Corn drought and flood and high temperature composite stress risk assessment method based on CEMI

Abstract

The invention discloses a CEMI-based maize drought and flood and high-temperature composite stress risk assessment method, which comprises the following steps: step 1, data identification and quantification: based on daily value data of a meteorological station, constructing maize drought and flood and high-temperature indexes, and identifying and quantifying maize drought, waterlogging and high temperature; step 2, constructing a composite event vibration level index: by fitting marginal distribution of drought, waterlogging and high-temperature strength values, constructing the composite event vibration level index CEMI, including a composite drought high-temperature vibration level index CDHMI and a composite waterlogging high-temperature vibration level index CWHMI; according to the method, agricultural meteorological disaster research is promoted to be converted from a single disaster seed to multiple disaster seeds, occurrence laws of CDHEs and CWHEs are identified by fitting marginal distribution and constructing CEMI, and potential influences of the occurrence laws on corn production are analyzed. Therefore, the multi-disaster coupling stress effect and the danger formation mechanism thereof are disclosed.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural meteorological disaster risk assessment, and specifically provides a method for assessing the risk of drought, waterlogging and high-temperature combined stress of maize based on CEMI. Background Technique

[0002] The IPCC's Sixth Assessment Report AR6 clearly points out that with the further intensification of future global warming, it is estimated that the intensity and frequency of extreme heat events, heavy precipitation, and agricultural ecological drought will increase. With the intensification of global climate change, the relationship between extreme climate drivers is changing, increasing the possibility of extreme events occurring simultaneously or successively. Due to the complexity and uncertainty of climate change, compound extreme events are often more destructive than single extreme events and pose a greater negative impact and unprecedented threat to agricultural production.

[0003] Scholars have used the construction of composite indices to quantify the intensity of compound extreme events. For example, the standardized composite event index SCEI constructed based on the standardized precipitation index SPI and the standardized temperature index STI, or the dry-hot vibration level index DHMI constructed using daily precipitation and daily maximum temperature data. Although these indices can better quantify the intensity of compound extreme events, they do not consider incorporating the properties of crops themselves into the scope of index construction. Hazard assessment is the first step in disaster risk evaluation and an important basis for disaster risk prevention.

[0004] However, the traditional assessment methods have the following disadvantages:

[0005] (1) At present, most of the research on the risk of agricultural meteorological disasters focuses on static assessment within a specific time range, lacking dynamic assessment of the risk of extreme events in different years and different growth stages within the crop growth season;

[0006] (2) The risk of traditional agricultural meteorological disasters is mostly roughly represented by the disaster frequency or the product of the disaster frequency and the grade, and fails to fully reflect the contribution of the disaster intensity to the risk. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for assessing the risk of drought, waterlogging and high-temperature combined stress of maize based on CEMI, so as to solve the problems raised in the above background technique, that is, at present, most of the research on the risk of agricultural meteorological disasters focuses on static assessment within a specific time range, lacking dynamic assessment of the risk of extreme events in different years and different growth stages within the crop growth season; the risk of traditional agricultural meteorological disasters is mostly roughly represented by the disaster frequency or the product of the disaster frequency and the grade, and fails to fully reflect the contribution of the disaster intensity to the risk.

[0008] To achieve the above object, the present invention provides the following technical solution: A method for assessing the risk of combined drought, waterlogging and high temperature stress in maize based on CEMI, comprising the following steps:

[0009] Step 1, data identification and quantification: Based on the daily value data of meteorological stations, construct maize drought, waterlogging and high temperature indicators to identify and quantify maize drought, waterlogging and high temperature;

[0010] Step 2, construct the combined event magnitude index: By fitting the marginal distributions of drought, waterlogging and high temperature intensity values, construct the combined event magnitude index CEMI, including the combined drought and high temperature magnitude index CDHMI and the combined waterlogging and high temperature magnitude index CWHMI;

[0011] Step 3, feature extraction: Adopt the method of run theory, and extract the features of combined maize drought and high temperature events CDHEs and combined waterlogging and high temperature events CWHEs based on CEMI, including the occurrence times, durations, severities and intensities of the events;

[0012] Step 4, quantitative assessment: Construct a probability density distribution curve through the time series data of CDHMI and CWHMI over the years, and combine the disaster-causing intensity characterized by the disaster loss coefficient to quantitatively assess the static risk levels at different growth stages and under different disaster intensities;

[0013] Step 5, dynamic assessment: Dynamically express the risk, calculate the survival probabilities of CDHMI and CWHMI each year using the survival function, construct a dynamic risk assessment model, and conduct dynamic assessment and zoning on the risk of combined drought, waterlogging and high temperature stress in the study area for maize.

[0014] As a preferred technical solution of the present invention, in the above Step 1, considering the response characteristics of maize to different stresses, the standardized precipitation crop water requirement index SPRI is used to characterize maize drought and waterlogging, and the standardized temperature index STI calculated using the cumulative probability distribution based on the daily average temperature is used to characterize high temperature, depicting the dynamic changes of drought, waterlogging and high temperature at different growth stages within the maize growth season.

[0015] As a preferred technical solution of the present invention, in the above Step 2, by fitting the marginal distributions of the standardized drought, waterlogging and high temperature intensity values, calculate their non-exceedance probabilities. Taking CDHMI as an example: Multiply the standardized drought intensity by the corresponding drought non-exceedance probability, and multiply the high temperature intensity by the corresponding high temperature non-exceedance probability, and then multiply the two results to construct CDHMI.

[0016] As a preferred technical solution of the present invention, in step three, based on the run theory, the characteristics of compound events with a duration of 2 days or more are extracted, the occurrence times, durations, severities and intensities of the compound events are counted, and their impacts on different growth stages within the maize growth season are analyzed.

[0017] As a preferred technical solution of the present invention, in step four, based on the time series data of multi-year CDHMI and CWHMI, the probability density distribution curves of the two are constructed, and the static hazard values under different growth stages and different disaster degrees are calculated respectively by multiplying the fitted probability density values by their corresponding disaster loss coefficients.

[0018] As a preferred technical solution of the present invention, in step five, the survival function is used to calculate the survival probabilities of annual CDHMI and CWHMI. By multiplying the multi-year static hazard values by the survival probabilities of the compound event magnitude index in the corresponding years, the dynamic hazard values of the years are calculated. Using ArcGIS 10.2 software, the spatial distribution of disasters is determined by the inverse distance weighted IDW interpolation method and divided into five hazard levels: extremely low, low, medium, high and extremely high. Finally, a dynamic evaluation model for the compound disaster hazard from the daily scale to the whole process of maize growth in the study area is established.

[0019] As a preferred technical solution of the present invention, the daily value data of the meteorological stations in step one include precipitation, average wind speed, average temperature, minimum temperature, maximum temperature, sunshine hours and average relative humidity, which are obtained from the National Meteorological Information Center - China Meteorological Data Network. The maize growth period is delimited, including the three-leaf stage - tasseling stage from May to June, i.e., the early growth stage, the tasseling stage - milk ripening stage in July, i.e., the middle growth stage, and the milk ripening stage - maturity stage from August to September, i.e., the late growth stage. Considering the different water requirements of maize in different months of the growth season, the potential evapotranspiration PET in the standardized precipitation evapotranspiration index SPEI is replaced by the water requirements of maize in different months, and the standardized precipitation crop water requirement index SPRI is constructed to identify the drought and waterlogging of maize. The calculation principle of the standardized temperature index STI is similar to that of the standardized precipitation index SPI, which is calculated based on the cumulative probability distribution of monthly or daily average temperature, and the Gamma probability distribution is used to describe the distribution change of temperature.

[0020] As a preferred technical solution of the present invention, in step two, multi-variable events are concerned, that is, the phenomenon that multiple driving factors and / or disaster-causing factors occur simultaneously in the same area and cause extreme impacts. In order to quantify the intensity of compound events, the compound drought and high temperature magnitude index CDHMI and the compound waterlogging and high temperature magnitude index CWHMI are constructed based on SPRI and STI, which are expressed as:

[0021] CDHMI = P ΔD (|D i -D th |)PΔH (H i -H th )=P ΔD (ΔD)P ΔH (ΔH)

[0022] CWHMI=P ΔW (|W i -W th |)P ΔH (H i -H th )=P ΔW (ΔW)P ΔH (ΔH)。

[0023] As a preferred technical solution of the present invention, in step three, based on the CEMI constructed above, the run theory is used to extract the CDHEs and CWHEs features respectively, the indicators in the time series are decomposed by the MATLAB tool, the duration, severity and intensity eigenvalue are obtained, the disaster events with a duration of one day are excluded, and the occurrence times of the compound events are counted.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. This method promotes the transformation of agricultural meteorological disaster research from single disaster types to multiple disaster types. By fitting the marginal distribution, constructing CEMI to identify the occurrence rules of CDHEs and CWHEs, and analyzing their potential impacts on maize production, so as to reveal the coupling stress effect of multiple disasters and its risk formation mechanism;

[0026] 2. A risk assessment model for maize drought, waterlogging and high temperature compound stress based on CEMI is provided. Based on the time series data of CEMI for many years, the present invention constructs a probability density distribution curve, quantifies the static risk values at different growth stages and different disaster degrees through the product of the fitted probability density value and the corresponding disaster loss coefficient, and further constructs a dynamic risk index in combination with the survival probability of the current year. The final risk zoning result provides an important scientific basis for the optimization of maize layout, the construction of irrigation projects and the formulation of disaster prevention and mitigation policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is the flow chart of the present invention;

[0028] Figure 2 is the conceptual diagram of the compound drought and high temperature magnitude index CDHMI and the compound waterlogging and high temperature magnitude index CWHMI constructed based on the standardized precipitation crop water requirement index SPRI and the standardized temperature index STI of the present invention;

[0029] Figure 3Spatial distribution maps of the occurrence times a1, a2, durations b1, b2, severities c1, c2, and intensities d1, d2 of the compound drought and heat events CDHEs and compound waterlogging and heat events CWHEs of the present invention;

[0030] Figure 4 Spatial zoning maps of the risks of the compound drought and heat events CDHEs and compound waterlogging and heat events CWHEs during the pre - growth stage a3, a4, mid - growth stage b3, b4, and late - growth stage c3, c4 of maize of the present invention;

[0031] Figure 5 Distribution of the risk centers and movement trajectories of the compound drought and heat events CDHEs and compound waterlogging and heat events CWHEs during the pre - growth stage a5, a6, mid - growth stage b5, b6, and late - growth stage c5, c6 of maize of the present invention;

[0032] Figure 6 Flow chart for the risk assessment of the combined drought, waterlogging and heat stress of maize of the present invention. Detailed implementation manners

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Please refer to Figure 1-6 , the present invention provides a method for risk assessment of combined drought, waterlogging and heat stress of maize based on CEMI, including the following steps:

[0035] Step 1: Data identification and quantification: Based on the daily data of meteorological stations, construct indices for drought, waterlogging and heat of maize to identify and quantify maize drought, waterlogging and heat;

[0036] Step 2: Construct the compound event magnitude index: By fitting the marginal distributions of drought, waterlogging and heat intensity values, construct the compound event magnitude index CEMI, including the compound drought - heat magnitude index CDHMI and the compound waterlogging - heat magnitude index CWHMI;

[0037] Step 3: Feature extraction: Adopt the method of run - length theory to extract the features of the compound drought and heat events CDHEs and compound waterlogging and heat events CWHEs of maize based on CEMI, including the occurrence times, durations, severities and intensities of the events;

[0038] Step 4. Quantitative assessment: Construct the probability density distribution curve through the time series data of CDHMI and CWHMI over the years, and combine with the disaster-causing intensity characterized by the disaster loss coefficient to quantitatively evaluate the static risk levels at different growth stages and different disaster intensities.

[0039] Step 5. Dynamic assessment: Dynamically express the risk, calculate the survival probability of CDHMI and CWHMI each year using the survival function, construct a dynamic risk assessment model, and conduct dynamic assessment and zoning on the risk of combined drought, waterlogging and high temperature stress of maize in the study area.

[0040] In Step 1, considering the response characteristics of maize to different stresses, the standardized precipitation crop water requirement index (SPRI) is used to characterize drought and waterlogging of maize, and the standardized temperature index (STI) calculated using the cumulative probability distribution based on the daily average temperature is used to characterize high temperature, depicting the dynamic change characteristics of drought, waterlogging and high temperature at different growth stages during the maize growing season.

[0041] In Step 2, by fitting the marginal distribution of the standardized drought, waterlogging and high temperature intensity values, calculate their non-exceedance probabilities. Taking CDHMI as an example: Multiply the standardized drought intensity by the corresponding drought non-exceedance probability, and multiply the standardized high temperature intensity by the corresponding high temperature non-exceedance probability, and then multiply the two results to construct CDHMI.

[0042] In Step 3, based on the run theory, extract the characteristics of compound events with a duration of 2 days or more, count the occurrence times, duration, severity and intensity of the compound events, and analyze their impacts on different growth stages during the maize growing season.

[0043] In Step 4, based on the time series data of CDHMI and CWHMI over the years, construct the probability density distribution curves of the two, and calculate the static risk values at different growth stages and different disaster levels respectively through the product of the fitted probability density values and their corresponding disaster loss coefficients.

[0044] In Step 5, use the survival function to calculate the survival probability of CDHMI and CWHMI each year. By multiplying the multi-year static risk values by the survival probability of the compound event magnitude index in the corresponding year, calculate the dynamic risk value of that year. Using ArcGIS 10.2 software, determine the spatial distribution of the disaster through the inverse distance weighting (IDW) interpolation method, and divide it into five risk levels: extremely low, low, medium, high and extremely high, and finally establish a dynamic evaluation model for the combined disaster risk from the daily scale to the whole process of maize growth in the study area.

[0045] In Step 1, the daily data of meteorological stations includes precipitation, average wind speed, average temperature, minimum temperature, maximum temperature, sunshine hours, and average relative humidity, which are obtained from the National Meteorological Information Center - China Meteorological Data Network. The growth period of maize is delimited, including the three-leaf to tasseling stage from May to June, i.e., the early growth period, the tasseling to milk-ripe stage in July, i.e., the middle growth period, and the milk-ripe to maturity stage from August to September, i.e., the late growth period. Considering that the water requirements of maize are different in different months of the growth season, the potential evapotranspiration PET in the standardized precipitation evapotranspiration index SPEI is replaced by the water requirements of maize in different months, and the standardized precipitation crop water requirement index SPRI is constructed to identify drought and waterlogging of maize. The calculation principle of the standardized temperature index STI is similar to that of the standardized precipitation index SPI, which is calculated based on the cumulative probability distribution of monthly or daily average temperature, and the Gamma probability distribution is used to describe the distribution change of temperature.

[0046] In Step 2, multivariate events are concerned, that is, the phenomenon that multiple driving factors and / or disaster-causing factors occur simultaneously in the same area and cause extreme impacts. In order to quantify the intensity of compound events, the compound drought and high temperature magnitude index CDHMI and the compound waterlogging and high temperature magnitude index CWHMI are constructed based on SPRI and STI, which are expressed as:

[0047] CDHMI = P ΔD (|D i -D th |)P ΔH (H i -H th ) = P ΔD (ΔD)P ΔH (ΔH)

[0048] CWHMI = P ΔW (|W i -W th |)P ΔH (H i -H th ) = P ΔW (ΔW)P ΔH (ΔH).

[0049] In Step 3, based on the above constructed CEMI, the run theory is used to extract the characteristics of CDHEs and CWHEs respectively. The indicators in the time series are decomposed by the MATLAB tool to obtain the characteristic values of duration, severity, and intensity. The disaster events with a duration of one day are excluded, and the occurrence times of compound events are counted.

[0050] In the present invention, in combination with the attached drawings of the specification Figure 2where "a" represents a compound drought and high temperature event lasting for 3 days, "b" represents a compound drought and high temperature event lasting for 4 days, and both "c" and "d" represent a compound waterlogging and high temperature event lasting for 3 days. Construction and field promotion and application of a dynamic assessment model for the risk of combined drought, waterlogging and high temperature stress on corn in the Songliao Plain,

[0051] Step 1: Select corn drought, waterlogging and high temperature indicators to identify and quantify drought, waterlogging and high temperature:

[0052] (1) Construct corn drought and waterlogging indicators:

[0053] Considering the different water requirements of corn in different months of the growing season, a standardized precipitation crop water requirement index (SPRI) is constructed to identify corn drought and waterlogging. This index adds the crop water requirements in different months of the corn growing season to the calculation on the basis of the standardized precipitation evapotranspiration index (SPEI). The crop coefficients for each growth stage of corn are: May (0.3 - 0.5), June (0.7 - 0.85), July (1.05 - 1.20), August (0.8 - 0.95), September (0.55 - 0.6); the specific formula is as follows:

[0054] (ET c ) i =K c,i xET0

[0055]

[0056] D i =P i -(ET c ) i

[0057]

[0058] Y=1 - F(x)

[0059]

[0060] When SPRI ≤ -0.5, drought occurs; when SPRI ≥ 0.5, waterlogging occurs.

[0061] (2) Construct corn high temperature indicators:

[0062] The calculation principle of the standardized temperature index (STI) is similar to that of the standardized precipitation index (SPI), and it is calculated based on the cumulative probability distribution of monthly or daily average temperatures; the specific formula is as follows:

[0063]

[0064] When STI ≥ 1.0, high temperature occurs.

[0065] Step 2: Construct the Composite Drought-High Temperature Magnitude Index (CDHMI) and the Composite Waterlogging-High Temperature Magnitude Index (CWHMI) by fitting the marginal distributions

[0066] Focusing only on multivariate events, i.e., the phenomenon where multiple driving factors and / or hazard factors occur simultaneously in the same area and cause extreme impacts, considering the composite drought-high temperature events (CDHEs, SPRI ≤ -0.5, STI ≥ 1) and the composite waterlogging-high temperature events (CWHEs, SPRI ≥ 0.5, STI ≥ 1), to quantify the intensity of the composite events, CDHMI and CWHMI are constructed based on SPRI and STI, expressed as:

[0067] CDHMI = P ΔD (|D i -D th |)P ΔH (H i -H th ) = P ΔD (ΔD)P ΔH (ΔH)

[0068] CWHMI = P ΔW (|W i -W th |)P ΔH (H i -H th ) = P ΔW (ΔW)P ΔH (ΔH),

[0069] where ΔD represents the degree of drought, which is the absolute value of the difference between the drought index SPRI value Di and the drought threshold Dth, and ΔW is the same; ΔH represents the degree of high temperature, which is the difference between the temperature index STI value Hi and the high temperature threshold Hth; P ΔD , P ΔW , P ΔH is obtained by converting ΔD, ΔW, ΔH to their non-exceedance probability ranges from 0 to 1;

[0070] Step 3: Extract the characteristics of the composite drought-high temperature events (CDHEs) and the composite waterlogging-high temperature events (CWHEs) based on the run theory, including the occurrence times, duration, severity, and intensity;

[0071] Occurrence times, the number of occurrences of CDHEs (CWHEs) where drought and high temperature (waterlogging and high temperature) occur simultaneously and last for 2 days or more in a specific time series;

[0072] Duration, the time interval from the start to the end of CDHEs and CWHEs;

[0073] Severity, the sum of indices during the duration of CDHEs and CWHEs;

[0074] Intensity, the ratio of the severity of CDHEs and CWHEs to their duration;

[0075] Step 4 constructs a probability density distribution curve through CDHMI and CWHMI time series data, and constructs a more accurate static hazard index by combining the disaster loss coefficient of the disaster-causing intensity. In order to dynamically express the hazard, the survival function is used to calculate the survival probability of CDHMI and CWHMI every year, constructs a dynamic hazard assessment model, and conducts dynamic assessment and zoning on the hazard of drought, waterlogging and high-temperature composite stress of maize in the study area;

[0076] Taking CDHEs as an example, the calculation formula is as follows:

[0077]

[0078] H ij (CDHEs n ) = Hi j (CDHEs) x CDF ij (CDHMIn),

[0079] In the formula: H ij (CDHEs) is the hazard of CDHEs with different degrees at different growth stages; PDF ij (CDHMI) is the fitted probability density value; w ij (CDHEs) is the disaster loss coefficient of CDHEs with different degrees at different growth stages; i represents the maize growth stage (including pre-growth, mid-growth and post-growth stages); j represents the disaster degree (including mild, moderate and severe), y ij (CDHEs) is the average yield reduction caused by CDHEs with different degrees at different growth stages, H ij (CDHEs n ) represents the hazard of CDHEs with different degrees at different growth stages in the nth year; CDF ij (CDHMI n ) represents the survival probability of CDHMI with different degrees at different growth stages n After obtaining the hazard values of the entire study area, standardize them so that all hazard values are between 0 and 1. Table 1 shows the disaster losses of CDHEs with different grades and different growth periods of maize:

[0080]

[0081] After evaluation, the higher-risk levels of CDHEs in the Songliao Plain mainly occur in the middle growth stage of maize, while the higher-risk levels of CWHEs mainly occur in the late growth stage of maize. The risk centers of CDHEs in each growth period all move from north to south, while the risk centers of CWHEs all move from south to north to varying degrees.

[0082] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the risk of drought, waterlogging and high temperature composite stress in corn based on CEMI, characterized in that, It includes the following steps: Step 1, data identification and quantification: Based on the daily value data of meteorological stations, construct maize drought, waterlogging and high temperature indices to identify and quantify maize drought, waterlogging and high temperature; Step 2, construct the composite event magnitude index: By fitting the marginal distributions of drought, waterlogging and high temperature intensity values, construct the composite event magnitude index CEMI, including the composite drought-high temperature magnitude index CDHMI and the composite waterlogging-high temperature magnitude index CWHMI; Step 3, feature extraction: Adopt the method of run theory, and based on CEMI, extract the features of maize composite drought-high temperature events CDHEs and composite waterlogging-high temperature events CWHEs, including the occurrence times, duration, severity and intensity of the events; Step 4, quantitative assessment: Construct a probability density distribution curve through the time series data of multi-year CDHMI and CWHMI, and combine with the disaster-causing intensity characterized by the disaster loss coefficient, so as to quantitatively evaluate the static risk level under different growth stages and different disaster intensities; Step 5, dynamic assessment: Dynamically express the risk, calculate the survival probabilities of annual CDHMI and CWHMI using the survival function, construct a dynamic risk assessment model, and dynamically assess and zone the risk of maize drought, waterlogging and high temperature composite stress in the study area.

2. The method for evaluating the risk of drought, waterlogging and high-temperature combined stress of corn based on CEMI according to claim 1, wherein: In the said Step 1, considering the response characteristics of maize to different stresses, use the standardized precipitation crop water requirement index SPRI to characterize maize drought and waterlogging, and use the standardized temperature index STI calculated based on the cumulative probability distribution of daily average temperature to characterize high temperature, and depict the dynamic change characteristics of drought, waterlogging and high temperature at different growth stages within the maize growth season.

3. A CEMI-based risk assessment method for drought, waterlogging and high-temperature composite stress of corn, characterized in that: In the said Step 2, by fitting the marginal distributions of the standardized drought, waterlogging and high temperature intensity values, calculate their non-exceedance probabilities. Taking CDHMI as an example: Multiply the standardized drought intensity by the corresponding drought non-exceedance probability, and multiply the high temperature intensity by the corresponding high temperature non-exceedance probability, and then multiply the two results to construct CDHMI.

4. A method for assessing the risk of combined drought, waterlogging and high temperature stress in maize based on CEMI according to claim 1, characterized in that: In the said Step 3, based on the run theory, extract the features of composite events with a duration of 2 days or more, count the occurrence times, duration, severity and intensity of the composite events, and analyze their impacts on different growth stages within the maize growth season.

5. A CEMI-based risk assessment method for drought, waterlogging and high-temperature combined stress of corn, characterized in that: In the said Step 4, based on the time series data of multi-year CDHMI and CWHMI, construct the probability density distribution curves of the two, and calculate the static risk values under different growth stages and different disaster degrees respectively through the product of the fitted probability density values and their corresponding disaster loss coefficients.

6. The method for evaluating the risk of combined drought, waterlogging and high temperature stress of corn based on CEMI according to claim 1, wherein: In the said Step 5, use the survival function to calculate the survival probabilities of annual CDHMI and CWHMI, calculate the dynamic risk value of the year by multiplying the multi-year static risk value by the survival probability of the composite event magnitude index in the corresponding year, use the ArcGIS10.2 software, determine the spatial distribution of disasters through the inverse distance weighting IDW interpolation method, and divide it into five risk levels: extremely low, low, medium, high and extremely high, and finally establish a dynamic evaluation model for the risk of composite disasters from the daily scale to the whole process of maize growth in the study area.

7. A method for assessing the risk of combined drought, waterlogging and high temperature stress in maize based on CEMI according to claim 1, characterized in that: The daily data of the meteorological stations in Step 1 include precipitation, average wind speed, average temperature, minimum temperature, maximum temperature, sunshine hours, and average relative humidity, which are obtained from the National Meteorological Information Center - China Meteorological Data Network. The maize growth period is demarcated, including the three-leaf stage to tasseling stage from May to June, i.e., the early growth stage; the tasseling stage to milk-ripening stage in July, i.e., the middle growth stage; and the milk-ripening stage to maturity stage from August to September, i.e., the late growth stage. Considering the different water requirements of maize in different months of the growth season, the potential evapotranspiration PET in the standardized precipitation evapotranspiration index SPEI is replaced by the water requirements of maize in different months, and the standardized precipitation crop water requirement index SPRI is constructed to identify drought and waterlogging of maize. The calculation principle of the standardized temperature index STI is similar to that of the standardized precipitation index SPI, which is calculated based on the cumulative probability distribution of monthly or daily average temperature, and the Gamma probability distribution is used to describe the distribution change of temperature.

8. A method for evaluating the risk of combined drought, waterlogging and high temperature stress of corn based on CEMI according to claim 1, characterized in that: In Step 2, multi-variable events are concerned, that is, the phenomenon that multiple driving factors and / or disaster-causing factors occur simultaneously in the same area and cause extreme impacts. In order to quantify the intensity of compound events, the compound drought and high temperature magnitude index CDHMI and the compound waterlogging and high temperature magnitude index CWHMI are constructed based on SPRI and STI, which are expressed as: CDHMI = P ΔD (|D i -D th |)P ΔH (H i -H th ) = P ΔD (ΔD)P ΔH (ΔH) CWHMI = P ΔW (|W i -W th |)P ΔH (H i -H th ) = P ΔW (ΔW)P ΔH (ΔH).

9. A CEMI-based risk assessment method for drought, waterlogging and high temperature composite stress of maize, characterized in that: In Step 3, based on the CEMI constructed above, the characteristics of CDHEs and CWHEs are extracted respectively using the run theory. The indicators in the time series are decomposed by the MATLAB tool to obtain the characteristic values of duration, severity, and intensity. The disaster events with a duration of one day are excluded, and the occurrence times of compound events are counted.

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