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JDR Vol.15 No.6 pp. 688-697
(2020)
doi: 10.20965/jdr.2020.p0688

Paper:

Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

Hiroyuki Hirashima*1,†, Tsutomu Iyobe*2, Katsuhisa Kawashima*3, and Hiroaki Sano*4

*1Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience (NIED)
Suyoshi, Nagaoka-shi, Niigata 940-0821, Japan

*2Research & Development Center, East Japan Railway Company, Saitama, Japan

*3Research Institute for Natural Hazards & Disaster Recovery, Niigata University, Niigata, Japan

*4National Research Institute for Earth Science and Disaster Resilience (NIED), Ibaraki, Japan

Received:
April 20, 2020
Accepted:
July 14, 2020
Published:
October 1, 2020
Keywords:
snow load alert, SNOWPACK model, snow water equivalent, roof snow removal, snow distribution
Abstract

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

Cite this article as:
H. Hirashima, T. Iyobe, K. Kawashima, and H. Sano, “Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan,” J. Disaster Res., Vol.15, No.6, pp. 688-697, 2020.
Data files:
References
  1. [1] S. Yamaguchi, S. Nakai, K. Iwamoto, and A. Sato, “Influence of Anomalous Warmer Winter on Statistics of Measured Winter Precipitation Data,” J. Appl. Meteor. Climatol., Vol.48, No.11, pp. 2403-2409, 2009.
  2. [2] M. Ishizaka, H. Motoyoshi, S. Yamaguchi, S. Nakai, T. Shiina, and K. Muramoto, “Relationships between snowfall density and solid hydrometeors, based on measured size and fall speed, for snowpack modeling applications,” The Cryosphere, Vol.10, pp. 2831-2845, 2016.
  3. [3] S. Margreth, “Falling snow and ice from buildings and structures: risk assessment and mitigation – two case studies,” 8th Int. Conf. on Snow Engineering, pp. 222-227, 2016.
  4. [4] M. Carter and R. Stangl, “Increasing Problems of Falling Ice and Snow on Modern Tall Buildings,” CTBUH J., 2012 Issue IV, pp. 24-28, 2012.
  5. [5] A. Nielsen, “Snow, Ice and Icicles on Roofs – Physics and Risks,” 6th Nordic Conf. on Building Physics in the Nordic Countries, pp. 562-569, 2005.
  6. [6] L. Makkonen, “A model of icicle growth,” J. of Glaciology, Vol.34, No.116, pp. 64-70, 1988.
  7. [7] T. Takahashi, D. Kounsana, W. Yamaguchi, and T. Motoyoshi, “Risk Assessment on Snow Removal Based on Heisei 18th. Snow Disaster,” J. of Snow Engineering, Vol.26, No.4, pp. 205-210, 2010 (in Japanese with English Abstract).
  8. [8] T. Kimura, “Observation of water equivalent of snow cover by metal wafer,” Report of the National Research Center for Disaster Prevention, No.31, pp. 203-217, 1983 (in Japanese with English abstract).
  9. [9] B. Henn, T. H. Painter, K. J. Bormann, B. McGurk, A. L. Flint, L. E. Flint, V. White, and J. D. Lundquist, “High-elevation evapotranspiration estimates during drought: Using streamflow and NASA airborne snow observatory SWE observations to close the upper tuolumne river basin water balance,” Water Resources Research, Vol.54, No.2, pp. 746-766. 2018.
  10. [10] W. T. Tinkham, A. M. S. Smith, H. P. Marshall et al., “Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest,” Remote Sens. Environ., Vol.141, pp. 105-115, 2014.
  11. [11] F. Avanzi, A. Bianchi, A. Cina, C. De Michele, P. Maschio, D. Pagliari, D. Passoni, L. Pinto, M. Piras, and L. Rossi, “Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation,” Remote Sensing, Vol.10, No.5, Article 765, 2018.
  12. [12] P. Da. Ronco, F. Avanzi, C. De Michele, C. Notarnicola, and B. Schaefli, “Comparing MODIS snow products Collection 5 with Collection 6 over Italian Central Apennines,” Int. J. of Remote Sensing, Vol.41, No.11, pp. 4174-4205, 2020.
  13. [13] J. Dozier, E. H. Bair, and R. E. Davis, “Estimating the spatial distribution of snow water equivalent in the world’s mountains,” WIREs Water, Vol.3, No.3, pp. 461-474, 2016.
  14. [14] P. D. Broxton, W. J. D. van Leeuwen, and J. A. Biederman, “Improving snow water equivalent maps with machine learning of snow survey and lidar measurements,” Water Resources Research, Vol.55, No.5, pp. 3739-3757, 2019.
  15. [15] T. H. Painter, D. F. Berisford, J. W. Boardman, K. J. Bormann, J. S. Deems, F. Gehrke et al., “The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo,” Remote Sensing of Environment, Vol.184, pp. 139-152, 2016.
  16. [16] T. Iyobe and K. Kawashima, “Development of Quasi-Real-Time Monitoring Systemfor Regional Snow Cover by Utilizing Multiple Snow Data,” J. of Snow Engineering of Japan, Vol.36, No.1, pp. 1-13, 2020 (in Japanese with English abstract).
  17. [17] M. Lehning, P. Bartelt, B. Brown, C. Fierz, and P. Satyawali, “A physical SNOWPACK model for the Swiss avalanche warning Part II. Snow microstructure,” Cold Regions Science and Technology, Vol.35, No.3, pp. 147-167, doi: 10.1016/S0165-232x(02)00073-3, 2002.
  18. [18] M. Lehning, P. Bartelt, B. Brown, and C. Fierz, “A physical SNOWPACK model for the Swiss avalanche warning Part III: meteorological forcing, thin layer formation and evaluation,” Cold Regions Science and Technology, Vol.35, No.3, pp. 169-184, doi: 10.1016/S0165-232X(02)00072-1, 2002.
  19. [19] P. Bartelt and M. Lehning, “A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model,” Cold Regions Science and Technology, Vol.35, No.3, pp. 123-145, 2002.
  20. [20] S. Yamaguchi, A. Sato, and M. Lehning, “Application of the numerical snowpack model (SNOWPACK) to the wet-snow region in Japan,” Annals of Glaciology, Vol.38, pp. 266-272, 2004.
  21. [21] H. Hirashima, K. Nishimura, E. Baba, A. Hachikubo, and M. Lehning, “SNOWPACK model simulations for snow in Hokkaido, Japan,” Annals of Glaciology, Vol.38, pp. 123-129. 2004.
  22. [22] H. Hirashima, K. Nishimura, S. Yamaguchi, A. Sato, and M. Lehning, “Avalanche forecasting in a heavy snowfall area using the snowpack model,” Cold Regions Science and Technology, Vol.51, No.2-3, pp. 191-203, 2008.
  23. [23] H. Hirashima, “Success and challenges of avalanche prediction using numerical snowpack model,” Seppyo, Vol.76, pp. 411-419, 2014 (in Japanese with English abstract).
  24. [24] H. Hirashima, “Numerical snowpack model simulation schemes for avalanche prediction in Japan,” Bulletin of Glaciological Research, Vol.37s, pp. 31-41, doi: 10.5331/bgr.18SW02, 2019.
  25. [25] H. Hirashima, S. Yamaguchi, K. Kosugi, M. Nemoto, T. Aoki, and S. Matoba, “Validation of the SNOWPACK model using snow pit observation data,” Seppyo, Vol.77, pp. 5-16, 2015 (in Japanese with English abstract).
  26. [26] J. Kondo, T. Nakamura, and T. Yamazaki, “Estimation of the solar and downward atmospheric radiation,” Tenki, Vol.38, pp. 41-48, 1991 (in Japanese).
  27. [27] S. Yamaguchi, O. Abe, S. Nakai, and A. Sato, “Recent fluctuations of meteorological and snow conditions in Japanese mountains,” Annals of Glaciology, Vol.52, No.58, pp. 209-215, 2011.
  28. [28] P. Mahajan and R. L. Brown, “A microstructure-based constitutive law or snow,” Annals of Glaciology, Vol.18, pp. 287-294, 1993.
  29. [29] V. Verjans, A. A. Leeson, C. M. Stevens, M. MacFerrin, B. Noël, and M. R. van den Broeke, “Development of physically based liquid water schemes for Greenland firn-densification models,” The Cryosphere, Vol.13, No.7, pp. 1819-1842, doi: 10.5194/tc-13-1819-2019, 2019.
  30. [30] K. Motoya, “Total snow water distributions in Tohoku region, Japan, averaged for twenty-seven years: Their application to heavy and light snowfall winters,” Seppyo, Vol.70, No.6, pp. 561-570, 2008 (in Japanese with English abstract).
  31. [31] Y. Tominaga, T. Okaze, and A. Mochida, “Validation of prediction method of roof snow depth for an isolated gable-roof building,” J. Struct. Constr. Eng., Vol.81, No.725, pp. 1051-1059, 2016 (in Japanese with English abstract).

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Last updated on Dec. 01, 2020