Skip to main content
Log in

Using remote sensing information to estimate snow hazard and extreme snow load in China

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The consideration of snow hazard and snow load is important for lightweight structures in cold regions. The assessment of spatial variation of the extreme snow hazard and ground snow load is complicated because the measurements of annual maximum snow depth or snow water equivalent are not always available or the spatial distribution of measuring stations is not sufficiently dense. An alternative is to use the available snow depth data derived from remote sensing for such an assessment. Several observations are made by carrying out such an assessment for the Mainland China using the derived data from 1979 to 2010. These include that there is no temporal trend in the annual maximum snow depth; the coefficient of variation of the annual maximum snow depth obtained by using the derived data is greater than that obtained by using the measurement data; and the results of the assessment by using the derived data may be useful to understand the spatial trends of the snow hazard and ground snow load. However, there are differences between the estimated snow hazard and ground snow load by using the derived snow depth from remote sensing and those estimated based on ground snow depth measurements.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

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

    Article  Google Scholar 

  • Armstrong RL, Brodzik MJ (2002) Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms. Ann Glaciol 34(1):38–44

    Article  Google Scholar 

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

    Google Scholar 

  • Belsley DA, Kuh E, Welsch RE (2005) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, Hoboken

    Google Scholar 

  • Biegus A, Rykaluk K (2009) Collapse of Katowice fair building. Eng Fail Anal 16(5):1643–1654

    Article  Google Scholar 

  • Chang ATC, Foster JL, Hall DK, Rango A, Hartline BK (1982) Snow water equivalent estimation by microwave radiometry. Cold Reg Sci Technol 5(3):259–267

    Article  Google Scholar 

  • Chang ATC, Foster JL, Hall DK (1987) Nimbus-7 SMMR derived global snow cover parameters. Ann Glaciol 9(9):39–44

    Article  Google Scholar 

  • Che T. and Dai L. (2011). Long-term snow depth dataset of China. Cold and Arid Regions Science Data Center at Lanzhou. doi:10.3972/westdc.001.2014.db

  • 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 

  • Che T, Li X, Jin R, Huang C (2014) Assimilating passive microwave remote sensing data into a land surface model to improve the estimation of snow depth. Remote Sens Environ 143:54–63

    Article  Google Scholar 

  • Chiles J, Delfiner P (1999) Geostatistics-modeling spatial uncertainty. Wiley, New York

    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 L, Che T (2014) Spatiotemporal variability in snow cover from 1987 to 2011 in northern China. J Appl Remote Sens. doi:10.1117/1.JRS.8.084693

    Google Scholar 

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

    Article  Google Scholar 

  • Foster JL, Chang ATC, Hall DK (1997) Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and a snow depth climatology. Remote Sens Environ 62(2):132–142

    Article  Google Scholar 

  • Foster JL, Hall DK, Eylander JB et al (2011) A blended global snow product using visible, passive microwave and scatterometer satellite data. Int J Remote Sens 32(5):1371–1395

    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)

  • Geis J, Strobel K, Liel A (2012) Snow-induced building failures. J Perform Constr Facil 26(4):377–388

    Article  Google Scholar 

  • Hallikainen MT, Jolma PA (1992) Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 SMMR data in Finland. IEEE Trans Geosci Remote Sens 30(1):124–131

    Article  Google Scholar 

  • Holicky M, Sykora M (2010) Failures of roofs under snow load: Causes and reliability analysis. In: Chen SE, de Leon AD, Dolhon AM, Drerup MJ, Parfitt MK (eds) Forensic engineering 2009: pathology of the built environment. ASCE Publications, Reston, Virginia, pp 444–453

    Google Scholar 

  • Hong HP, Ye W (2014) Analysis of extreme ground snow loads for Canada using snow depth records. Nat Hazards 73(2):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 (1990) L-moments: analysis and estimation of distributions using linear combinations of order statistics. J R Stat Soc B 52:105–124

    Google Scholar 

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

    Book  Google Scholar 

  • Huang X, Deng J, Ma X, Wang Y, Feng Q, Hao X, Liang T (2016) Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China. Cryosphere 10(5):2453–2463

    Article  Google Scholar 

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

    Google Scholar 

  • Kelly RE, Chang AT, Tsang L, Foster JL (2003) A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans Geosci Remote Sens 41(2):230–242

    Article  Google Scholar 

  • Larue F, Royer A, De Sève D, Langlois A, Roy A, Brucker L (2017) Validation of GlobSnow-2 snow water equivalent over Eastern Canada. Remote Sens Environ 194:264–277

    Article  Google Scholar 

  • Li X-L, Zhang F-M, Wang C-H (2012) Comparison and analysis of snow depth over China, observation and derived from remote sensing. J Glaciol Geocryol 34(4):755–764 (in Chinese)

    Google Scholar 

  • Li D, Durand M, Margulis SA (2017) Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation. Water Resour Res 53(1):647–671

    Article  Google Scholar 

  • Ma LJ, Qin DH (2012) Spatial temporal characteristics of observed key parameters for snow cover in china during 1957–2009. J Glaciol Geocryol 34(1):1–11 (in Chinese)

    Google Scholar 

  • Madsen H, Krenk S, Lind NC (2006) Methods of structural safety. Courier Dover Publications, Mineola

  • Mo HM, Fan F, Hong HP (2015a) Snow hazard estimation and mapping for a province in northeast China. Nat Hazards 1–16. doi:10.1007/s11069-014-1566-9

  • Mo HM, Fan F, Hong HP (2015b) Application of region of influence approach to estimate extreme snow load for a northeastern Province in China. In: 12th international conference on application of statistics and probability in civil engineering, ICASP12, Vancouver, Canada, 12–15 July 2015

  • 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 

  • NBCC (2010) National building code of Canada. National research council of Canada (NRCC), Ottawa, Canada

  • 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 

  • Rango A, Chang ATC, Foster JL (1979) The utilization of spaceborne microwave radiometers for monitoring snowpack properties. Nord Hydrol 10(1):25–40

    Google Scholar 

  • Savoie MH, Armstrong RL, Brodzik MJ, Wang JR (2009) Atmospheric corrections for improved satellite passive microwave snow cover retrievals over the Tibet Plateau. Remote Sens Environ 113(12):2661–2669

    Article  Google Scholar 

  • Schanda E, Matzler C, Kunzi K (1983) Microwave remote sensing of snow cover. Int J Remote Sens 4(1):149–158

    Article  Google Scholar 

  • Smith T, Bookhagen B (2016) Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia. Remote Sens Environ 181:174–185

    Article  Google Scholar 

  • Sturm M, Holmgren J, Liston GE (1995) A seasonal snow cover classification system for local to global applications. J Clim 8(5):1261–1283

    Article  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 11:1380–1394

    Article  Google Scholar 

  • Tait A, Armstrong R (1996) Evaluation of SMMR satellite-derived snow depth using ground-based measurements. Int J Remote Sens 17(4):657–665

    Article  Google Scholar 

  • Takala M, Luojus K, Pulliainen J et al (2011) Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sens Environ 115(12):3517–3529

    Article  Google Scholar 

  • Wang YQ, Hu ZW, Shi YJ, Zhang Y, Liu M (2009) Analysis and reflection on snow disaster accidents of steel structures of light-weight buildings with portal frames. China Civ Eng J 42(3):65–70 (in Chinese)

    Google Scholar 

  • Yin H, Cao C, Xu M, Chen W, Ni X, Chen X (2016) Long-term snow disasters during 1982–2012 in the Tibetan Plateau using satellite data. Geomat Nat Hazards Risk. doi:10.1080/19475705.2016.1238851

    Google Scholar 

Download references

Acknowledgements

Financial support received from the National Natural Science Foundation of China (Grant No. 51478147) and from the National Science and Engineering Research Council of Canada is much appreciated. The authors are grateful to Heilongjiang Bureau of Meteorology for providing part of the observed snow depth data used in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. Fan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, H.M., Hong, H.P. & Fan, F. Using remote sensing information to estimate snow hazard and extreme snow load in China. Nat Hazards 89, 1–17 (2017). https://doi.org/10.1007/s11069-017-2939-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-017-2939-7

Keywords

Navigation