An irrigation evaluation method and sudden drought response measures under sudden drought conditions

Abstract

This invention discloses an irrigation assessment method and response measures for sudden drought conditions. The method includes: acquiring basic data and identifying sudden drought events; calculating the integrated ideal irrigation mitigation potential under gridded sudden drought conditions; assessing water resource constraints; comprehensively assessing and outputting the actual irrigation mitigation potential under actual constraints; and providing decision support and measure recommendations. This invention organically integrates climate zoning statistical methods and crop growth model simulation results through a Bayesian model averaging framework, constructing equipment effect functions and water resource effect functions. It productizes and couples the integrated ideal irrigation mitigation potential with equipment capacity and water resource constraints, achieving a precise transformation from ideal mitigation potential to actual achievable capacity, ultimately serving the precise transformation of hierarchical decision-making. This invention can accurately assess the actual irrigation mitigation potential of agriculture under sudden drought conditions, providing more accurate information for early warning of agricultural drought and optimal allocation of water resources.

Description

Technical Field

[0001] This invention relates to an agricultural water resources management and irrigation assessment method, and more particularly to an irrigation assessment method and drought response measures under sudden drought conditions. Background Technology

[0002] A surge drought is a rapidly developing, intense, and short-term extreme drought event characterized by a sharp drop in soil moisture within a short period, posing a serious threat to crop growth and final yield. Compared to a slow-developing drought, a surge drought can cause irreversible damage to crops within weeks, catching agricultural systems off guard. Existing assessment methods often target general, slow-developing drought conditions or fail to distinguish between the different impact mechanisms of surge droughts and slow droughts, leading to inaccurate assessments of irrigation mitigation potential for surge drought events. Traditional methods, when calculating the relationship / impact between irrigation and yield, lack extreme scenario simulations specific to the characteristics of surge droughts, and a framework for systematically coupling statistical methods with mechanistic models, equipment capacity, and water resource constraints, making it difficult to support effective early warning of agricultural drought and precise water resource allocation decisions.

[0003] A sudden drought is a rapidly developing, intense, and short-term extreme drought event characterized by a sharp decline in soil moisture within weeks, posing a rapid and severe stress on crop growth. However, existing mainstream irrigation assessment and water resource planning methods, whether for long-term forecasting or current situation analysis, suffer from systemic deficiencies, such as insufficient targeting of sudden drought events and a lack of deep integration with engineering realities. This leads to problems like delayed early warnings and inaccurate decision-making in responding to such sudden disasters. For example, patent CN106570627B, "A Method for Calculating Crop Irrigation Water Demand under Future Climate Conditions," provides a future irrigation water demand prediction scheme based on climate model correction and water balance. However, this scheme relies entirely on historical statistics and seasonal to interannual averages or probability distributions of future climate scenarios, failing to capture and simulate the rapid onset and evolution dynamics of sudden droughts on a "weekly scale," and thus cannot guarantee the timeliness of early warnings for sudden droughts. Furthermore, this method focuses on calculating "theoretical irrigation water demand," resulting in a relatively simplistic assessment method, and the accuracy of the obtained theoretical irrigation water demand does not meet the needs of actual emergency management. Summary of the Invention

[0004] Purpose of the invention: To address the above-mentioned problems, this invention proposes an irrigation assessment method and response measures for sudden drought conditions. This method can accurately quantify the actual irrigation mitigation potential under sudden drought conditions, providing a scientific basis for agricultural management decisions in response to sudden drought, and offering precise guidance strategies for irrigation measures under sudden drought conditions.

[0005] Technical solution: The irrigation assessment method under sudden drought conditions described in this invention includes:

[0006] Acquire agricultural data, meteorological data, soil and water data, water resource data, and irrigation equipment data;

[0007] The system identifies sudden drought events based on soil water data; it calculates the statistical irrigation mitigation potential under sudden drought conditions using statistical methods based on agricultural data, whereby the irrigation mitigation potential is used to characterize the yield loss that irrigation can mitigate; and it obtains simulated crop yields using crop growth models based on meteorological data, and calculates the simulated irrigation mitigation potential under sudden drought conditions based on the simulated crop yields.

[0008] Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the combined ideal irrigation mitigation potential under the condition of sudden drought is calculated using the Bayesian method.

[0009] The water resource constraint ratio is calculated based on water resource data, and the irrigation facility status index is calculated based on irrigation equipment data.

[0010] Based on the combined ideal irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned sudden drought conditions, the actual irrigation mitigation potential under the constraints of water resources and irrigation facilities is calculated.

[0011] Irrigation effectiveness is assessed based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the described drought conditions.

[0012] Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the integrated ideal irrigation mitigation potential under the condition of sudden drought is calculated using a Bayesian method, including: calculating the integration weight using a Bayesian method based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, and then weighting and integrating the statistical irrigation mitigation potential and the simulated irrigation mitigation potential according to the integration weight to obtain the integrated ideal irrigation mitigation potential under the condition of sudden drought.

[0013] The fusion weights are calculated using the Bayesian method, and the formula is as follows:

[0014] ;

[0015] In the formula, For the weighted fusion, Given the simulation results of the k-th crop growth model, the statistical method calculates the conditional probability of result O, where O is the irrigation mitigation potential result calculated by the statistical method. For the k-th crop growth model, Let be the prior probability of the result of the k-th crop growth model. For the first Prior probabilities of a crop growth model Let K be the number, and K be the total number of crop growth models.

[0016] The water resource constraint ratio is calculated based on water resource data, including: obtaining agricultural available water volume based on water resource data; calculating fusion weights using a Bayesian method based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential; weighting and fusing crop irrigation water requirements obtained from multiple crop growth models based on the fusion weights to obtain the fused crop irrigation water requirement; and calculating the water resource constraint ratio based on the ratio of the agricultural available water volume to the fused crop irrigation water requirement. The agricultural available water volume includes blue water and green water, where the available blue water volume is a set proportion of local runoff, and the available green water volume is the effective rainfall during the crop growing season.

[0017] The integrated formula for calculating crop irrigation water requirement is as follows:

[0018] ;

[0019] In the formula, This indicates the combined irrigation water requirements for crops; Indicates the weights for weighted fusion; This represents the crop irrigation water requirement obtained from the k-th crop growth model, where K is the total number of crop growth models.

[0020] The actual irrigation mitigation potential under the aforementioned sudden drought conditions is calculated using the following formula:

[0021]

[0022] In the formula, To assess the actual irrigation mitigation potential under sudden drought conditions, This provides potential for integrated ideal irrigation to alleviate drought conditions. For the device effect function, This is the water resource effect function.

[0023] The device effect function is:

[0024] ;

[0025] In the formula, For indicators of irrigation facility conditions, This represents the efficiency coefficient for the equipment type.

[0026] The water resource effect function is:

[0027] ;

[0028] In the formula, This represents the water resource constraint ratio.

[0029] This invention proposes a drought response measure based on the above-mentioned irrigation assessment method, comprising:

[0030] Acquire agricultural data, meteorological data, soil and water data, water resource data, and irrigation equipment data;

[0031] The system identifies sudden drought events based on soil water data; it calculates the statistical irrigation mitigation potential under sudden drought conditions using statistical methods based on agricultural data, whereby the irrigation mitigation potential is used to characterize the yield loss that irrigation can mitigate; and it obtains simulated crop yields using crop growth models based on meteorological data, and calculates the simulated irrigation mitigation potential under sudden drought conditions based on the simulated crop yields.

[0032] Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the combined ideal irrigation mitigation potential under the condition of sudden drought is obtained;

[0033] The water resource constraint ratio is calculated based on water resource data, and the irrigation facility status index is calculated based on irrigation equipment data.

[0034] Based on the combined ideal irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned sudden drought conditions, the actual irrigation mitigation potential under the constraints of water resources and irrigation facilities is calculated.

[0035] Response measures should be implemented based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the described drought conditions.

[0036] Based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned drought conditions, the system is divided into six decision intervals according to priority, and corresponding countermeasures are implemented according to the decision intervals.

[0037] Beneficial Effects: Compared with existing technologies, this invention has the following advantages: 1. This invention is specifically designed for sudden drought events. By introducing a Bayesian model averaging framework, it integrates the statistical robustness of climate analogy methods with the mechanistic foresight of global grid crop models, generating a "fusion irrigation mitigation potential" dataset with lower uncertainty than any single data source, providing reliable input for subsequent assessments. 2. By constructing two core models, the equipment effect function f(E) and the water resource effect function g(R), and combining them with a product coupling method, it achieves a precise and dynamic transformation from "ideal irrigation mitigation potential" to "actually achievable capacity." f(E) distinguishes the efficiency of different irrigation technologies (drip irrigation, sprinkler irrigation, flood irrigation); g(R), through a piecewise function based on the natural index, reasonably characterizes the nonlinear effect of water resources from shortage to saturation. 3. This invention comprehensively considers the characteristics of sudden drought, the efficiency of different irrigation technologies, and the water resource saturation effect, making the assessment results more realistic and overcoming the shortcomings of traditional methods that are too idealistic. 4. Through multi-scenario simulation and explicit spatial output, this invention can not only assess the current risk of sudden drought but also provide early warnings of potential agricultural water resource risks and opportunities arising from exacerbated sudden droughts due to future climate change. This provides support for both long-term and short-term decisions regarding drought-resistant water conservancy projects, irrigation facility upgrades, and emergency water resource allocation. This invention enables dynamic and precise assessment of irrigation disaster reduction capabilities under realistic constraints, thus providing a direct basis for tiered and categorized emergency response and resource allocation. Attached Figure Description

[0038] Figure 1 This is a flowchart of the irrigation assessment method under sudden drought conditions described in this invention. Detailed Implementation

[0039] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0040] The irrigation assessment method under sudden drought conditions described in this invention is illustrated in the flowchart below. Figure 1 As shown below. The steps are explained in detail below, and there is no specific order between the steps.

[0041] Step 1: Obtain agricultural data, meteorological data, soil water data, water resource data, and irrigation equipment data; identify sudden drought events based on soil water data.

[0042] This step involves acquiring basic data and identifying sudden drought events. It involves acquiring global-scale agricultural data (crop yield, phenological stage, and proportion of irrigation facilities), meteorological data (daily precipitation, daily maximum temperature, daily minimum temperature, and crop reference evapotranspiration), soil water data (top and root soil moisture), and water resource data (grid runoff data). The basic data also includes irrigation facility data; this embodiment uses irrigation facility coverage area data.

[0043] Soil water data were fitted with an empirical distribution function to transform it into soil moisture quantile values, and drought events were identified according to the following rules: (1) A drought event was considered to have started when the soil moisture quantile was below 40%; a drought event was considered to have ended when the soil moisture quantile recovered to or exceeded 40%. (2) There was at least one moment in a drought event when the soil moisture quantile was below 20%. (3) The drought lasted at least two weeks. Based on the above rules, the drought formation time was defined as the time it took for the soil moisture quantile to decrease from 40% to 20% in a given drought event. Events with a drought formation time of less than 20 days were further identified as sudden drought events.

[0044] Step 2: Based on agricultural data, the statistical irrigation mitigation potential under sudden drought conditions is calculated using statistical methods. The irrigation mitigation potential is used to characterize the yield loss that irrigation can mitigate. Based on meteorological data, the simulated crop yield is obtained using a crop growth model. The simulated irrigation mitigation potential under sudden drought conditions is calculated based on the simulated crop yield. Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the fusion ideal irrigation mitigation potential under sudden drought conditions is calculated using a Bayesian method.

[0045] This step aims to calculate the simulated irrigation mitigation potential under gridded flash drought conditions by integrating climate zoning statistical methods with crop growth process modeling simulation methods, and obtain the optimal estimate through Bayesian averaging. Irrigation mitigation potential characterizes the relative yield loss caused by water stress that irrigation measures can offset.

[0046] 1. Calculation of irrigation mitigation potential under sudden drought conditions based on climate zone division.

[0047] First, based on crop phenological data, the duration of each grid-by-grid crop growth stage (start of growth - end of growth) is obtained, and the growing season for each grid-by-grid crop is identified. Then, the multi-year average temperature and multi-year average total precipitation within the growing season of each grid-by-grid crop are calculated using the following formulas:

[0048] ;

[0049] ;

[0050] in, The grid represents the multi-year growing season average temperature, where n represents the year of the meteorological data, and i represents the i-th year. This represents the average temperature during the growing season of a given year. The average total precipitation over the multi-year growing season, representing the grid. It represents the total precipitation during the growing season of a given year.

[0051] Based on the above calculations, all global grids and Each region is evenly divided into 10 intervals (e.g., the multi-year average temperature ranges from 0-30°C, divided into 10 average temperature groups; the multi-year average total precipitation ranges from 0-1500 mm, divided into 10 total precipitation groups), resulting in 100 climate zones with different temperature and precipitation conditions (10 x 10). Within each climate zone, irrigation yield and rainfed yield are defined.

[0052] Irrigation yield: The 95th quantile of crop yield across all grids (including irrigated and rainfed farmland) within year y and climate zone c. This represents the highest yield level achievable under optimal management (including full irrigation) within that climate zone.

[0053] The formula for calculating irrigation yield is as follows:

[0054] ;

[0055] in, This represents the irrigation yield in year i, climate zone c. This represents the crop yield of the m-th grid within climate zone c in year i. i represents year i, c represents climate zone c, and M represents the total number of grids within climate zone c. This represents the 95th quantile within the calculated set. This represents the highest achievable yield level under optimal management (including adequate irrigation) within that climate zone.

[0056] Similarly, rainfed yield is defined as the 95th quantile of crop yield in a grid within year y and climate zone c that simultaneously meets both of the following conditions: 1) the grid experienced a flash drought event that year; and 2) the grid's irrigation facility share E is less than 0.1. This represents the highest yield level achievable in a rainfed agricultural system under flash drought stress and with virtually no irrigation available.

[0057] Based on the above definition, the formula for calculating rainfed yield is:

[0058] ;

[0059] in, This represents the rainfed yield in year i, climate zone c. This represents the crop yield of the j-th grid within climate zone c that meets the conditions in year i. It is an arid type. This indicates that a sudden drought event occurred in the j-th grid within climate zone c in the i-th year. It represents the percentage of irrigation equipment in grid j. This represents the 95th quantile within the filtered set. This indicates a relatively high yield level achievable in rainfed agriculture systems under sudden drought stress and with little to no irrigation available.

[0060] Based on the irrigation yield and rainfed yield calculated above, the irrigation mitigation potential under sudden drought conditions can be further calculated using statistical methods. The calculation formula is as follows:

[0061] ;

[0062] in This represents the irrigation mitigation potential under sudden drought conditions in year i and climate zone c, calculated using statistical methods.

[0063] Finally, the average of the multi-year calculation results is taken to obtain the irrigation mitigation potential of climate zone c under multi-year average drought conditions calculated using statistical methods. The calculation formula is as follows:

[0064] ;

[0065] 2. Crop growth model calculation model simulates the irrigation mitigation potential under sudden drought conditions.

[0066] This section uses crop growth process models (one or more models can be used) to simulate yield at the grid scale. First, based on the drought event calculated in step one, the meteorological conditions corresponding to multi-year drought events in the grid are averaged to obtain the average meteorological conditions under drought conditions for each grid. The obtained average meteorological conditions under drought conditions are used to drive the crop growth model, and two simulations are performed for each grid: 1) Full irrigation scenario, that is, simulating unlimited water supply for crops in the model to obtain the grid irrigation yield. Furthermore, the model results can also directly output the parameters for achieving grid irrigation yield levels during periods of sudden drought stress. Additional irrigation water is needed. 2) Rainfed scenario, which is a simulation under natural conditions to obtain the grid-based rainfed yield. Based on the simulation results, the irrigation mitigation potential under the simulated flash drought conditions using a gridded model was calculated. The calculation formula is as follows:

[0067] ;

[0068] in, This represents the simulated irrigation mitigation potential under sudden drought conditions obtained from the model, and grid represents the grid.

[0069] 3. Multi-source data fusion based on Bayesian model averaging.

[0070] The purpose of this section is to organically integrate statistical results based on climate zoning with model simulation results based on crop growth processes. Climate zoning methods are statistically robust, but their spatial resolution is limited by climate zones; model simulations can provide continuous gridded data, but they suffer from structural uncertainties. The Bayesian Model Averaging (BMA) framework generates a probabilistic optimal estimate that combines the advantages of both methods by assigning weights to each data source.

[0071] First, we construct the Bayesian framework, assuming the result of the statistical method is O, and the set of model results is S = { ..., }, where S1 is the calculation method for climate zone division, ..., Let there be k crop growth models. According to Bayes' theorem, the model... The posterior probability is:

[0072] ;

[0073] Where O represents the irrigation mitigation potential calculated using statistical methods. The results of the irrigation mitigation potential calculated by the model simulation; For prior probabilities, we assume that all models are equally probable, i.e. =1 / K; Given the simulation results of the k-th crop growth model, the statistical method calculates the conditional probability of the result O, which is the likelihood function of the model and is used to measure the degree of agreement between the simulation results and the statistical results.

[0074] Then calculate the model. weight Determined by its likelihood function:

[0075] ;

[0076] in, This represents the weight assigned to each data source; all weights The sum equals 1. Based on the above calculations, the optimal weights are obtained. Applying these weights to the grid scale, the final result is the fused ideal irrigation mitigation potential under the fused gridded sudden drought conditions:

[0077] ;

[0078] in, This represents the potential for combined ideal irrigation mitigation under sudden drought conditions on a grid-by-grid basis, after integrating statistical results and model simulation results. This represents the irrigation mitigation potential under sudden drought conditions calculated using a statistical method based on climate zoning. This indicates the potential for irrigation to alleviate drought conditions based on model simulation calculations. The weights obtained from the calculation results of the climatological zone statistical method; This represents the weights obtained from the model simulation results; additionally, each model can output the gridded crop irrigation water requirement under fully irrigated conditions. We also performed Bayesian fusion on the irrigation water requirements of multiple models to obtain the fused crop irrigation water requirements. The calculation formula is as follows:

[0079] ;

[0080] in, This represents the crop irrigation water requirement, which incorporates the simulation results from all crop models. This represents the weights obtained from each model simulation result; This represents the crop irrigation water requirement simulated by each crop model. Based on the above calculations, we obtain the combined ideal irrigation mitigation potential under gridded sudden drought conditions and the combined crop irrigation water requirement under full irrigation conditions, which combine statistical reliability and spatial detail. This method retains the robustness of statistical methods while leveraging the spatial advantages of model simulation, providing more reliable basic data for subsequent evaluations.

[0081] Step 3: Calculate the water resource constraint ratio based on water resource data, and calculate the irrigation facility status index based on irrigation equipment data.

[0082] This step aims to assess the availability of the water resources required to realize irrigation potential, quantifying the hard constraints of water resources on the ideal irrigation mitigation potential. In this embodiment, the irrigation facility status indicator uses the proportion of irrigated facility coverage area to cultivated land area.

[0083] Agricultural water availability refers to the total amount of water resources that can be used for agricultural irrigation without compromising the sustainability of the ecosystem. The calculation formula is as follows:

[0084] ;

[0085] in, This represents the total amount of water available for agricultural use during the growing season for a given grid (unit: mm). This represents the amount of blue water available for agriculture, the available amount of which is determined by the local runoff (Q), but the basic ecological water requirement to ensure river health must be deducted. The calculation formula is as follows:

[0086] ;

[0087] in, The growing season runoff depth of the grid (unit: mm).

[0088] This refers to the amount of green water available for agriculture, specifically the effective rainfall stored in the soil and directly utilized by crops. The calculation formula is as follows:

[0089] ;

[0090] ;

[0091] in, This represents the effective rainfall (in mm) on day d of the crop growing season on the grid, where D is the total number of days in the growing season. This represents the rainfall on day d during the crop growing season on the grid (unit: mm).

[0092] Based on the above formula, we can obtain the crop irrigation water requirement under fully irrigated conditions on the grid. and agricultural water availability We use water resource constraint ratios To measure the degree of water resource constraints on a grid-by-grid basis, when A value greater than 1 indicates that the available water supply is greater than or equal to the irrigation demand, and water resources are not a constraint; otherwise, it indicates that the available water supply cannot meet the irrigation demand, and water resources constitute a constraint. The calculation formula is as follows:

[0093] ;

[0094] Step 4: Based on the ideal irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned drought conditions, calculate the actual irrigation mitigation potential under the constraints of water resources and irrigation facilities.

[0095] A comprehensive assessment and output of the potential for irrigation to alleviate agricultural drought under actual constraints.

[0096] This step calculates the actual achievable irrigation mitigation potential under sudden drought conditions by coupling the ideal irrigation mitigation potential, irrigation equipment capacity, and water resource constraints, and generates results that can directly serve decision-making. This is achieved through a device effect function constructed based on irrigation equipment capacity. and the water resource effect function constructed based on the water resource constraint ratio The final result is obtained by coupling the fusion ideal irrigation mitigation potential calculated in step two.

[0097] Device effect function The aim is to quantify the impact of irrigation infrastructure coverage and efficiency on the potential for irrigation mitigation, using the following formula:

[0098] ;

[0099] in, The value represents the proportion of the area covered by irrigation facilities within the grid to the cultivated land area, ranging from 0 to 1; 'a' is the equipment type efficiency coefficient, which is 1.0 for drip irrigation, 0.8 for sprinkler irrigation, and 0.5 for flood irrigation.

[0100] Water resource effect function The aim is to quantify the impact of water availability on the potential for irrigation mitigation, and the calculation formula is as follows:

[0101] ;

[0102] Ultimately, the potential of integrated ideal irrigation to alleviate drought conditions will be realized. Equipment effect function Water resource effect function By coupling these parameters, we can ultimately arrive at the practically achievable irrigation mitigation potential under sudden drought conditions. The calculation formula is as follows:

[0103] ;

[0104] It is a value between 0 and 1 (i.e., 0% to 100%), representing the grid's actual ability to alleviate agricultural drought conditions after taking into account realistic equipment and water resource constraints.

[0105] The drought response measures described in this invention provide decision support and measure recommendations based on steps one to four above. They also include:

[0106] Step 5: Evaluate the irrigation effect based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the described drought conditions.

[0107] This step is based on the potential for mitigating sudden agricultural droughts under actual water resource constraints. The proportion of irrigation facilities in cultivated land area and water resource constraints All assessment results were divided into four decision-making intervals, and each interval was matched with corresponding emergency measures for the current year and future development plans. The decision-making interval criteria and recommended measures are shown in the table below:

[0108] .

Claims

1. A method for assessing irrigation under sudden drought conditions, characterized in that, include: Acquire agricultural data, meteorological data, soil and water data, water resource data, and irrigation equipment data; Identifying sudden drought events based on soil water data; Based on agricultural data, statistical methods are used to calculate the statistical irrigation mitigation potential under sudden drought conditions. The irrigation mitigation potential is used to characterize the yield loss that irrigation can mitigate. Based on meteorological data, crop growth models were used to simulate crop yields, and the simulated irrigation mitigation potential under sudden drought conditions was calculated based on the simulated crop yields. Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the combined ideal irrigation mitigation potential under the condition of sudden drought is calculated using the Bayesian method. The water resource constraint ratio is calculated based on water resource data, and the irrigation facility status index is calculated based on irrigation equipment data. Based on the combined ideal irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned sudden drought conditions, the actual irrigation mitigation potential under the constraints of water resources and irrigation facilities is calculated. Irrigation effectiveness is assessed based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the described drought conditions.

2. The irrigation assessment method under sudden drought conditions according to claim 1, characterized in that: Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the integrated ideal irrigation mitigation potential under the condition of sudden drought is calculated using a Bayesian method, including: calculating the integration weight using a Bayesian method based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, and then weighting and integrating the statistical irrigation mitigation potential and the simulated irrigation mitigation potential according to the integration weight to obtain the integrated ideal irrigation mitigation potential under the condition of sudden drought.

3. The irrigation assessment method under sudden drought conditions according to claim 2, characterized in that: The fusion weights are calculated using the Bayesian method, and the formula is as follows: ； In the formula, For the weighted fusion, Given the simulation results of the k-th crop growth model, the statistical method calculates the conditional probability of result O, where O is the irrigation mitigation potential result calculated by the statistical method. For the k-th crop growth model, Let be the prior probability of the result of the k-th crop growth model. For the first Prior probabilities of a crop growth model Let K be the number, and K be the total number of crop growth models.

4. The irrigation assessment method under sudden drought conditions according to claim 1, characterized in that: The water resource constraint ratio is calculated based on water resource data, including: obtaining agricultural available water volume based on water resource data; calculating fusion weights using a Bayesian method based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential; weighting and fusing crop irrigation water requirements obtained from multiple crop growth models based on the fusion weights to obtain the fused crop irrigation water requirement; and calculating the water resource constraint ratio based on the ratio of the agricultural available water volume to the fused crop irrigation water requirement. The agricultural available water volume includes blue water and green water, where the available blue water volume is a set proportion of local runoff, and the available green water volume is the effective rainfall during the crop growing season.

5. The irrigation assessment method under sudden drought conditions according to claim 1, characterized in that: The integrated formula for calculating crop irrigation water requirement is as follows: ； In the formula, This indicates the combined irrigation water requirements for crops; Indicates the weights for weighted fusion; This represents the crop irrigation water requirement obtained from the k-th crop growth model, where K is the total number of crop growth models.

6. The irrigation assessment method under sudden drought conditions according to claim 1, characterized in that: The actual irrigation mitigation potential under the aforementioned sudden drought conditions is calculated using the following formula: ； In the formula, To assess the actual irrigation mitigation potential under sudden drought conditions, This provides potential for integrated ideal irrigation to alleviate drought conditions. For the device effect function, This is the water resource effect function.

7. The irrigation assessment method under sudden drought conditions according to claim 6, characterized in that: The device effect function is: ； In the formula, For indicators of irrigation facility conditions, This is the efficiency coefficient for the equipment type.

8. The irrigation assessment method under sudden drought conditions according to claim 6, characterized in that: The water resource effect function is: ； In the formula, This represents the water resource constraint ratio.

9. A measure to cope with sudden drought, characterized in that, include: Acquire agricultural data, meteorological data, soil and water data, water resource data, and irrigation equipment data; Identifying sudden drought events based on soil water data; Based on agricultural data, statistical methods are used to calculate the statistical irrigation mitigation potential under sudden drought conditions. The irrigation mitigation potential is used to characterize the yield loss that irrigation can mitigate. Based on meteorological data, crop growth models were used to simulate crop yields, and the simulated irrigation mitigation potential under sudden drought conditions was calculated based on the simulated crop yields. Based on the statistical irrigation mitigation potential and the simulated irrigation mitigation potential, the combined ideal irrigation mitigation potential under the condition of sudden drought is obtained; The water resource constraint ratio is calculated based on water resource data, and the irrigation facility status index is calculated based on irrigation equipment data. Based on the combined ideal irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned sudden drought conditions, the actual irrigation mitigation potential under the constraints of water resources and irrigation facilities is calculated. Response measures should be implemented based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the described drought conditions.

10. The drought response measures according to claim 9, characterized in that: Based on the actual irrigation mitigation potential, water resource constraint ratio, and irrigation facility status indicators under the aforementioned drought conditions, the system is divided into six decision intervals according to priority, and corresponding countermeasures are implemented according to the decision intervals.

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