davies, D. L., and D. W. Bouldin. 1979. “A cluster separation measure.” IEEE Trans. Pattern Anal. Mach. Intell. 1 (2): 224–227.
Deo, R. C., P. Samui, and D. Kim. 2016. “Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models.” Stochastic Environ. Res. Risk Assess. 30 (6): 1769–1784. https://doi.org/10.1007/s00477-015-1153-y.
Dibike, Y. B., P. Gachon, A. St-Hilaire, T. B. M. J. Ouarda, and V. T. V. Nguyen. 2008. “Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada.” Theor. Appl. Climatol. 91 (1–4): 149–170. https://doi.org/10.1007/s00704-007-0299-z.
Diez, E., C. Primo, J. A. Garcia-Moya, J. M. Gutiérrez, and B. Orfila. 2005. “Statistical and dynamical downscaling of precipitation over Spain from DEMETER seasonal forecasts.” Tellus A: Dyn. Meteorol. Oceanogr. 57 (3): 409–423. https://doi.org/10.1111/j.1600-0870.2005.00130.x.
D’Onofrio, D., E. Palazzi, J. von Hardenberg, A. Provenzale, and S. Calmanti. 2014. “Stochastic rainfall downscaling of climate models.” J. Hydrometeorol. 15 (2): 830–843. https://doi.org/10.1175/JHM-D-13-096.1.
Doty, B., and J. L. Kinter, III. 1993. “The grid analysis and display system (GrADS): A desktop tool for earth science visualization.” In Proc., American Geophysical Union 1993 Fall Meeting, 6–10. Washington, DC: American Geophysical Union.
Eden, J. M., and M. Widmann. 2014. “Downscaling of GCM-simulated precipitation using model output statistics.” J. Clim. 27 (1): 312–324. https://doi.org/10.1175/JCLI-D-13-00063.1.
Éireann, M. 2009. “Climate of Ireland.” Met Eireann. Accessed September 1, 2009. https://www.met.ie/climate/available-data/historical-data.
Fowler, H. J., S. Blenkinsopa, and C. Tebaldi. 2007. “Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modeling.” Int. J. Climatol. 27 (12): 1547–1578. https://doi.org/10.1002/joc.1556.
Fowler, H. J., C. G. Kilsby, P. E. O’Connell, and A. Burton. 2005. “A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change.” J. Hydrol. 308 (1–4): 50–66. https://doi.org/10.1016/j.jhydrol.2004.10.021.
Fowler, H. J., and R. L. Wilby. 2010. “Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk.” Water Resour. Res. 46 (3): W03525. https://doi.org/10.1029/2008WR007636.
Gestel, T. V., J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. D. Moor, and J. Vandewalle. 2004. “Benchmarking least squares support vector machine classifiers.” Mach. Learn. 54 (1): 5–32. https://doi.org/10.1023/B:MACH.0000008082.80494.e0.
Ghosh, S., and P. P. Mujumdar. 2008. “Statistical downscaling of GCM simulations to streamflow using relevance vector machine.” Adv. Water Resour. 31 (1): 132–146. https://doi.org/10.1016/j.advwatres.2007.07.005.
Haylock, M. R., G. C. Cawley, C. Harpham, R. L. Wilby, and C. M. Goodess. 2006. “Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios.” Int. J. Climatol. 26 (10): 1397–1415. https://doi.org/10.1002/joc.1318.
Hellström, C., D. Chen, C. Achberger, and J. Räisänen. 2001. “Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation.” Clim. Res. 19 (1): 45–55. https://doi.org/10.3354/cr019045.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Joshi, D., A. St-Hilaire, T. B. M. J. Ouarda, and A. Daigle. 2015. “Statistical downscaling of precipitation and temperature using sparse Bayesian learning, multiple linear regression and genetic programming frameworks.” Can. Water Resour. J. 40 (4): 392–408. https://doi.org/10.1080/07011784.2015.1089191.
Landman, W. A., M. J. Kgatuke, M. Mbedzi, A. Beraki, A. Bartman, and A. D. Piesanie. 2009. “Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective.” Int. J. Climatol.: J. R. Meteorol. Soc. 29 (11): 1535–1549. https://doi.org/10.1002/joc.1766.
Li, H., J. Sheffield, and E. F. Wood. 2010. “Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching.” J. Geophys. Res. 115 (D10): 1–20. https://doi.org/10.1029/2009JD012882.
Likas, A., N. Vlassis, and J. J. Verbeek. 2003. “The global -means clustering algorithm.” Pattern Recognit. 36 (2): 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2.
Meinshausen, M., et al. 2011. “The RCP greenhouse gas concentrations and their extensions from 1765 to 2300.” Clim. Change 109 (1–2): 213–241. https://doi.org/10.1007/s10584-011-0156-z.
Murphy, J. 1999. “An evaluation of statistical and dynamical techniques for downscaling local climate.” J. Clim. 12 (8): 2256–2284. https://doi.org/10.1175/1520-0442(1999)012%3C2256:AEOSAD%3E2.0.CO;2.
Nguyen, V. T. V., T. D. Nguyen, and P. Gachon. 2006. “On the linkage of large-scale climate variability with local characteristics of daily precipitation and temperature extremes: An evaluation of statistical downscaling methods.” In Advances in geosciences: Volume 4: Hydrological science (HS), 1–9. Singapore: World Scientific.
Nolan, P., J. O’Sullivan, and R. McGrath. 2017. “Impacts of climate change on mid-twenty-first-century rainfall in Ireland: A high-resolution regional climate model ensemble approach.” Int. J. Climatol. 37 (12): 4347–4363. https://doi.org/10.1002/joc.5091.
Okkan, U., and O. Fistikoglu. 2014. “Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M.” Theor. Appl. Climatol. 117 (1–2): 343–361. https://doi.org/10.1007/s00704-013-1005-y.
Okkan, U., and G. Inan. 2015. “Bayesian learning and relevance vector machines approach for downscaling of monthly precipitation.” J. Hydrol. Eng. 20 (4): 04014051. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001024.
Olsson, J., K. Berggren, M. Olofsson, and M. Viklander. 2009. “Applying climate model precipitation scenarios for urban hydrological assessment: A case study in Kalmar City, Sweden.” Atmos. Res. 92 (3): 364–375. https://doi.org/10.1016/j.atmosres.2009.01.015.
Olsson, J., C. B. Uvo, K. Jinno, A. Kawamura, K. Nishiyama, N. Koreeda, T. Nakashima, and O. Morita. 2004. “Neural networks for rainfall forecasting by atmospheric downscaling.” J. Hydrol. Eng. 9 (1): 1–12. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:1(1).
Prudhomme, C., N. Reynard, and S. Crooks. 2002. “Downscaling of global climate models for flood frequency analysis: Where are we now?” Hydrol. Process. 16 (6): 1137–1150. https://doi.org/10.1002/hyp.1054.
Rashid, M. M., S. Beecham, and R. K. Chowdhury. 2016. “Statistical downscaling of rainfall: A non-stationary and multi-resolution approach.” Theor. Appl. Climatol. 124 (3–4): 919–933. https://doi.org/10.1007/s00704-015-1465-3.
Sachindra, D. A., A. W. M. Ng, S. Muthukumaran, and B. J. C. Perera. 2016. “Impact of climate change on urban heat island effect and extreme temperatures: A case-study.” Q. J. R. Meteorolog. Soc. 142 (694): 172–186. https://doi.org/10.1002/qj.2642.
Srinivas, V. V., B. Basu, N. Nagesh Kumar, and S. K. Jain. 2014. “Multi-site downscaling of maximum and minimum temperature using support vector machine.” Int. J. Climatol. 34 (5): 1538–1560. https://doi.org/10.1002/joc.3782.
Taylor, K. E. 2001. “Summarizing multiple aspects of model performance in a single diagram.” J. Geophys. Res.: Atmos. 106 (D7): 7183–7192. https://doi.org/10.1029/2000JD900719.
Tipping, M. E. 2001. “Sparse Bayesian learning and the relevance vector machine.” J. Mach. Learn. Res. 1 (Jun): 211–244.
Vapnik, V. 1995. The nature of statistical learning theory. New York: Springer.
Vrac, M., P. Marbaix, D. Peillard, and P. Naveau. 2007a. “Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe.” Clim. Past 3 (4): 669–682. https://doi.org/10.5194/cp-3-669-2007.
Vrac, M., and P. Naveau. 2007. “Stochastic downscaling of precipitation: From dry events to heavy rainfalls.” Water Resour. Res. 43: W07402. https://doi.org/10.1029/2006WR005308.
Vrac, M., M. Stein, and K. Hayhoe. 2007b. “Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing.” Clim. Res. 34 (3): 169–184. https://doi.org/10.3354/cr00696.
Wetterhall, F., A. BÃrdossy, D. Chen, S. Halldin, and C.-Y. Xu. 2006. “Daily precipitation-downscaling techniques in three Chinese regions.” Water Resour. Res. 42 (11): W11423. https://doi.org/10.1029/2005WR004573.
Wilby, R. L., L. E. Hay, W. J. Gutowski, Jr., R. W. Arritt, E. S. Takle, Z. Pan, G. H. Leavesle, and M. P. Clark. 2000. “Hydrological responses to dynamically and statistically downscaled climate model output.” Geophys. Res. Lett. 27 (8): 1199–1202. https://doi.org/10.1029/1999GL006078.
Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks. 1998. “Statistical downscaling of general circulation model output: A comparison of methods.” Water Resour. Res. 34 (11): 2995–3008. https://doi.org/10.1029/98WR02577.
Wilks, D. S., and R. L. Wilby. 1999. “The weather generation game: A review of stochastic weather models.” Prog. Phys. Geogr. 23 (3): 329–357. https://doi.org/10.1177/030913339902300302.
Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier. 2004. “Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.” Clim. Change 62 (1–3): 189–216. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Dec 8, 2018
Accepted: Oct 18, 2019
Published online: Feb 27, 2020
Published in print: May 1, 2020
Discussion open until: Jul 27, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.