A method for determining gridded crop drought vulnerability curves based on improved water sensitivity

Abstract

The present invention belongs to the technical field of hydrology and water resources, and relates to a method for determining a grid crop drought vulnerability curve based on improved water sensitivity, the method comprising: embedding the influencing factors of water stress on photosynthesis into a DSSAT model; respectively calculating the standard root mean square error of the aboveground biomass simulation value before and after the improvement of the DSSAT model; collecting basic data in the study area, including meteorological data, geographical data, crop data and soil data, and dividing the study area into grids; using the GLUE method to calculate winter wheat genetic parameters according to phenological period data; using an improved water-sensitive grid crop simulation system, constructing a drought disaster intensity DHI index as an indicator for evaluating wheat drought disaster factors; and defining a physical vulnerability curve based on the drought damage intensity index and the corresponding yield loss rate. The present invention establishes the relationship between grid drought damage intensity and crop yield loss based on the physical process of agricultural drought.

Description

Technical Field

[0001] The invention belongs to the technical field of hydrology and water resources, and in particular relates to a method for determining a gridded crop drought vulnerability curve based on improved water sensitivity. Background Art

[0002] With global warming, extreme climate events in my country are increasing. Drought has become one of the most common disasters, and the frequency and intensity of drought disasters have shown a clear upward trend in recent years. The impact of drought is wide and severe, affecting my country's agricultural production, threatening food security, water supply security and social stability. Therefore, conducting vulnerability assessment of crop water loss under drought stress plays an important guiding role in reducing the risk of agricultural drought disasters and enhancing drought resistance management capabilities.

[0003] Current research mainly selects drought vulnerability indicators, which can be evaluated from four perspectives: water resources, agricultural economy, society, and drought prevention and control. There are also methods that construct an indicator system from three aspects: exposure, sensitivity, and adaptability, and use comprehensive weight analysis methods such as entropy weight method or analytic hierarchy process to evaluate regional drought vulnerability. This type of method is relatively macroscopic and cannot achieve dynamic evaluation of agricultural drought vulnerability from the perspective of mechanism process. In addition, there are methods that use various crop growth models to evaluate agricultural drought risks based on the main crops in the region, such as AquaCrop, APSIM, EPIC (Environmental Policy Integrated Climate Model), DSSAT model, etc., which are mainly used for single-point simulation and are rarely applied to large areas. Moreover, the DSSAT model is more sensitive to nitrogen than water, and the accuracy of simulating the impact of water stress on crop yield is low. Therefore, the present invention provides a method for determining a gridded crop drought vulnerability curve based on improved water sensitivity. Summary of the invention

[0004] The present invention aims to provide a method for determining a grid crop drought vulnerability curve based on improved water sensitivity, which can more accurately simulate the growth and development process of grid crops and establish the relationship between grid drought hazard intensity and crop yield loss, namely, the vulnerability curve, based on the physical process of agricultural drought.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following method for determining a gridded crop drought vulnerability curve based on improved water sensitivity, which mainly includes the following steps:

[0006] S1. Couple the stress mechanism of soil moisture content on plant transpiration and photosynthesis behavior. Use the relationship between the actual available soil water content and the critical available soil water content that affects photosynthesis production to construct the influencing factor of water stress on photosynthesis, reflecting the reduction effect of photosynthesis under water shortage. Embed it into the water module in the DSSAT model source code, compile it using Visual Studio software, and generate the improved DSSAT model executable file;

[0007] S2. Import winter wheat related data sets into the improved water-sensitive DSSAT model, simulate the aboveground biomass using the improved DSSAT model, and calculate the standard root mean square error nRMSE of the aboveground biomass simulation values ​​before and after the DSSAT model improvement;

[0008] S3. Collect basic data in the study area, including meteorological data, geographic data, crop data and soil data, divide the study area into 1km×1km grids, and pre-process the meteorological data and phenological period data into grids, and then input them into the corresponding meteorological files and management files respectively. The model simulation time is 1970-2018, and the simulation step is day;

[0009] S4. Based on the phenological data, the GLUE method was used to calculate the genetic parameters. The number of calculations was set to 1000 times. The degree of simulation between the simulation results and the measured results was used as the evaluation standard to determine the winter wheat vernalization coefficient P1V, light sensitivity coefficient P1D, accumulated temperature during the filling period P5, number of grains per kilogram of canopy during the flowering period G1, grain weight under optimal conditions G2, stem weight at maturity under non-stress conditions G3, and leaf thermal spacing PHINT;

[0010] S5. Using the improved water-sensitive grid crop simulation system, we set up full irrigation scenarios and full rain-fed scenarios, simulated the growth process of winter wheat on a 1km grid in Handan City from 1970 to 2018, extracted the water stress value and number of days affected by water stress in each growth period from the grid daily output results of the model, constructed the drought disaster intensity DHI index as an indicator for evaluating wheat drought disaster factors, and calculated the DHI index for each grid;

[0011] S6. The winter wheat yield under the fully irrigated scenario of each 1km grid minus the winter wheat yield under the fully rain-fed scenario was used as the loss value affected by drought. The ratio of the loss value to the multi-year maximum yield of the corresponding grid was used as the drought loss rate. Based on the drought hazard intensity index and the corresponding drought loss rate, the physical vulnerability curve was defined. The logistic regression analysis method was used to fit the relationship between the two, and the winter wheat drought vulnerability curve of the 1km grid was obtained.

[0012] As a preferred technical solution of the present invention, in S1, the water absorption of the crop is calculated by the following formula:

[0013] T ai =min(T pi ,T pi *f,U xi )

[0014]

[0015] θ ti =(1-p)(θ fc -θ pwp )+θ pwp

[0016] Among them, T ai is the actual water absorption of the crop on day i, T pi is the water requirement of the crop on the i-th day, U xi is the simulated crop root water absorption on the i-th day, f is the factor affecting water stress on photosynthesis, θ i is the actual soil moisture content, in cm 3 / cm 3 ,θ pwp is the wilting moisture content, in cm 3 / cm 3 ,θ ti is the critical soil moisture content that affects photosynthetic production, in cm 3 / cm 3 , p is the proportion of soil water available for plant absorption during photosynthesis, θ fc is the field water holding capacity, in cm 3 / cm 3 .

[0017] As a preferred technical solution of the present invention, in S2, the standard root mean square error nRMSE is calculated by the following formula:

[0018]

[0019] Among them, X model,i represents the aboveground biomass simulated by the model, X obs,i represents the measured aboveground biomass, and n represents the number of simulations.

[0020] As a preferred technical solution of the present invention, in S3, data preprocessing includes the following steps:

[0021] S31. Interpolate the time series of temperature, precipitation and sunshine hours at the meteorological station;

[0022] S32. Calculate the solar radiation required to be input into the DSSAT model based on the sunshine hours. The calculation formula is as follows:

[0023]

[0024] Among them, R S is the solar radiation, in MJ / m 2 , a s 、b s are empirical parameters related to air quality, with values ​​of 0.18 and 0.55 respectively, n is the sunshine hours in h, N is the available sunshine hours in h, R a is the incident radiation from the upper boundary of the atmosphere, in MJ / m 2 ;

[0025] S33, expand the meteorological station data into 1km grid data, and use the Kriging interpolation method to estimate the attribute value of the unknown point according to the temperature, precipitation and solar radiation of the known meteorological stations, where the calculation formula of the Kriging interpolation method is:

[0026]

[0027] Among them, Z(x i ) is x i Meteorological data at, i = 1, 2, ..., n, Z * (x0) is the interpolation result at the interpolation point x0, λ i is the weight coefficient, C(x i ,x j ) is Z(x i ) and Z(x j ), the covariance function Z(x j ) is x j Meteorological data at, j = 1, 2, ..., n, i ≠ j, μ is the Lagrange multiplier, n is the number of observation points;

[0028] S34. A model is established based on the relationship between DEM digital elevation data, longitude and latitude, and the crop growth and development data of the site. The phenological period of each grid point is calculated to achieve gridding of the phenological period. A regional expansion model is constructed based on known site data. The three features of the site's longitude and latitude and elevation are used as model inputs, and the flowering period and full growth period length of winter wheat are used as model outputs. The established flowering period model and maturity period model are as follows:

[0029] A=1.052*lon+5.513*lat+0.027*dem-126.625

[0030] M=1.217*lon+4.822*lat+0.031*dem-80.192

[0031] Where A is the simulated DAP of the flowering period, M is the simulated DAP of the maturity period, lon is the latitude, lat is the longitude, and dem is the digital elevation data in meters.

[0032] As a preferred technical solution of the present invention, in S5, the drought disaster intensity DHI index and the water stress value are calculated by the following formula:

[0033]

[0034] CWSI i =1-ET / ET0

[0035] Among them, DHI xy is the drought severity index of the yth grid in year x, CWSI i is the water stress index on the i-th day, ET is the actual evapotranspiration, ET0 is the potential evapotranspiration, and n is the number of days of water stress.

[0036] As a preferred technical solution of the present invention, in S6, the drought loss rate of winter wheat is calculated by the following formula:

[0037]

[0038] Among them, YL xy is the drought loss rate of the indicator in the growing period of the yth grid in year x, Y1 and Y2 are the values ​​of the indicators under the full irrigation scenario and the full rain-fed scenario, respectively, and maxY1 is the maximum value of the indicator under the full irrigation scenario in the simulated year of the corresponding grid.

[0039] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0040] 1. The stress mechanism of soil moisture content on plant transpiration and photosynthesis behavior is coupled. The relationship between the actual available soil water content and the critical available soil water content that affects photosynthesis production is used to construct the influencing factor of water stress on photosynthesis, reflecting the reduction effect of photosynthesis under water shortage. It improves the sensitivity of crop growth to water stress, can significantly improve the simulation accuracy of aboveground biomass, can more accurately simulate the growth process of crops under water stress, and is conducive to simulating and predicting crop yield losses under different drought conditions.

[0041] 2. Carry out raster crop simulation in the region, interpolate the site data, build a regional expansion model to expand the surface, etc. to obtain a 1km input data set, and simulate the daily growth and development process of crops in a 1km grid under water stress. At the same time, according to the water stress value and the number of days, a grid-based drought hazard intensity index DHI can be constructed. The simulation results of this method have spatial heterogeneity and can provide richer information on crop growth and drought risk.

[0042] 3. The drought damage intensity index and yield loss rate were extracted from the yield loss process of crops under water stress under drought disasters, and the relationship between the two was fitted with a logistic curve. The crop drought vulnerability curve was constructed from the perspective of physical processes, which can be used to assess crop losses under different intensities of drought-causing factors, and is conducive to the dynamic assessment of agricultural drought risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a graph of simulated aboveground biomass values ​​before and after the DSSAT model of the present invention is improved;

[0044] Figure 2 It is a simulation result diagram of flowering period and maturity period after the genetic parameters of the present invention are localized;

[0045] Figure 3 It is a comparison chart of simulation results of the flowering stage and mature stage area expansion model of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention.

[0047] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0048] The research area of ​​the present invention is Handan City, Hebei Province. The meteorological data adopts the daily value data of meteorological observation stations and automatic stations from 1960 to 2020 provided by the National Meteorological Information Center, including daily maximum temperature, daily minimum temperature, precipitation, sunshine hours, etc. The DEM data comes from SRTM, provided by the Resources and Environmental Science and Data Center, with a spatial resolution of 1km. The crop data adopts the ten-day data set of crop growth and development and farmland soil moisture of agricultural meteorological stations in North China from 1970 to 2020, which comes from the China Meteorological Data Network, including the basic information of the station, including station number, name, longitude and latitude, altitude, basic growth information of winter wheat, etc. The soil data comes from the Chinese soil data set for land surface simulation. The source data of this data set comes from the 1:1 million Chinese soil map and 8595 soil profiles of the second soil census. The data include profile depth, soil layer thickness, sand, silt, clay, gravel, bulk density, porosity, soil structure, soil color, pH, organic matter, nitrogen, phosphorus, potassium, exchangeable cations, exchangeable hydrogen, aluminum, calcium, magnesium, potassium, sodium ions and root mass. The data is in raster format and stored in NetCDF format with a spatial resolution of 30 arc seconds. The soil vertical data is divided into 8 layers with a maximum depth of 2.3 meters. The data is processed in each step in combination with GIS software and programming software.

[0049] The present invention provides a method for determining a gridded crop drought vulnerability curve based on improved water sensitivity, which is mainly achieved by executing the following steps:

[0050] S1. Couple the stress mechanism of soil moisture content on plant transpiration and photosynthesis behavior. Use the relationship between the actual available soil water content and the critical available soil water content that affects photosynthesis production to construct the influencing factor of water stress on photosynthesis, reflecting the reduction effect of photosynthesis under water shortage. Embed it into the water module in the DSSAT model source code, compile it using Visual Studio software, and generate the improved DSSAT model executable file;

[0051] S2. Import winter wheat related data sets into the improved water-sensitive DSSAT model, simulate the aboveground biomass using the improved DSSAT model, and calculate the standard root mean square error nRMSE of the aboveground biomass simulation values ​​before and after the DSSAT model improvement;

[0052] Specifically, the aboveground biomass simulation values ​​of the four grid points before and after the DSSAT model improvement are referenced Figure 1 As shown in the table below, the standard root mean square error nRMSE of the aboveground biomass simulation values ​​of the four grid points before and after the DSSAT model improvement is calculated is shown in the table below:

[0053] nRMSE of aboveground biomass simulation values ​​before and after model improvement

[0054]

[0055] S3. Collect basic data in the study area, including meteorological data (site data), geographic data (1km grid data), crop data (site data) and soil data (1km grid data), divide the study area into 1km×1km grids, and pre-process the meteorological data and phenological period data into grids, and then input them into the corresponding meteorological files (meteorological files are files containing data such as temperature, rainfall, radiation, evapotranspiration, etc.) and management files (management files are files containing information such as variety processing, sowing, irrigation, fertilization, simulation control and harvest time). The model simulation time is 1970-2018, and the simulation step is day;

[0056] S4. Based on the phenological data, the GLUE method was used to calculate the genetic parameters. The number of calculations was set to 1000 times. The degree of simulation between the simulation results and the measured results was used as the evaluation standard to determine the winter wheat vernalization coefficient P1V, light sensitivity coefficient P1D, accumulated temperature during the filling period P5, number of grains per kilogram of canopy during the flowering period G1, grain weight under optimal conditions G2, stem weight at maturity under non-stress conditions G3, and leaf thermal spacing PHINT;

[0057] Specifically, the simulation results of flowering and maturity after localization of genetic parameters are shown in Figure 2 The range of winter wheat genetic parameters is shown in the following table:

[0058] Winter wheat genetic parameter range table

[0059]

[0060]

[0061] S5. Using the improved water-sensitive grid crop simulation system, we set up full irrigation scenarios and full rain-fed scenarios, simulated the growth process of winter wheat on a 1km grid in Handan City from 1970 to 2018, extracted the water stress value and number of days affected by water stress in each growth period from the grid daily output results of the model, constructed the drought disaster intensity DHI index as an indicator for evaluating wheat drought disaster factors, and calculated the DHI index for each grid;

[0062] S6. The winter wheat yield under the fully irrigated scenario of each 1km grid minus the winter wheat yield under the fully rain-fed scenario was used as the loss value affected by drought. The ratio of the loss value to the multi-year maximum yield of the corresponding grid was used as the drought loss rate. Based on the drought hazard intensity index and the corresponding drought loss rate, the physical vulnerability curve was defined. The logistic regression analysis method was used to fit the relationship between the two, and the winter wheat drought vulnerability curve of the 1km grid was obtained.

[0063] Furthermore, in S1, the water absorption of crops is calculated by the following formula:

[0064] T ai =min(T pi ,T pi *f,U xi )

[0065]

[0066] θ ti =(1-p)(θ fc -θ pwp )+θ pwp

[0067] Among them, T ai is the actual water absorption of the crop on the i-th day simulated by the model, T pi is the water requirement of the crop on the i-th day, U xi is the simulated crop root water absorption on the i-th day, f is the factor affecting water stress on photosynthesis, θ i is the actual soil moisture content, in cm 3 / cm 3 ,θ pwp is the wilting moisture content, in cm 3 / cm 3 ,θ ti is the critical soil moisture content that affects photosynthetic production, in cm 3 / cm 3 , p is the proportion of soil water available for plant absorption during photosynthesis, θ fc is the field water holding capacity, in cm 3 / cm 3 .

[0068] Furthermore, in S2, the standard root mean square error nRMSE is calculated by the following formula:

[0069]

[0070] Among them, X model,i represents the aboveground biomass simulated by the model, X obs,irepresents the measured aboveground biomass, and n is the number of simulations.

[0071] Furthermore, in S3, data preprocessing includes the following steps:

[0072] S31. Interpolate the time series of temperature, precipitation and sunshine hours at the meteorological station;

[0073] S32. Calculate the solar radiation required to be input into the DSSAT model based on the sunshine hours. The calculation formula is as follows:

[0074]

[0075] Among them, R S is the solar radiation, in MJ / m 2 , a s 、b s are empirical parameters related to the air quality, with values ​​of 0.18 and 0.55 respectively. n is the sunshine hours, which refers to the actual number of hours the sun shines on a place, in h. N is the available sunshine hours, which refers to the time it takes for the sun's center to shine on the ground from the eastern horizon to the western horizon without any shielding, in h. R a is the incident radiation from the upper boundary of the atmosphere, in MJ / m 2 ;

[0076] S33, expand the meteorological station data into 1km grid data, and use the Kriging interpolation method to estimate the attribute value of the unknown point according to the temperature, precipitation and solar radiation of the known meteorological stations, where the calculation formula of the Kriging interpolation method is:

[0077]

[0078] Among them, Z(x i ) is x i Meteorological data at, i = 1, 2, ..., n, Z * (x0) is the interpolation result at the interpolation point x0, λ i is the weight coefficient, C(x i ,x j ) is Z(x i ) and Z(x j ), the covariance function Z(x j ) is x j Meteorological data at, j = 1, 2, ..., n, i ≠ j, μ is the Lagrange multiplier, n is the number of observation points;

[0079] S34. Build a model based on the relationship between DEM data, longitude and latitude, and the crop growth and development data of the site, calculate the phenological period of each grid point, realize the gridding of the phenological period, build a regional expansion model based on known site data, use the longitude and latitude and elevation of the site as model input, and the flowering period and full growth period length of winter wheat as model output. Specifically, the crop growth and development data set includes a total of 700 sets of data for the flowering period and a total of 700 sets of data for the maturity period. 80% of the data are randomly selected from the sample to build the model, and 20% of the data are used to verify the model. Refer to Figure 3 As shown, a multivariate linear equation between phenological period and longitude, latitude and altitude was established, and the fitting result passed the significance test of P<0.05. The established flowering period model and maturity period model are as follows:

[0080] A=1.052*lon+5.513*lat+0.027*dem-126.625

[0081] M=1.217*lon+4.822*lat+0.031*dem-80.192

[0082] Wherein, A is the simulated flowering period, M is the simulated maturity period, lon is the latitude, lat is the longitude, and dem is the digital elevation data in meters.

[0083] Model R 2 They reached 0.78 and 0.84 respectively, proving that this method can simulate the flowering and maturity periods of the study area very well. Therefore, the verified regional extension model was used to input the elevation, longitude and latitude of the winter wheat planting area in the entire study area, simulate the length of the entire growth period, and obtain the regional gridded winter wheat flowering and maturity period data.

[0084] Furthermore, in S5, the drought disaster intensity DHI index and water stress value are calculated by the following formula:

[0085]

[0086] CWSI i =1-ET / ET0

[0087] Among them, DHI xy is the drought severity index of the yth grid in year x, CWSI i is the water stress index on the i-th day, ET is the actual evapotranspiration, ET0 is the potential evapotranspiration, and n is the number of days of water stress.

[0088] Furthermore, in S6, the drought loss rate of winter wheat is calculated by the following formula:

[0089]

[0090] Among them, YL xy is the loss rate of the indicator due to drought during the growth period of the yth grid in year x, Y1 and Y2 are the values ​​of the indicator under the full irrigation scenario and the full rain-fed scenario, respectively, and maxY1 is the maximum value of the indicator under the full irrigation scenario in the simulated year of the corresponding grid.

[0091] In summary, the method based on the improved water-sensitive grid crop drought vulnerability curve of the present invention can more accurately simulate the growth and development process of grid crops, and establish the relationship between grid drought hazard intensity and crop yield loss, namely, the vulnerability curve, based on the physical process of agricultural drought. It is a reliable and effective tool for realizing dynamic assessment of agricultural drought risk and reducing drought losses.

[0092] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0094] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0096] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a gridded crop drought vulnerability curve based on improved water sensitivity, characterized in that: The steps include: S1. Couple the stress mechanism of soil moisture content on plant transpiration and photosynthesis behavior. Use the relationship between the actual available soil water content and the critical available soil water content that affects photosynthesis production to construct the influencing factor of water stress on photosynthesis, reflecting the reduction effect of photosynthesis under water shortage. Embed it into the water module in the DSSAT model source code, compile it using Visual Studio software, and generate the improved DSSAT model executable file; S2. Import winter wheat related data sets into the improved water-sensitive DSSAT model, simulate the aboveground biomass using the improved DSSAT model, and calculate the standard root mean square error nRMSE of the aboveground biomass simulation values ​​before and after the DSSAT model improvement; S3. Collect basic data in the study area, including meteorological data, geographic data, crop data and soil data, divide the study area into 1km×1km grids, and pre-process the meteorological data and crop data into grids, and then input them into the corresponding meteorological files and management files respectively. The model simulation time is 1970-2018, and the simulation step is day; S4. Based on the phenological data, the GLUE method was used to calculate the genetic parameters. The number of calculations was set to 1000 times. The degree of simulation between the simulation results and the measured results was used as the evaluation standard to determine the winter wheat vernalization coefficient P1V, light sensitivity coefficient P1D, accumulated temperature during the filling period P5, number of grains per kilogram of canopy during the flowering period G1, grain weight under optimal conditions G2, stem weight at maturity under non-stress conditions G3, and leaf thermal spacing PHINT; S5. Using the improved water-sensitive grid crop simulation system, we set up full irrigation scenarios and full rain-fed scenarios, simulated the growth process of winter wheat on a 1km grid in Handan City from 1970 to 2018, extracted the water stress value and number of days affected by water stress in each growth period from the grid daily output results of the model, constructed the drought disaster intensity DHI index as an indicator for evaluating wheat drought disaster factors, and calculated the DHI index for each grid; S6. The winter wheat yield under the fully irrigated scenario of each 1km grid was subtracted from the winter wheat yield under the fully rain-fed scenario as the loss value affected by drought. The ratio of the loss value to the multi-year maximum yield of the corresponding grid was used as the drought loss rate. Based on the drought damage intensity index and the corresponding drought loss rate, the physical vulnerability curve was defined. The logistic regression analysis method was used to fit the relationship between the two, and the winter wheat drought vulnerability curve of the 1km grid was obtained. It is characterized in that, in S1, the water absorption of the crop is calculated by the following formula: T ai =min(T pi ,T pi *f,U xi ) i ti =(1-p)(θ) fc -θ pwp )+θ pwp Among them, T ai is the actual water absorption of the crop on day i, T pi is the water requirement of the crop on the i-th day, U xi is the simulated crop root water absorption on the i-th day, f is the factor affecting water stress on photosynthesis, θ i is the actual soil moisture content, in cm 3 / cm 3 ,θ pwp is the wilting moisture content, in cm 3 / cm 3 ,θ ti is the critical soil moisture content that affects photosynthetic production, in cm 3 / cm 3 , p is the proportion of soil water available for plant absorption during photosynthesis, θ fc is the field water holding capacity, in cm 3 / cm 3 .

2. The method for determining a gridded crop drought vulnerability curve based on improved water sensitivity according to claim 1, characterized in that: In S2, the standard root mean square error nRMSE is calculated by the following formula: Among them, X model,i represents the aboveground biomass simulated by the model, X obs,i represents the measured aboveground biomass, and n represents the number of simulations.

3. The method for determining a gridded crop drought vulnerability curve based on improved water sensitivity according to claim 1, characterized in that: In S3, data preprocessing includes the following steps: S31. Interpolate the time series of temperature, precipitation and sunshine hours at the meteorological station; S32. Calculate the solar radiation required to be input into the DSSAT model based on the sunshine hours. The calculation formula is as follows: Among them, R S is the solar radiation, in MJ / m 2 , a s , b s are empirical parameters related to air quality, with values ​​of 0.18 and 0.55 respectively, n is the sunshine hours in h, N is the available sunshine hours in h, R a is the incident radiation from the upper boundary of the atmosphere, in MJ / m 2 ; S33, the meteorological station data is expanded into 1km grid data, and the attribute values ​​of unknown points are estimated by using the Kriging interpolation method according to the temperature, precipitation and solar radiation of the known meteorological stations, wherein the calculation formula of the Kriging interpolation method is: Among them, Z(x i ) is x i Meteorological data at, i = 1, 2, ..., n, Z * (x0) is the interpolation result at the interpolation point x0, λ i is the weight coefficient, C(x i ,x j ) is Z(x i ) and Z(x j ), the covariance function Z(x j ) is x j Meteorological data at, j = 1, 2, ..., n, i ≠ j, μ is the Lagrange multiplier, n is the number of observation points; S34. A model is established based on the relationship between DEM digital elevation data, longitude and latitude, and the crop growth and development data of the site. The phenological period of each grid point is calculated to achieve gridding of the phenological period. A regional expansion model is constructed based on known site data. The three features of the site's longitude and latitude and elevation are used as model inputs, and the flowering period and full growth period length of winter wheat are used as model outputs. The established flowering period model and maturity period model are as follows: A=1.052*lon+5.513*lat+0.027*dem-126.625 M=1.217*lon+4.822*lat+0.031*dem-80.192 Where A is the simulated DAP of the flowering period, M is the simulated DAP of the maturity period, lon is the latitude, lat is the longitude, and dem is the digital elevation data in meters.

4. The method for determining a gridded crop drought vulnerability curve based on improved water sensitivity according to claim 1, characterized in that: In S5, the drought disaster intensity DHI index and water stress value are calculated by the following formula: CWSI i =1-ET / ET0 Among them, DHI xy is the drought severity index of the yth grid in year x, CWSI i is the water stress index on the i-th day, ET is the actual evapotranspiration, ET0 is the potential evapotranspiration, and n is the number of days of water stress.

5. The method for determining gridded crop drought vulnerability curve based on improved water sensitivity according to claim 1, characterized in that: In S6, the drought loss rate of winter wheat is calculated by the following formula: Among them, YL xy is the loss rate of the indicator due to drought during the growing period of the yth grid in year x, Y1 is the value of the indicator under the full irrigation scenario of the yth grid, Y2 is the value of the indicator under the full rain-fed scenario of the yth grid, and maxY1 is the maximum value of the indicator under the full irrigation scenario in the simulated year of the yth grid.

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