KR20150080210A - System for abnormal agroclimatic index and evaluation method therefor - Google Patents

Abstract

The present invention relates to a system of an agro-climatic index under abnormal climate and a method for evaluating an agro-climatic index under abnormal climate by using the same and, in particular, to a system for evaluating an agro-climatic index under abnormal climate with six surrogate parameters selected by considering parameters affecting an agricultural field and a method for evaluating an agro-climatic index under abnormal climate by using the same. The present invention provides a system of an agro-climatic index under abnormal climate and a method for evaluating an agro-climatic index under abnormal climate by using the same, which enable a user to subjectively and clearly identify a condition under which abnormal climate occurs by seasons and by regions. In addition, the present invention provides a system of an agro-climatic index under abnormal climate, which is tailored to the Korean peninsula to enable a user involved in an agricultural field vulnerable to a climate change to be prepared for a natural disaster caused by a climate change, and a method for evaluating an agro-climatic index under abnormal climate by using the same. Furthermore, the present invention provides a system of an agro-climatic index under abnormal climate, which is configured so that climate-causing disasters in an agricultural field are properly reflected in the six surrogate parameters selected by considering parameter affecting an agricultural field, and a method for evaluating an agro-climatic index under abnormal climate by using the same.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an agricultural climate index system and an agricultural climate index system,

The present invention relates to an agricultural abnormal climate index system and a method for evaluating an agricultural abnormal climate index using the same, and more particularly, to a system and method for evaluating agriculture abnormal climate index with six proxy variables selected in consideration of variables affecting the agricultural sector And a method for evaluating agricultural abnormal climate indexes.

Recently, accelerated global warming has caused weather changes in many parts of the world. Drought and floods occurred on the Korean peninsula several times, causing damage to people and property. Especially recently, the damage of ecosystem due to shortage of sunshine is increasing. Climate change trends on the Korean peninsula are already being detected not only in the meteorological field but also in a wide range of ecological, environmental, and water resources. In particular, the agricultural sector is severely damaged by the weather, which is directly related to the production of agricultural products and directly to the crisis. Accordingly, there is a need for a customized climate change vulnerability assessment index system for the Korean peninsula and an evaluation method using the index system to prepare for disasters caused by climate change in the agricultural field.

1. Korean Registered Patent No. 1055291 (Registered on August 2, 2011)

It is an object of the present invention to provide an agricultural abnormal climate index system and a method of evaluating the agricultural abnormal climate index using the same, which can provide a climate change vulnerability assessment index so that a disaster caused by agricultural climate change can be prepared in advance.

According to another aspect of the present invention, there is provided a surveillance system including a surrogate parameter calculation unit for collecting and standardizing meteorological information to calculate surrogate parameters for meteorological information, And a risk indicator indicating the degree of the risk.

The surrogate parameter calculation unit includes a weather information collection module for collecting weather information, and a parameter standardization module for standardizing the weather information collected by the weather information collection module. However, the present invention is not limited to this, and the proxy parameter calculation unit may include a weather information prediction module for predicting weather information and a parameter standardization module for standardizing the weather information predicted by the weather information prediction module. The variable normalization module includes a jet score module for standardizing the value of the weather information by a Z-score method, and a section score for applying surrogate scores to the values of the weather information standardized in the jet score module, Application module.

The section score applying module assigns a score of 100 to 10 for 5? To 5? Standardized weather information values in the jet score module, and a score of 10 for every range of the standardized weather information value 1? do. The meteorological information includes temperature, precipitation, precipitation, wind speed, cloudiness, and solar radiation.

The limit level comparator includes a limit level setting module for setting a limit level of the risk exposure condition for each surrogate variable, a limit level table for storing and managing the limit level, and a limit level table for managing the limit level stored in the limit level table, And a limit level comparison module for comparing the proxy variable values derived from the module. The threshold level of the risk exposure condition by the proxy variable is such that the daily maximum temperature is 33 ° C or higher, the drought is the SPI index is -1 or less, the daylight hours are less than 6 hours, 13 ° C or less, the maximum rainfall of the day is 80 mm or more, and the strong wind is the maximum wind speed of 14 m / s or more.

Wherein the risk display unit displays a surrogate variable value exceeding the limit level by a different color for each surrogate variable value on a map segmented by administrative district and a different brightness according to the magnitude of surrogate variable value, And a graph display module for displaying a proxy variable value in a radial graph.

Calculating a surrogate variable for the meteorological information by collecting and standardizing the meteorological information of the surrogate variable calculation unit; calculating a risk by comparing the surrogate variable with a preset threshold level by a threshold level comparator; The method comprising the steps of: a) displaying an indicator of the agricultural climate index.

Wherein the proxy parameter calculation unit collects and standardizes the weather information to calculate a surrogate variable for the weather information includes a step of collecting weather information by the weather information collecting module and a step of standardizing the weather information collected by the weather information collecting module . However, the present invention is not limited to this, and the proxy parameter calculation unit may collect and standardize the weather information to calculate the surrogate parameter for the weather information, including the steps of: predicting the weather information by the weather information prediction module; And standardizing the weather information predicted by the weather information prediction module. The standardization of the weather information includes standardizing the value of the weather information by a Z score method by a jet score module and applying a section score to a value of standard weather information in the jet score module, And then substituting the parameters.

The section score applying module assigns a score of 100 to 10 for 5? To 5? Standardized weather information values in the jet score module, and a score of 10 for every range of the standardized weather information value 1? do. The meteorological information includes temperature, precipitation, precipitation, wind speed, cloudiness, and solar radiation.

The step of calculating the risk by comparing the surrogate variable with a preset threshold level by comparing the threshold level comparing unit includes the steps of setting the threshold level of the risk expression condition for each surrogate variable through the threshold level setting module, Level and a surrogate variable value derived from the interval score applying module are compared by a limit level comparing module. The threshold level of the risk exposure condition by the proxy variable is such that the daily maximum temperature is 33 ° C or higher, the drought is the SPI index is -1 or less, the daylight hours are less than 6 hours, 13 ° C or less, the maximum rainfall of the day is 80 mm or more, and the strong wind is the maximum wind speed of 14 m / s or more.

The step of displaying the risk level by the risk level display unit may further include displaying the proxy level value exceeding the threshold level in a color different from each other according to the proxy variable value on the map segmented by region by the administration zone, And displaying the surrogate variable value in a radial graph by the graph display module.

The present invention can provide an agricultural abnormal weather index system which can clearly and objectively judge the occurrence of an abnormal weather by season / region and an agricultural abnormal weather index evaluation method using the same.

Further, the present invention can provide a customized climate change vulnerability agricultural climate index system for the Korean peninsula and a method for evaluating an agricultural climate index using the same, which can prepare for a disaster caused by climate change in the agricultural field.

In addition, the present invention can provide an agricultural weather indicator system and an evaluation method of an agricultural weather indicator using the six representative variables selected in consideration of the parameters affecting the agricultural field, have.

1 is a conceptual diagram of an agricultural weather index system according to the present invention;
Fig. 2 is an illustration of a result of standardization of temperature information in an agricultural weather index system according to the present invention. Fig.
3 is an illustration of color and lightness according to surrogate values of an agricultural weather forecasting system according to the present invention.
4 is a graph displayed in an agricultural weather index system according to the present invention.
FIG. 5 is an exemplary screen of an end result applying the agricultural weather index system according to the present invention. FIG.
6 is a flowchart of a method for evaluating an agricultural abnormal climate index according to the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

It will be apparent to those skilled in the art that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, It is provided to let you know. Like reference numerals refer to like elements throughout.

1 is a conceptual diagram of an agricultural weather index system according to the present invention.

As shown in FIG. 1, the agricultural abnormal climate index system according to the present invention includes a surrogate parameter calculation unit 100, a threshold level comparator 200, and a risk indicator 300.

The proxy parameter calculation unit 100 includes a weather information collection module 110 or a weather information prediction module 120 and a variable standardization module 130 for collecting and standardizing variables that are weather information, .

The weather information collecting module 110 collects weather information from a nationwide manned observatory to calculate surrogate variables. Here, surrogate variables include heatwaves, droughts, sunshine hours, cold weather, dows and strong winds. In order to calculate such proxy variables, the present invention collects and utilizes weather information such as temperature, precipitation, number of precipitation, wind speed, cloudiness, and solar radiation observed at a nationwide manned observatory, Is passed to the parameter normalization module. However, the present invention is not limited thereto, and the weather information prediction module 120 may be provided in addition to the weather information collection module 110 or the weather information collection module 110.

Since the meta information collected or predicted by the meteorological information collecting module 110 or the meteorological information predicting module 120 is different from each other in units, the meta information standardization module 130 must pass all the meteorological information, i.e., the parameters, through the standardization method. Accordingly, in the present invention, the variable standardization module uses a Z-score (standard score) method to standardize variables and convert them into surrogate variables. To this end, the variable standardization module 130 uses the Z score module 132 and the interval And a score application module 134.

The jet score module 132 compares the variables of different units collected in the weather information collecting module. The jet score module 132 divides the difference between the variables collected in the weather information collecting module 110 and the average value of the variables by the standard deviation .

The section score applying module 134 applies the section score to the result value calculated by the jet score module 132. [

Table 1 shows the interval scores for surrogate variable values in the agricultural abnormal climate index system according to the present invention.

σ section score σ section score 5σ or more 100  1σ to -1σ 50 5σ to 4σ 90 -1σ to -2σ 40 4σ to 3σ 80 -2σ to -3σ 30 3σ to 2σ 70 -3σ to -4σ 20 2σ to 1σ 60 -4σ to -5σ 10

Referring to Table 1, in the present invention, when the proxy variable value calculated by the jet score module 132 is 5σ or more, the interval score is 100, and when the value between 5σ and 4σ is 90, A score of 100 to 10 is given. Here, the surrogate variable value is given a score of 10 for every positive range of positive values and negative integer values. For example, in the case of hot weather, the risk is expressed when the daily maximum temperature observation or predicted value exceeds 33 캜 as shown in Fig.

The limit level comparator 200 compares the surrogate variable with a set threshold level and includes a threshold level setting module 210 and a threshold level table 220 and a threshold level comparing module 230.

The limit level setting module 210 sets a risk exposure condition for each surrogate variable. That is, since the proxy variable value not exceeding a certain level does not depend on the risk expression condition, the limit level for the proxy variable value is set. Since the surrogate variable of the present invention is six, the limit level setting module 210 includes the overheat limit level setting module 211, the drought limit level setting module 211, A cold limit threshold level setting module 213, a cold limit threshold level setting module 214, a DOW threshold level setting module 215, and a strong wind threshold level setting module 216. [ In addition, each of the threshold level setting modules sets a threshold level for each of the heatwaves, drought, sunshine hours, cold weather, dow, and strong winds, and the respective threshold levels are shown in Table 2 below.

Table 2 shows the risk exposure conditions for surrogate variables in the agricultural abnormal climate index system according to the present invention.

Surrogate variable Condition Risk scorching heat Day when the maximum temperature is over 33 ℃ High temperature stress drought A day when the SPI index is below 1 Criteria for drought concerns Daylight hours Sunlight hours less than 6 hours Daylight Time Interference Cold weather When the daily minimum temperature is below 13 ℃ Criteria for inhibiting growth Dow When the maximum precipitation is more than 80mm Heavy rainfall frequency gale When the maximum wind speed is more than 14m / s Strong wind

As shown in Table 2, the heat stress is expressed on the day when the daily maximum temperature is 33 ° C or higher, and the drought is based on the Standard Precipitation Index (SPI). The sunshine hours are based on the average sunshine hours of April to June of about 6 hours from the past 1920-2010 Korea ASOS past observations. In cold weather, the risk is expressed when the daily minimum temperature is less than 13 ℃, when the maximum daily precipitation is 80mm or more, and when the maximum wind speed is 14m / s or more.

The threshold level table 220 stores and manages the threshold level values for the heatwaves, droughts, daylight hours, cold weather, dows, and strong winds set in the threshold level setting module 210.

The limit level comparison module 230 compares the result value calculated in the interval score application module 134, that is, the proxy variable value, with the limit level value stored in the limit level table 220. [

The risk display unit 300 displays a surrogate variable value or all surrogate variable values exceeding the limit level value in the limit level comparison module 230. [ For this, the risk display unit 300 includes a spatial display module 310 and a graph display module 320.

Figure 3 is an illustration of color and lightness according to surrogate values of an agricultural weather index system according to the present invention.

The spatial display module 310 displays on the map a proxy variable value that exceeds the threshold level value. For example, you can use ArcGIS to map agricultural disaster values to a map of the Korean peninsula. Here, the spatial display module 310 can display different colors for the proxy variable values as shown in FIG. In addition, the magnitude of the proxy variable value can be expressed by the brightness of the color. Since only one color can be displayed in one administrative area, only the proxy values exceeding the limit level value are displayed on the map. Of course, when there are several proxy variable values exceeding the limit level value, only the proxy variable values exceeding the limit level value are displayed in color in the administrative area. Accordingly, the present invention displays all the surrogate variable values that can not be displayed in the spatial display module 310 through the separate graph display module 320.

4 is a graph displayed in an agricultural weather index system according to the present invention.

The graph display module 320 displays a surrogate variable value as a graph. In the present invention, as shown in FIG. 4, the graph display module 320 displays a proxy variable value in a hexagonal radial graph. In a hexagonal radial graph, the bias of the proxy variable is a meteorological disaster affecting agriculture. These meteorological disasters may not be a single disaster, but multiple disasters may occur at the same time.

5 is an exemplary screen of an end result of applying the agricultural weather index system according to the present invention.

Referring to FIG. 5, in the 1970s, only the temperature value of the Korean peninsula was shown on the map of the Korean peninsula. However, since the maximum value of the drought in the hexagonal radial graph exceeded the threshold value, . Therefore, the map of the Korean peninsula and the hexagonal radial graph should be displayed at the same time, so that the index value can be calculated by time and space. Since each crop has a different threshold temperature, it is easy to set an agricultural climate index for a crop by applying specific limits for each crop. Therefore, this index can be used to easily derive ideal climatic indices for individual crops and fruit trees, if knowledge of the physiological temperature of the crops desired by the farmers is known.

INDUSTRIAL APPLICABILITY As described above, the present invention can provide an agricultural weather index system that can clearly and objectively judge the occurrence of abnormal climates by season / region. In addition, the present invention can provide a customized climate change vulnerability index system for the Korean Peninsula that can prepare for a disaster caused by climate change in the agricultural field. In addition, the present invention can provide an agricultural weather index system constituted so that six proxy variables selected in consideration of the parameters affecting the agricultural field can appropriately reflect the weather disaster in the agricultural field.

Hereinafter, a method for calculating the agricultural abnormal climate index according to the present invention will be described with reference to the drawings. Hereinafter, the description of the agricultural abnormal climate index system according to the present invention, which will be described later, will be omitted or briefly explained.

6 is a flowchart of a method for calculating an agricultural abnormal climate index according to the present invention.

As shown in FIG. 6, the method for calculating the agricultural abnormal climate index according to the present invention includes a step S1 of calculating a surrogate variable, a step S2 of comparing a threshold level, and a step S3 of displaying a risk degree do.

In the step S1 of calculating the surrogate variable, the surrogate variable calculating section collects and standardizes the surrogate variable to calculate the surrogate variable. To this end, step S1 of calculating a surrogate variable includes step S1-1 of collecting weather information or step S1-2 of predicting weather information, and step S1-3 of standardizing surrogate variables do.

In the step (S1-1) of collecting the weather information, the weather information collecting module collects weather information from the nationwide manned observing stations. As described above, the surrogate variable includes a temperature, a precipitation, a wind speed, a cloud, an irradiation amount And the like, that is, variables.

The step (S1-2) of predicting the weather information predicts the weather information instead of the step (S1-1) of collecting the weather information. That is, in the case of the step S1-1 of collecting the weather information, it is possible to standardize the parameters of the present (including the past when the past weather information is collected) And to standardize future variables.

The step of normalizing the variable S1-3 includes the steps of collecting the meteorological information (S1-1) or the meteorological information collected in the step S1-2 for predicting the meteorological information, that is, .

In the step S2 of comparing the threshold level, the threshold level comparing unit compares the preset threshold level with the proxy value. The step S2 of comparing the threshold level for this purpose includes the step S2-1 of setting the threshold level and the step S2-2 of comparing the threshold level.

In step S2-1 of setting the limit level, the risk expression condition for each proxy variable is set through the limit level setting module. Since the target to set the limit level is a hot spot, a drought, a sunshine hour, a cold weather, a drought and a strong wind, the step of setting the threshold level includes setting a threshold level, setting a drought threshold level, Setting a cold limit level, setting a DOW limit level, and setting a strong wind threshold level.

In step S2-2, the threshold level is compared with the surrogate level in step S2-1.

The step of displaying the risk (S3) displays the risk based on the proxy variable value. This includes a space exposing step S3-1 and a step S3-2 of displaying a graph.

In the space exposing step (S3-1), the space exposing module displays on the map the surrogate variable value exceeding the threshold level value derived in the step S2-2 of comparing the threshold level. This, as described above, can be used to display agricultural weather disaster values on a Korean Peninsula map using ArcGIS.

In a step S3-2 of displaying a graph, the graph display module displays a surrogate variable value as a graph. This can be expressed as a hexagonal radial graph. Here, in the hexagonal radial graph, a proxy variable value that is biased by the graph is a meteorological disaster affecting agriculture.

INDUSTRIAL APPLICABILITY As described above, the present invention can provide a method for evaluating agricultural weather indexes that can be clearly and objectively judged by taking into account the occurrence of abnormal climates in different periods and regions. In addition, the present invention can provide a method of evaluating a customized climate change vulnerability index on the Korean Peninsula that can prepare for a disaster caused by climate change in the agricultural field. In addition, the present invention can provide a method for evaluating an agricultural weather index, in which six proxy variables selected in consideration of the parameters affecting the agricultural sector are appropriately reflected in the agricultural disaster.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the appended claims. You will understand.

100: substitution variable calculation unit 110: variable collection module
120: Variable Normalization Module 122: Jet Score Module
124: interval score application module 200: limit level comparator
210: Limit level setting module 220: Limit level table
230: Limit level comparison module 300: Risk indicator
310: Graph Display Module 320: Spatial Display Module

Claims (18)

A proxy variable calculation unit for collecting and standardizing the weather information to calculate a proxy variable for the weather information,
A limit level comparator for comparing the proxy variable with a predetermined limit level to calculate a risk level;
And a risk indicator indicating the risk. The method according to claim 1,
Wherein the substitute variable calculating unit comprises:
A weather information collection module for collecting weather information, and
And a parameter standardization module for standardizing the weather information collected by the weather information collection module. The method according to claim 1,
Wherein the substitute variable calculating unit comprises:
A weather information prediction module for predicting weather information, and
And a parameter standardization module for standardizing the weather information predicted by the weather information prediction module. The method according to claim 2 or 3,
Wherein the variable standardization module comprises:
A jet score module for standardizing the value of the weather information by a Z-Score method, and
And a section score applying module for applying the interval score to the value of the standardized weather information in the jet score module to substitute parameter. The method of claim 4,
Wherein the interval score application module assigns a standardized weather information value in the jet score module to a score of 100 to 10 for 5σ to -5σ,
And an interval index score of 10 is provided for every range of the standardized weather information value 1σ. The method of claim 5,
The surrogate variables include hot flashes, droughts, sunshine hours, cold weather, dows and strong winds,
Wherein the weather information includes a temperature, a precipitation, a precipitation number, a wind speed, a cloud, and an irradiation amount. The method of claim 6,
Wherein the limit level comparison unit comprises:
A limit level setting module for setting a limit level of the risk exposure condition for each surrogate variable;
A limit level table for storing and managing the limit level, and
And a threshold level comparison module for comparing the threshold level stored in the threshold level table with a proxy value derived from the interval score application module. The method of claim 7,
The limit level of the risk exposure condition for each proxy variable,
The above-mentioned hot-water temperature is higher than or equal to 33 ° C,
The drought is characterized by an SPI index of less than or equal to -1,
The above-mentioned daylight hours may be set such that the daylight hours are less than 6 hours,
The cold weather has a daily minimum temperature of 13 ° C or lower,
The dow has a maximum precipitation of 80 mm or more,
Wherein the strong wind is at a maximum wind speed of 14 m / s or higher. The method of claim 8,
The risk-
A spatial expression module for expressing surrogate variable values exceeding the threshold level by a different color for each surrogate variable value and a different brightness value according to the size of the surrogate variable on a map segmented by regions by administrative regions,
And a graph display module for displaying the proxy variable value in a radial graph. Collecting and standardizing the meteorological information of the surrogate parameter calculation unit to calculate surrogate parameters for the meteorological information,
Comparing the surrogate variable with a predetermined threshold level to calculate a risk level;
And the risk indicator is displayed by the risk indicator. The method of claim 10,
Wherein the proxy parameter calculation unit collects and standardizes the weather information to calculate a surrogate variable for the weather information,
The weather information collection module collecting the weather information, and
And standardizing the weather information collected by the weather information collecting module. The method of claim 10,
Wherein the proxy parameter calculation unit collects and standardizes the weather information to calculate a surrogate variable for the weather information,
A step in which the weather information prediction module predicts the weather information, and
And standardizing the weather information predicted by the weather information prediction module. The method according to claim 11 or 12,
The standardization of the weather information includes:
Standardizing the value of the weather information by a Jet Score module in a Z-Score method, and
Wherein the step of applying the interval score to the value of the weather information standardized in the jet score module includes applying the interval score to the proxy parameter. 14. The method of claim 13,
Wherein the interval score application module assigns a standardized weather information value in the jet score module to a score of 100 to 10 for 5σ to -5σ,
And an interval score of 10 is given for every range of the standardized weather information value 1σ. 15. The method of claim 14,
The surrogate variables include hot flashes, droughts, sunshine hours, cold weather, dows and strong winds,
Wherein the weather information includes a temperature, a precipitation, a precipitation number, a wind speed, a cloud, and an irradiation amount. 16. The method of claim 15,
The step of calculating the risk by comparing the surrogate variable with a predetermined threshold level by the threshold level comparing unit,
Setting a threshold level of risk exposure condition for each surrogate variable through a threshold level setting module; and
Comparing the threshold level stored in the threshold level table with a surrogate variable value derived from the interval score application module by a threshold level comparison module. 18. The method of claim 16,
The limit level of the risk exposure condition for each proxy variable,
The above-mentioned hot-water temperature is higher than or equal to 33 ° C,
The drought is characterized by an SPI index of less than or equal to -1,
The above-mentioned daylight hours may be set such that the daylight hours are less than 6 hours,
The cold weather has a daily minimum temperature of 13 ° C or lower,
The dow has a maximum precipitation of 80 mm or more,
Wherein said strong wind is at a maximum wind speed of 14 m / s or higher. 18. The method of claim 17,
The step of displaying the risk by the risk indicator comprises:
Expressing surrogate variable values exceeding the threshold level by a different color for each surrogate variable value and a different brightness value according to the magnitude of surrogate variable on a map segmented by administrative district by administrative space,
And displaying the proxy variable value in a radial graph by the graphical display module.

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