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Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling

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Abstract

Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010–2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial–temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann–Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima.

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References

  • Arora M, Goel NK, Singh P (2005) Evalution of temperature trends over India. J Hydrol Sci 50(1):81–93

    Article  Google Scholar 

  • Aziz OIA, Burn DH (2006) Trends and variability in the hydrological regime of the Mackenzie River Basin. J Hydrol 319(1–4):282–294

    Article  Google Scholar 

  • Bate BC, Kundzewicz ZW, Wu S, Palutikof J (eds) (2008) Climate change and water. Technical paper of the Intergovernmental panel on climate change. IPCC Secretariat, Geneva, pp 15–18

  • Burn DH, Elnur MAH (2002) Detection of hydrologic trends and variability. J Hydrol 255:107–122

    Article  Google Scholar 

  • Chen H, Guo SL, Xu C-Y, Singh VP (2007) Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin. J Hydrol 344:171–184

    Article  Google Scholar 

  • Chen H, Guo J, Xiong W, Guo S-L, Xu C-Y (2010) Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Adv Atmos Sci 27(2):274–284

    Article  Google Scholar 

  • Chu JT, Xia J, Xu C-Y, Singh VP (2010) Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River of China. Theor Appl Climatol 99:149–161

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307:145–163

    Article  Google Scholar 

  • Fowler HJ, Blenkinsopa S, Tebaldib C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578

    Article  Google Scholar 

  • Ghosh S, Mujumdar PP (2008) Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv Water Resour 31(1):132–146

    Article  Google Scholar 

  • Gregory JM, Wigley TML, Jones PD (1993) Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals. Clim Dyn 8:299–310

    Article  Google Scholar 

  • Harpham C, Wilby RL (2005) Multi-site downscaling of heavy daily precipitation occurrence and amounts. J Hydrol 312:235–255

    Article  Google Scholar 

  • Hessami M, Gachon P, Ouarda T, St-Hilaire A (2008) Automated regression-based Statistical Downscaling Tool. Environ Model Softw 23:813–834

    Article  Google Scholar 

  • Hoerl AE, Kennard RW (1970) Ridge regression: application to nonorthogonal problems. Technometrics 12:69–82

    Article  Google Scholar 

  • Huang J, Zhang J, Zhang ZX, Xu C-Y, Wang B, Yao J (2010) Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch Environ Res Risk Assess. doi:10.1007/s00477-010-0441-9

  • Huth R (2002) Statistical downscaling of daily temperature in Central Europe. J Clim 15:1731–1742

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: Synthesis Report Forth Assessment Report of the Intergovernmental panel on climate change, Geneva, Switzerland

  • Kendall MG (1975) Rank correlation methods. Griffin, London

    Google Scholar 

  • Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric test against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M Thiele-Eic I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys. doi:10.1029/2009RG000314

  • McCuen RH (2003) Modeling hydrologic change. CRC Press, Boca Raton, pp 261–263

    Google Scholar 

  • Srikanthan R, McMahon TA (2001) Stochastic generation of annual, monthly and daily climate data: a review. Hydrol Earth Syst Sci 5(4):653–670

    Article  Google Scholar 

  • Su BD, Jiang T, Shi YF, Becker S, Gemmer M (2004) Observed precipitation trends in the Yangtze catchment from 1951 to 2002. J Geogr Sci 2(14):204–218

    Google Scholar 

  • Su BD, Xiao B, Zhu DM, Jiang T (2005) Trends in frequency of precipitation extremes in the Yangtze River basin, China: 1960–2003. J Hydrol Sci 50(3):479–492

    Article  Google Scholar 

  • Su BD, Kundzewicz ZW, Jiang T (2009) Simulation of extreme precipitation over the Yangtze River Basin using Wakeby distribution. Theor Appl Climatol 96:209–219

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3–4):621–640

    Article  Google Scholar 

  • Wetterhall F, Halldin S, Xu C-Y (2005) Statistical precipitation downscaling in Central Sweden with the analogue method. J Hydrol 306:174–190

    Article  Google Scholar 

  • Wilby RL (1998) Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices. Clim Res 10:163–178

    Article  Google Scholar 

  • Wilby RL, Wigley TML (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20:641–661

    Article  Google Scholar 

  • Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225(1–2):67–91

    Article  Google Scholar 

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:147–159

    Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA). http://ipcc-ddc.cru.uea.ac.uk/guidelines/StatDown_Guide.pdf

  • Wilby RL, Beven KJ, Reynolds N (2008) Climate change and fluvial flood risk in the UK: more of the same? Hydrol Process 22(14):2511–2523

    Article  Google Scholar 

  • Wilks DS (1989) Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour Res 25:1429–1439

    Article  Google Scholar 

  • Xu C-Y (1999) From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Prog Phys Geogr 23:229–249

    Google Scholar 

  • Xu C-Y, Gong LB, Jiang T, Chen DL (2006) Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J Hydrol 327:81–93

    Article  Google Scholar 

  • Xu Y, Xu C, Gao X, Luo Y (2009) Projected changes in temperature and precipitation extremes over the Yangtze River Basin of China in the 21st century. Quat Int 208:44–52

    Article  Google Scholar 

  • Yang G, Weng L, Li L (2007) Yangtze Conservation and Development Report 2007. Science Press, Beijing

    Google Scholar 

  • Yue S, Pilon P, Cavadias G (2002) Power of the Mann–Kendall test and the Spearman’s rho test for detecting monotonic trends in hydrological time series. J Hydrol 259:254–271

    Article  Google Scholar 

  • Zetterqvist L (1991) Statistical estimation and interpretation of trends in water quality time series. Water Resour Res 27(7):1637–1648

    Article  CAS  Google Scholar 

  • Zhang Q, Jiang T, Gemmer M, Becker S (2005) Precipitation, temperature and discharge analysis from 1951 to 2002 in the Yangtze Catchment, China. J Hydrol Sci 50(1):65–80

    Article  Google Scholar 

  • Zhang Q, Liu C-L, Xu C-Y, Xu YP, Jiang T (2006) Observed trends of annual maximum water level and streamflow during past 130 years in the Yangtze River basin, China. J Hydrol 324:255–265

    Article  Google Scholar 

  • Zhang Q, Xu C-Y, Zhang ZX, Chen DY, Liu C-L, Lin H (2008) Spatial and temporal variability of precipitation maxima during 1960–2005 in the Yangtze River basin and possible association with large-scale circulation. J Hydrol 353:215–227

    Article  Google Scholar 

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Acknowledgments

The study is financially supported by the National Program on Key Basic Research Project (973 Program) (2010CB428405) and National Natural Science Fund of China (50809049). The authors are greatly appreciated the Canadian Climate Change Scenarios Network (CCCSN) for providing the downscaling tool (ASD) and the reanalysis products of the NCEP and HadCM3 outputs for the downscaling tool. The authors also thank the National Climate Centre of China for supplying the daily precipitation data of meteorological stations in the Yangtze River Basin.

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Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, People’s Republic of China

    Jing Guo, Hua Chen, Shenglian Guo & Jiali Guo

  2. Department of Geosciences, University of Oslo, P. O. Box 1047, Blindern, 0316, Oslo, Norway

    Chong-Yu Xu

  3. HydroChina Huadong Engineering Corporation, Hangzhou, 310014, People’s Republic of China

    Jing Guo

  4. Department of Earth Sciences, Uppsala University, Uppsala, Sweden

    Chong-Yu Xu

Authors
  1. Jing Guo
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  2. Hua Chen
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  3. Chong-Yu Xu
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  4. Shenglian Guo
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  5. Jiali Guo
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Correspondence to Hua Chen.

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Guo, J., Chen, H., Xu, CY. et al. Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling. Stoch Environ Res Risk Assess 26, 157–176 (2012). https://doi.org/10.1007/s00477-011-0464-x

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