Zhou Bingrong, Li Fengxia, Shen Shuanghe, et al. Forecasting model of snow calamity and GIS application of spacial analysis technology in Qinghai Plateau. J Appl Meteor Sci, 2007, 18(3): 373-379.
Citation:
Zhou Bingrong, Li Fengxia, Shen Shuanghe, et al. Forecasting model of snow calamity and GIS application of spacial analysis technology in Qinghai Plateau. J Appl Meteor Sci, 2007, 18(3): 373-379.
Zhou Bingrong, Li Fengxia, Shen Shuanghe, et al. Forecasting model of snow calamity and GIS application of spacial analysis technology in Qinghai Plateau. J Appl Meteor Sci, 2007, 18(3): 373-379.
Citation:
Zhou Bingrong, Li Fengxia, Shen Shuanghe, et al. Forecasting model of snow calamity and GIS application of spacial analysis technology in Qinghai Plateau. J Appl Meteor Sci, 2007, 18(3): 373-379.
In recent years, with the application of 3S technology in monitoring disaster field, utilizing the technology of GIS spacial analysis, the minimum unit of analytic target could become the concrete geographical unit. As a result, spacial analysis precision is improved. The foundation of the function of snow calamity fragility degrees can lead to the realization of integrated and stepwise forecasting, warning and assessment. GIS and RS technology are applied, and the result on relation between quantity of precipitation and depth of snow is used, then the functions are defined, which are the function of frangibility on livestock husbandry and function of frangibility on social economic level. The Forecasting Model of Snow Calamity on Meteorology and the Forecasting Model of Snow Calamity on Remote Sensing are established. Then, the snow calamity of the Plateau is forecasted step by step, in order to find out the mechanism of snow calamity in different areas, to improve the capability of forecasting snow calamity, and to give the way against the calamity. It is regarded that the mechanism of snow calamity in south Qinghai is different from that in areas around Qinghai Lake. Although snow is a factor causing snow calamity in every area, its effects are more severe in south Qinghai than in the areas around Qinghai Lake. Snow of similar degree may cause disaster, but it may also affect animal husbandry in areas around Qinghai Lake only slightly. Snow calamity is not the only nature disaster, the unreasonable activity of human beings also affect the occurrence and the development of snow calamity. At last, snow calamity which appears in Qinghai Plateau is forecasted using this model. Result shows the model can forecast snow calamity in the Plateau, and enhance forecasting ability of snow calamity in the Plateau.
Fig.
1
Plan of snow calamity forecasting and evaluating (a) snow calamity forecasting for meteorology, (b) snow calamity forecasting for remote sensing
Fig.
2
Spacial analysis of snow calamity animal husbandry fragility degree in Qinghai
(a) distribution of pasturage yield, (b) classification of grassland type, (c) distribution of animal numbers, (d) distribution of fragility degree on livestock husbandry
Fig.
3
Compartmentalizing by snow calamity fragility degree in Qinghai Plateau
(a) result of compartmentalizing by function of fragility degree on livestock husbandry, (b) result combined by function of fragility degree on society economic level and function of fragility degree on livestock husbandry
Fig.
4
Snow calamity special analysis about two type of forecasting models (a) classification by J(P, t), (b) result of meteorological snow calamity forecasting model, (c) classification by snow depth utilizing remote sensing, (d) result of remote sensing snow calamity forecasting model
Figure 1. Plan of snow calamity forecasting and evaluating (a) snow calamity forecasting for meteorology, (b) snow calamity forecasting for remote sensing
Figure 2. Spacial analysis of snow calamity animal husbandry fragility degree in Qinghai
Figure 3. Compartmentalizing by snow calamity fragility degree in Qinghai Plateau
Figure 4. Snow calamity special analysis about two type of forecasting models (a) classification by J(P, t), (b) result of meteorological snow calamity forecasting model, (c) classification by snow depth utilizing remote sensing, (d) result of remote sensing snow calamity forecasting model
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