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Extreme snow hazard and ground snow load for China

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Abstract

The ground snow load is used as the reference snow load to estimate the design snow load on roofs. The ground snow load is recommended in Chinese load code for the design of building structures in the applicable jurisdiction; this load needs to be updated regularly by integrating new available snow measurements and new analysis techniques. This study is concentrated on the estimation of extreme snow depth and ground snow load and on snow hazard mapping in China by using historical snow measurement data. A probabilistic model of the snowpack bulk density was developed. For the extreme value analysis of annual maximum snow depth, both the at-site analysis and region of influence approach were applied. Also, several commonly used probabilistic models and distribution fitting methods were considered for the extreme value analysis. For the annual maximum snow depth, it was identified from the at-site analysis results that the number of sites where the lognormal distribution is preferred is greater than that where the Gumbel distribution is preferred. The 50-year return period value obtained from the ROI approach is insensitive to whether the three-parameter lognormal distribution or the generalized extreme value distribution is adopted. Maps of annual maximum snow depth and ground snow load were developed. Comparison of the estimated ground snow load to that recommended in the design code was presented, and potential updating to the ground snow load in the design code was suggested.

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References

  • Acreman MC, Wiltshire SE (1987) Identification of regions for regional flood frequency analysis. Eos Trans Am Geophys Union 68(44):1262

    Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  • ASCE (2010) Minimum design loads for buildings and other structures (ASCE/SEI 7-10). American Society of Civil Engineering, Reston

    Google Scholar 

  • Burn DH (1990) Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour Res 26(10):2257–2265

    Article  Google Scholar 

  • Che T, Xin L, Jin R, Armstrong R, Zhang T (2008) Snow depth derived from passive microwave remote-sensing data in China. Ann Glaciol 49(1):145–154

    Article  Google Scholar 

  • Chilès JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New York

    Book  Google Scholar 

  • CMA (2007) Specifications for surface meteorological observation. China Meteorological Press, Beijing (in Chinese)

    Google Scholar 

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London

    Book  Google Scholar 

  • Dai LY, Che T (2010) The spatio-temporal distribution of snow density and its influence factors from 1999 to 2008 in China. J Glaciol Geocryol 32(5):861–866 (in Chinese)

    Google Scholar 

  • Dai LY, Che T (2014) Spatiotemporal variability in snow cover from 1987 to 2011 in northern China. J Appl Remote Sens 8(1):084693

    Article  Google Scholar 

  • Dai LY, Che T, Wang J, Zhang P (2012) Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China. Remote Sens Environ 127:14–29

    Article  Google Scholar 

  • Ellingwood B, Galambos TV, MacGregor JG, Cornell CA (1980) Development of a probability based load criterion for American National Standard A58: building code requirements for minimum design loads in buildings and other structures (Vol. 577). US Department of Commerce, National Bureau of Standards

  • Ellingwood B, Redfield RK (1983) Ground snow loads for structural design. J Struct Eng ASCE 109(4):950–964

    Article  Google Scholar 

  • GB-50009 (2012) Load code for the design of building structures (GB 50009-2012). Ministry of Housing and Urban-Rural Development of the People’s Republic of China. China Architecture & Building Press, Beijing (in Chinese)

  • Hong HP, Ye W (2014) Analysis of extreme ground snow loads for Canada using snow depth records. Nat Hazards 73:355–371

    Article  Google Scholar 

  • Hong HP, Li SH, Mara T (2013) Performance of the generalized least-squares method for the extreme value distribution in estimating quantiles of wind speeds. J Wind Eng Ind Aerodyn 119:121–132

    Article  Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  • Jin XY, Zhao JD (2012) Development of the design code for building structures in China. Struct Eng Int 22(2):195–201

    Article  Google Scholar 

  • Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2003) ArcGIS 9, using ArcGIS geostatistical analyst. Environmental Systems Research Institute (ESRI), Redlands

    Google Scholar 

  • Lee KH, Rosowsky DV (2005) Site-specific snow load models and hazard curves for probabilistic design. Natural Hazards Review 6(3):109–120

    Article  Google Scholar 

  • Ma L-J, Qin D-H (2012) Spatial-temporal characteristics of observed key parameters for snow cover in China during 1957–2009. J Glaciol Geocryol 32(5):861–866 (in Chinese)

    Google Scholar 

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability. California, USA, vol 1, no 14, pp 281–297

  • Madsen HO, Krenk S, Lind NC (2006) Methods of structural safety. Courier Corporation

  • Martin ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Res Res 36:737–744

    Article  Google Scholar 

  • Mo HM, Fan F, Hong HP (2015a) Snow hazard estimation and mapping for a province in northeast China. Nat Hazards 77(2):543–558

    Article  Google Scholar 

  • Mo HM, Hong HP, Fan F (2015b) Estimating wind hazard for China using surface observations and reanalysis data. J. Wind Eng Aerodyn Ind 143:19–33

    Article  Google Scholar 

  • Mo HM, Fan F, Hong HP (2015c) Application of region of influence approach to estimate extreme snow load for a northeastern province in China, ICASP12 conference, Vancouver

  • NBCC (2010) National Building Code of Canada. Institute for Research in Construction, National Research Council of Canada, Ottawa

    Google Scholar 

  • Newark MJ, Welsh LE, Morris RJ, Dnes WV (1989) Revised ground snow loads for the 1990 National Building Code of Canada. Can J Civ Eng 16(3):267–278

    Article  Google Scholar 

  • O’Rourke MJ, Wrenn P (2007) Snow loads: a guide to the use and understanding of the snow load provisions of ASCE 7–05. ASCE, Reston

    Book  Google Scholar 

  • Sack RL (2015) Ground snow loads for the Western United States: state of the art. J Struct Eng ASCE, 04015082

  • Sturm M, Taras B, Liston GE, Derksen C, Jonas T, Lea J (2010) Estimating snow water equivalent using snow depth data and climate classes. J Hydrometeor 11(6):1380–1394

    Article  Google Scholar 

  • Takahashi T, Kawamura T, Kuramota K (2001) Estimation of ground snow load using snow layer model. J Struct Construct Eng AIJ 545:35–40

    Google Scholar 

  • Tobiasson W, Greatorex A (1997). Database and methodology for conducting site specific snow load case studies for the United States. In: Snow engineering: recent advances: proceedings of the third international conference, Sendai, Japan, 26–31 May 1996. CRC Press, New York, pp 249–256

  • Ye W, Hong HP, Wang JF (2015) Comparison of spatial interpolation techniques for extreme wind speeds over Canada. J Comput Civil Eng ASCE 29(6), November/December, 04014095

  • Ye W, Hong HP, Mo HM (2016) A comparison of ground snow load estimated using at-site analysis, regional frequency analysis, and region of influence approach, Report BLWT-2-2016, Boundary Layer Wind Tunnel Laboratory, the University of Western Ontario

  • Zhou XY, Zhang YQ, Gu M, Li JL (2013) Simulation method of sliding snow load on roofs and its application in some representative regions of China. Nat Hazards 67(2):295–320

    Article  Google Scholar 

  • Zhou XY, Li JL, Gu M, Sun LL (2015) A new simulation method on sliding snow load on sloped roofs. Nat Hazards 77(1):39–65

    Article  Google Scholar 

Download references

Acknowledgments

Financial support received from National Natural Science Foundation of China (No. 51478147 and No. 41271087), National Science and Engineering Research Council of Canada (RGPIN-2016-04814) and the University of Western Ontario is much acknowledged. We thank two reviewers for their constructive comments which helped us to improve the manuscript

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Correspondence to H. P. Hong.

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Mo, H.M., Dai, L.Y., Fan, F. et al. Extreme snow hazard and ground snow load for China. Nat Hazards 84, 2095–2120 (2016). https://doi.org/10.1007/s11069-016-2536-1

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  • DOI: https://doi.org/10.1007/s11069-016-2536-1

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