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Estimating and mapping snow hazard based on at-site analysis and regional approaches

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Abstract

The estimation of snow hazard and load faces the small sample size effect because of the short snow depth record at a station. To reduce such an effect, we propose to estimate the return period value of the annual maximum ground snow depth S, sT, for Canada sites by applying the regional frequency analysis (RFA) and the region of influence approach (ROIA). The use of RFA and ROIA to map Canadian snow hazard is new. The comparison of their performance for snow hazard mapping has not been explored in the literature. We also consider the at-site analysis approach (ASA) for estimating sT by using three often used probability distributions for S. A comparison of the estimated sT by using the three approaches (ASA, RFA, ROIA) indicates that there is considerable scatter between the estimated sT value although the identified overall spatial trends of sT are similar. It is shown that the two-parameter lognormal distribution for S at most Canadian sites, based on the at-site analysis, is preferred; this differs from the Gumbel distribution used to develop the design snow load in Canadian structural design code. The new findings indicate that it is valuable to consider the lognormal distribution for developing design snow load for Canadian sites.

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Data availability statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. These include some of the distribution parameters and estimated return period values.

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 (1998) Information theory and an extension of the maximum likelihood principle. In: Selected papers of Hirotugu Akaike (pp. 199–213). Springer, New York, NY

  • Bartlett FM, Hong HP, Zhou W (2003) Load factor calibration for the proposed 2005 edition of the National Building Code of Canada: statistics of loads and load effects. Can J Civ Eng 30(2):429–439

  • Bartlett FM, Hong HP, Zhou W (2003b) Load factor calibration for the proposed 2005 edition of the National Building Code of Canada: companion-action load combinations. Can J Civ Eng 30(2):440–448

  • Beale MH, Hagan MT, Demuth HB (2012) Neural network toolbox, user’s guide, The MathWorks, Inc., Natick, Massachusetts

  • Bernard E, Naveau P, Vrac M, Mestre O (2013) Clustering of maxima: spatial dependencies among heavy rainfall in France. J Clim 26(20):7929–7937

    Article  Google Scholar 

  • Blanchet J, Davison AC (2011) Spatial modeling of extreme snow depth. Ann Appl Stat 5(3):1699–2264

    Article  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 

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

    Book  Google Scholar 

  • Croce P, Formichi P, Landi F, Marsili F (2019) Harmonized European ground snow load map: analysis and comparison of national provisions. Cold Regions Sci Technol 168:102875

  • DeBock DJ, Liel AB, Harris JR, Ellingwood BR, Torrents JM (2017) Reliability-based design snow loads. I: site-specific probability models for ground snow loads. J Struct Eng 143(7):04017046

  • Deng J, Pandey MD (2008) Estimation of the maximum entropy quantile function using fractional probability weighted moments. Struct Saf 30(4):307–319

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Goel NK, Burn DH, Pandey MD, An Y (2004) Wind quantile estimation using a pooled frequency analysis approach. J Wind Eng Ind Aerodyn 92(6):509–528

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Book  Google Scholar 

  • Hong HP, Ye W (2014a) Analysis of extreme ground snow loads for Canada using snow depth records. Nat Hazards. https://doi.org/10.1007/s11069-014-1073-z

    Article  Google Scholar 

  • Hong HP, Ye W (2014b) Estimating extreme wind speed based on regional frequency analysis. Struct Saf 47:67–77

    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 

  • Hong HP, Ye W, Li SH (2016) Sample size effect on the reliability and calibration of design wind load. Struct Infrastruct Eng 12(6):752–764

    Article  Google Scholar 

  • Hosking JRM (1990) L-moments: Analysis and estimation of distributions using linear combinations of order statistics. J R Stat Soc B 52(1):105–124

    Google Scholar 

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

    Book  Google Scholar 

  • Hosking JRM, Wallis JR, Wood EF (1985) Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics 27(3):251–261

    Article  Google Scholar 

  • Kohonen T (2001) Self-organizing maps. Springer, Berlin-Heidelberg

    Book  Google Scholar 

  • Li Y, van de Lindt JW (2012) Loss-based formulation for multiple hazards with application to residential buildings. Eng Struct 38:123–133

    Article  Google Scholar 

  • Liel AB, DeBock DJ, Harris JR, Ellingwood BR, Torrents JM (2017) Reliability-based design snow loads. II: reliability assessment and mapping procedures. J Struct Eng 143(7):04017047

  • Lin G-F, Chen L-H (2006) Identification of homogeneous regions for regional frequency analysis using the self-organizing map. J Hydrol 324(1):1–9

    Article  Google Scholar 

  • Lu LH, Stedinger JR (1992) Sampling variance of normalized GEV/PWM quantile estimators and a regional homogeneity test. J Hydrol 138(1–2):223–245

    Article  Google Scholar 

  • 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 

  • MATLAB version 7.13 (2011) Computer software. The MathWorks Inc., Natick, Massachusetts

    Google Scholar 

  • Mo HM, Dai LY, Fan F, Che T, Hong HP (2016) Extreme snow hazard and ground snow load for China. Nat Hazards 84(3):2095–2120

    Article  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 

  • Newark MJ (1984) A new look at ground snow loads in Canada. Proceedings, Eastern snow conference, vol. 29, 41st annual meeting, Washington, DC, pp 37–48

  • NRCC (2010) National building code of Canada. Institute for Research in Construction, National Research Council of Canada, Ottawa, Ontario

  • Odry J, Boucher MA, Cantet P, Lachance-Cloutier S, Turcotte R, St-Louis PY (2020) Using artificial neural networks to estimate snow water equivalent from snow depth. Can Water Resour J/revue Canadienne Des Ressources Hydriques 45(3):252–268

    Article  Google Scholar 

  • Pandey MD (2000) Direct estimation of quantile functions using the maximum entropy principle. Struct Saf 22(1):61–79

    Article  Google Scholar 

  • Qiang S, Zhou X, Gu M (2018) Research on reliability of steel roof structures subjected to snow loads at representative sites in China. Cold Reg Sci Technol 150:62–69

    Article  Google Scholar 

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

  • Schellander H, Hell T (2018) Modeling snow depth extremes in Austria. Nat Hazards 94(3):1367–1389

    Article  Google Scholar 

  • Singh VP (1998) Entropy-based parameter estimation in hydrology. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  • 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 

  • Tadić MP, Zaninović K, Jurković RS (2015) Mapping of maximum snow load values for the 50-year return period for Croatia. Spatial Stat 14:53–69

    Article  Google Scholar 

  • Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley, Boston

    Google Scholar 

  • Taylor DA, Allen DE (2000) Statistical variation and duration of snow load on flat roofs in Canada. Institute for Research in Construction, National Research Council of Canada, Ottawa, Ont. Unpublished study

  • 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 (p 249–256). CRC Press

  • Weiss J, Bernardara P, Benoit M (2014) Formation of homogeneous regions for regional frequency analysis of extreme significant wave heights. J Geophys Res: Oceans 119(5):2906–2922

    Article  Google Scholar 

  • Winkler M, Schellander H, Gruber S (2021) Snow water equivalents exclusively from snow depths and their temporal changes: the Δ snow model. Hydrol Earth Syst Sci 25(3):1165–1187

    Article  Google Scholar 

Download references

Acknowledgements

HMM gratefully acknowledges the scholarship (Number 201206120219) received from the China Scholarship Council and funding received from the National Natural Science Foundation of China (No. 51808169 and 51927813). Funding received from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2016-04814) is acknowledged. We thank two anonymous reviewers for their constructive comments and suggestions on the earlier draft of this paper.

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Authors and Affiliations

  1. Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, People’s Republic of China

    H. M. Mo

  2. Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Haihe Rd, P.O. Box 2619, Nangang District, Harbin, 150090, People’s Republic of China

    H. M. Mo

  3. Dept. of Civil and Environmental Eng, University of Western Ontario, London, N6A 5B9, Canada

    W. Ye & H. P. Hong

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  1. H. M. Mo
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  2. W. Ye
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  3. H. P. Hong
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Correspondence to H. M. Mo.

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Mo, H.M., Ye, W. & Hong, H.P. Estimating and mapping snow hazard based on at-site analysis and regional approaches. Nat Hazards 111, 2459–2485 (2022). https://doi.org/10.1007/s11069-021-05144-3

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