CN117688290B - Drought disaster comprehensive risk early warning method and system based on grading - Google Patents
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Abstract
The invention provides a drought disaster comprehensive risk early warning method and system based on grading, wherein the method comprises the following steps: establishing influence factors of weather drought, preprocessing weather drought data, classifying weather drought grades, normalizing weather drought data sets of all sites, constructing risk indexes of weather drought disasters, expanding early warning grades of the risk indexes of the weather drought disasters, and inputting calculation results into an early warning system for early warning display; according to the invention, month relative wettability index, cultivated area and vegetation area influence factors are introduced, and the grades are classified based on the history month relative wettability index mode, so that the existing system is researched and simplified, the weather drought grade and the drought disaster risk grade are integrated to perform weather drought comprehensive risk early warning, the early warning is visualized by utilizing the system and equipment, and the efficiency and the credibility of the drought risk early warning are improved.
Description
Technical Field
The invention belongs to the technical field of meteorological monitoring, and particularly relates to a drought disaster comprehensive risk early warning method and system based on grading.
Background
Meteorological drought refers to the phenomenon that in a certain period of time, the evaporation amount and precipitation amount of water are unbalanced in a certain place, and the water expenditure is larger than the water income, so that the water shortage of the surface is caused. The weather drought not only causes agriculture yield reduction, food shortage, property loss and even social fluctuation, but also brings a series of secondary disasters in the aspect of environment.
Weather drought is the cause of other types of drought and the basis for monitoring and assessment. The comprehensive meteorological drought index (CI) and the meteorological drought comprehensive monitoring index (MCI) are respectively provided in 2006 and 2012 in China, and the drought frequency distribution of different areas and the season distribution characteristics of different grades of drought in year in China are reflected well.
Although the new drought early warning method based on the MCI method is obviously improved compared with the CI method, the method has the advantages that because more data are needed, some data are not easy to obtain or have poor stability, and the method is limited in practical early warning application, so that a new drought disaster early warning method needs to be researched.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a drought disaster comprehensive risk early warning method and system based on grading, which are constructed through drought grading, so that the drought disaster comprehensive risk early warning technical level is improved, and technical support is provided for the operation of drought disaster comprehensive early warning business and disaster early warning information release.
According to the drought disaster comprehensive risk early warning method, month relative wettability indexes, cultivated land areas and vegetation area influence factors are introduced, the existing system is researched and simplified through grading, and the weather drought comprehensive risk early warning is carried out by integrating weather drought grades and drought disaster risk early warning grades, so that the early warning accuracy is ensured; and the early warning is visualized by using the system, so that the drought risk early warning efficiency and reliability are improved.
The invention provides a drought disaster comprehensive risk early warning method based on grading, which comprises the following steps:
S1, constructing an influence factor of weather drought disaster risk, wherein the influence factor of the weather drought disaster risk comprises a month relative wettability index x 1, a cultivated area x 2 and a vegetation area x 3;
S2, preprocessing weather drought data of each site/region:
S21, calculating potential evapotranspiration PET, wherein the calculation formula is as follows:
Wherein PET is the potential amount of evapotranspiration per month in millimeters per month; t i is the average air temperature per month in degrees Celsius; h is annual caloric index; a is a constant;
S22, calculating a month relative wettability index MI by using the month accumulated precipitation P and the potential evapotranspiration PET in the same period, wherein the formula is as follows:
Wherein, the unit of P is millimeter/month;
S3, judging whether drought occurs or not: drawing a relative wettability index (MI) distribution histogram of each site/region history for 30 years month by month, carrying out weather drought judgment and drought classification based on the MI distribution histogram, marking as A, wherein A=0, 1, 2, 3 or 4, judging the region drought according to the value of A, and specifically comprising the following substeps:
S31, calculating the value of A: determining a value based on a difference delta between the relative wettability index MI i for the month of the region and the mode MI m for the month of the history of the month of the region,
Δ＝MIi-MIm，i＝1，2……12；
When the value of delta is greater than or equal to 0, the value of A is 0, when the value of delta is greater than c and less than 0, the value of A is 1, when the value of delta is greater than d and less than c, the value of A is 2, when the value of delta is greater than e and less than d, the value of A is 3, and when the value of delta is less than e, the value of A is 4; wherein the value of c is-0.2, the value of d is-0.4, and the value of e is-0.6;
S32, judging the drought of the region according to the value of A: when the value A is 0, judging that drought does not occur in the area, and when the value A is 1,2, 3 or 4, judging that drought occurs in the area and entering the steps S4-S6 to perform drought disaster risk early warning and display;
s4, carrying out normalization processing on meteorological drought data sets of all sites/regions, wherein a calculation formula is as follows:
Wherein x i is an influence factor, and the value of i is 1,2 or 3; s i is disaster factor of x i after normalization, and x i max and x i min are maximum value and minimum value of x i respectively; when i takes a value of 1, x 1 max and x 1 min are the maximum value and the minimum value of the month and month relative wettability index MI in 30 years of the site/region history; when i takes a value of 2 or 3, x i max and x i min are the maximum and minimum values of x i in all sites/regions of the whole research area; finally, the disaster causing factor value after normalization of each influencing factor is between 0 and 1;
S5, constructing a weather drought disaster risk index I, wherein the I is expressed as follows:
I＝(1-s1)*(β2s2+β3s3)
Wherein, β 2 and β 3 are expert weights or the area ratios occupied by the references x 2 and x 3, and satisfy β 2+β3 =1;
S6, carrying out drought disaster risk early warning grade division by combining the weather drought disaster risk indexes I of each site, recording as B, inputting the calculation result of the B into an early warning system for early warning display, and specifically comprising the following sub-steps:
s61, dividing a weather drought disaster risk index I into 4 grades, and obtaining a first percentile breakpoint P1, a second percentile breakpoint P2 and a third percentile breakpoint P3 which are arranged from low to high to form 5 grades of early warning comprising a non-early warning layer, primary early warning, secondary early warning, tertiary early warning and quaternary early warning;
S62, respectively carrying out early warning display in five colors in an early warning system.
Preferably, step S21 specifically includes the steps of:
the monthly caloric index H i is calculated by the formula:
the annual heat index H is calculated as follows:
The constant a is calculated from the following formula:
a＝6.45×10-7H3-7.71×10-5H2+1.792×10-2H+0.49。
Preferably, in step S61, a natural breakpoint method or an empirical value in the ArcGIS map is adopted to form a corresponding drought disaster comprehensive risk early warning grade standard, so that the weather drought disaster risk index I is divided into 4 grades.
Preferably, in step S62, early warning display is performed in the early warning system in five colors of green, yellow, orange, powder and red, respectively.
The invention also provides a drought disaster comprehensive risk early warning system based on grading for realizing the method, which comprises a data acquisition module, an operation module, a display module and an early warning module, wherein the data acquisition module acquires weather drought data sets of all sites, the operation module processes the data sets by using the drought disaster comprehensive risk early warning method based on grading, the display module displays weather drought disaster conditions by using an ArcGIS map, and the early warning module displays the drought disaster comprehensive risk early warning conditions by different colors.
Compared with the prior art, the invention has the technical effects that:
1. According to the drought disaster comprehensive risk early warning method based on grading, an existing system is researched and simplified through grading, month relative wettability indexes, cultivated area and vegetation area influence factors are introduced, a 30-year month relative wettability index MI distribution histogram of each site/region history is drawn, a new drought judgment standard is provided, a weather drought disaster risk index and weather drought grading principle is constructed, and drought risk early warning efficiency and reliability are improved.
2. According to the drought disaster comprehensive risk early warning system based on grading, the data acquisition module, the operation module, the display module and the early warning module are reasonably arranged, so that early warning efficiency and accuracy are improved, the data acquisition module can acquire weather drought data sets of all sites, the operation module processes the data sets by using a drought disaster comprehensive risk early warning method based on grading, the display module can display weather drought disaster conditions by using an ArcGIS map, the early warning module can display the drought disaster comprehensive risk early warning conditions through different colors, and the weather drought conditions of all sites are continuously updated by the cooperation of the modules, so that drought early warning is visualized and systemized.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings.
FIG. 1 is a flow chart of a drought disaster comprehensive risk early warning method based on grading;
FIG. 2 is a schematic diagram of a drought disaster comprehensive risk early warning system based on grading;
fig. 3 (a) -3 (g) are 30 year MI distribution histograms of 4-10 month history of 97 sites of inner mongolia in the example of the present invention.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Fig. 1 shows a drought disaster comprehensive risk early warning method based on grading, which comprises the following steps:
S1, constructing a meteorological drought influence factor:
The influence factors of the weather drought are determined according to the traditional climate theory and the weather drought index, and are selected and calculated from the weather drought data set, and the influence factors comprise: a relative lunar wettability index x 1, a cultivated area x 2, and a vegetation area x 3.
S2, preprocessing weather drought data of each site/region:
S21, calculating potential evapotranspiration PET, wherein the calculation formula is as follows:
wherein PET is the potential amount of evapotranspiration per month in millimeters per month; t i is the average air temperature per month in degrees Celsius; h is annual caloric index; a is a constant.
The step S21 specifically includes the following steps:
the monthly caloric index H i is calculated by the formula:
The annual heat index H is calculated as follows:
The constant a is calculated from the following formula:
a＝6.45×10-7H3-7.71×10-5H2+1.792×10-2H+0.49
the value criteria for drought severity class a are shown in table 1 below:
TABLE 1
S22, calculating a month relative wettability index MI by using the month accumulated precipitation P and the potential evapotranspiration PET in the same period, wherein the formula is as follows:
where P is in mm/month.
S3, judging whether drought occurs or not: drawing a relative wettability index (MI) distribution histogram of each site/region history for 30 years month by month, carrying out weather drought judgment and drought classification based on the MI distribution histogram, marking as A, wherein A=0, 1, 2, 3 or 4, judging the region drought according to the value of A, and specifically comprising the following substeps:
S31, calculating the value of A: determining a value based on a difference delta between the relative wettability index MI i for the month of the region and the mode MI m for the month of the history of the month of the region,
Δ＝MIi-MIm，i＝1，2……12；
When the value of delta is greater than or equal to 0, the value of A is 0, when the value of delta is greater than c and less than 0, the value of A is 1, when the value of delta is greater than d and less than c, the value of A is 2, when the value of delta is greater than e and less than d, the value of A is 3, and when the value of delta is less than e, the value of A is 4; wherein the value of c is-0.2, the value of d is-0.4, and the value of e is-0.6.
S32, judging the drought of the region according to the value of A: and when the value A is 0, judging that drought does not occur in the area, and when the value A is 1,2,3 or 4, judging that drought occurs in the area and entering the steps S4-S6 to perform drought disaster risk early warning and display.
S4, carrying out normalization processing on meteorological drought data sets of all sites/regions, wherein a calculation formula is as follows:
Wherein x i is an influence factor, and the value of i is 1,2 or 3; s i is disaster factor of x i after normalization, and x i max and x i min are maximum value and minimum value of x i respectively; when i takes a value of 1, x 1 max and x 1 min are the maximum value and the minimum value of the month and month relative wettability index MI in 30 years of the site/region history; when i takes a value of 2 or 3, x i max and x i min are the maximum and minimum values of x i in all sites/regions of the whole research area; finally, the disaster causing factor value after normalization of each influencing factor is between 0 and 1.
S5, constructing a weather drought disaster risk index I, wherein the I is expressed as follows:
I＝(1-*1)*(β2s2+β3s3)
Wherein, β 2 and β 3 are expert weights or the area ratios occupied by the references x 2 and x 3, and satisfy β 2+β3 =1;
S6, carrying out drought disaster risk early warning grade division by combining the weather drought disaster risk indexes I of each site, recording as B, inputting the calculation result of the B into an early warning system for early warning display, and specifically comprising the following sub-steps:
s61, dividing a weather drought disaster risk index I into 4 grades, and obtaining a first percentile breakpoint P1, a second percentile breakpoint P2 and a third percentile breakpoint P3 which are arranged from low to high to form 5 grades of early warning comprising a non-early warning layer, primary early warning, secondary early warning, tertiary early warning and quaternary early warning;
S62, respectively carrying out early warning display in five colors in an early warning system.
In the embodiment, step S6 adopts a natural breakpoint method or an empirical value in the ArcGIS map to form a corresponding drought disaster comprehensive risk early warning level standard, divides the weather drought disaster risk index I into 4 levels, and obtains a first percentile breakpoint P1, a second percentile breakpoint P2 and a third percentile breakpoint P3 which are arranged from low to high, namely, forms a 5-level standard comprising a non-early warning layer, a first-level early warning, a second-level early warning, a third-level early warning and a fourth-level early warning, and is respectively and correspondingly marked as b=1, 2,3, 4 and 5.
In other application embodiments, the drought disaster comprehensive risk early warning method obtained in the steps S5 to S6 is also applicable to the technical field of grading early warning of other meteorological elements. Preferably, there are 1 or more stations to be interpolated in step S4, 1 or more first interpolation points in the stations to be interpolated in step S41, and no less than 2 reference stations in step S42.
In another aspect of the present invention, a drought disaster comprehensive risk early warning system based on grading is provided for the foregoing method, as shown in fig. 2, where the early warning system includes a data acquisition module 101, an operation module 102, a display module 103 and an early warning module 104, where the data acquisition module 101 acquires weather drought data sets of each site, the operation module 102 processes the data sets by using a drought disaster comprehensive risk early warning method based on grading, the display module 103 displays weather drought disaster conditions by using an ArcGIS map, and the early warning module 104 displays the drought disaster comprehensive risk early warning conditions by using different colors.
Specific examples:
Taking inner mongolia as an example, fig. 3 (a) -3 (g) show 30 years MI distribution histograms of inner mongolia for 4 months-10 months history. We propose a new drought criterion: according to the growth rule of crops and vegetation, whether a certain period (month) of a certain region is determined as drought is related to the historical relative humidity of the region in addition to the relative humidity of the period of the region. If the relative wettability of the region is low and the frequency of occurrence of low relative wettability over a historical period is low, we determine the region as drought. From the figure, it can be seen that inner mongolia mostly occurs in summer (6 months-9 months) in arid weather.
The invention provides a class system of drought disaster comprehensive risk early warning method based on class division, which introduces month relative wettability index, cultivated area and vegetation area influence factors, researches and simplifies the existing system through class division, synthesizes weather drought class and drought disaster risk early warning class to perform weather drought comprehensive risk early warning, and utilizes the system and equipment to visualize early warning, thereby improving the efficiency and reliability of drought risk early warning.
Finally, what should be said is: the above embodiments are merely for illustrating the technical aspects of the present invention, and it should be understood by those skilled in the art that although the present invention has been described in detail with reference to the above embodiments: modifications and equivalents may be made thereto without departing from the spirit and scope of the invention, which is intended to be encompassed by the claims.
Claims (5)
1. The drought disaster comprehensive risk early warning method based on grading is characterized by comprising the following steps of:
S1, constructing an influence factor of weather drought disaster risk, wherein the influence factor of the weather drought disaster risk comprises a month relative wettability index x 1, a cultivated area x 2 and a vegetation area x 3;
S2, preprocessing weather drought data of each site/region:
S21, calculating potential evapotranspiration PET, wherein the calculation formula is as follows:
Wherein PET is the potential amount of evapotranspiration per month in millimeters per month; t i is the average air temperature per month in degrees Celsius; h is annual caloric index; a is a constant;
S22, calculating a month relative wettability index MI by using the month accumulated precipitation P and the potential evapotranspiration PET in the same period, wherein the formula is as follows:
Wherein, the unit of P is millimeter/month;
S3, drawing a relative wettability index MI distribution histogram of each site/region for 30 years month, dividing drought grades based on the MI distribution histogram, marking as A, wherein A=0, 1, 2, 3 or 4, judging regional drought according to the value of A, and specifically comprising the following substeps:
S31, calculating the value of A: determining a value based on a difference delta between the relative wettability index MI i for the month of the region and the mode MI m for the month of the history of the month of the region,
Δ＝MIi-MIm，i＝1，2......12；
When the value of delta is greater than or equal to 0, the value of A is 0, when the value of delta is greater than c and less than 0, the value of A is 1, when the value of delta is greater than d and less than c, the value of A is 2, when the value of delta is greater than e and less than d, the value of A is 3, and when the value of delta is less than e, the value of A is 4; wherein the value of c is-0.2, the value of d is-0.4, and the value of e is-0.6;
s32, judging the drought of the region according to the value of A: when the value of A is 0, judging that drought does not occur in the area, and when the value of A is 1, 2,3 or 4, judging that drought occurs in the area and executing the steps S4-S6 to perform drought disaster risk early warning and display;
s4, carrying out normalization processing on meteorological drought data sets of all sites/regions, wherein a calculation formula is as follows:
Wherein x i is an influence factor, and the value of i is 1,2 or 3; s i is disaster factor of x i after normalization, and x i max and x i min are maximum value and minimum value of x i respectively; when i takes a value of 1, x 1 max and x 1 min are the maximum value and the minimum value of the month and month relative wettability index MI in 30 years of the site/region history; when i takes a value of 2 or 3, x i max and x i min are the maximum and minimum values of x i in all sites/regions of the whole research area; finally, the disaster causing factor value after normalization of each influencing factor is between 0 and 1;
S5, constructing a weather drought disaster risk index I, wherein the I is expressed as follows:
I＝(1-s1)*(β2s2+β3s3)
Wherein, beta 2 and beta 3 are expert weights or the occupied area proportion of the reference x 2 and x 3 is set, and beta 2+β3 =1 is satisfied;
S6, carrying out drought disaster risk early warning grade division by combining the weather drought disaster risk indexes I of each site, recording as B, inputting the calculation result of the B into an early warning system for early warning display, and specifically comprising the following sub-steps:
s61, dividing a weather drought disaster risk index I into 4 grades, and obtaining a first percentile breakpoint P1, a second percentile breakpoint P2 and a third percentile breakpoint P3 which are arranged from low to high to form 5 grades of early warning comprising a non-early warning layer, primary early warning, secondary early warning, tertiary early warning and quaternary early warning;
S62, respectively carrying out early warning display in five colors in an early warning system.
2. The drought disaster comprehensive risk early warning method based on grading according to claim 1, wherein the step S21 specifically comprises the following steps:
the monthly caloric index H i is calculated by the formula:
the annual heat index H is calculated as follows:
The constant a is calculated from the following formula:
a＝6.75×10-7H3-7.71×10-5H2+1.792×10-2H+0.49。
3. the grading-based drought disaster comprehensive risk early warning method according to claim 1, wherein the method comprises the following steps of: in the step S61, a natural breakpoint method or an empirical value in the ArcGIS map is adopted to form a corresponding drought disaster comprehensive risk early warning grade standard, so that the weather drought disaster risk index I is divided into 4 grades.
4. The grading-based drought disaster comprehensive risk early warning method according to claim 1, wherein the method comprises the following steps of: in step S62, early warning display is performed in the early warning system with five colors of green, yellow, orange, powder and red, respectively.
5. The early warning system for realizing the grading-based drought disaster comprehensive risk early warning method according to any one of claims 1 to 4 is characterized by comprising a data acquisition module, an operation module, a display module and an early warning module which are in data communication with each other, wherein the data acquisition module acquires weather drought data sets of all sites, the operation module processes the data sets by using the grading-based drought disaster comprehensive risk early warning method, the display module displays weather drought disaster conditions by using an ArcGIS map, and the early warning module displays the drought disaster comprehensive risk early warning conditions by different colors.
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| CN113849763A (en) * | 2021-11-22 | 2021-12-28 | 中国农业科学院农业资源与农业区划研究所 | Winter wheat-summer corn drought disaster risk assessment method, storage medium and terminal |
| CN115203625A (en) * | 2022-07-29 | 2022-10-18 | 应急管理部国家减灾中心 | Drought and waterlogging index data missing value interpolation method and device |
| CN116090644A (en) * | 2023-02-01 | 2023-05-09 | 山东农业大学 | Drought disaster early warning method for apple orchard |
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