Confidence Interval Assessment to Estimate Dry and Wet Spells under Climate Change in Shahrekord Station, Iran
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 7
Abstract
Global warming and its resulting climate change will affect different elements, such as water resources, in the future. One effect is that rainfall becomes very difficult to predict, as it is under the influence of several different elements. In this study, which considers Shahrekord synoptic station in Iran, various sources of uncertainty in rainfall prediction in the future and its effect on dry and wet spells are investigated. In the present research, CCSIRO, CGCM, ECHO-G, HADCM3, ECHAM, and PCM Atmospheric and Ocean General Circulation Model (AOGCM) models and , , , and emission scenarios under three downscaling methods are examined. The results indicate a significant impact of the various downscaling methods on increasing the uncertainty band in rainfall estimation for the future. The AOGCM models in all of the scenarios except are in agreement. The results of wet and dry spells estimation display a long-duration drought at the beginning of the upcoming 30-year period, followed by a long-duration wet spell.
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References
Busuioc, A., Chen, D., and Helistrom, C. (2001). “Performance of statistical downscaling models in GCM validation and regional climate change estimates: Application for Swedish precipitation.” Int. J. Clim., 21(5), 557–578.
Crawford, T., Betts, N., and Mortlock, D. F. (2007). “GCM grid-box choice and predictor selection associated with statistical downscaling of daily precipitation over Northern Ireland.” Clim. Res., 34(2), 145–160.
Dibike, B. Y., and Coulibaly, P. (2005). “Hydrological impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models.” J. Hydrol., 307(1–4), 145–163.
DiCiccio, T. J., and Efron, B. (1996). “Bootstrap confidence intervals (with discussion).” Stat. Sci., 11(3), 189–228.
Efron, B., and Tibshirani, R. J. (1993). An introduction to the bootstrap (monographs on statistics and applied probability), Chapman & Hall/CRC, New York.
Farzaneh, M. R., Eslamian, S. S., Samadi, Z., and Akbarpour, A. (2012). “An appropriate general circulation model (GCM) to investigate climate change.” Int. J. Hydrol. Sci. Tech., 2(1), 34–47.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005). “Very high resolution interpolated climate surfaces for global land areas.” Int. J. Clim., 25(15), 1965–1978.
Hughes, J. P., and Guttorp, P. (1994). “A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena.” Water Resour. Res., 30(5), 1535–1546.
Hulme, M., and Brown, O. (1998). “Portraying climate scenario uncertainties in relation to tolerable regional climate change.” Clim. Res., 10(1), 1–14.
Hulme, M., Jiang, T., and Wigley, T. M. L. (1995). SCENGEN, a climate change scenario generator: User manual, Climatic Research Unit, Univ. of East Anglia, Norwich, UK.
Kang, B., and Ramirez, J. A. (2007). “Response of streamflow to weather variability under climate change in the Colorado Rockies.” J. Hydrol. Eng., 12(1), 63–72.
Karamouz, M., Hosseinpour, A., and Nazif, S. (2011). “Improvement of urban drainage system performance under climate change impact: Case study.” J. Hydrol. Eng., 16(5), 395–412.
New, M., Hulme, M., and Jones, P. D. (2000). “Representing twentieth century space-time climate variability. Part 2: Development of 1901–96 monthly grids of terrestrial surface climate.” J. Clim., 13(13), 2217–2238.
Nicks, A. D., Lane, L. J., and Gander, G. A. (1995). “Weather generator.” Water erosion prediction project: Hillslope profile and watershed model documentation, D. C. Flanagan and M. A. Nearing, eds., USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, IN.
Prudhomme, C., Jakob, D., and Svensson, C. (2003). “Uncertainty and climate change impact on the flood regime of small UK catchments.” J. Hydrol., 277(1–2), 1–23.
Racsko, P., Szeidl, L., and Semenov, M. (1991). “A serial approach to local stochastic weather models.” Ecol. Model., 57(1–2), 27–41.
Richardson, C. W. (1981). “Stochastic simulation of daily precipitation, temperature, and solar radiation.” Water Resour. Res., 17(1), 182–190.
Sajad-Khan, M., Coulibaly, P., and Dibike, Y. (2006). “Uncertainty analysis of statistical downscaling methods.” J. Hydrol., 319(1–4), 357–382.
Samadi, S., Ehteramian, K., and Sari Sarraf, B. (2011). “SDSM ability in simulate predictors for climate detecting over Khorasan province.” Procedia Soc. Behav. Sci., 19, 741–749.
Semenov, M. A. (2007). “Developing of high-resolution UKCUP02-based climate change scenarios in the UK.” Agric. Forest Meteorol., 144(1–2), 127–138.
Semenov, M. A., and Barrow, E. M. (1997). “Use of a stochastic weather generator in the development of climate change scenarios.” Clim. Change, 35(4), 397–414.
Semenov, M. A., Brooks, R. J., Barrow, E. M., and Richardson, C. W. (1998). “Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates.” Clim. Res., 10(2), 95–107.
Schubert, S. (1998). “Downscaling local extreme temperature changes in south-eastern Australia from the CSIRO Mark2 GCM.” Int. J. Climatol., 18(13), 1419–1438.
Schuols, J., and Abbaspour, K. C. (2007). “Using monthly weather statistics to generate daily data in a SWAT model application to West Africa.” Ecol. Model., 201(3–4), 301–311.
Wilby, R. L., Conway, D., and Jones, P. D. (2002a). “Prospects for downscaling seasonal precipitation variability using conditioned weather generator parameters.” Hydrol. Process., 16(6), 1215–1234.
Wilby, R. L., Dawson, C. W., and Barrow, E. M. (2002b). “SDSM—A decision support tool for the assessment of regional climate change impacts.” Environ. Model. Soft., 17(2), 147–159.
Wilby, R. L., Tomlinson, O. J., and Dawson, C. W. (2003). “Multi-site simulation of precipitation by conditional resampling.” J. Clim. Res., 23(3), 183–194.
Wilks, D. S. (1992). “Adapting stochastic weather generation algorithms for climate change studies.” Clim. Change, 22(1), 67–84.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 7 • July 2013
Pages: 911 - 918
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Aug 18, 2011
Accepted: Jul 6, 2012
Published online: Aug 7, 2012
Published in print: Jul 1, 2013
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