Agricultural water safety risk assessment method based on climate change

Abstract

The invention provides an agricultural water safety risk assessment method based on climate change, and the method comprises the steps: sampling the crop distribution data of a target region according to a 0.1-degree spatial resolution, and determining an agricultural risk region affecting the water safety based on a target grid comprising at least one effective pixel; in a set future climate scene, the disastrous property, the exposure degree and the vulnerability of the agricultural risk area are quantified to obtain respective corresponding index data; wherein the disastrous property is used for representing a natural disaster event influencing agricultural water safety; the exposure degree is used for representing the production potential of land and climate affected by natural disaster events; the vulnerability is used for representing the sensitivity and toughness of the agricultural system to natural disaster events; and based on the quantified index data of disastrous, exposure and vulnerability, evaluating the agricultural water safety risk under the future climate scene. According to the method, transferable insights are provided for agricultural water safety risk assessment and sustainable development strategies of similar agricultural ecological regions in the world.

Description

Technical Field

[0001] The present application belongs to the technical field of agricultural water security risk assessment, and in particular relates to an agricultural water security risk assessment method based on climate change. Background Art

[0002] There is a critical link between hydrological dynamics and global food security. At the same time, climate change is reshaping hydrological processes through rising temperatures and altered precipitation patterns, posing unprecedented challenges to agricultural water management. Climate-driven water scarcity and extreme weather events are now the primary risks facing global agricultural systems. Anthropogenic climate forcing exacerbates the volatility of the hydrological cycle, increasing the spatiotemporal complexity of extreme weather events. Given the water security risks posed by growing global food demand and the expected expansion of irrigated farmland, taking the necessary adaptive measures is crucial to ensuring agricultural water security and safeguarding the well-being of the world's people.

[0003] Existing research on agricultural water security primarily relies on the assessment of selected indicators, including water quantity, water quality, socioeconomics, and technological infrastructure. However, the inherent complexity and causal relationships among these indicators make it challenging to construct comprehensive indicators based on multiple factors. Furthermore, many studies focus on assessing whether a region has achieved "agricultural water security." However, assessments of agricultural water security often involve unpredictable and diverse indicators (such as irrigation water pollutant concentrations and groundwater extraction rates), which limits the results' usefulness in guiding the risk-averse behavior of agricultural stakeholders under global climate change and policy-driven factors. These studies overlook the fact that agricultural water security issues are driven by agricultural water security risks.

[0004] Furthermore, a large number of studies have focused primarily on single drivers of agricultural water insecurity, including hydrological anomalies, crop yield losses, and water quality degradation, while ignoring the exposure and vulnerability of affected humans or ecosystems to agricultural insecurity. Consequently, there is currently a lack of a systematic approach to assessing agricultural water security risks and developing corresponding adaptive management strategies. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a climate change-based agricultural water security risk assessment method to provide transferable insights for agricultural water security risk assessment and agricultural sustainable development strategies in similar agricultural ecological zones around the world.

[0006] This application provides a climate change-based agricultural water security risk assessment method, including:

[0007] Crop distribution data in the target area is sampled at a spatial resolution of 0.1°, and agricultural risk areas that affect water security are identified based on target grids containing at least one valid pixel.

[0008] Under a set future climate scenario, the hazard, exposure, and vulnerability of the agricultural risk area are quantified to obtain corresponding indicator data. The hazard is used to characterize natural disaster events that affect agricultural water security; the exposure is used to characterize the production potential of land and climate affected by the natural disaster event; and the vulnerability is used to characterize the sensitivity and resilience of the agricultural system to the natural disaster event.

[0009] Based on the quantified indicator data of disaster risk, exposure and vulnerability, the agricultural water security risks of the target area under future climate scenarios are assessed.

[0010] Furthermore, the catastrophic nature of agricultural water security is quantified by:

[0011] Calculating the weighted average precipitation of each natural disaster event during its respective duration; wherein the natural disaster events include: drought and flood events;

[0012] Based on the weighted average precipitation on the same day in different years, the Gamma function is used for distribution fitting to obtain the standardized weighted average precipitation index of the day;

[0013] Calculate the cumulative absolute value of the standardized weighted average precipitation index of each natural disaster event during its respective duration to characterize the intensity of the natural disaster event;

[0014] The copula function is used to calculate the joint probability distribution of the intensity of each natural disaster event and its corresponding duration, so as to quantify the catastrophic nature of the agricultural water security.

[0015] Furthermore, it is characterized in that the weighted average precipitation WAP of each day is obtained by the following formula:

[0016]

[0017] Where N is the maximum number of days before the current day, the current day is defined as n = 0, P n is the precipitation on the nth day before the day before, a represents the contribution intensity of the precipitation on the previous day to the flood severity on the current day, a=e -bΔt <1;

[0018] The following Gamma function is used to fit the distribution to obtain the standardized weighted average precipitation index SWAP for each day:

[0019]

[0020] Where γ is the shape parameter, β is the scale parameter, and Γ represents the Gamma function;

[0021] The following Copula function is used to obtain the joint probability distribution of the intensity of each natural disaster event and its corresponding duration:

[0022] H=C(F J (j),F D (d));

[0023] Where C is the joint distribution probability of intensity and duration, F J (j) is the cumulative distribution kernel function of intensity, F D (d) is the cumulative distribution kernel function of duration.

[0024] Furthermore, it is characterized in that the exposure to agricultural water security is quantified by:

[0025] Determining a land use sub-exposure index based on the area of ​​farmland in the agricultural risk area and the corresponding weight; wherein the land use type further includes: forest land, grassland, water, residential land and unused land;

[0026] The minimum values ​​of the production potential determined by the annual average temperature, the production potential determined by the annual precipitation, and the production potential determined by the evapotranspiration are obtained respectively to characterize the climate production potential;

[0027] The climate production potential is normalized to obtain the climate production potential sub-exposure index;

[0028] The exposure of the agricultural water security is characterized based on the land use sub-exposure index and the climate production potential sub-exposure index.

[0029] Furthermore, it is characterized in that the climate production potential is characterized by:

[0030] Using the Miami model, the production potential W determined by the annual average temperature is obtained by the following formulas: T and the production potential W determined by annual precipitation R :

[0031]

[0032] W R =3000×(1-e -0.000664R );

[0033] Where T is the annual average temperature and R is the annual precipitation;

[0034] Using the Thornthwaite Memorial Model, the production potential W determined by evapotranspiration is obtained by the following formula: V :

[0035] WV =3000×[1-e -0.0009695(AETP-20) ];

[0036] Where AETP is the actual annual average evapotranspiration, which is obtained by the following formula:

[0037]

[0038] The climate production potential CPP is obtained by the following formula:

[0039] CPP=min(W T ,W R ,W V ).

[0040] Furthermore, it is characterized in that the vulnerability of agricultural water security is quantified by:

[0041] Using daily average temperature, daily precipitation, and their interaction as explanatory variables and daily irrigation water demand as the response variable, a multiple linear regression model was used to obtain a linear relationship between climate change and irrigation water demand, thereby characterizing the sensitivity of agricultural water security.

[0042] Obtaining the daily precipitation and determining the water supply status based on whether the daily precipitation meets the crop water demand; wherein the water supply status includes: satisfactory and unsatisfactory;

[0043] The ratio of the probability of the daily water supply status being both satisfactory and unsatisfactory to the probability of the daily water supply status being only unsatisfactory is calculated to characterize the resilience of the agricultural water security; wherein the resilience is used to characterize the ability of farmland to recover from insufficient green water supply;

[0044] The ratio of the sensitivity to the resilience is used to characterize the vulnerability of the agricultural water security.

[0045] Furthermore, it is characterized in that the future climate scenarios include: moderate forcing scenario SSP2-4.5 and high forcing scenario SSP5-8.5 specified by the IPCC.

[0046] Furthermore, it is characterized in that the assessment of agricultural water security risks of the target area under future climate scenarios based on the quantified indicator data of disaster risk, exposure, and vulnerability includes:

[0047] Multiply the quantified indicators of disaster risk, exposure, and vulnerability to obtain the agricultural water security risk index of the target area under the future climate scenario;

[0048] Taking the current time as the starting year and 20-year intervals, the future is divided into three periods: early, mid-term, and long-term;

[0049] The corresponding agricultural water security risk level is assessed based on the agricultural water security risk index in the future period; wherein the agricultural water security risk level includes: slight risk, low risk, medium risk, high risk, and severe risk.

[0050] This application proposes a climate change-based agricultural water security risk assessment method that selects climate change-related hazard, exposure, and vulnerability as water security risk assessment indicators. These indicators are quantified under future climate scenarios to assess future water security risks. This application can identify evolving agricultural water security risks across time and space, providing transferable insights for ensuring sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of a method for agricultural water security risk assessment based on climate change provided in an embodiment of the present application is shown;

[0052] Figure 2 A schematic diagram of the agricultural water security risk levels of black soil granaries in different future climate scenarios and different future periods provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of this technical solution more clear, the following technical solution is further described in detail in conjunction with specific implementation methods. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of this technical solution.

[0054] Example 1:

[0055] Please refer to Figure 1 Flowchart of the agricultural water security risk assessment method based on climate change is shown.

[0056] like Figure 1 As shown, the method includes:

[0057] S101. Sample crop distribution data of the target area at a spatial resolution of 0.1°, and determine agricultural risk areas that affect water security based on a target grid containing at least one valid pixel.

[0058] In this step, to avoid including areas too far from farmland in the scope of agricultural water security risk assessment, the crop distribution data sampled at a resolution of 30 m were resampled at a spatial resolution of 0.1° to obtain several target grids. When a target grid contained at least one original valid pixel (i.e., crop coverage > 0), the target grid was determined to belong to the agricultural risk area. The agricultural risk area was determined based on all target grids that met the above conditions.

[0059] S102. Under the set future climate scenario, quantify the disaster risk, exposure, and vulnerability of the agricultural risk area to obtain corresponding indicator data.

[0060] The future climate scenarios include the moderate forcing scenarios SSP2-4.5 and the high forcing scenarios SSP5-8.5 specified by the IPCC. The severity of natural disasters is used to characterize the impact of natural disasters on agricultural water security; the exposure level is used to characterize the production potential of land and climate affected by these natural disasters; and the vulnerability level is used to characterize the sensitivity and resilience of agricultural systems to these natural disasters.

[0061] In this step, IPCC refers to the Intergovernmental Panel on Climate Change (IPCC). SSP2-4.5 and SSP5-8.5 are two shared socioeconomic pathway scenarios. Specifically, SSP2-4.5 assumes moderate greenhouse gas emissions and technological progress, reflecting a moderate climate change response strategy. Under this scenario, the magnitude and speed of global climate change are relatively controllable, but may still lead to changes in resource allocation and environmental pressures. SSP5-8.5 assumes a high-intensity greenhouse gas emissions and technological development pathway, reflecting insufficient responses to climate change or lagging technological progress. This scenario may lead to more severe climate change, exacerbating extreme weather events and the risk of resource shortages. SSP2-4.5 and SSP5-8.5 correspond to their respective datasets, which contain meteorological data (including at least precipitation, temperature, and disaster events) that can reflect future climate change trends.

[0062] In specific implementation, the catastrophic nature of agricultural water security is quantified through the following methods:

[0063] Step 201: Calculate the weighted average precipitation of each natural disaster event during its respective duration.

[0064] The natural disaster events include: drought and flood events.

[0065] Step 202: Based on the weighted average precipitation on the same day in different years, a distribution fitting is performed using a Gamma function to obtain a standardized weighted average precipitation index for the day.

[0066] Step 203: Calculate the cumulative absolute value of the standardized weighted average precipitation index of each natural disaster event within its respective duration to characterize the intensity of the natural disaster event.

[0067] Step 204: Calculate the joint probability distribution of the intensity of each natural disaster event and its corresponding duration using a Copula function to quantify the catastrophic nature of the agricultural water security.

[0068] Here, the above steps 201-204 can be expressed as the following mathematical function. Specifically, the weighted average precipitation WAP of each day is obtained by the following formula:

[0069]

[0070] Where N is the maximum number of days before the current day, the current day is defined as n = 0, Pn is the precipitation on the nth day before the current day, a represents the contribution intensity of the precipitation on the previous day to the flood severity on the current day, a = e-bΔt < 1;

[0071] The following Gamma function is used to fit the distribution to obtain the standardized weighted average precipitation index SWAP for each day:

[0072]

[0073] Where γ is the shape parameter, β is the scale parameter, and Γ represents the Gamma function;

[0074] The following Copula function is used to obtain the joint probability distribution of the intensity of each natural disaster event and its corresponding duration:

[0075] H=C(F J (j),F D (d));

[0076] Where C is the joint distribution probability of intensity and duration, F J (j) is the cumulative distribution kernel function of intensity, F D (d) is the cumulative distribution kernel function of duration.

[0077] In addition, exposure to agricultural water security was quantified by:

[0078] Step 301: Determine a land use sub-exposure index based on the area of ​​the region whose land use type is farmland in the agricultural risk area and the corresponding weight.

[0079] Among them, the land use types also include: forest land, grassland, water, residential land and unused land.

[0080] In this step, the weights corresponding to each land use type are set to 0.4, 0.08, 0.12, 0.15, 0.24, and 0.1, respectively.

[0081] Step 302: Obtain the minimum values ​​of the production potential determined by the annual average temperature, the production potential determined by the annual precipitation, and the production potential determined by the evapotranspiration, respectively, for characterizing the climate production potential.

[0082] Step 303: Normalize the climate production potential to obtain a climate production potential sub-exposure index.

[0083] Step 304: Characterize the exposure of the agricultural water security based on the land use sub-exposure index and the climate production potential sub-exposure index.

[0084] In this step, the land use sub-exposure index and the climate production potential sub-exposure index are added together and the average value is taken to obtain the quantitative value of the exposure to agricultural water security.

[0085] Here, the above steps 301-304 can be expressed as the following mathematical function, specifically:

[0086] The climate production potential is characterized by:

[0087] Using the Miami model, the production potential W determined by the annual average temperature is obtained by the following formulas: T and the production potential W determined by annual precipitation R :

[0088]

[0089] W R =3000×(1-e -0.000664R );

[0090] Where T is the annual average temperature and R is the annual precipitation;

[0091] Using the Thornthwaite Memorial Model, the production potential W determined by evapotranspiration is obtained by the following formula: V :

[0092] W V =3000×[1-e -0.0009695(AETP-20) ];

[0093] Where AETP is the actual annual average evapotranspiration, which is obtained by the following formula:

[0094]

[0095] The climate production potential CPP is obtained by the following formula:

[0096] CPP=min(W T ,W R ,W V ).

[0097] Furthermore, the vulnerability of agricultural water security was quantified by:

[0098] Step 401: Using daily average temperature, daily precipitation, and their interaction term as explanatory variables and daily irrigation water demand as the response variable, a multiple linear regression model is used to obtain a linear relationship between climate change and irrigation water demand, thereby characterizing the sensitivity of agricultural water security.

[0099] Among them, the two refer to the daily average temperature and daily precipitation.

[0100] In this step, the Z score is first used to standardize the values ​​of each variable (response variable and explanatory variable) to ensure the comparability of the regression coefficients; then, based on the standardized values ​​of each variable, the regression coefficient of each explanatory variable is obtained using a multiple linear regression model.

[0101]

[0102] Where S r is the standardized regression coefficient of each variable, and β is the original regression coefficient.

[0103] Step 402: Obtain the precipitation for the day, and determine the water supply status based on whether the precipitation for the day meets the crop water demand; wherein the water supply status includes: satisfactory and unsatisfactory.

[0104] Step 403: Calculate the ratio of the probability that the daily water supply status is both satisfactory and unsatisfactory to the probability that the daily water supply status is only unsatisfactory, so as to characterize the resilience of the agricultural water security.

[0105] Among them, the resilience is used to characterize the ability of farmland to recover from insufficient green water supply.

[0106] In this step, the resilience of agricultural water security is calculated using the following formula:

[0107]

[0108] Where GWS t is the precipitation on day t, FAI represents unsatisfactory status, and SAT represents satisfactory status.

[0109] Step 404: Use the ratio of the sensitivity to the resilience to characterize the vulnerability of the agricultural water security.

[0110] S103. Based on the quantified indicator data of disaster risk, exposure, and vulnerability, assess the agricultural water security risks of the target area under future climate scenarios.

[0111] In specific implementation, the agricultural water security risks of the target areas under future climate scenarios will be assessed through the following methods:

[0112] Step 1031: Multiply the quantified indicator data of disaster risk, exposure, and vulnerability to obtain the agricultural water security risk index of the target area under the future climate scenario.

[0113] Step 1032: Using the current time as the starting year and 20-year intervals, divide the future into three periods: early, mid-term, and long-term.

[0114] Step 1033: Evaluate the corresponding agricultural water security risk level based on the agricultural water security risk index in the future period.

[0115] Among them, the agricultural water safety risk levels include: slight risk, low risk, medium risk, high risk, and severe risk.

[0116] Example 2:

[0117] The second embodiment of the present application is an example analysis of the Black Soil Granary, an important grain-producing area in China. The agricultural development in the region faces problems such as intensified drought and flood disasters and increased irrigation demand. The frequency and intensity of drought and flood disasters have increased, resulting in serious threats to agricultural production and food security. The Black Soil Granary is located at 115°30′~135°30′ east longitude and 38°43′~53°30′ north latitude (i.e., the Northeast region), covering a total area of ​​approximately 1.45 million square kilometers. It is a strategic agricultural area with a temperate monsoon climate and a significant latitudinal thermal gradient. The annual precipitation is approximately 337-871 mm, of which the summer rainfall pattern is dominant.

[0118] The agricultural water security risk assessment method based on climate change as described in Example 1 was used to assess the agricultural water security risk of the black soil granary under two future climate scenarios, SSP2-4.5 and SSP5-8.5, and the following results were obtained:

[0119] Please refer to Figure 2 The following diagram shows the agricultural water security risk levels for the Black Soil Granary under different future climate scenarios and different future periods. To compare the impact of climate change on agricultural water security risks, this application also assesses agricultural water security risks for the baseline period (1999-2018). Figure 2In the figure, A is a schematic diagram of the spatial distribution of water security risks in the baseline period; C, D, and E are schematic diagrams of the spatial distribution of water security risks in the future period under the SSP2-4.5 future climate scenario; F, G, and H are schematic diagrams of the spatial distribution of water security risks in the future period under the SSP5-8.5 future climate scenario; the colors from light to dark are used in each figure to represent different water security risk levels (mild risk, low risk, moderate risk, high risk, and severe risk), among which the pie charts represent the area proportion of regions with different water security risk levels; B is the average value of the agricultural water security risk index under different scenarios and different periods, among which BP, NT, MT, and DF represent the baseline period, short-term, medium-term, and long-term, respectively; I is the average value of the agricultural water security risk index of each prefecture-level city pair under different scenarios and different periods.

[0120] like Figure 2 As shown in Figures A and 2B, during the baseline period (1999-2018), the average risk index for the Black Soil Granary was 0.061, with 30% of cities experiencing high or severe risk. Under the SSP2-4.5 scenario, the average risk index for the Black Soil Granary is projected to peak at 0.068 in the near term, then gradually decline, reaching 0.061 in the medium term and further decreasing to 0.056 in the long term. Under the SSP5-8.5 scenario, the average risk index for the Black Soil Granary peaks at 0.067 in the near term, then decreases to 0.062 in the medium term before rising again to 0.063 in the long term.

[0121] From the perspective of the proportion of high-risk and severe-risk cities, in both scenarios, the proportion first reaches a peak and then decreases. Figure 2 As shown in CE, under the SSP2-4.5 scenario, the proportion of cities with high and severe risks is 50% in the near term, drops to 42.5% in the medium term, and further decreases to 25% in the long term. Figure 2 As shown in FH, under the SSP5-8.5 scenario, the proportion of cities with high and severe risks is 50% in the short term, which drops sharply to 32.5% in the medium term and further drops to 25% in the long term.

[0122] From the perspective of spatial distribution, whether in the baseline period or the two future scenarios, cities with high and severe risks are mainly concentrated in the Sanjiang Plain, Songnen Plain and Liaoning Province. Figure 2 As shown in CE, under the SSP2-4.5 scenario, six cities in Liaoning Province will show high and severe risk situations for a long time, namely Chaoyang, Dalian, Fuxin, Jinzhou, Shenyang and Tieling; one city (Songyuan) in Jilin Province and three cities (Jixi, Jiamusi and Qiqihar) in Heilongjiang Province will show high and severe risk situations respectively; there is no city in the eastern part of Inner Mongolia showing high and severe risk situations. Figure 2As shown in Figure 5-6, under the SSP5-8.5 scenario, three cities each in Liaoning, Jilin and Heilongjiang provinces will show long-term high and severe risk situations, namely Dalian, Huludao and Shenyang in Liaoning Province, Baicheng, Siping and Songyuan in Jilin Province, and Hegang, Jixi and Jiamusi in Heilongjiang Province.

[0123] As shown in Figure 1, during the baseline period, Shenyang had the highest average water security risk index. Under the SSP2-4.5 scenario, 24, 14, and 11 cities will experience higher average risks than during the baseline period in the near, medium, and long term, respectively. Among these, Tonghua, Panjin, and the Greater Khingan Range region will experience the most significant increases in risk, with increases of 66.19%, 3.59%, and 18.84% in the near, medium, and long term, respectively. In this scenario, Shenyang will continue to have the highest average water security risk index, with increases of 0.163 (near, medium, and long term), 0.150 (medium, and long term), and 0.126 (long term), respectively. Under the SSP5-8.5 scenario, 22, 18, and 19 cities will experience higher risks than during the baseline period in the near, medium, and long term, respectively. Among these, Chaoyang, Panjin, and the Greater Khingan Range region will experience the most significant increases in risk, with increases of 68.80%, 26.42%, and 27.17% in the near, medium, and long term, respectively. In the future period under this scenario, Jiamusi will be the city with the highest average water security risk index, which are 0.144 (short term), 0.139 (medium term) and 0.142 (long term).

[0124] The above content is only a preferred embodiment of the present invention. For ordinary technicians in this field, many changes can be made in the specific implementation methods and application scopes based on the ideas of the present technical content. As long as these changes do not deviate from the concept of the present invention, they all fall within the scope of protection of the present invention.

Claims

1. A method for agricultural water security risk assessment based on climate change, characterized in that: The method comprises: Crop distribution data in the target area is sampled at a spatial resolution of 0.1°, and agricultural risk areas that affect water security are identified based on target grids containing at least one valid pixel. Under a set future climate scenario, the hazard, exposure, and vulnerability of the agricultural risk area are quantified to obtain corresponding indicator data. The hazard is used to characterize natural disaster events that affect agricultural water security; the exposure is used to characterize the production potential of land and climate affected by the natural disaster event; and the vulnerability is used to characterize the sensitivity and resilience of the agricultural system to the natural disaster event. Based on the quantified indicator data of disaster risk, exposure and vulnerability, the agricultural water security risks of the target area under future climate scenarios are assessed.

2. The method according to claim 1, wherein The hazard level of agricultural water security is quantified by: Calculating the weighted average precipitation of each natural disaster event during its respective duration; wherein the natural disaster events include: drought and flood events; Based on the weighted average precipitation on the same day in different years, the Gamma function is used for distribution fitting to obtain the standardized weighted average precipitation index of the day; Calculate the cumulative absolute value of the standardized weighted average precipitation index of each natural disaster event during its respective duration to characterize the intensity of the natural disaster event; The copula function is used to calculate the joint probability distribution of the intensity of each natural disaster event and its corresponding duration, so as to quantify the catastrophic nature of the agricultural water security.

3. The method according to claim 2, wherein The weighted average precipitation WAP for each day is obtained by the following formula: Where N is the maximum number of days before the current day, the current day is defined as n = 0, P n is the precipitation on the nth day before the day before, a represents the contribution intensity of the precipitation on the previous day to the flood severity on the current day, a=e -bΔt <1; The following Gamma function is used to fit the distribution to obtain the standardized weighted average precipitation index SWAP for each day: Where γ is the shape parameter, β is the scale parameter, and Γ represents the Gamma function; The following Copula function is used to obtain the joint probability distribution of the intensity of each natural disaster event and its corresponding duration: H=C(F J (j),F D (d)); Where C is the joint distribution probability of intensity and duration, F J (j) is the cumulative distribution kernel function of intensity, F D (d) is the cumulative distribution kernel function of duration.

4. The method according to claim 1, wherein Exposure to agricultural water security was quantified by: Determining a land use sub-exposure index based on the area of ​​farmland in the agricultural risk area and the corresponding weight; wherein the land use type further includes: forest land, grassland, water, residential land and unused land; The minimum values ​​of the production potential determined by the annual average temperature, the production potential determined by the annual precipitation, and the production potential determined by the evapotranspiration are obtained respectively to characterize the climate production potential; The climate production potential is normalized to obtain the climate production potential sub-exposure index; The exposure of the agricultural water security is characterized based on the land use sub-exposure index and the climate production potential sub-exposure index.

5. The method according to claim 4, wherein The climate production potential is characterized by: Using the Miami model, the production potential W determined by the annual average temperature is obtained by the following formulas: T and the production potential W determined by annual precipitation R : W R =3000×(1-e -0.000664R ); Where T is the annual average temperature and R is the annual precipitation; Using the Thornthwaite Memorial Model, the production potential W determined by evapotranspiration is obtained by the following formula: V : W V =3000×[1-e -0.0009695(AETP-20) ]; Where AETP is the actual annual average evapotranspiration, which is obtained by the following formula: The climate production potential CPP is obtained by the following formula: CPP=min(W T ,IN R ,IN V )。 6. The method according to claim 1, wherein The vulnerability of agricultural water security is quantified by: Using daily average temperature, daily precipitation, and their interaction as explanatory variables and daily irrigation water demand as the response variable, a multiple linear regression model was used to obtain a linear relationship between climate change and irrigation water demand, thereby characterizing the sensitivity of agricultural water security. Obtaining the daily precipitation and determining the water supply status based on whether the daily precipitation meets the crop water demand; wherein the water supply status includes: satisfactory and unsatisfactory; The ratio of the probability of the daily water supply status being both satisfactory and unsatisfactory to the probability of the daily water supply status being only unsatisfactory is calculated to characterize the resilience of the agricultural water security; wherein the resilience is used to characterize the ability of farmland to recover from insufficient green water supply; The ratio of the sensitivity to the resilience is used to characterize the vulnerability of the agricultural water security.

7. The method according to claim 1, wherein The future climate scenarios include: moderate forcing scenario SSP2-4.5 and high forcing scenario SSP5-8.5 specified by the IPCC.

8. The method according to claim 1, wherein The assessment of agricultural water security risks in the target area under future climate scenarios based on quantified indicators of hazard, exposure, and vulnerability includes: Multiply the quantified indicators of disaster risk, exposure, and vulnerability to obtain the agricultural water security risk index of the target area under the future climate scenario; Taking the current time as the starting year and 20-year intervals, the future is divided into three periods: early, mid-term, and long-term; The corresponding agricultural water security risk level is assessed based on the agricultural water security risk index in the future period; wherein the agricultural water security risk level includes: slight risk, low risk, medium risk, high risk, and severe risk.

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