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.
Similar content being viewed by others
References
Acreman MC, Wiltshire SE (1987) Identification of regions for regional flood frequency analysis. Eos Trans Am Geophys Union 68(44):1262
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723
ASCE (2010) Minimum design loads for buildings and other structures (ASCE/SEI 7-10). American Society of Civil Engineering, Reston
Burn DH (1990) Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour Res 26(10):2257–2265
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
Chilès JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New York
CMA (2007) Specifications for surface meteorological observation. China Meteorological Press, Beijing (in Chinese)
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London
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)
Dai LY, Che T (2014) Spatiotemporal variability in snow cover from 1987 to 2011 in northern China. J Appl Remote Sens 8(1):084693
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
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
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
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
Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323
Jin XY, Zhao JD (2012) Development of the design code for building structures in China. Struct Eng Int 22(2):195–201
Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2003) ArcGIS 9, using ArcGIS geostatistical analyst. Environmental Systems Research Institute (ESRI), Redlands
Lee KH, Rosowsky DV (2005) Site-specific snow load models and hazard curves for probabilistic design. Natural Hazards Review 6(3):109–120
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)
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
Mo HM, Fan F, Hong HP (2015a) Snow hazard estimation and mapping for a province in northeast China. Nat Hazards 77(2):543–558
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
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
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
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
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
Takahashi T, Kawamura T, Kuramota K (2001) Estimation of ground snow load using snow layer model. J Struct Construct Eng AIJ 545:35–40
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
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
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-016-2536-1