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Estimation of snow damage in an urban area with heavy snowfall

Published online by Cambridge University Press:  20 January 2017

Seiji Kamimura
Affiliation:
Nagaoka University of Technology, Nagaoka, Niigata 940–21, Japan
Teruyoshi Umemura
Affiliation:
Nagaoka University of Technology, Nagaoka, Niigata 940–21, Japan
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Abstract

Type
Research Article
Copyright
Copyright © International Glaciological Society 1993

Summary

In cities bordering the Sea of Japan where snowfall is heavy, residents cannot maintain their urban life without snow damage reduction or snow disaster prevention systems. In planning those systems an economic estim-ation is necessary, which requires quantification of the amount of snow damage occurring in a given place.

We have defined amounts of snow damage per day expressed in terms of money, considering it intrinsic to place and existing materials; its value is estimated for a 20 block section in Nagaoka City described in a previous paper (Uemura and others, 1992), for 24 January 1983. Here we give a convenient way to calculate an annual amount of snow damage, as follows.

The daily amount of snow damage, h (Yen d−1, or Yen m2d−1), has been expressed as

(1)

where k represents the seasonal drop in utilization, e expense in non-snowy times and e″ the expense only in snowy times. The annual amount of snow damage can be expressed as an accumulation of the daily amount of snow damage shown in Equation (1):

where Σ means accumulation of daily amounts for 365 days. If we take annual mean seasonal drop factor as k =

then the annual amount of snow damage, H, is shown as follows,
(2)

Table 1. Calculated annual amounts of snow damage for the urban area of Nagaoka City in

Usually E is known from past data and the daily amount is expressed as e = E/365 on average, so it follows that

. Now we use the following approximation: if any measures are taken to a part of a snow damage appraisal factor (SDAF), the value of k is expressed as the value of unity minus the ratio of the area of the part. If the area does not change during snow-cover days, by using the number of snow-cover days, D, k becomes
(3)

because k is constant during snow-cover days and zero on other days.

Using this method, we calculate an annual amount of snow damage to the SDAF existing in the place to be assessed, and show the amounts in every district in Nagaoka City for representative amounts of snow.

It has been made clear, by calculation of areal mean amounts of snow damage to SDAF (shown in Fig. 1), that snow damage is already considerably reduced by snow-removing machines and snow-melting pipe systems in roads and parking lots, but remains quite high in sidewalks and public and private areas left without any measures.

Fig. 1. Areal annual amount of snow damage for 6 SDAF: abundance (1985–86), average (1982–83) and scarcity (1988–89).

Annual amounts of snow have an approximately linear effect on the amount of snow damage, through the amount of snowfall, maximum snow depth or snow-cover days. No catastrophic effects have been observed as far as the present calculation is concerned.

Economic estimation of construction of snow-removing ditches using the amount of snow damage has been made in an urban area of 20.45 km2. Construction is economical in the commercial district and neighborhood in a year of snow scarcity, and also in years of snow abundance in residential districts.

Acknowledgements

We thank Nagaoka City Office which supplied much of the data used. This study was supported in part by members of the Conference of Development and Research for an Integrated System of Removal, Storage and Air-conditioning Use of Snow.

References

Umemura, T. Kamimura, S. Otaki, H. 1992 Snow damage on an urban area with heavy snow fall: definition of the amount of snow damage and appraisal Journal of Natural Disaster Science 13(1), 111.Google Scholar
Figure 0

Table 1. Calculated annual amounts of snow damage for the urban area of Nagaoka City in

Figure 1

Fig. 1. Areal annual amount of snow damage for 6 SDAF: abundance (1985–86), average (1982–83) and scarcity (1988–89).