Hail disaster risk assessment method

Abstract

The invention discloses a hail disaster risk assessment method, and relates to the technical field of natural disaster monitoring identification and risk assessment. Comprising the following steps: acquiring X-band radar observation data in an evaluation area, calculating the occurrence frequency of a hail disaster, and taking the occurrence frequency of the hail disaster as a disaster-inducing factor risk index VE; extracting elevation information of a sub-grid in the grid, and taking an elevation standard deviation as a disaster-pregnant environment sensitivity index VH; calculating the planting density of the evaluation unit, and taking the planting density of the evaluation unit as a physical exposure evaluation index VS of the disaster-bearing body; and combining the disaster-inducing factor risk index VE, the disaster-pregnant environment sensitivity index VH and the disaster-bearing body physical exposure index VS to obtain a hail disaster risk assessment index V in the assessment region. According to the method, the data identified as the hail is selected from all areas of the affected area in combination with the observation data of the X-band radar, so that the hail occurrence frequency is calculated, and the statistics of the hail occurrence frequency is more comprehensive and accurate.

Description

Technical Field

[0001] The invention relates to the technical field of natural disaster monitoring, identification and risk assessment, and in particular to a method for hail disaster risk assessment. Background Art

[0002] Hail, as a natural disaster, is very dangerous and has a great impact, which can easily cause huge losses to agriculture. Under the condition that the occurrence of natural disasters cannot be controlled, risk assessment of hail and identification of high-risk areas can provide an important basis for disaster prevention and mitigation in agriculture and reduce agricultural losses.

[0003] In the prior art, the evaluation indicators for the danger of hail disaster-causing factors are hail intensity index, hail duration index, hail diameter and other indicators. The acquisition of evaluation factors is based on ground meteorological observations, with artificial ground investigation as the main path, and the data recorded by ground observation stations are used to count various indicators of hail disasters.

[0004] The defects of the above-mentioned existing technology are: various indicators of hail disasters are counted through data from ground observation stations. Due to the suddenness, localization, and short-term nature of hail disasters, and the influence of the distribution of ground observation stations, the monitoring means based on ground observation stations have huge monitoring omissions, and the observation data have uneven spatial distribution and gaps with the actual situation, which directly affect the accuracy and precision of the hail disaster risk assessment results. Summary of the invention

[0005] Based on this, it is necessary to provide a method for hail disaster risk assessment in response to the above technical issues.

[0006] An embodiment of the present invention provides a method for assessing hail disaster risk, comprising:

[0007] Obtain X-band radar observation data in the assessment area, calculate the frequency of hail disasters based on the radar observation data, and use the frequency of hail disasters as the disaster risk index VE;

[0008] Obtain digital elevation model data, extract elevation information from the digital elevation model, calculate the elevation standard deviation σ of the assessment area, and use the elevation standard deviation σ as the disaster-prone environmental sensitivity index VH;

[0009] Obtain the planting structure of cultivated land, calculate the planting density of the assessment area through the planting structure of cultivated land, and use the planting density of the assessment unit as the physical exposure evaluation index VS of the disaster-prone body;

[0010] The weighted quadrature is used to combine the hazard index VE of the disaster-causing factor, the sensitivity index VH of the disaster-prone environment, and the physical exposure index VS of the disaster-bearing body to obtain the hail disaster risk assessment index V in the assessment area.

[0011] In addition, the calculation of the frequency of occurrence of hail disasters includes:

[0012] Obtain the X-band radar sounding data for the whole year and interpolate it into an array corresponding to radar grid points;

[0013] After processing, five polarization parameters are formed, namely, radar reflectivity factor dBz, differential reflectivity factor Zdr, differential propagation phase shift rate Kdp, zero correlation coefficient CC, and temperature T. The fuzzy membership functions of the five polarization parameters are calculated respectively:

[0014] Beta_dBz=1 / (1+((dBz-m) / a)**(2*b))

[0015] Beta_Zdr=1 / (1+((Zdr-m) / a)**(2*b))

[0016] Beta_Kdp=1 / (1+((Kdp-m) / a)**(2*b))

[0017] Beta_CC=1 / (1+((CC-m) / a)**(2*b))

[0018] Beta_T=1 / (1+((Tm) / a)**(2*b));

[0019] Among them, m represents the center point, a represents the width, and b represents the slope;

[0020] Select the polarization weight function weights: {'dBz':1.5,'Zdr':0.8,'Kdp':1.0,'CC':0.8,'T':0.4};

[0021] Calculate precipitation particle phase identification HCL:

[0022] HCL=(Beta_Kdp*weights['Kdp']+Beta_Zdr*weights['Zdr']+Beta_CC*weights

[0023] ['CC'] / (weights['Kdp'])+weights['Zdr']+weights['CC'])*Beta_T*Beta_dBz)

[0024] According to HCL, data identified as hail are selected, the number of hail occurrences is counted, and the frequency of hail disasters is obtained.

[0025] In addition, the calculated elevation standard deviation σ is:

[0026]

[0027] Among them, h i and is the elevation and average elevation information of the sub-grid, and n is the number of grids in the sub-grid.

[0028] In addition, the planting density of the assessment unit is used as the physical exposure evaluation index VS of the hazard-prone body:

[0029] VS i =SA i / SG i

[0030] Among them, VS i is the agricultural disaster-prone body evaluation index of the i-th grid, SA i is the number of agricultural land subgrids contained in the i-th grid, SG i is the total number of subgrids contained in the i-th grid.

[0031] In addition, the hail disaster risk assessment index V is:

[0032] V=VE WE *VH WH *VS WS

[0033] Among them, VE is the hazard index of disaster-causing factors, WE is the weight of the hazard index VE, VH is the disaster-prone environmental sensitivity index, WH is the weight of the disaster-prone environmental sensitivity index VH, VS is the physical exposure index of the disaster-prone body, and WS is the weight of the physical exposure index VS of the disaster-prone body;

[0034] The larger the hail disaster risk assessment index V, the higher the comprehensive risk of hail disaster; the smaller the hail disaster risk assessment index V, the lower the comprehensive risk of hail disaster.

[0035] In addition, the weights of each index are obtained using the expert scoring method. The weight WE of the disaster risk index VE is 0.5, the weight WH of the disaster-prone environmental sensitivity index VH is 0.2, and the weight WS of the disaster-prone body physical exposure index VS is 0.3.

[0036] Compared with the prior art, the above-mentioned method for assessing the risk of hail disasters provided by the embodiment of the present invention has the following beneficial effects:

[0037] Combined with X-band radar observation data, data identified as hail are selected in all areas of the disaster-stricken area to calculate the frequency of hail occurrence, which makes the statistics of hail occurrence frequency more comprehensive and accurate; the hail disaster risk assessment is carried out by combining the danger of disaster factors, the sensitivity of the disaster-prone environment and the physical exposure of the disaster-bearing body. The assessment results are more comprehensive and accurate, which can provide an important basis for agricultural disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic flow chart of a method for assessing hail disaster risk provided in one embodiment;

[0039] Figure 2 A schematic diagram of parameters used by radars of different wavebands in a method for assessing hail disaster risk provided in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] In one embodiment, a method for assessing hail disaster risk is provided, such as Figure 1 As shown, the method includes:

[0042] 1. Identification of hail occurrence areas. Using X-band radar observation data, the polarization radar precipitation particle phase recognition capability was constructed based on the scattering theory of different particles on electromagnetic waves. Different precipitation particle phases (mainly light rain, rain, ice crystals, polymers, wet snow, vertically aligned ice, low-density graupel, high-density graupel, hail, large raindrops, etc.) have different scattering characteristics for electromagnetic waves in a certain polarization state due to their different shapes, sizes, densities and orientations in space. Therefore, different polarization parameters can be obtained using radar, such as Figure 2 As shown in the figure, the information obtained by combining polarization parameters can be used to determine the phase state of precipitation particles in clouds. At present, both the S-band radar and X-band radar in Beijing can obtain polarization parameters such as Zdr, Kdp, and CC. With the help of conventional reflectivity factors and nearby sounding data, dual-linear polarization Doppler radar can better identify the phase state of precipitation particles in various clouds.

[0043] a. Fuzzy membership function

[0044] Beta_dBz=1 / (1+((dBz-m) / a)**(2*b))

[0045] Beta_Zdr=1 / (1+((Zdr-m) / a)**(2*b))

[0046] Beta_Kdp=1 / (1+((Kdp-m) / a)**(2*b))

[0047] Beta_CC=1 / (1+((CC-m) / a)**(2*b))

[0048] Beta_T=1 / (1+((Tm) / a)**(2*b))

[0049] b. Integrated output

[0050] Polarization weight function selection:

[0051] {'dBz':1.5,'Zdr':0.8,'Kdp':1.0,'CC':0.8,'T':0.4}

[0052] HCL=(Beta_Kdp*weights['Kdp']+Beta_Zdr*weights['Zdr']+Beta_CC*weights['CC'] / (weights['Kdp'])+weights['Zdr']+weights['CC'])*Beta_T*Beta_dBz).

[0053] First, the sounding data for the whole year is read and interpolated into an array corresponding to the radar grid points; the X-band radar data of the Beijing network from 2018 to 2020 is read, and after processing, the five parameters of dBz, Zdr, Kdp, CC, and T are used to identify the phase state of precipitation particles, and the data and product images identified as hail are selected. In 2021, the Beijing Daxing S-band radar (dual polarization upgrade business operation) data and X-band radar data are used, and after processing, the five parameters of dBz, Zdr, Kdp, CC, and T are used to identify the phase state of precipitation particles, as shown in Table 1, and the data and product images identified as hail are selected.

[0054] HCL type HCL type 1 Drizzle 6 VerticalIce 2 Rain 7 LDGraupel 3 IceCrystals 8 HDGraupel 4 Aggregates 9 Hail 5 WetSnow 10 BigDrops

[0055] 2. Using the above algorithm, calculate the spatial grid data of the daily occurrence of hail in a certain area in a long-term series (more than 5 years).

[0056] 3. Risk assessment of agricultural hail disasters.

[0057] The risk of hail disaster is evaluated from three aspects: the danger of disaster-causing factors, the sensitivity of the disaster-prone environment, and the physical exposure of the disaster-bearing body.

[0058] Hail weather is a disaster factor, and the frequency of hail is used to assess its risk level (hazard). This is one of the historical data assessment methods and is the most widely used method in meteorological disaster risk assessment. Generally speaking, assuming that the physical exposure of the disaster-bearing body is the same, the higher the frequency of hail, the higher the risk level and the greater the risk of disaster. Therefore, the frequency of hail disasters is selected as the risk index (VE) of the disaster factor. The frequency results are obtained from steps 1 and 2 above.

[0059] Hail disasters are the product of the occurrence, development and rapid movement of strong convective clouds. The steepness of the terrain has an important influence on the formation of hail clouds. Therefore, the standard deviation of terrain elevation is selected as the disaster-prone environmental sensitivity index (VH). The standard deviation of terrain elevation can be obtained through digital elevation model data. In the 500m×500m digital elevation model data, the sub-grid elevation information within the grid can be extracted, and the standard deviation of each grid elevation can be calculated, that is:

[0060]

[0061] Where: h i and is the elevation and average elevation information of the sub-grid, and n is the number of grids in the sub-grid.

[0062] The physical exposure of a disaster-prone body (also known as vulnerability) is the number or value of the disaster-prone bodies exposed to the influence of risk factors. The physical exposure of a disaster-prone body depends on the hazard of the risk factor and the total number of risk-prone bodies in the region, reflecting the total number of risk-prone bodies that may suffer losses under the influence of risk factors of a certain intensity. In terms of risk, the greater the physical exposure, the weaker the disaster response capacity and the greater the disaster risk.

[0063] Historical data statistics show that agriculture is one of the most common victims of hail in Beijing. Therefore, this report selects the main agricultural types in Beijing - farmland, facility agriculture, and forestry and fruit as the main disaster-prone bodies for hail disaster risk assessment and analysis. Agriculture is the main disaster-prone body of hail disasters, and its disaster-prone body is mainly directly reflected by the planting structure of cultivated land. The planting density of the assessment unit is selected as the physical exposure evaluation index (VS) of the disaster-prone body, and its expression is:

[0064] VS i =SA i / SG i

[0065] Among them, VS i is the agricultural disaster-prone body evaluation index of the i-th grid, SA i is the number of agricultural (farmland / facility agriculture / special fruit and vegetable) land subgrids contained in the i-th grid, SG i is the total number of subgrids contained in the i-th grid.

[0066] The hail disaster risk assessment index in the assessment area is obtained by combining the hazard factor hazard index (VE), the disaster-prone environmental sensitivity index (VH), and the physical exposure index of the disaster-bearing body (VS) with weighted product.

[0067] V=VE WE *VH WH *VS WS

[0068] Among them, WE, WH and WS are the weights of each index, which are obtained by expert scoring method and are assigned weights of 0.5, 0.2 and 0.3 respectively.

[0069] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for hail disaster risk assessment, characterized in that: include: Obtain X-band radar observation data within the assessment area, calculate the frequency of hail disasters based on the radar observation data, and use the frequency of hail disasters as the disaster risk index VE; Obtain digital elevation model data, extract elevation information from the digital elevation model, calculate the elevation standard deviation σ of the assessment area, and use the elevation standard deviation σ as the disaster-prone environmental sensitivity index VH; Obtain the cultivated land planting structure, calculate the planting density of the assessment area based on the cultivated land planting structure, and use the planting density of the assessment unit as the physical exposure evaluation index VS of the disaster-prone body; The weighted quadrature method is used to combine the hazard index VE of the disaster-causing factor, the sensitivity index VH of the disaster-prone environment, and the physical exposure index VS of the disaster-bearing body to obtain the hail disaster risk assessment index V in the assessment area.

2. The method for hail disaster risk assessment according to claim 1, wherein: The calculation of the frequency of hail disasters includes: Obtain the X-band radar sounding data for the entire year and interpolate it into an array corresponding to radar grid points; After processing, five polarization parameters are formed: radar reflectivity factor dBz, differential reflectivity factor Zdr, differential propagation phase shift rate Kdp, zero correlation coefficient CC, and temperature T. The fuzzy membership functions of the five polarization parameters are calculated respectively: Beta_dBz=1 / (1+((dBz-m) / a)**(2*b)) Beta_Zdr=1 / (1+((Zdr-m) / a)**(2*b)) Beta_Kdp=1 / (1+((Kdp-m) / a)**(2*b)) Beta_CC=1 / (1+((CC-m) / a)**(2*b)) Beta_T=1 / (1+((Tm) / a)**(2*b)); Among them, m represents the center point, a represents the width, and b represents the slope; Select the polarization weight function weights: {'dBz':1.5,'Zdr':0.8,'Kdp':1.0,'CC':0.8,'T':0.4}; Calculate precipitation particle phase identification HCL: HCL=(Beta_Kdp*weights['Kdp']+Beta_Zdr*weights['Zdr']+Beta_CC*weights['CC'] / (weights['Kdp'])+weights['Zdr']+weights['CC'])*Beta_T*Beta_dBz) According to HCL, data identified as hail are selected, the number of hail occurrences is counted, and the frequency of hail disasters is obtained.

3. The method for hail disaster risk assessment according to claim 1, wherein: The calculated elevation standard deviation σ is: Among them, h i and is the elevation and average elevation information of the subgrid, and n is the number of grids in the subgrid.

4. The method for hail disaster risk assessment according to claim 1, wherein: The planting density of the assessment unit is used as the physical exposure evaluation index VS of the hazard-bearing body: VS i =SA i / SG i Among them, VS i is the physical exposure evaluation index of the agricultural disaster-prone body in the i-th grid, SA i is the number of agricultural land subgrids contained in the i-th grid, SG i is the total number of subgrids contained in the i-th grid.

5. The method for hail disaster risk assessment according to claim 1, wherein: The hail disaster risk assessment index V: V=VE WE *VH WH *VS WS Among them, VE is the hazard index of the hazard factor, WE is the weight of the hazard index VE, VH is the disaster-prone environmental sensitivity index, WH is the weight of the disaster-prone environmental sensitivity index VH, VS is the physical exposure index of the hazard-prone body, and WS is the weight of the physical exposure index VS of the hazard-prone body; The larger the hail disaster risk assessment index V, the higher the comprehensive risk of hail disaster; the smaller the hail disaster risk assessment index V, the lower the comprehensive risk of hail disaster.

6. A method for assessing hail disaster risk according to claim 5, characterized in that: The weights of each index are obtained using the expert scoring method. The weight WE of the disaster-causing factor risk index VE is 0.5, the weight WH of the disaster-prone environmental sensitivity index VH is 0.2, and the weight WS of the disaster-prone body physical exposure index VS is 0.3.

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