A grid-based regional climate event risk assessment method based on losses

Abstract

The present invention discloses a gridded regional climate event risk assessment method based on loss, including S1, determining the regional climate event to be risk assessed, and obtaining the gridded meteorological element data corresponding to the regional climate event in the study area; S2, extracting the historical regional climate events in the study area according to the obtained gridded meteorological element data, establishing a historical event case library, and identifying event characteristics; S3, calculating the event intensity and the number of affected objects within its coverage according to the identified event characteristics, and determining the degree of loss affected by the previous events; S4, calculating the recurrence period of the degree of loss affected by the previous events, determining the threshold for dividing the risk level, and then assessing the loss risk of the regional climate event. The present invention comprehensively considers the possible losses caused by regional climate events for risk assessment, can provide scientific emergency management and decision-making support for regional climate events such as high temperature and drought, and improves regional disaster prevention and mitigation capabilities.

Description

Technical Field

[0001] The present invention belongs to the technical field of climate event identification, and in particular relates to a loss-based gridded regional climate event risk assessment method. Background Art

[0002] Regional climate events are usually characterized by long duration, high intensity, and wide coverage, causing significant damage to ecosystems, land resources, and environmental health. Therefore, scientifically and systematically assessing the risks of regional climate events, especially the potential losses to agriculture and the ecological environment, has become an important topic in the field of meteorological disaster prevention and mitigation. Effective risk assessment methods can support the formulation of accurate meteorological disaster warning mechanisms and emergency response strategies, and reduce the adverse effects of extreme climate events.

[0003] Among the existing related methods, first, the traditional regional climate event assessment method relies on meteorological station observation data, but meteorological stations are unevenly distributed geographically, especially in complex terrain or remote areas, where stations are scarce and it is difficult to fully capture the spatial distribution characteristics of regional climate events. Secondly, most of the existing assessment methods focus on the abnormal meteorological events themselves, without considering the extent of the losses that abnormal weather events may cause to society, economy and environment, and the possible losses are the key issues in risk assessment. Finally, the existing risk assessment methods focus on overall zoning, but lack risk assessment of an event process, and cannot meet the needs of risk warning for a disastrous weather process in current meteorological business services. Summary of the invention

[0004] In response to the above-mentioned deficiencies in the prior art, the loss-based grid-based regional climate event risk assessment method provided by the present invention comprehensively quantifies the degree of loss caused by regional climate events by comprehensively considering the intensity of regional climate events and the number of affected objects within the coverage area.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a grid-based regional climate event risk assessment method based on loss, comprising the following steps:

[0006] S1. Determine the regional climate events to be risk assessed and obtain the gridded meteorological element data corresponding to the regional climate events in the study area;

[0007] S2. Based on the acquired gridded meteorological element data, extract historical regional climate events in the study area, establish a historical event case library, and identify event characteristics;

[0008] S3. Based on the identified event characteristics, calculate the intensity of regional climate events and extract the number of affected objects within their coverage area to determine the extent of losses caused by previous regional climate events;

[0009] S4. Calculate the recurrence period of the loss degree of different affected objects caused by regional climate events, determine the thresholds for different risk levels, and then evaluate the loss risk of regional climate events.

[0010] Furthermore, in step S1, the regional climate events include regional precipitation events, regional drought events, regional high temperature events and regional low temperature events, and the corresponding meteorological element gridded data include daily precipitation, daily maximum temperature and daily minimum temperature.

[0011] Furthermore, in step S2, the method for establishing a historical event case library is:

[0012] S21. Analyze the gridded meteorological element data. When the grid points of abnormal meteorological element data on a certain day appear in a continuous space, it is regarded as the start date of the regional climate event.

[0013] S22, determining whether the grid points of abnormal meteorological element data that appear continuously on the next day overlap with those on the previous day;

[0014] If yes, proceed to step S23;

[0015] If not, that is, the abnormal grid points do not appear in a continuous manner or appear in a continuous manner but do not overlap with the previous day, then proceed to step S24;

[0016] S23, determining that the regional climate event continues, and returning to step S22;

[0017] S24, determine that the regional climate event ends, determine its end date, and proceed to step S25;

[0018] S25. Repeat steps S21 to S24 to identify several regional climate events, and then establish a historical event case library.

[0019] Furthermore, in step S2, the event characteristics are characteristics that describe the coverage, duration, abnormal amplitude of meteorological elements and continuity of the event process of the regional climate event.

[0020] Furthermore, the step S3 includes the following sub-steps:

[0021] S31. Obtain spatial distribution data of objects affected by regional climate events, and count the number of affected objects within the coverage of previous regional climate events;

[0022] S32. Calculate the intensity of regional climate events;

[0023] S33. Multiply the number of objects affected by each regional climate event within its coverage area by the event intensity, and perform normalization to obtain the degree of loss caused by the impact of each regional climate event.

[0024] Furthermore, the step S32 is specifically as follows:

[0025] The duration of regional climate events, the abnormal amplitude of meteorological elements and the continuity of event processes are standardized, and their respective weights are determined and then weighted summed to obtain the intensity of regional climate events;

[0026] Among them, j Intensity of sub-regional climate events P j It is expressed as:

[0027]

[0028]

[0029] In the formula, Indicates the regional climate event i The weight of the feature, R i Indicates the regional climate event i The standardized value of the feature, n represents the total number of features, x i Indicates i The original value of the feature, x imin Represents the first of all historical regional climate events i The minimum value of the features, x imax Indicates i The maximum value of the features.

[0030] Further, in step S33, j The extent of damage caused by sub-regional climate events L j for:

[0031]

[0032] In the formula, Z j Indicates j The number of affected objects within the coverage of this event. P j Indicates j The intensity of sub-regional climate events, ( Z j × Pj ) max Represents the regional climate events in the historical event case library Z j × P j The maximum value of .

[0033] Furthermore, the step S4 includes the following sub-steps:

[0034] S41. Determine the optimal marginal distribution function of the loss degree of different impact objects caused by regional climate events;

[0035] S42. Calculate the return period of the loss degree of different affected objects due to regional climate events based on the determined optimal marginal distribution function;

[0036] S43. Determine the thresholds for different risk levels based on the recurrence periods of the various degrees of loss of different affected objects caused by regional climate events, and then assess the loss risks of regional climate events.

[0037] Furthermore, the step S41 is specifically as follows:

[0038] Fit the marginal distribution function to the degree of loss, and use the P value calculated by the KS test to test the goodness of fit of each marginal distribution function. P The marginal distribution function with the largest value is taken as the optimal marginal distribution function ;

[0039] Among them, the candidate marginal distribution functions for fitting include normal distribution, Poisson distribution, exponential distribution, extreme value distribution, generalized extreme value distribution, gamma distribution and Rayleigh distribution.

[0040] The beneficial effects of the present invention are:

[0041] The present invention comprehensively considers the losses that regional climate events may cause to agriculture and the environment, and provides a method for comprehensively assessing the loss risks of regional climate events. It can provide more scientific emergency management and decision-making support for regional climate events such as high temperature and drought, effectively reduce the losses caused by disasters, and significantly improve regional disaster prevention and mitigation capabilities, which are specifically embodied in:

[0042] (1) High-precision grid-based risk assessment: The present invention uses grid-based meteorological element data to comprehensively and accurately capture the spatial distribution characteristics of regional climate events, which can better reflect the actual impact range of regional climate events.

[0043] (2) Risk assessment based on loss: In the risk assessment process, the present invention not only takes into account the intensity characteristics of the regional climate event itself, but also comprehensively considers the number of various affected objects within the coverage of the event, comprehensively quantifies the degree of loss caused by the regional climate event on the study area, and can more accurately assess the loss risk of regional climate events.

[0044] (3) Ability to conduct risk assessment on an event process: The present invention can conduct loss risk assessment on all regional climate events that have occurred, helping to transform the current warning of disastrous weather events into warning of potential losses of events, thereby improving regional disaster prevention and mitigation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of the loss-based gridded regional climate event risk assessment method provided by the present invention.

[0046] Figure 2 The present invention provides changes in the areas of cultivated land, forest land and grassland within the coverage area of ​​83 regional high temperature events identified in Jiangsu Province from 1990 to 2021 as the events cover the area.

[0047] Figure 3 The present invention provides the intensity of 83 regional high temperature events identified in Jiangsu Province from 1990 to 2021.

[0048] Figure 4 The present invention provides the degree of loss of cultivated land, forest land and grassland affected by 83 regional high temperature events identified in Jiangsu Province from 1990 to 2021.

[0049] Figure 5 The optimal marginal distribution of the degree of loss when the affected object of regional high temperature events identified in Jiangsu Province from 1990 to 2021 is cultivated land is provided in the present invention.

[0050] Figure 6 The optimal marginal distribution of the degree of loss when the affected object is forest land due to regional high temperature events identified in Jiangsu Province from 1990 to 2021 provided by the present invention.

[0051] Figure 7 The optimal marginal distribution of the degree of loss when the affected object is grassland in the regional high temperature events identified in Jiangsu Province from 1990 to 2021 provided by the present invention.

[0052] Figure 8 The present invention provides the identification process of the regional high temperature event in Jiangsu Province from June 6, 2021 to June 7, 2021.

[0053] Fig. 9The distribution of cultivated land, woodland and grassland affected by the regional high temperature event from June 6 to June 7, 2021 in Jiangsu Province provided by the present invention. DETAILED DESCRIPTION

[0054] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0055] The embodiment of the present invention provides a grid-based regional climate event risk assessment method based on loss, such as Figure 1 As shown, the following steps are included:

[0056] S1. Determine the regional climate events to be risk assessed and obtain the gridded meteorological element data corresponding to the regional climate events in the study area;

[0057] S2. Based on the acquired gridded meteorological element data, extract historical regional climate events in the study area, establish a historical event case library, and identify event characteristics;

[0058] S3. Based on the identified event characteristics, calculate the intensity of regional climate events and extract the number of affected objects within their coverage area to determine the extent of losses caused by previous regional climate events;

[0059] S4. Calculate the recurrence period of the loss degree of different affected objects caused by regional climate events, determine the thresholds for different risk levels, and then evaluate the loss risk of regional climate events.

[0060] In step S1 of the embodiment of the present invention, regional climate events include regional precipitation events, regional drought events, regional high temperature events, and regional low temperature events, and the corresponding meteorological element grid data include daily precipitation, daily maximum temperature, and daily minimum temperature.

[0061] In step S2 of the embodiment of the present invention, the method for establishing a historical event case library is specifically as follows:

[0062] S21. Analyze the gridded meteorological element data. When the grid points of abnormal meteorological element data on a certain day appear in a continuous space, it is regarded as the start date of the regional climate event.

[0063] S22, determining whether the grid points of abnormal meteorological element data that appear continuously on the next day overlap with those on the previous day;

[0064] If yes, proceed to step S23;

[0065] If not, that is, the abnormal grid points do not appear in a continuous manner or appear in a continuous manner but do not overlap with the previous day, then proceed to step S24;

[0066] S23, determining that the regional climate event continues, and returning to step S22;

[0067] S24, determine that the regional climate event ends, determine its end date, and proceed to step S25;

[0068] S25. Repeat steps S21 to S24 to identify several regional climate events, and then establish a historical event case library.

[0069] Specifically, specific standards for identifying regional climate events are defined based on the definitions of different regional climate events. The standards that need to be determined in this embodiment include: standards for determining whether grid meteorological elements are abnormal, standards for continuous appearance in space, and standards for whether the two days before and after overlap. Based on these determined standards, regional climate events over the past 30 years or more are identified, and a historical event case library is established.

[0070] In step S2 of the embodiment of the present invention, the identified event features are features that describe the coverage, duration, magnitude of meteorological element anomalies, and continuity of the event process of the regional climate event.

[0071] In a specific embodiment, taking a regional high temperature event as an example, the identified event features include coverage area, duration days, average maximum temperature, maximum daily maximum temperature and event process continuity.

[0072] Step S3 of the embodiment of the present invention includes the following sub-steps:

[0073] S31. Obtain spatial distribution data of objects affected by regional climate events, and count the number of affected objects within the coverage of previous regional climate events;

[0074] S32. Calculate the intensity of regional climate events;

[0075] S33. Multiply the number of objects affected by each regional climate event within its coverage area by the event intensity, and perform normalization to obtain the degree of loss caused by the impact of each regional climate event.

[0076] In a specific embodiment, taking a regional high temperature event as an example, the affected objects within its coverage include cultivated land, forest land and grassland. Therefore, in step S31, it is first necessary to collect grid data on the distribution of cultivated land, forest land and grassland in the study area, and count the number of affected objects according to the coverage of the regional high temperature event.

[0077] In this embodiment, step S32 is specifically as follows:

[0078] The duration of regional climate events, the abnormal amplitude of meteorological elements and the continuity of event processes are standardized, and their respective weights are determined and then weighted summed to obtain the intensity of regional climate events;

[0079] Among them, j Intensity of sub-regional climate events P j It is expressed as:

[0080]

[0081]

[0082] In the formula, Indicates the regional climate event i The weight of the feature, R i Indicates the regional climate event i The standardized value of the feature, n represents the total number of features, x i Indicates i The original value of the feature, x imin Represents the first of all historical regional climate events i The minimum value of the features, x imax Indicates i The maximum value of the features.

[0083] In step S33 of this embodiment, j The extent of damage caused by sub-regional climate events L j for:

[0084]

[0085] In the formula, Z j Indicates j The number of affected objects within the coverage of this event. P j Indicates j The intensity of sub-regional climate events, ( Z j × P j ) max Represents the regional climate events in the historical event case library Z j × Pj The maximum value of .

[0086] Step S4 in the embodiment of the present invention includes the following sub-steps:

[0087] S41. Determine the optimal marginal distribution function of the loss degree of different impact objects caused by regional climate events;

[0088] S42. Calculate the return period of the loss degree of different affected objects due to regional climate events based on the determined optimal marginal distribution function;

[0089] S43. Determine the thresholds for different risk levels based on the recurrence periods of the various degrees of loss of different affected objects caused by regional climate events, and then assess the loss risks of regional climate events.

[0090] Step S41 of this embodiment is specifically as follows:

[0091] Fit the marginal distribution function to the degree of loss, and use the P value calculated by the KS test to test the goodness of fit of each marginal distribution function. P The marginal distribution function with the largest value is taken as the optimal marginal distribution function ;

[0092] Among them, the candidate marginal distribution functions for fitting include normal distribution, Poisson distribution, exponential distribution, extreme value distribution, generalized extreme value distribution, gamma distribution and Rayleigh distribution.

[0093] In step S42 of this embodiment, the recurrence period T ( x ) is calculated as follows:

[0094]

[0095] In step S43 of this embodiment, in a specific example, when selecting a specific recurrence period, such as 1 year, 5 years, 10 years, 50 years, and 100 years, as different risk level thresholds, it is appropriate that the proportion of cases divided into extremely high, high, medium, and low risk levels is close to 1:2:3:4.

[0096] In the embodiment of the present invention, a regional high temperature event in Jiangsu Province is taken as an example to illustrate the specific implementation process of the above risk assessment method.

[0097] In this embodiment, the historical event case library period is 1990-2021, and the degree of loss of cultivated land, forest land and grassland within the event coverage is used as the basis for risk assessment.

[0098] S1. Obtain the daily maximum temperature grid data required for identifying regional high temperature events in Jiangsu Province from 1990 to 2021. Specifically, obtain the daily maximum temperature grid data of Jiangsu Province from 1990 to 2021 with a resolution of 0.25×0.25° from the National Climate Center. The number of grids is 155, and the coverage area is 105219km. 2 .

[0099] S2. Based on the obtained daily maximum temperature grid data of Jiangsu Province, the identification criteria for regional high temperature events are determined. A historical regional high temperature event case library is established, and the characteristics of each event in the case library are extracted, including the coverage area, duration, average daily maximum temperature, maximum daily maximum temperature, and event process continuity.

[0100] S21. If the daily maximum temperature is greater than or equal to 35℃, it is considered high temperature. If the area of ​​high temperature grids connected in space on a certain day is greater than or equal to 20% of the area of ​​Jiangsu Province, it is considered to be a high temperature patch, which can be regarded as the beginning of a regional high temperature event. Among them, the grid in at least one direction of the eight directions of a high temperature grid connected in space, namely, up, down, left, right, upper left, lower left, upper right and lower right, is a high temperature grid; 20% of the area of ​​Jiangsu Province is 31 grids (155×20%=31);

[0101] S22, determine whether the overlapping area of ​​the area where high temperature appears continuously on the second day and the area where high temperature appears continuously on the previous day reaches 10% of the area of ​​the study area, that is, greater than or equal to 15 grids;

[0102] If yes, proceed to step S23;

[0103] If not, that is, the high temperature does not appear continuously or appears continuously but the overlapping area with the previous day is less than 10%, go to step S24;

[0104] S23, determining that the regional high temperature event continues, and returning to step S22;

[0105] S24, determine that the regional climate event ends, determine its end date, and proceed to step S25;

[0106] S25. Repeat steps S21 to S24 to identify several regional high temperature events, and include regional high temperature events that last for more than or equal to 2 days into the historical event case library.

[0107] According to the above process, a total of 83 events were identified in the historical case database of regional high temperature events in Jiangsu Province from 1990 to 2021.

[0108] Furthermore, the various characteristics of the 83 regional high temperature events identified in Jiangsu Province from 1990 to 2021 were extracted, including coverage area, duration, average daily maximum temperature, maximum daily maximum temperature, and event process continuity. Table 1 shows the maximum, minimum, and average values ​​of the characteristics of each event in the extracted case library. The specific calculation method of each event characteristic is as follows:

[0109] ① Coverage area A: The total area of ​​grids with continuous high temperatures during a regional high temperature event in Jiangsu Province / km 2 .

[0110] ② Duration days D: The number of days from the start to the end of a regional high temperature event in Jiangsu Province / d.

[0111] ③Average daily maximum temperature I: The average value of the daily maximum temperature of all high temperature grids during a regional high temperature event in Jiangsu Province / ℃.

[0112] ④ Maximum daily maximum temperature M: the maximum value of the daily maximum temperature of all high temperature grids during a regional high temperature event in Jiangsu Province / ℃.

[0113] ⑤ Event process continuity Q: The average value of the ratio of the overlapping area of ​​high temperature grids on the two days before and after a regional high temperature event in Jiangsu Province to the area of ​​high temperature grids on the previous day. The calculation formula is as follows:

[0114]

[0115] in, Q For the continuity of the event process, D is the number of days it lasts, A i For an event i The area of ​​high temperature grids connected to the sky, C i,i+1 For the i Heaven and Earth i +The area of ​​the overlapping zone where high temperature occurs continuously for 1 day.

[0116] Table 1: Maximum, minimum and average values ​​of each characteristic indicator of regional high temperature events identified in Jiangsu Province from 1990 to 2021

[0117]

[0118] S3. Based on the identified event characteristics, calculate the intensity of regional high temperature events and extract the amount of affected cultivated land, forest land and grassland within their coverage area, and determine the extent of losses caused by previous regional high temperature events;

[0119] S31. Obtain the spatial distribution data of the objects affected by the regional high temperature events, and count the number of cultivated land, forest land and grassland affected within the coverage area according to the coverage area of ​​previous regional high temperature events;

[0120] Specifically, the gridded land use data of Jiangsu Province with a resolution of 1 km in 2020 was obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences, from which the spatial distribution of cultivated land, forest land, and grassland was extracted. The cultivated land area of ​​the province is 63,357 km 2 , the forest area is 2394km 2 , the grassland area is 824km 2 Furthermore, the areas of cultivated land, forest land and grassland affected by each regional high temperature event in the case database were counted. Figure 2 The changes in the areas of cultivated land, forest land and grassland within the coverage of 83 regional high temperature events identified in Jiangsu Province from 1990 to 2021 depend on the area covered by the events. Figure 2 As shown, within the coverage area of ​​83 regional high temperature events identified in Jiangsu Province from 1990 to 2021, the areas of cultivated land, forest land and grassland increased with the increase of regional high temperature coverage area.

[0121] S32. Calculate the intensity of regional high temperature events;

[0122] Specifically, the maximum and minimum method is used to standardize the extracted event features. The required maximum and minimum values ​​are shown in Table 1. Secondly, the entropy weight method is used to determine the weight of each event feature. Finally, the weighted summation is used to calculate the intensity of all regional high temperature events:

[0123] P =0.73 D +0.11 I +0.12 M +0.05 Q

[0124] in, P is the intensity of regional high temperature events. The intensity of 83 regional high temperature events in Jiangsu Province during the study period is shown in Figure 3 .

[0125] S33. Multiply the areas of cultivated land, forest land and grassland affected by each regional high temperature event within its coverage area by the event intensity, and perform normalization to obtain the degree of loss of each affected object caused by each regional high temperature event.

[0126] Specifically, the areas of cultivated land, forest land and grassland affected by previous regional high temperature events were normalized and multiplied by the corresponding event intensity to obtain the degree of loss of cultivated land, forest land and grassland caused by each regional high temperature event in the historical case library. Figure 4The extent of loss of cultivated land, forest land and grassland affected by 83 regional high temperature events identified in Jiangsu Province from 1990 to 2021.

[0127] S4. Calculate the recurrence period of the impact of regional high temperature events on the degree of loss of cultivated land, forest land and grassland, determine the thresholds for different risk levels, and then evaluate the loss risk of regional high temperature events.

[0128] S41. Determine the optimal marginal distribution function of the loss degree of cultivated land, forest land and grassland caused by regional high temperature events;

[0129] Specifically, marginal distribution fitting was performed for the loss of cultivated land, forest land and grassland respectively. The candidate marginal distribution functions included normal distribution (NORM), Poisson distribution (POISS), exponential distribution (EXP), extreme value distribution (EV), generalized extreme value distribution (GEV), gamma distribution (GAM) and Rayleigh distribution (RAYL). The P value calculated by Kolmogorov-Smimov (KS) test was used to test the goodness of fit of each marginal distribution. The one with the largest P value was the optimal marginal distribution function. . Figure 5 , Figure 6 and Figure 7 They are the optimal marginal distributions of cultivated land, forest land and grassland affected by regional high temperature events in the historical case database. The results show that the loss of cultivated land, forest land and grassland all obey Rayleigh distribution.

[0130] S42. Calculate the return period of the loss degree of cultivated land, forest land and grassland affected by regional high temperature events based on the determined optimal marginal distribution function;

[0131] Specifically, the recurrence period thresholds of regional high temperature events affecting the loss degree of cultivated land, forest land and grassland in Jiangsu Province were obtained, as shown in Table 2. From 1990 to 2021, there were two events in Jiangsu Province with a recurrence period of more than 50 years that affected the loss degree of cultivated land, forest land and grassland identified in the regional high temperature events.

[0132] Table 2: Thresholds of various return periods for the degree of loss of cultivated land, forest land and grassland caused by regional high temperature events in Jiangsu Province from 1990 to 2021

[0133]

[0134] S43. Determine the thresholds for different risk levels based on the recurrence period of the degree of loss of cultivated land, forest land and grassland caused by regional high temperature events, and then assess the loss risk of regional high temperature events.

[0135] Specifically, when selecting a specific return period threshold as the loss threshold for different risk levels, it is advisable that the ratio of events with extremely high, high, medium and low risk levels divided is close to 1:2:3:4. Table 3 shows the loss risk level thresholds for the impact of regional high temperature events on cultivated land, forest land and grassland in Jiangsu Province. Based on this table, the loss level of any regional high temperature event can be divided into risk levels.

[0136] Table 3: Risk level thresholds for the degree of loss of cultivated land, forest land and grassland due to regional high temperature events in Jiangsu Province

[0137]

[0138] Furthermore, a loss risk assessment was conducted for a regional high temperature event identified in Jiangsu Province from June 6 to June 7, 2021:

[0139] Figure 8 The identification process of the regional high temperature event in Jiangsu Province from June 6 to June 7, 2021 is shown. Figure 8 As shown in the figure, although there is a high temperature grid greater than or equal to 35°C on June 5, 2021, the area of ​​the contiguous high temperature grid is 13921 km 2 , accounting for only 13.2% of Jiangsu Province, less than 20% of Jiangsu Province, so there was no regional high temperature event on the 5th. On the 6th, the area of ​​the grid with high temperature and contiguous was 64002 km 2 The high temperature coverage area reached 60.8% of the area of ​​Jiangsu Province, which is greater than the spatial contiguous threshold of 20%, which meets the definition of the beginning of a regional high temperature event in this invention; on the 7th, not only the continuous high temperature grid area was 76627 km 2 , and the overlapping area with the high temperature grid on the 6th is 62670 km 2 , reaching 59.6% of the area of ​​Jiangsu Province, which is greater than the overlapping area of ​​10% specified in the present invention. Therefore, the 6th and 7th belong to the same regional high temperature event, and this regional high temperature event continues on the 7th, and the continuity of the event process is 0.98; the area with high temperature on the 8th is 15231 km 2 , accounting for only 14.5% of the area of ​​Jiangsu Province, and this regional high temperature event has ended. In summary, Jiangsu Province has identified a regional high temperature event lasting 2 days, starting on June 6, 2021 and ending on June 7, 2021.

[0140] Fig. 9 The distribution of cultivated land, forest land and grassland affected by the regional high temperature event from June 6 to June 7, 2021, identified in Jiangsu Province, is shown. Fig. 9 As shown, the area covered by this event is 78437km 2, of which the areas of cultivated land, forest land and grassland are 54678 km 2 2006 km 2 、779 km 2 . It was calculated that the duration was 2 days, the average daily maximum temperature was 36.3℃, the maximum daily maximum temperature was 38.1℃, and the continuity of the event process was 0.98. The intensity of this event was calculated to be 0.17. The loss degree of cultivated land affected by this regional high temperature event was 0.15, the loss degree of forest land was 0.12, and the loss degree of grassland was 0.16. Referring to Table 3, the loss risk level of cultivated land, forest land, and grassland affected by this event is low risk, and the recurrence period of the event does not exceed 2 years.

[0141] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0142] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A grid-based regional climate event risk assessment method based on loss, characterized in that: The following steps are involved: S1. Determine the regional climate events to be risk assessed and obtain the gridded meteorological element data corresponding to the regional climate events in the study area; S2. Based on the acquired gridded meteorological element data, extract historical regional climate events in the study area, establish a historical event case library, and identify event characteristics; S3. Based on the identified event characteristics, calculate the intensity of regional climate events and extract the number of affected objects within their coverage area to determine the extent of losses caused by previous regional climate events; S4. Calculate the recurrence period of the loss degree of different affected objects caused by regional climate events, determine the thresholds for different risk levels, and then evaluate the loss risk of regional climate events; The step S3 comprises the following sub-steps: S31. Obtain spatial distribution data of objects affected by regional climate events, and count the number of affected objects within the coverage of previous regional climate events; S32. Calculate the intensity of regional climate events; S33, multiplying the number of objects affected by each regional climate event within its coverage area by the event intensity, and performing normalization processing to obtain the degree of loss caused by each regional climate event; The step S32 is specifically as follows: The duration of regional climate events, the abnormal amplitude of meteorological elements and the continuity of event processes are standardized, and their respective weights are determined and then weighted summed to obtain the intensity of regional climate events; Among them, j Intensity of sub-regional climate events P j It is expressed as: In the formula, Indicates the regional climate event i The weight of the feature, R i Indicates the regional climate event i The standardized value of the feature, n represents the total number of features, x i Indicates i The original value of the feature, x imin Represents the first of all historical regional climate events i The minimum value of the features, x imax Indicates i The maximum value of features; In step S33, j The extent of damage caused by sub-regional climate events L j for: In the formula, Z j Indicates j The number of affected objects within the coverage of this event. P j Indicates j The intensity of sub-regional climate events, ( Z j × P j ) max Represents the regional climate events in the historical event case library Z j × P j The maximum value of .

2. The grid-based regional climate event risk assessment method based on loss according to claim 1 is characterized in that: In step S1, the regional climate events include regional precipitation events, regional drought events, regional high temperature events and regional low temperature events, and the corresponding meteorological element grid data include daily precipitation, daily maximum temperature and daily minimum temperature.

3. The grid-based regional climate event risk assessment method based on loss according to claim 1 is characterized in that: In step S2, the method for establishing a historical event case library is: S21. Analyze the gridded meteorological element data. When the grid points of abnormal meteorological element data on a certain day appear in a continuous space, it is regarded as the start date of the regional climate event. S22, determining whether the grid points of abnormal meteorological element data that appear continuously on the next day overlap with those on the previous day; If yes, proceed to step S23; If not, that is, the abnormal grid points do not appear in a continuous manner or appear in a continuous manner but do not overlap with the previous day, then proceed to step S24; S23, determining that the regional climate event continues, and returning to step S22; S24, determine that the regional climate event ends, determine its end date, and proceed to step S25; S25. Repeat steps S21 to S24 to identify several regional climate events, and then establish a historical event case library.

4. The grid-based regional climate event risk assessment method based on loss according to claim 1 is characterized in that: In step S2, the event characteristics are characteristics that describe the coverage, duration, abnormal amplitude of meteorological elements and continuity of the event process of the regional climate event.

5. The grid-based regional climate event risk assessment method based on loss according to claim 1 is characterized in that: The step S4 comprises the following sub-steps: S41. Determine the optimal marginal distribution function of the loss degree of different impact objects caused by regional climate events; S42. Calculate the return period of the loss degree of different affected objects due to regional climate events based on the determined optimal marginal distribution function; S43. Determine the thresholds for different risk levels based on the recurrence periods of the various degrees of loss of different affected objects caused by regional climate events, and then assess the loss risks of regional climate events.

6. The grid-based regional climate event risk assessment method based on loss according to claim 5 is characterized in that: The step S41 is specifically as follows: Fit the marginal distribution function to the degree of loss, and use the P value calculated by the KS test to test the goodness of fit of each marginal distribution function. P The marginal distribution function with the largest value is taken as the optimal marginal distribution function ; Among them, the candidate marginal distribution functions for fitting include normal distribution, Poisson distribution, exponential distribution, extreme value distribution, generalized extreme value distribution, gamma distribution and Rayleigh distribution.

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