CN103593582A - Area snow disaster risk estimation method - Google Patents
Area snow disaster risk estimation method Download PDFInfo
- Publication number
- CN103593582A CN103593582A CN201310631992.3A CN201310631992A CN103593582A CN 103593582 A CN103593582 A CN 103593582A CN 201310631992 A CN201310631992 A CN 201310631992A CN 103593582 A CN103593582 A CN 103593582A
- Authority
- CN
- China
- Prior art keywords
- snow
- risk
- disaster
- snow disaster
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an area snow disaster risk estimation method. The method includes the following steps that an area snow disaster risk estimation model is called; a risk estimation plug-in is established according to the area snow disaster risk estimation model; whether an interesting area is divided to be a pasturing area or not is determined; under the condition that the interesting area is divided to be the pasturing area, pasture height distribution data, snow depth distribution data and snow distribution range data of the interesting area are input into the risk estimation plug-in, wherein the pasture height distribution data, the snow depth distribution data and the snow distribution range data are obtained from different data sources. The snow distribution range data include snow duration. The risk estimation plug-in is used for executing the following operation that the height ratio of snow and pasture is calculated according to the pasture height distribution data and the snow depth distribution data; a snow disaster grade list is provided, and the snow disaster grade list is relevant to the height ratio of the snow and the pasture and the snow duration. A corresponding snow disaster grade value is found in the snow disaster grade list according to the calculated height ratio of the pasture and the snow and the snow duration; snow disaster risks are estimated according to the snow disaster grade value.
Description
Technical field
The present invention relates to a kind of region snow disaster evaluation of risk method.
Background technology
Snow disaster is the product that natural snowfall acts on human society, is a kind of performance of relation between man and nature.Because the final hazard-affected body of snow disaster is the aggregate of the mankind and human society, as grassland, livestock, Architectural Equipment etc., so, only have and cause the snowfall of direct or indirect infringement just can be called as snow disaster to a part or whole part of hazard-affected body.
Grassland snow disaster in pastoral area refers to the animal husbandry area that relies on native pasture to herd, due to winter half year snowfall too much and accumulated snow blocked up, the snow deposit length of holding time, affects herding normal grazing activity, livestock because freezing, the hungry a kind of disaster that occurs the phenomena of mortality.Harm to animal husbandry, is mainly that accumulated snow is covered grassland, and surpasses certain depth, though some accumulated snow is not dark, density is larger, or snow face icing forms nilas, livestock is difficult to push aside snow deposit and pastures, and causes hunger, and nilas also easily scratches the hoof wrist of sheep and horse sometimes, cause frostbite, cause livestock thin and weak, usually cause livestock breeding miscarriage, newborn animal survival rate is low, the old and the weak cub is lived in hunger and cold, and death increases.
In vast non-pastoral area, snowfall and accumulated snow, can have a strong impact on lifeline engineering such as even destroying traffic, communication on a large scale, and people's production and life are threatened.
In Chinese high latitude, high height above sea level and arid area, snow disaster in various degree almost all can occur every year, particularly in the Inner Mongol, Xinjiang, the large main pastoral area in Qinghai and Tibet four, more serious snow disaster has been periodic appearance, and these regional snow disasters are also often relevant with plateau accumulated snow, and in different regions, the concrete manifestation of snow disaster is different again, as in Qinghai-xizang Plateau Region, herbage is short, slight accumulated snow just may cause snow disaster, and in Northern Part of Xinjiang, meadow height is high compared with Qinghai-xizang Plateau Region, the accumulated snow is here conventionally also deep, snow disaster in the Inner Mongol is usually accompanied by again strong wind weather, the snow disaster forms such as snowstorm that form.Because that pastoral area, grassland is mainly positioned at is hard to get to, backward in economy, produce low backcountry and minority area, the nomadic main mode of production in some pastoral areas that remains, in addition government to the management on grassland and plan improper, add some herdsman's idea consciousness not enough, adopt predation formula to manage, make grassland ecology out of trim, so once snow disaster in pastoral area occurs, will cause a large amount of livestocks frozen by low temperature snow and be difficult to existence, especially winter-spring season, snow and ice cover grassland, livestock cannot search for food, livestock is in batch compeled by hunger and cold, occurs dead in flakes phenomenon.
In addition, due to deep accumulated snow, traffic is seriously obstructed, and communication line interrupts, and daily life has been caused to very large impact.
Summary of the invention
For solving problems of the prior art, the object of this invention is to provide a kind of region snow disaster evaluation of risk method, the method comprises: call region snow disaster risk estimation model; According to this region snow disaster risk estimation model, create evaluation of risk plug-in unit; Determine whether area-of-interest is divided into pastoral area; In the situation that described area-of-interest is divided into pastoral area, the herbage height distributed data of the area-of-interest obtaining from different data sources to described evaluation of risk plug-in unit input, snow depth distributed data, distribution of Snow Cover Over range data, wherein this distribution of Snow Cover Over range data comprises the accumulated snow duration; Described evaluation of risk plug-in unit is used for carrying out following operation: according to described natural plant height degree distributed data and snow depth distributed data, calculate the careless aspect ratio of snow; Snow disaster table of grading is provided, and this snow disaster table of grading is associated with the careless aspect ratio of described snow and accumulated snow duration; According to the careless aspect ratio of the snow calculating and accumulated snow duration, from described snow disaster table of grading, find corresponding snow disaster grade point; According to this snow disaster grade point, estimate snow disaster risk.
Other features and advantages of the present invention partly in detail are described the embodiment subsequently.
Accompanying drawing explanation
Accompanying drawing is to be used to provide a further understanding of the present invention, and forms a part for instructions, is used from explanation the present invention, but is not construed as limiting the invention with embodiment one below.In the accompanying drawings:
Fig. 1 is the process flow diagram of snow disaster evaluation of risk method according to the embodiment of the present invention;
Fig. 2 is the process flow diagram of the step of evaluation of risk plug-in unit execution according to the embodiment of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is elaborated.Should be understood that, embodiment described herein only, for description and interpretation the present invention, is not limited to the present invention.
According to the snow disaster machine-processed research of causing disaster, the danger of snow disaster is mainly the result of meteorologic factor effect, and the formation of snow disaster need to possess certain condition, and being not has snowfall just to have snow disaster.The danger of snow disaster mainly refers to that accumulated snow is blocked up, coverage is very large and be accompanied by the weather of low temperature and strong wind, can make large quantities of livestocks die of hunger the phenomenon of freezing to death because eating less than grass, meanwhile, a large amount of accumulated snow also can work the mischief to herdsman's life, as herdsman's frostbite, freeze to death, suffer from snow blindness etc.Other areas in China, the harm of a large amount of snowfalls is mainly snowfall and the impact of low temperature on people's normal life, as the Traffic interruption causing, power communication equipment are endangered etc.In a word, the calamity factor that causes of the harm of snow disaster is a large amount of snowfall and cooling, different according to the region of snowfall and cooling, the loss in various degree that the adaptability difference of snow is caused.In snow disaster evaluation of risk, the main factor of considering comprises the factor based on meteorological such as snow depth, accumulated snow duration, low temperature continuous days.
The main hazard-affected body of snow disaster disaster is livestock, snow disaster in pastoral area be livestock because of snow cover grassland eat less than grass in addition low temperature cause " starving calamity "; And pastoral area provides a large amount of meat, leather, dairy produces every year, a large amount of livestock death must cause large economic loss.So, consider that the hazard-affected body of snow disaster mainly carries out around livestock, comprise the meadow situation of depending on for existence and the opposing situation of livestock self.In addition snow disaster will be considered the infringement of road traffic is that the grade of road and road are by the scope of snow cover and length etc.
Therefore the region snow disaster evaluation of risk method that, embodiments of the present invention provide can be considered the situation in yardstick and He Fei pastoral area, pastoral area.
Yardstick can be divided into the yardstick of range of countries level and the regional scale in range of countries.According to yardstick and He Fei pastoral area, pastoral area, embodiments of the present invention can provide three kinds of region snow disaster risk estimation model:
Model 1: for the snow disaster risk estimation model in country scale, pastoral area;
Model 2: for the snow disaster risk estimation model in regional scale, pastoral area; And
Model 3: for the snow disaster risk estimation model in country scale, non-pastoral area.
To respectively these three kinds of models be described below.
Model 1
The snow disaster in pastoral area of the applicable national yardstick of this model is estimated, buries the situation of herbage height by judgement accumulated snow duration and accumulated snow, identifies high risk zone, then for high risk zone, call region, pastoral area estimation model and further estimate in the scope in national pastoral area.
This model is usingd the data of accumulated snow and herbage two aspects as input item, form input with raster data, in model, by the relative height of judgement accumulated snow duration and snow depth and herbage, with the cause disaster table of grading contrast of given snow disaster, to on grid map one by one picture dot judgement may there is the degree of snow disaster, the raster data of snow disaster grade point that records each picture dot, as snow disaster risk rating scheme, is exported to snow disaster high risk area.Because snow disaster has continuation, in model, by this cumulative amount of accumulated snow duration, consider the continuation of snow disaster.
Index Analysis on Selecting: obtain accumulated snow duration and Precipitation amount, snow depth, temperature on average etc. because have good correlativity by analysis, therefore can use the accumulated snow duration as the factor of the representative reaction accumulated snow extent of injury, and avenge the extent of injury that careless aspect ratio can roughly be reacted pastoral area after accumulated snow, it is as shown in table 1 that this model is chosen the factor:
Table 1
| Because of sublayer | Indicator layer |
| Dangerous | The accumulated snow duration |
| Fragility | Avenge careless aspect ratio |
Avenge the calculating of careless aspect ratio
H=H
1/H
2
H---the snow grass aspect ratio of each picture dot on grid map, unit is %;
H
1---represent the snow depth of each picture dot on grid map, unit is centimetre;
H
2---represent the herbage height of each picture dot on grid map, unit is centimetre.
Above-mentioned accumulated snow duration, snow depth and herbage highly can obtain from different data sources.These data sources can be such as being Earth System Science Data shared platform, National Agricultural Science data sharing center etc.
According to snow disaster risk class table (following table 2), find corresponding snow disaster risk class.
Table 2
In table 3, the accumulated snow duration has been carried out to classification, for example, according to 3,5,7 days be boundary, to be divided into four intervals, can give corresponding value: 0,1,2,3.
In addition, according to the given grade interval of the careless aspect ratio of snow, can take respectively 30%, 40%, 50%, 70%, 90% as boundary, to be divided into 6 intervals, give corresponding value: O, 1,2,3,4,5.
The careless aspect ratio of table 3 moderate snow and accumulated snow duration are replaced by corresponding value, and respective items addition, snow disaster comprehensive classification exponential quantity can be obtained.For example, O, 1 corresponding devoid of risk; 2,3 corresponding low-risks; 4,5 corresponding risks; The corresponding excessive risk of 6-8, as shown in table 3 below.
Table 3
| Snow disaster risk class | Comprehensive classification value |
| Excessive risk | 6、7、8 |
| Risk | 4、5 |
| Low-risk | 2、3 |
| Devoid of risk | 0、1 |
Model 2
This model causes the condition of a disaster condition for the accumulated snow of pastoral area, region scope, meadow situation, livestock situation and combat a natural disaster a plurality of factors of information summary, according to formation mechanism and the harmfulness of snow disaster, estimate to analyze the scope that may produce snow disaster, and the snow disaster in scope is estimated to obtain snow disaster risk class.This model is chosen and is caused the calamity factor, pregnant calamity environment, the data of hazard-affected body and four aspects of anti-disaster ability are as input item, with the form input of raster data, in model, by the standardization of raster data, the weighted value definite according to expert's scoring, does weighted stacking analysis again, and the degree of snow disaster may occur the judgement of picture dot one by one on grid map, using the raster data of snow disaster grade point that records each picture dot as snow disaster risk rating scheme, output snow disaster risk evaluation result.
It is as shown in table 4 for the dangerous factor of snow disaster evaluation of risk and the index of the fragility factor that this model is chosen.
Table 4
The definition of snow disaster risk class can be as shown in table 5.
Table 5
These parameters (factor) can obtain from different data sources.Such data source can comprise Meteorological Science Data shared platform, national Population and Health Platform of Scientific data Sharing etc.
Avenge similar in careless aspect ratio, the calculating of accumulated snow duration and processing and model 1, repeat no more.
The calculating of grassland utilization intensity
First need to calculate theoretical animal number
Hay yield computing formula:
Bg---the hay yield monthly of each picture dot on grid map, unit is g/ (m
2)
NPP---the net primary productivity monthly of each picture dot on grid map
S
bn---Grassland Biomass is to NPP conversion coefficient, g/gC, and numerical value is 0.45
S
ug---meadow under ground portion and Aboveground Biomass of Young scale-up factor.
Table 6 provides the underground and Aboveground Biomass of Young scale-up factor table of comparisons of the different grassland vegetation types of example.
Table 6
| Grassland types | S ug | Grassland types | S ug |
| Warm nature meadow steppe class | 5.26 | Cold Desert grassland class | 7.89 |
| Temperate steppe class | 4.25 | Temperate steppe Desert | 7.89 |
| Alpine meadow grassland class | 7.91 | Warm nature Desert | 7.89 |
| High-cold steppe class | 4.25 | Cold Desert class | 7.89 |
| Warm nature desert steppe class | 7.89 | Hot thick grass class | 4.42 |
| Hot filling thick grass class | 4.42 | Warm property thick grass class | 4.42 |
| Warm property is filled with thick grass class | 4.42 | Lowland meadow class | 6.31 |
| Mountain Meadow class | 6.23 | Alpine meadow class | 7.92 |
| Bog | 15.68 | ? | ? |
Carrying computing formula:
CA---the Carrying whole month (for example ，Yi Yangwei unit)
B
g---the hay yield monthly of each picture dot on grid map
C
use---the utilization factor of livestock to herbage, the type on different meadows has different forage grass utilization rates;
Table 7 provides take the different grassland types grassland utilization of the example rate table of comparisons that sheep is example.
Table 7
| Grassland types | C use |
| Grassy marshland class group | 60％ |
| Grassland class group | 50％ |
| Desert group | 40％ |
| (filling) thick grass class group and bog group | 55％ |
U
g---the hay amount that each sheep unit needs every day, kg/d, numerical value is 2.0
DAY---of that month number of days.
Then calculate actual animal number
The quantity of the actual livestock searching for food on the actual animal number unit of being illustrated in pasture, represents computing formula here by the density of livestock on meadow:
C=M/S
The live-stock inventory amount of M---certain administrative unit
The grassland area of S---certain administrative unit
Finally calculate grassland utilization intensity:
E=C/CA
E---the grassland utilization intensity of each picture dot on grid map, the % of unit.
C---actual animal number (take grid as unit, i.e. actual livestock number on each grid picture dot)
CA---Carrying (take grid as unit, the livestock number that meadow on each grid picture dot can be supported).
GDP density calculation:
GDP density calculation formula:
G
i=M
i/S
i
The quantity of I---administrative unit
G
i---the GDP density of i administrative unit
M
i---the GDP total value of i administrative unit
S
i---the area of i administrative unit
Can be normalized (standardization) processing to the above-mentioned dangerous factor and the fragility factor.Normalized can be the method that well known to a person skilled in the art, its effect is that required multi-source data in estimating is carried out to nondimensionalization processing.
After being normalized, can give weights to the above-mentioned factor, then weighted sum.Weights determine can be based on the dangerous factor and/or fragility factor pair snow disaster influence degree.The weights that following table 8 shows example distribute, but it is disclosed to it will be understood by those skilled in the art that weights allocation scheme is not limited in table 8.
Table 8
After the above-mentioned factor is weighted to summation, snow disaster risk index can be obtained, according to this index, snow disaster degree of risk can be judged.Also can carry out classification and to each grade of assignment to risk index, as shown in table 9.
Table 9
| Snow disaster grade | Devoid of risk | Low-risk | Risk | Excessive risk |
| Risk index scope | ＜20 | 21-40 | 41-65 | ＞65 |
| Grid assignment | 4 | 3 | 2 | 1 |
Model 3
This model carries out snow disaster estimation for non-pastoral area scope, and formation mechanism and harm according to snow disaster in non-pastoral area scope, mainly impact traffic lines, estimates to analyze the harm that snow disaster may cause traffic lines.
Factorial analysis: the major area that this model is considered is (China) vast non-pastoral area, these regional Winter Snows are commonplace, snowfall major effect road traffic, and in model, mainly still consider the factor aspect meteorological, application Snowstorm Forecast product, and traffic lines distributed data, index is chosen as shown in table 10.
Table 10
| Because of sublayer | Indicator layer |
| Dangerous | Snowstorm Forecast product |
| Fragility | Traffic lines distribute |
The dangerous factor in table 10 and the fragility factor can be obtained from different data sources, and such data source can comprise China Meteorological Platform of Scientific data Sharing, national fundamental geographic information system database.
Snowstorm Forecast product comprises Snowstorm Forecast data, can carry out rasterizing to Snowstorm Forecast data, to form Snowstorm Forecast distribution plan;
Traffic lines distributed data is carried out to rasterizing, to form traffic lines distribution plan, and this traffic lines distribution plan is carried out to binaryzation;
The traffic lines distribution plan of the Snowstorm Forecast distribution plan of formation and binaryzation is superposeed and analyzed, to determine, distributed by the traffic lines that snow disaster affects.Can snowfall thickness according to weather report determine that this is subject to the distribute degree of susceptibility of upper diverse location of traffic lines that snow disaster affects.
Three kinds of snow disaster risk estimation model more than introducing, the snow disaster evaluation of risk method that embodiments of the present invention provide can be used at least one or the combination in any in above-mentioned three kinds of models.This snow disaster evaluation of risk method will be described below.
Fig. 1 is the process flow diagram of region snow disaster evaluation of risk method according to the embodiment of the present invention.As shown in Figure 1, according to an embodiment of the invention, provide a kind of region snow disaster evaluation of risk method, the method can comprise:
Call region snow disaster risk estimation model;
According to this region snow disaster risk estimation model, create evaluation of risk plug-in unit;
Determine whether area-of-interest is divided into pastoral area;
In the situation that described area-of-interest is divided into pastoral area, the herbage height distributed data of the area-of-interest obtaining from different data sources to described evaluation of risk plug-in unit input, snow depth distributed data, distribution of Snow Cover Over range data, wherein this distribution of Snow Cover Over range data comprises the accumulated snow duration;
Evaluation of risk plug-in unit is for execution area snow disaster evaluation of risk.
Wherein, determining that whether area-of-interest is divided into pastoral area can be that this evaluation of risk plug-in unit is carried out, can be also manually to judge.
In embodiments of the present invention, can set up region snow disaster risk estimation model according to required function, and distribute ID with easy-to-look-up and call can to this model.For example, three kinds of models describing before can setting up, and distribute an ID to each model.In the preferred embodiment of the present invention, can carry out this model by the form of plug-in unit.Setting up region snow disaster risk estimation model modeling method used and program language can be method and the program language that programming those skilled in the art know.
In addition, the method can also comprise:
According to described evaluation of risk plug-in unit, produce user interface;
Described evaluation of risk plug-in unit is associated with described user interface.
Can carry out user interactions to trigger Plugin events by user interface, thereby enable/stop using this plug-in unit.
Fig. 2 is the process flow diagram of the step of evaluation of risk plug-in unit execution according to the embodiment of the present invention.As shown in Figure 2, specifically, evaluation of risk plug-in unit can be carried out following operation:
According to described natural plant height degree distributed data and snow depth distributed data, calculate the careless aspect ratio of snow;
Snow disaster table of grading is provided, and this snow disaster table of grading is associated with the careless aspect ratio of described snow and accumulated snow duration;
According to the careless aspect ratio of the snow calculating and accumulated snow duration, from described snow disaster table of grading, find corresponding snow disaster grade point;
According to this snow disaster grade point, estimate snow disaster risk.
Above-mentioned method can for example use a model and 1 carry out.
Wherein, described snow disaster table of grading can be determined according to the historical statistical data of former snow disaster.
Alternatively, if determine and have excessive risk subregion according to said method, 2 pairs of these excessive risk subregions that can use a model carry out snow disaster evaluation of risk.Specifically, for this excessive risk subregion:
From different data sources, obtain at least one dangerous factor and at least one the fragility factor being associated with this excessive risk subregion, wherein, this danger factor comprises low temperature duration, accumulated snow duration, avenges at least one in careless aspect ratio, and this fragility factor comprises at least one in grassland utilization intensity, forage reserves, GDP density, composition of livestock herds;
Described at least one dangerous factor and at least one fragility factor are normalized;
To at least one dangerous factor and at least one fragility factor after processing, give weights;
At least one dangerous factor and at least one fragility factor and corresponding weights are weighted to summation, to calculate snow disaster risk index;
According to the snow disaster risk index calculating, from the snow disaster risk index grade point table of comparisons, find out corresponding snow disaster risk class.
Alternatively, in the situation that described area-of-interest is divided into non-pastoral area, can use a model and 3 carry out snow disaster evaluation of risk, so the method can also comprises:
From different data sources, obtain Snowstorm Forecast data, traffic lines distributed data;
Described Snowstorm Forecast data are carried out to rasterizing, to form Snowstorm Forecast distribution plan;
Described traffic lines distributed data is carried out to rasterizing, to form traffic lines distribution plan, and this traffic lines distribution plan is carried out to binaryzation;
The traffic lines distribution plan of the Snowstorm Forecast distribution plan of formation and binaryzation is superposeed, to determine, distributed by the traffic lines that snow disaster affects.
Be subject on basis that traffic lines that snow disaster affects distribute having determined, snowfall thickness according to weather report determines that this is distributed by traffic lines that snow disaster affects and goes up the degree of susceptibility of diverse location.
It will be understood by those skilled in the art that said method provided by the invention can realize with modular form by software programming.Applicable programming language can comprise such as but not limited to C language, VB, Java etc.Can also set up disaster risk estimation model etc. by XML technology.
The disaster evaluation of risk method that embodiments of the present invention provide, practical business demand for Ministry of Civil Affairs's country's mitigation center, guaranteeing on scientific basis, take into full account domestic all kinds of science data (weather data, remote sensing image data, geologic data, terrain data, hydrographic data, crop type distribution and the growth conditions data etc.) property obtained and degree of share at present, design and Implement the region snow disaster evaluation of risk method towards mitigation business.Through tracking and checking for many years, model accuracy is higher, meets the business demand of owner unit.
Aspect snow disaster evaluation of risk under national yardstick, from May 3rd, 2010 17Shi Qi， Ministry of Land and Resources, China Meteorological platform combine the forecast of issue geological hazard meteorological.Meanwhile, based on this technical method, make national geological disaster risk and estimate thematic product, by comparative analysis, two series products are basically identical to the space distribution scope of snow disaster early warning.
Aspect snow disaster evaluation of risk under regional scale, participate in the data of modeling, in the situation that fiducial interval is 95%, actual conditions are that 0 the judgment accuracy that snow disaster does not occur is 85.5%, actual conditions are that the judgment accuracy of 1 generation snow disaster is 74.4%, to modeling data, to sentence accuracy be 80.0% in total returning, and this illustrates that this technical method is to the good predictive ability of having of study area snow disaster.
Below describe by reference to the accompanying drawings the preferred embodiment of the present invention in detail; but; the present invention is not limited to the detail in above-mentioned embodiment; within the scope of technical conceive of the present invention; can carry out multiple simple variant to technical scheme of the present invention, these simple variant all belong to protection scope of the present invention.
It should be noted that in addition each the concrete technical characterictic described in above-mentioned embodiment, in reconcilable situation, can combine by any suitable mode.For fear of unnecessary repetition, the present invention is to the explanation no longer separately of various possible array modes.
In addition, between various embodiment of the present invention, also can carry out combination in any, as long as it is without prejudice to thought of the present invention, it should be considered as content disclosed in this invention equally.
Claims (5)
1. a region snow disaster evaluation of risk method, the method comprises:
Call region snow disaster risk estimation model;
According to this region snow disaster risk estimation model, create evaluation of risk plug-in unit;
Determine whether area-of-interest is divided into pastoral area;
In the situation that described area-of-interest is divided into pastoral area, the herbage height distributed data of the area-of-interest obtaining from different data sources to described evaluation of risk plug-in unit input, snow depth distributed data, distribution of Snow Cover Over range data, wherein this distribution of Snow Cover Over range data comprises the accumulated snow duration;
Described evaluation of risk plug-in unit is used for carrying out following operation:
According to described natural plant height degree distributed data and snow depth distributed data, calculate the careless aspect ratio of snow;
Snow disaster table of grading is provided, and this snow disaster table of grading is associated with the careless aspect ratio of described snow and accumulated snow duration;
According to the careless aspect ratio of the snow calculating and accumulated snow duration, from described snow disaster table of grading, find corresponding snow disaster grade point;
According to this snow disaster grade point, estimate snow disaster risk.
2. method according to claim 1, the method also comprises:
According to described evaluation of risk plug-in unit, produce user interface;
Described evaluation of risk plug-in unit is associated with described user interface.
3. method according to claim 1, wherein, the in the situation that of there is excessive risk subregion in estimating described area-of-interest, for this excessive risk subregion, described evaluation of risk plug-in unit is also for carrying out following operation:
From different data sources, obtain at least one dangerous factor and at least one the fragility factor being associated with this excessive risk subregion, wherein, this danger factor comprises low temperature duration, accumulated snow duration, avenges at least one in careless aspect ratio, and this fragility factor comprises at least one in grassland utilization intensity, forage reserves, GDP density, composition of livestock herds;
Described at least one dangerous factor and at least one fragility factor are normalized;
To at least one dangerous factor and at least one fragility factor after processing, give weights;
At least one dangerous factor and at least one fragility factor and corresponding weights are weighted to summation, to calculate snow disaster risk index;
According to the snow disaster risk index calculating, from the snow disaster risk index grade point table of comparisons, find out corresponding snow disaster risk class.
4. method according to claim 1, wherein, in the situation that described area-of-interest is divided into non-pastoral area, described evaluation of risk plug-in unit is also for carrying out following operation:
From different data sources, obtain Snowstorm Forecast data, traffic lines distributed data;
Described Snowstorm Forecast data are carried out to rasterizing, to form Snowstorm Forecast distribution plan;
Described traffic lines distributed data is carried out to rasterizing, to form traffic lines distribution plan, and this traffic lines distribution plan is carried out to binaryzation;
The traffic lines distribution plan of the Snowstorm Forecast distribution plan of formation and binaryzation is superposeed, to determine, distributed by the traffic lines that snow disaster affects.
5. method according to claim 4, described evaluation of risk plug-in unit also for:
Be subject on basis that traffic lines that snow disaster affects distribute having determined, snowfall thickness according to weather report determines that this is distributed by traffic lines that snow disaster affects and goes up the degree of susceptibility of diverse location.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310631992.3A CN103593582A (en) | 2013-11-29 | 2013-11-29 | Area snow disaster risk estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310631992.3A CN103593582A (en) | 2013-11-29 | 2013-11-29 | Area snow disaster risk estimation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103593582A true CN103593582A (en) | 2014-02-19 |
Family
ID=50083719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310631992.3A Pending CN103593582A (en) | 2013-11-29 | 2013-11-29 | Area snow disaster risk estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103593582A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780433A (en) * | 2016-11-16 | 2017-05-31 | 清华大学 | Accumulated snow phenology information fusion method and system based on multi- source Remote Sensing Data data |
| CN109871988A (en) * | 2019-01-28 | 2019-06-11 | 河海大学 | A kind of flood forecasting early warning precision analytical method |
| CN111291945A (en) * | 2019-04-16 | 2020-06-16 | 中国水利水电科学研究院 | Dynamic assessment method for dry disaster loss of animal husbandry |
| CN111401689A (en) * | 2020-02-19 | 2020-07-10 | 远景智能国际私人投资有限公司 | Method, device and equipment for determining snowfall date of photovoltaic station and storage medium |
| CN111626599A (en) * | 2020-05-22 | 2020-09-04 | 广东省突发事件预警信息发布中心(广东省人工影响天气中心) | Meteorological disaster risk studying and judging method and system |
| CN113642877A (en) * | 2021-08-05 | 2021-11-12 | 中国农业科学院草原研究所 | Snow disaster situation assessment method and system based on actual disaster damage of herdsmen |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002218082A (en) * | 2001-01-17 | 2002-08-02 | Alpha Kogaku Kk | Weather disaster prevention information monitoring apparatus |
| JP2007066034A (en) * | 2005-08-31 | 2007-03-15 | Yukio Yazaki | Disaster notification system |
| CN101799561A (en) * | 2010-02-05 | 2010-08-11 | 民政部国家减灾中心 | Snow disaster remote sensing monitoring simulation evaluation method based on disaster reduction small satellite |
| CN102646219A (en) * | 2012-02-27 | 2012-08-22 | 兰州大学 | Method for pre-warning snow disaster in pasturing area |
-
2013
- 2013-11-29 CN CN201310631992.3A patent/CN103593582A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002218082A (en) * | 2001-01-17 | 2002-08-02 | Alpha Kogaku Kk | Weather disaster prevention information monitoring apparatus |
| JP2007066034A (en) * | 2005-08-31 | 2007-03-15 | Yukio Yazaki | Disaster notification system |
| CN101799561A (en) * | 2010-02-05 | 2010-08-11 | 民政部国家减灾中心 | Snow disaster remote sensing monitoring simulation evaluation method based on disaster reduction small satellite |
| CN102646219A (en) * | 2012-02-27 | 2012-08-22 | 兰州大学 | Method for pre-warning snow disaster in pasturing area |
Non-Patent Citations (3)
| Title |
|---|
| 仝川等: "温带草原放牧场积雪灾害分级评价的遥感分析", 《内蒙古大学学报（自然科学版）》 * |
| 张旭: "基于多源遥感数据的雪盖信息提取算法及其应用研究", 《中国优秀硕士学位论文全文数据库》 * |
| 王博等: "基于GIS的内蒙古中部牧区雪灾风险评估模型研究", 《安徽农业科学》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780433A (en) * | 2016-11-16 | 2017-05-31 | 清华大学 | Accumulated snow phenology information fusion method and system based on multi- source Remote Sensing Data data |
| CN106780433B (en) * | 2016-11-16 | 2019-10-11 | 清华大学 | Accumulated snow phenology information fusion method and system based on multi- source Remote Sensing Data data |
| CN109871988A (en) * | 2019-01-28 | 2019-06-11 | 河海大学 | A kind of flood forecasting early warning precision analytical method |
| CN109871988B (en) * | 2019-01-28 | 2021-06-01 | 河海大学 | Flood forecast early warning precision analysis method |
| CN111291945A (en) * | 2019-04-16 | 2020-06-16 | 中国水利水电科学研究院 | Dynamic assessment method for dry disaster loss of animal husbandry |
| CN111401689A (en) * | 2020-02-19 | 2020-07-10 | 远景智能国际私人投资有限公司 | Method, device and equipment for determining snowfall date of photovoltaic station and storage medium |
| CN111401689B (en) * | 2020-02-19 | 2023-08-04 | 远景智能国际私人投资有限公司 | Determination method, device and equipment for snowfall date of photovoltaic station and storage medium |
| CN111626599A (en) * | 2020-05-22 | 2020-09-04 | 广东省突发事件预警信息发布中心(广东省人工影响天气中心) | Meteorological disaster risk studying and judging method and system |
| CN113642877A (en) * | 2021-08-05 | 2021-11-12 | 中国农业科学院草原研究所 | Snow disaster situation assessment method and system based on actual disaster damage of herdsmen |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103593582A (en) | Area snow disaster risk estimation method | |
| Park et al. | Simulating hydrologic responses to alternate grazing management practices at the ranch and watershed scales | |
| Liu et al. | Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts | |
| Thuy et al. | Vulnerability of rice production in Mekong River Delta under impacts from floods, salinity and climate change | |
| Bleich et al. | Habitat use by a desert ungulate: predicting effects of water availability on mountain sheep | |
| CN103577719A (en) | Method for estimating regional snow disaster risk | |
| Qiu et al. | Impacts of snow disaster on rural livelihoods in southern Tibet-Qinghai Plateau | |
| Girard et al. | Seasonal variation in habitat selection by free-ranging feral horses within Alberta’s forest reserve | |
| CN103577720A (en) | Method for estimating regional drought risk | |
| CN103605901A (en) | Regional drought risk assessment method | |
| Yerli et al. | An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey) | |
| Kahn et al. | ASKBILL as a web-based program to enhance sheep well-being and productivity | |
| Farazmand et al. | Determining the factors affecting rangeland suitability for livestock and wildlife grazing. | |
| Enari et al. | Estimation of abundance and distribution of Japanese macaques using track counts in snow | |
| Tilse et al. | Mapping the impact of subsoil constraints on soil available water capacity and potential crop yield | |
| Taper et al. | The phenology of space: spatial aspects of bison density dependence in Yellowstone National Park | |
| Taylor et al. | The influence of surface layer salinity on wintertime convection in Wilkinson Basin, Gulf of Maine | |
| Meng et al. | Differing migration patterns and year-round habitat-use of allopatric nesting Eastern Tundra Bean Geese (Anser fabalis serrirostris) in East Asia | |
| Marker | Spatial scale and nest distribution of little penguins (Eudyptula minor) | |
| Farhan et al. | Future Scenario of Spatiotemporal Changes in Land Use and Land Cover Using CA-Markov Model, GIS and Remote Sensing Applications | |
| Collins et al. | Dove habitat association based on remotely sensed land cover types in South Texas | |
| Kolar | Pronghorn migration and resource selection in southwestern North Dakota | |
| Russell | Kiggavik Project effects: Energy-protein and population modeling of the Qamanirjuaq caribou herd | |
| Hao et al. | Sensitivity Analysis of APSIM Wheat Yield Predictions | |
| Boulanger et al. | Analysis of environmental, temporal, and spatial factors affecting demography of the Bathurst and Bluenose-East caribou herds |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140219 |