Skip to main content

Advertisement

SpringerLink
Log in

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Anandhi A, Srinivas VV, Nanjundiah RS, Nagesh Kumar D (2008) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 28:401–420

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Nagesh Kumar D, Nanjundiah RS (2009) Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 29:583–603

    Article  Google Scholar 

  • Bates BC, Kundzewicz ZW, Wu S, Palutikof J (2008) Climate change and water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva

  • Beniston M (2003) Climate change in mountain regions: a review of possible impacts. Climatic Change 59:5–31

    Article  Google Scholar 

  • Bellone E, Hughes JP, Guttorp P (2000) A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Clim Res 15:1–12

    Article  Google Scholar 

  • Chandler RE (2002) GLIMCLIM: generalised linear modelling for daily climate series (Software and User Guide). Department of Statistical Science, University College London

  • Chandler RE, Wheater HS (2002) Analysis of rainfall variability using generalised linear models—a case study from the west of Ireland. Water Resour Res 38:W1192

    Article  Google Scholar 

  • Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor method: impacts on low flows in the River Thames, United Kingdom. Climatic Change 69:245–268

    Article  Google Scholar 

  • Fealy R, Sweeney J (2007) Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalized linear modelling approach. Int J Climatol 27:2083–2094

    Article  Google Scholar 

  • Frost AJ, Charles SP, Timbal B, Chiew FHS, Mehrotra R, Nguyen KC, Chandler RE, McGregor J, Fu G, Kirono DGC, Fernandez E, Kent D (2011) A comparison of multi-site daily rainfall downscaling techniques under Australian conditions. J Hydrol 408:1–18

    Article  Google Scholar 

  • Forney GD Jr (1978) The Viterbi algorithm. Proc IEEE 61:268–278

    Article  Google Scholar 

  • Ghosh S (2010) SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. J Geophys Res 115:D22102. doi:10.1029/2009JD013548

    Article  Google Scholar 

  • Hu Y, Maskey S, Uhlenbrook S (2012) Trends in temperature and precipitation extremes in the Yellow River source region, China. Clim Change 110:403–429. doi:10.1007/s10584-011-0056-2

    Google Scholar 

  • Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrological phenomena. Water Resour Res 30(5):1535–1546

    Article  Google Scholar 

  • Hughes JP, Guttorp P, Charles SP (1999) A non-homogeneous hidden Markov model for precipitation occurrence. Appl Stat 48(1):15–30

    Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP-NCAR 40-year reanalysis project. Bull Am Meteor Soc 77:437–471

    Article  Google Scholar 

  • Kioutsioukis I, Melas D, Zanis P (2008) Statistical downscaling of daily precipitation over Greece. Int J Climatol 28:679–691

    Article  Google Scholar 

  • Kirshner S (2005a) Modeling of multivariate time series using hidden Markov models, PhD thesis, University of California, Irvine

  • Kirshner S (2005b) The MVNHMM Toolbox. University of California, Irvine, (http://www.stat.purdue.edu/~skirshne/MVNHMM/) Accessed June 2011

  • Lan Y, Zhao G, Zhang Y, Wen J, Liu J, Hu X (2010) Response of runoff in the source region of the Yellow River to climate warming. Quaternary Int 226:60–65. doi:10.1016/j.quaint.2010.03.006

    Article  Google Scholar 

  • Liu X, Chen B (2000) Climatic warming in the Tibetan plateau during recent decades. Int J Climatol 20:1729–1742

    Article  Google Scholar 

  • Liu Z, Xu Z, Charles SP, Fu G, Liu L (2011) Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. Int J Climatol. doi:10.1002/joc.2211

  • Liu W, Fu G, Liu C, Charles SP (2012) A comparison of three multi-site statistical downscaling models for daily rainfall in the North Chain Plain. Theor Appl Climatol. doi:10.1007/S00704-012-0692-0

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, ThemeßI, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent development to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. doi:10.1029/2009RG000314

    Article  Google Scholar 

  • Mehrotra R, Sharma A, Cordery I (2004) Comparison of two approaches for downscaling synoptic atmospheric patterns to multisite precipitation occurrence. J Geophys Res 109:D14107. doi:10.1029/2004JD004823

    Article  Google Scholar 

  • Meehl GA, Arblaster JM, Tebaldi C (2005) Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys Res Lett 32:L18719. doi:10.1029/2005GL023680

    Article  Google Scholar 

  • Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Process 16:1137–1150

    Article  Google Scholar 

  • Schmidli J, Frei C, Vidale PL (2006) Downscaling from GCM precipitation: a benchmark for dynamic and statistical downscaling methods. Int J Climatol 26:679–689

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol. doi:10.1016/j.jhydrol.2006.04.030

  • Tryhorn L, DeGaetano A (2010) A comparison of techniques for downscaling extreme precipitation over the Northeastern United States. Int J Climatol. doi:10.1002/joc.2208

  • von Storch H (1999) On the use of “Inflation” in statistical downscaling. J Climate 12:3505–3506

    Article  Google Scholar 

  • Wetterhall F, Bárdossy A, Chen D, Halldin S, Xu C (2006) Daily precipitation downscaling techniques in three Chinese regions. Water Resour Res 42:W11423. doi:10.1029/2005WR004573

    Article  Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Progr Phys Geogr 21:530–548

    Article  Google Scholar 

  • Wilby RL, Wigley TML (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20(6):641–661

    Article  Google Scholar 

  • Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34:2995–3008

    Article  Google Scholar 

  • Wilby RL, Dawson CW, Barrow EW (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Modell Softw 17(2):145–157

    Article  Google Scholar 

  • Wilby RL, Tomlinson OJ, Dawson CW (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23:183–194

    Article  Google Scholar 

  • Xu C (1999) Climate change and hydrological models: a review of existing gaps and recent research developments. Water Resour Manag 13(5):369–382

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Xu Z, Zhao F, Li J (2009b) Response of streamflow to climate change in the headwater catchment of the Yellow River basin. Quaternary Int 208:62–75

    Article  Google Scholar 

  • Xu C, Luo Y, Xu Y (2011) Projected changes of precipitation extremes in river basins over China. Quaternary Int 244:149–158

    Article  Google Scholar 

  • Yang C, Chandler RE, Isham VS, Wheater HS (2005) Spatial-temporal rainfall simulation using generalised linear models. Water Resour Res 41:W11415

    Google Scholar 

  • Yang T, Hao X, Shao Q, Xu C, Zhao C, Chen X, Wang W (2012) Multi-model ensemble projections in temperature and precipitation extremes of the Tibetan Plateau in the 21st century. Global Planet Change 80–81:1–13

    Article  Google Scholar 

Download references

Acknowledgments

This study was jointly supported by UNESCO-IHE Institute for Water Education, Rijkswaterstaat (the Ministry of Transport, Public Works and Water Management), Netherlands, and Yellow River Conservancy Commission, China. Special thanks to Dr. Richard Chandler and Dr. Sergey Kirshner for advice on GLIMCLIM and NHMM, respectively.

Author information

Authors and Affiliations

  1. Yellow River Conservancy Commission, Zhengzhou, 450003, People’s Republic of China

    Yurong Hu

  2. UNESCO-IHE Institute for Water Education, PO Box 3015, 2601 DA, Delft, The Netherlands

    Yurong Hu, Shreedhar Maskey & Stefan Uhlenbrook

  3. Department of Water Resources, Delft University of Technology, PO Box 5048, 2600 GA, Delft, The Netherlands

    Stefan Uhlenbrook

Authors
  1. Yurong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shreedhar Maskey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Uhlenbrook
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yurong Hu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hu, Y., Maskey, S. & Uhlenbrook, S. Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theor Appl Climatol 112, 447–460 (2013). https://doi.org/10.1007/s00704-012-0745-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-012-0745-4

Keywords

Search

Navigation