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Climate Dynamics

, Volume 47, Issue 1–2, pp 191–209 | Cite as

Assessment of climate change impacts on watershed in cold-arid region: an integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis method

  • X. W. Zhuang
  • Y. P. LiEmail author
  • G. H. Huang
  • J. Liu
Article

Abstract

An integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis (MGCM-SWG–SCA) method is developed, through incorporating multiple global climate models (MGCM), stochastic weather generator (SWG), and stepwise-clustered hydrological model (SCHM) within a general framework. MGCM-SWG–SCA can investigate uncertainties of projected climate changes as well as create watershed-scale climate projections from large-scale variables. It can also assess climate change impacts on hydrological processes and capture nonlinear relationship between input variables and outputs in watershed systems. MGCM-SWG–SCA is then applied to the Kaidu watershed with cold-arid characteristics in the Xinjiang Uyghur Autonomous Region of northwest China, for demonstrating its efficiency. Results reveal that the variability of streamflow is mainly affected by (1) temperature change during spring, (2) precipitation change during winter, and (3) both temperature and precipitation changes in summer and autumn. Results also disclose that: (1) the projected minimum and maximum temperatures and precipitation from MGCM change with seasons in different ways; (2) various climate change projections can reproduce the seasonal variability of watershed-scale climate series; (3) SCHM can simulate daily streamflow with a satisfactory degree, and a significant increasing trend of streamflow is indicated from future (2015–2035) to validation (2006–2011) periods; (4) the streamflow can vary under different climate change projections. The findings can be explained that, for the Kaidu watershed located in the cold-arid region, glacier melt is mainly related to temperature changes and precipitation changes can directly cause the variability of streamflow.

Keywords

Climate change Cold-arid Multi-GCM Stochastic weather generator Stepwise cluster analysis Watershed 

Notes

Acknowledgments

This research was supported by the Natural Sciences Foundation of China (51379075 and 51190095), the National Natural Science Foundation for Distinguished Young Scholar (51225904), the State Key Laboratory of Desert and Oasis Ecology at Xinjiang Institute of Ecology and Geography, and the Fundamental Research Funds for the Central Universities (2015XS96). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and helpful suggestions. The authors would also like to thank Dr. X.Q. Wang at the University of Regina providing the packagee of rSCA.

References

  1. Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow dominated regions. Nature 438:303–309CrossRefGoogle Scholar
  2. Chen YN (2014) Water resources research in the arid region of northwest China. Chinese Science Press, BeijingCrossRefGoogle Scholar
  3. Chen H, Guo JL, Zhang ZX, Xu CY (2013) Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future. Theor Appl Climatol 113:363–375CrossRefGoogle Scholar
  4. Chen YN, Yang Q, Luo Y, Shen YJ, Pan XL, Li LH, Li ZQ (2012) Ponder on the issue of water resources in the arid region of northwest China. Arid Land Geogr 35(1):1–9Google Scholar
  5. Chiew FHS, Whetton PH, McMahon TA, Pittock AB (1995) Simulation of the impacts of climate change on runoff and soil moisture in Australian catchments. J Hydrol 167(1):121–147CrossRefGoogle Scholar
  6. Ding YJ, Liu SY, Li J, Shangguan DL (2006) The retreat of glaciers in response to recent climate warming in western China. Ann Glaciol 43(1):97–105CrossRefGoogle Scholar
  7. Dou Y, Chen X, Bao AM, Li LH (2011) The simulation of snowmelt runoff in the ungauged Kaidu river basin of TianShan Mountains, China. Environ Earth Sci 62:1039–1045CrossRefGoogle Scholar
  8. Duan JP, Wang LL, Ren JW, Li L (2009) Progress in glacier variations in China and its sensitivity to climatic change during the past century. Prog Geogr 28(2):231–237Google Scholar
  9. Etemadi H, Samadi S, Sharifikia M (2014) Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Clim Dyn 42:2899–2920CrossRefGoogle Scholar
  10. Huang GH (1992) A stepwise cluster analysis method for predicting air quality in an urban environment. Atmos Environ 26(3):349–357CrossRefGoogle Scholar
  11. Huang GH, Huang YF, Wang GQ, Xiao HN (2006) Development of a forecasting system for supporting remediation design and process control based on NAPL biodegradation simulation and stepwise-cluster analysis. Water Resour Res 42:W06413Google Scholar
  12. Huntington TG (2006) Evidence for intensification of the global water cycle: review and synthesis. J Hydrol 319:83–95CrossRefGoogle Scholar
  13. Institute of Geography Research (IGR) (1980) Hydrological analysis and experiments, special issue of geography. Science Press, BeijingGoogle Scholar
  14. IPCC (2007) Freshwater resources and their management. Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  15. Kennedy WJ, Gentle JE (1981) Statistics: textbooks and monographs. Marcel Dekker, New YorkGoogle Scholar
  16. Labat D, Godderis Y, Probst JL, Guyot JL (2004) Evidence for global runoff increase related to climate warming. Adv Water Resour 27(6):631–642CrossRefGoogle Scholar
  17. Li LH, Simonovic SP (2002) System dynamics model for predicting floods from snowmelt in North American Prairie Watersheds. Hydrol Process 16(13):2645–2666CrossRefGoogle Scholar
  18. Lin KR, Zhai WL, Huang SX, Liu ZF (2015) An evaluation of the effect of future climate on runoff in the Dongjiang river basin, south China. Remote Sens GIS Hydrol Water Resour 368:257–262Google Scholar
  19. Ling HB, Xu HL, Fu JY (2013) Temporal and spatial variation in regional climate and its impact on runoff in Xinjiang, China. Water Resour Manag 27:381–399CrossRefGoogle Scholar
  20. Ma ZZ, Wang ZJ, Xia T, Gippel CJ, Speed R (2014) Hydrograph-based hydrologic alteration assessment and its application to the Yellow River. J Environ Inform 23(1):1–13CrossRefGoogle Scholar
  21. Mansuer S, Chu XZ (2007) Study on the change of climate and runoff volumes of the Tarim river basin in recent 40 years. Areal Res Dev 26(4):97–101Google Scholar
  22. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, Part I—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  23. Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region. Clim Dyn 43:2663–2680CrossRefGoogle Scholar
  24. Qin XS, Huang GH, Chakma A (2007) A stepwise-inference-based optimization system for supporting remediation of petroleum-contaminated sites. Water Air Soil Pollut 185:349–368CrossRefGoogle Scholar
  25. Rahmani MA, Zarghami M (2015) The use of statistical weather generator, hybrid data driven and system dynamics models for water resources management under climate change. J Environ Inform 25(1):23–35CrossRefGoogle Scholar
  26. Rao CR (1965) Linear statistical inference and its applications. Wiley, New YorkGoogle Scholar
  27. Regonda SK, Rajagopalan B, Clark M, Pitlick J (2005) Seasonal cycle shifts in hydroclimatology over the western United States. J Clim 18(2):372–384CrossRefGoogle Scholar
  28. Semenov MA (2002) LARS-WG: a stochastic weather generator for use in climate impact studies. http://www.rothamsted.ac.uk/masmodels/larswg.php User Manual: 1–27
  29. Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Chang 35:397–414CrossRefGoogle Scholar
  30. Shangguan DH, Liu SY, Ding YJ, Ding LF, Xu JL, Jing L (2009) Glacier changes during the last forty years in the Tarim interior River basin, northwest China. Prog Nat Sci 19:727–732CrossRefGoogle Scholar
  31. Souvignet M, Heinrich J (2011) Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation. Theor Appl Climatol 106:229–244CrossRefGoogle Scholar
  32. Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 versus CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744CrossRefGoogle Scholar
  33. Swain S, Hayhoe K (2015) CMIP5 projected changes in spring and summer drought and wet conditions over North America. Clim Dyn 44:2737–2750CrossRefGoogle Scholar
  34. Tanasienko AA, Chumbaev AS (2008) Features of snowmelt runoff waters in the Cis-Salair region in an extremely snow-rich hydrological year. Contemp Probl Ecol 1(6):687–696CrossRefGoogle Scholar
  35. Teutschbein C, Wetterhall F, Seibert J (2011) Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Clim Dyn 37:2087–2105CrossRefGoogle Scholar
  36. Wang R, Ernst G, Gao QZ (2003) The recent change of water level in the Bosten Lake and analysis of its causes. J Glaciol Geocryol 25:60–64Google Scholar
  37. Wilks SS (1962) Mathematical statistics. Wiley, New YorkGoogle Scholar
  38. Xu JH, Chen YN, Ji MH, Lu F (2008) Climate change and its effects on runoff of Kaidu river, Xinjiang, China: a multiple time-scale analysis. Chin Geogr Sci 18(4):331–339CrossRefGoogle Scholar
  39. Zhang H, Huang GH (2013) Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation. Clim Dyn 40:805–821CrossRefGoogle Scholar
  40. Zhang YC, Li BL, Bao AM, Zhou CH, Chen X, Zhang XR (2007) Study on snowmelt runoff simulation in the Kaidu river basin. Sci China Ser D: Earth Sci 50(1):26–35CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • X. W. Zhuang
    • 1
  • Y. P. Li
    • 1
    • 2
    Email author
  • G. H. Huang
    • 1
    • 2
  • J. Liu
    • 1
  1. 1.MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, Sino-Canada Resources and Environmental Research AcademyNorth China Electric Power UniversityBeijingChina
  2. 2.Environmental Systems Engineering Program, Faculty of Engineering and Applied ScienceUniversity of ReginaReginaCanada

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