Method for evaluating influence of extreme rainfall on crops based on multi-factor coupling

Abstract

The invention discloses a method for evaluating the influence of extreme rainfall on crops based on multi-factor coupling, and belongs to the technical field of hydrology and water resources. The method comprises the following steps: firstly, acquiring extreme rainfall intensity, soil type, crop distribution and topographic data of a research area, dividing the area into grid units, and calculating the soil water content; calculating a waterlogging stress index according to the soil water content, and obtaining the soil stress intensity in combination with the drainage capacity and the terrain gradient; determining a vulnerability index according to the growth period and the stress degree of the crops; and finally, calculating a comprehensive influence index through nonlinear coupling of the soil stress intensity and the crop vulnerability index, and constructing a four-level risk classification system to evaluate the crop disaster risk. According to the method, the soil hydrological process, the terrain drainage condition and the dynamic characteristics of the crop growth period are comprehensively considered, the influence of extreme rainfall on crops is accurately evaluated, and a scientific basis is provided for agricultural disaster prevention and reduction.

Description

Technical Field

[0001] This invention relates to the field of hydrology and water resources technology, and in particular to a method for assessing the impact of extreme precipitation on crops based on multi-factor coupling. Background Technology

[0002] Extreme rainfall not only directly affects crop growth but also triggers secondary disasters such as soil waterlogging and nutrient loss. Traditional crop disaster assessment methods often focus on single-factor analysis, such as considering only rainfall thresholds or crop growth stage characteristics, which makes it difficult to comprehensively reflect the combined impact of extreme rainfall on crops. This simplified assessment approach often leads to inaccurate risk assessment and fails to provide effective support for agricultural disaster prevention and mitigation.

[0003] Most existing crop waterlogging assessment techniques neglect the impact of key factors such as soil type, topography, and drainage capacity on waterlogging formation, leading to significant discrepancies between assessment results and actual conditions. They lack dynamic consideration of crop sensitivity at different growth stages, failing to accurately reflect the differentiated responses of crops to waterlogging stress at different growth stages. Traditional assessment methods often integrate influencing factors using linear superposition or simple weighting, failing to fully reflect the nonlinear coupling relationships and synergistic effects between multiple factors. Furthermore, current assessment methods also have significant limitations in spatial accuracy and practicality. On the one hand, assessments based on large-scale statistical data are insufficient to meet the needs of refined agricultural management and cannot provide targeted guidance for field-level disaster response. On the other hand, while complex physical process models offer high accuracy, they require numerous parameters and detailed initial conditions, making them difficult to apply in areas where data acquisition is challenging.

[0004] Therefore, there is an urgent need to develop a method for assessing the impact of extreme precipitation on crops that can comprehensively consider multiple influencing factors and is easy to apply in practice. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art. By comprehensively considering multiple factors such as extreme precipitation intensity, soil hydrological characteristics, topographic conditions, drainage capacity, and dynamic characteristics of crop growth period, a soil waterlogging stress index and a crop vulnerability index are constructed. A nonlinear coupling method is used to calculate the comprehensive impact index, thereby achieving accurate assessment of disaster risk for different regions and different crop types.

[0006] To achieve the above objectives, this invention provides a method for assessing the impact of extreme precipitation on crops based on multi-factor coupling, the method comprising the following steps: Step S1: Obtain extreme precipitation intensity, soil type data, crop type distribution data and topographic data of the study area, divide the study area into regular grid units, and calculate the precipitation input and soil moisture content of each grid unit.

[0007] Step S2: Calculate the soil waterlogging stress index of each grid cell based on soil moisture content, and obtain the soil stress intensity driven by waterlogging considering the effects of drainage capacity and topographic slope.

[0008] Step S3: Determine the precipitation sensitivity coefficient based on the current growth stage of the crop and the soil waterlogging stress index of each grid unit to obtain the crop vulnerability index.

[0009] Step S4: Calculate the comprehensive impact index of each grid unit based on the soil stress intensity driven by waterlogging and the crop vulnerability index. Construct a crop disaster risk level classification based on the comprehensive impact index and assess the crop disaster risk level of each grid unit.

[0010] Furthermore, the soil moisture content is determined by precipitation input, evapotranspiration output, surface runoff, and deep infiltration. The method for calculating the soil moisture content is as follows: for grid cells... At any moment Precipitation input Based on extreme precipitation intensity calculate: ;in, Extreme precipitation intensity, For grid cells area, This is the calculation period.

[0011] Grid cells At any moment Soil moisture content : ;in, This represents the soil moisture content at the previous moment. For a moment The actual evaporation rate For a moment Surface runoff, For a moment The amount of deep leakage, This refers to the depth of the crop root system.

[0012] Furthermore, grid cells Actual evaporation and surface runoff The calculation method is as follows: based on the mesh element Crop type determines crop coefficient Reference evaporation rate is ,time Actual evaporation for: ;in, Soil moisture stress coefficient: ;in, Field holding capacity; For the wilting point, take 0.05 for sandy soil, 0.10 for loam, and 0.15 for clay.

[0013] Surface runoff The calculation method is as follows: ;in, For the maximum potential retention, , For grid cells The number of curves is determined by soil type and land use type, and is obtained by consulting the SCS runoff curve number table.

[0014] Furthermore, the soil waterlogging stress index The calculation method is as follows: calculate the grid cell. Deviation from field capacity soil moisture content: ;when When the soil is waterlogged, it indicates the presence of soil waterlogging stress; the soil waterlogging stress index. ,when hour: ;in, For grid cells The number of days of continuous flooding This is the time accumulation factor, and its value range is... Rice: 0.3%, corn: 0.2%, wheat: 0.15%, soybean: 0.2%; when hour, .

[0015] Furthermore, based on the grid cells Soil texture type determines saturated hydraulic conductivity Field water holding capacity saturated water content When the soil texture is sandy, , , When the soil texture type is loam, , , When the soil texture is clay, , , .

[0016] Furthermore, the amount of deep leakage Calculated based on saturated hydraulic conductivity and soil moisture content: ;in, The infiltration index is used to characterize the control effect of soil texture on the infiltration process. It is determined according to the soil texture, with 2 for sandy soil, 3 for loam, and 4 for clay.

[0017] Furthermore, the method for calculating the soil stress intensity driven by waterlogging is as follows: considering the influence of drainage capacity and topographic slope, an effective drainage coefficient is defined. : ;in, For reference hydraulic conductivity, a value of 50 mm / h is used. The slope of the ground. The slope enhancement coefficient is denoted by [value]. According to the slope of the terrain Confirmed: When When the value is 0.2, When the value is 0.35, Take 0.5 at a time. This is a correction factor for drainage facilities; natural drainage is taken as... Artificial drainage system The soil stress intensity driven by waterlogging. : , Used to characterize the waterlogging stress intensity of each grid cell under the current soil moisture conditions.

[0018] Furthermore, the dynamic vulnerability index is calculated as follows: Obtain the sowing date of crops in each grid unit and divide the crop growth period into seedling stage. 1. Vegetative growth period Reproductive growth period and maturity According to the number of days after sowing Determine the current reproductive stage ; Define grid cells Crop precipitation sensitivity coefficient : ;in Based on the sensitivity coefficient, rice Corn wheat harvest Soybean Extract ; Crop vulnerability index for: ;in, This refers to the total number of days in the crop's entire growth period. The fertility process regulation index is set at 0.3. For crop type coefficients, rice is taken as follows: Corn wheat harvest Soybean Extract ; This characterizes the sensitivity of crops in each grid unit to waterlogging stress at the current growth stage.

[0019] Furthermore, the comprehensive impact index is constructed by coupling waterlogging-driven soil stress intensity. and crop vulnerability index For grid cells Calculate its comprehensive impact index : ;in, The coupling index is set to 2.5.

[0020] Furthermore, a four-level classification of crop disaster risk levels is constructed based on the comprehensive impact index. : .

[0021] Compared with the prior art, the beneficial effects of the present invention are: This invention establishes a multi-factor coupled evaluation framework that comprehensively considers the physical mechanisms of soil moisture dynamics, waterlogging stress formation, and crop response during extreme precipitation, overcoming the limitations of traditional single-factor analysis methods. The parameters required by the method are easily obtained from conventional observation and remote sensing data, making it highly operable and applicable. It can provide strong technical support for agricultural disaster risk management and emergency response in different regions, helping to improve agricultural disaster resistance and mitigation capabilities and ensure food security. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for assessing the impact of extreme precipitation on crops based on multi-factor coupling, according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of this invention, not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Example In this embodiment, a plain agricultural area in an arid region was selected as the study area. This area covers approximately 500 square kilometers and mainly cultivates crops such as rice, corn, wheat, and soybeans. The region is influenced by a continental climate, which frequently induces extreme precipitation events in summer. In this embodiment, precipitation events with hourly precipitation ≥10.0 mm are defined as extreme precipitation events.

[0025] First, the distribution of extreme precipitation intensity was obtained through meteorological station observation data and radar inversion data. In a typical extreme precipitation event, the precipitation intensity reached 30 mm per hour and lasted for more than 6 hours. The distribution of soil types in the study area was obtained through soil survey data. The area mainly includes three types: sandy soil, loam, and clay. Loam accounts for the largest proportion, about 60%, sandy soil accounts for about 25%, and clay accounts for about 15%. The spatial distribution of crop types was obtained using remote sensing imagery and agricultural statistics. Combined with crop sowing date information, the crop type and growth stage of each grid unit were determined.

[0026] The study area was divided into regular grid cells of 500m × 500m, totaling 2000 grid cells. Each grid cell corresponds to a computing node, storing basic information such as soil type, crop type, and terrain slope for that area. For boundary grid cells, considering the hydraulic connection with adjacent cells, a flux exchange method was used to handle boundary conditions.

[0027] like Figure 1 The diagram shows a flowchart of a method for assessing the impact of extreme precipitation on crops based on multi-factor coupling, according to the present invention. The method includes the following steps: Step S1: Obtain extreme precipitation intensity, soil type data, crop type distribution data and topographic data of the study area, divide the study area into regular grid units, and calculate the precipitation input and soil moisture content of each grid unit.

[0028] The soil moisture content is determined by precipitation input, evapotranspiration output, surface runoff, and deep infiltration. The calculation method for the soil moisture content is as follows: for grid cells... At any moment Precipitation input Based on extreme precipitation intensity calculate: ;in, Extreme precipitation intensity, For grid cells area, For the calculation period; Grid cells At any moment Soil moisture content : ;in, This represents the soil moisture content at the previous moment. For a moment The actual evaporation rate For a moment Surface runoff, For a moment The amount of deep leakage, This refers to the depth of the crop root system.

[0029] Soil moisture content calculation follows the water balance principle, which states that the change in soil moisture equals the difference between the input and output. Precipitation is the primary source of water input; during extreme precipitation events, a large amount of precipitation enters the soil in a short period, causing a rapid increase in soil moisture content. Evapotranspiration is an important pathway for water output, comprising both soil evaporation and crop transpiration, and is influenced by meteorological conditions, crop type, and soil moisture status.

[0030] Surface runoff is closely related to rainfall intensity and soil infiltration capacity. When rainfall intensity exceeds soil infiltration capacity, excess water is discharged as surface runoff. In flat areas, surface runoff is relatively small, but in areas with a certain slope, the rate of runoff generation is significantly faster. Deep percolation refers to the movement of water into deeper soil layers below the root zone, and it is closely related to soil hydraulic conductivity. Sandy soils have larger pores, resulting in faster deep percolation, while clay soils have smaller pores, leading to slower percolation.

[0031] For example, in a grid cell for loam soil, soil moisture content rapidly increases from an initial value of 0.20 at the beginning of extreme precipitation. When the precipitation input is 20 mm, after deducting 2 mm of evapotranspiration, 5 mm of surface runoff, and 3 mm of deep infiltration, the net increase in moisture is 10 mm, and the soil moisture content increases to 0.24. As precipitation continues, when the soil moisture content approaches the saturation point of 0.45, surface runoff and deep infiltration increase significantly, and the rate of increase in soil moisture content slows down.

[0032] Grid cells Actual evaporation and surface runoff The calculation method is as follows: based on the mesh element Crop type determines crop coefficient Reference evaporation rate is ,time Actual evaporation for: ;in, Soil moisture stress coefficient: ;in, Field holding capacity; For the wilting point, take 0.05 for sandy soil, 0.10 for loam, and 0.15 for clay.

[0033] Actual evapotranspiration reflects the ability of crops and soil to transport water to the atmosphere. Between field capacity and saturation water content, soil moisture is sufficient, crop transpiration is unrestricted, and actual evapotranspiration is close to potential evapotranspiration. When soil moisture content is below field capacity, crops begin to experience water stress, roots struggle to absorb water, transpiration rates decrease, and actual evapotranspiration significantly reduces. Conversely, when soil moisture content exceeds saturation water content, soil pores are filled with water, aeration deteriorates, root respiration is hindered, and transpiration also weakens.

[0034] Different crop types exhibit varying transpiration characteristics. Rice, as an aquatic crop, possesses strong waterlogging tolerance and can maintain a high transpiration rate even under excessively wet soil conditions. In contrast, dryland crops such as corn and wheat show a significant decrease in root activity and inhibition of transpiration when the soil is excessively wet. For instance, at a soil moisture content of 0.50 (exceeding the saturation moisture content of 0.45), the evapotranspiration coefficient of rice is approximately 0.90, while that of corn may drop to 0.60.

[0035] Surface runoff The calculation method is as follows: ;in, For the maximum potential retention, , For grid cells The number of curves is determined by soil type and land use type, and is obtained by consulting the SCS runoff curve number table.

[0036] The formation process of surface runoff reflects the redistribution of precipitation on the land surface. The runoff curve number is a comprehensive parameter characterizing the runoff generation characteristics of a watershed, reflecting the influence of factors such as soil type, land use, and anterior soil moisture content. A higher runoff curve number indicates stronger runoff generation capacity. Sandy soils, due to rapid infiltration, have lower runoff curve numbers, generally between 60 and 75. Clay soils, with slower infiltration, have higher runoff curve numbers, reaching 80-90. Farmland typically has runoff curve numbers between these two extremes, with well-cultivated farmland having a runoff curve number of approximately 70-80.

[0037] In practical applications, it is necessary to consult the standard curve number table based on the specific soil type and crop planting conditions; for example, for loam farmland planted with corn, the curve number is generally taken as around 75. If the rainfall in this plot is 30 mm, according to the formula, the maximum potential retention is approximately 85 mm. Since the rainfall is less than 20% (17 mm) of the retention, no surface runoff will occur at this time. However, when the rainfall increases to 50 mm, it exceeds the threshold, and surface runoff begins to occur, with a runoff volume of approximately 8 mm, accounting for 16% of the rainfall.

[0038] The soil waterlogging stress index The calculation method is as follows: calculate the grid cell. Deviation from field capacity soil moisture content: ;when When the soil is waterlogged, it indicates the presence of soil waterlogging stress; the soil waterlogging stress index. ,when hour: ;in, For grid cells The number of days of continuous flooding This is the time accumulation factor, and its value range is... Rice: 0.3%, corn: 0.2%, wheat: 0.15%, soybean: 0.2%; when hour, .

[0039] The severity of soil waterlogging stress depends not only on the current soil moisture content but also on the duration of waterlogging. Short-term excessive soil moisture may only cause minor effects, but if waterlogging persists for several days or even longer, it can severely damage crop roots. When roots are in a hypoxic environment for a long time, respiration is hindered, absorption function declines, and ultimately crop growth stagnation or even death occurs.

[0040] The time accumulation coefficient reflects the differences in the tolerance of different crops to the duration of waterlogging. Rice, having adapted to the paddy field environment for a long time, has well-developed aeration tissue and can tolerate a relatively long period of flooding, hence its time accumulation coefficient is set relatively high. Dryland crops such as corn, wheat, and soybeans lack this adaptability, and the duration of waterlogging has a more significant impact on them, resulting in a relatively lower accumulation coefficient.

[0041] Taking wheat-grown fields as an example, when the soil moisture content exceeds field capacity by 0.05 to 0.30, the baseline value of the soil waterlogging stress index is 0.25. If this excessively wet state lasts only one day, the stress index is 0.29 after considering the cumulative effect over time. However, if it lasts for five days, the stress index will increase to 0.44, showing a significant cumulative damage effect.

[0042] The deep leakage Calculated based on saturated hydraulic conductivity and soil moisture content: ;in, The infiltration index characterizes the control effect of soil texture on the infiltration process. It is determined based on soil texture: 2 for sandy soil, 3 for loam, and 4 for clay. The infiltration rate is mainly affected by the soil's saturated hydraulic conductivity and current moisture content. Saturated hydraulic conductivity reflects the connectivity of soil pores and its water conduction capacity. Sandy soil has large, well-connected pores, high hydraulic conductivity, and rapid water infiltration. Clay soil has small pores, mostly capillary pores, low hydraulic conductivity, and slow infiltration.

[0043] The permeability index reflects the non-linear effect of soil moisture content on the permeability rate. When soil moisture content is low, water is mainly adsorbed by soil particles and is difficult to move, resulting in minimal permeability. As moisture content increases, large pores begin to fill with water, accelerating the permeability rate. When approaching saturation, almost all pores are filled with water, and the permeability rate reaches its maximum.

[0044] For example, for sandy soil plots with a saturated hydraulic conductivity of 100 mm / h and a permeability index of 2, the permeability rate is approximately 32 mm / h when the soil moisture content is 0.20 (57% of the saturated moisture content of 0.35). When the moisture content increases to 0.30 (86% of the saturated moisture content), the permeability rate increases to 74 mm / h. This rapid, deep permeability allows sandy soil plots to recover to normal moisture levels more quickly after extreme rainfall, resulting in a relatively low risk of waterlogging.

[0045] Step S2: Calculate the soil waterlogging stress index of each grid cell based on soil moisture content, and obtain the soil stress intensity driven by waterlogging considering the effects of drainage capacity and topographic slope.

[0046] According to grid cells Soil texture type determines saturated hydraulic conductivity Field water holding capacity saturated water content When the soil texture is sandy, , , When the soil texture type is loam, , , When the soil texture is clay, , , .

[0047] The soil stress weighting index is calculated by considering the effects of drainage capacity and topographic slope, and defining the effective drainage coefficient. : ;in, For reference hydraulic conductivity, a value of 50 mm / h is used. The slope of the ground. The slope enhancement coefficient is denoted by [value]. According to the slope of the terrain Confirmed: When When the value is 0.2, When the value is 0.35, Take 0.5 at a time. This is a correction factor for drainage facilities; natural drainage is taken as... Artificial drainage system The soil stress intensity driven by waterlogging. : , Used to characterize the waterlogging stress intensity of each grid cell under the current soil moisture conditions.

[0048] The effective drainage coefficient comprehensively reflects the natural drainage capacity of a plot of land and the effect of human-made drainage measures. The saturated hydraulic conductivity of the soil is the foundation of natural drainage capacity, determining the speed at which water seeps downwards and flows laterally through soil pores. Topographic slope enhances the gravitational drainage effect; on sloping plots, water flows more easily down the slope, reducing the risk of waterlogging. Artificial drainage facilities, such as field drainage ditches and underground drainage systems, can significantly improve drainage efficiency.

[0049] In actual agricultural production, drainage conditions vary greatly across different plots of land. Plains farmland are flat and have slow natural drainage; without adequate drainage facilities, they are highly susceptible to waterlogging. Hilly and mountainous areas have undulating terrain, with significant gravity drainage, resulting in a relatively lower risk of waterlogging. However, excessively steep slopes can lead to soil erosion, necessitating appropriate soil and water conservation measures.

[0050] Taking loamy farmland as an example, its saturated hydraulic conductivity is 50 mm / h. If the plot has a slope of 3 degrees, is relatively flat, with a slope enhancement factor of 0.2, and only has simple natural drainage ditches, with a drainage facility correction factor of 0.6, then the effective drainage coefficient is approximately 0.64. This indicates that the actual drainage capacity of this plot is only 64% of the reference standard, making it prone to severe waterlogging stress during extreme rainfall. If a subsurface drainage system is constructed on this plot, the drainage facility correction factor increases to 0.9, and the effective drainage coefficient increases to 0.95, significantly reducing the risk of waterlogging.

[0051] The soil stress intensity driven by waterlogging is obtained by dividing the soil waterlogging stress index by the effective drainage coefficient, which corrects for the actual stress level. In plots with strong drainage capacity, even if the soil moisture content is high, the actual stress intensity is low because the water can be discharged in time. Conversely, in plots with poor drainage, even if the initial waterlogging is not severe, the stress will continue to increase because the water is difficult to drain.

[0052] Step S3: Determine the precipitation sensitivity coefficient based on the current growth stage of the crop and the soil waterlogging stress index of each grid unit to obtain the crop vulnerability index.

[0053] The crop vulnerability index is calculated as follows: obtain the sowing date of the crop in each grid unit, and divide the crop growth period into seedling stage. 1. Vegetative growth period Reproductive growth period and maturity According to the number of days after sowing Determine the current reproductive stage .

[0054] Define grid cells Crop precipitation sensitivity coefficient : ;in Based on the sensitivity coefficient, rice Corn wheat harvest Soybean Extract .

[0055] Crop vulnerability index for: ;in, This refers to the total number of days in the crop's entire growth period. The fertility process regulation index is set at 0.3. For crop type coefficients, rice is taken as follows: Corn wheat harvest Soybean Extract ; This characterizes the sensitivity of crops in each grid unit to waterlogging stress at the current growth stage.

[0056] Crops exhibit significant differences in their response to waterlogging stress across different growth stages. During the seedling stage, the root system is underdeveloped, making them less resistant to adverse environments. However, above-ground growth is minimal, water requirements are low, and they can tolerate mild waterlogging. The vegetative growth stage is a period of rapid crop growth, requiring ample water and nutrient supply. At this stage, the crop has established a relatively complete root system and possesses a certain degree of adaptive and regulatory capacity.

[0057] The reproductive growth stage is the most critical period in a crop's life cycle, encompassing important physiological processes such as flowering, pollination, and grain formation. During this period, crops are extremely sensitive to environmental conditions; any adverse factor can lead to severe yield losses. Waterlogging stress can cause decreased pollen viability, hindered pollination, and poor grain filling, resulting in irreversible damage to the final yield. Therefore, the rainfall sensitivity coefficient for the reproductive growth stage is set to the highest value of 1.

[0058] As crops mature and grains become plump, their sensitivity to waterlogging decreases. However, excessive soil moisture can still affect grain quality and delay harvest. Overall, crop sensitivity to disasters decreases as the growth process progresses, reflected in vulnerability calculations through the growth process regulation index. Rice, as an aquatic crop, is most tolerant of waterlogging, but excessive flooding can still cause damage at certain growth stages, such as the booting stage. Corn is relatively sensitive to waterlogging, especially during the tasseling and silking stages, where even short-term water accumulation can severely impact pollination. Wheat is relatively tolerant of waterlogging, but waterlogging during the jointing to heading stage significantly reduces yield. Soybeans are also sensitive to waterlogging, with the flowering and pod-setting stages being the most vulnerable.

[0059] For example, for maize in its reproductive growth stage, the precipitation sensitivity coefficient is 1. Assuming 100 days have passed since sowing, and the entire growth period is 130 days, the growth process accounts for 77%. After considering the adjustment of the growth process, and multiplying by the maize type coefficient of 1.0, the final vulnerability index is approximately 0.54. This value indicates that the maize in this plot has a high sensitivity to waterlogging stress and requires special attention.

[0060] Step S4: Calculate the comprehensive impact index of each grid unit based on the soil stress intensity driven by waterlogging and the crop vulnerability index. Construct a crop disaster risk level classification based on the comprehensive impact index and assess the crop disaster risk level of each grid unit.

[0061] The comprehensive impact index is constructed by coupling waterlogging-driven soil stress intensity. and crop vulnerability index For grid cells Calculate its comprehensive impact index : ;in, The coupling index is set to 2.5.

[0062] A four-level classification of crop disaster risk levels is constructed based on the comprehensive impact index. : .

[0063] When the coupling index is 1, the formula degenerates into a linear weighted average. As the coupling index increases, the nonlinear characteristics are enhanced, the comprehensive influence of the high-value region is further amplified, while the low-value region is relatively compressed. This invention uses 2.5 as the coupling index. After extensive experimental verification, this value can better balance the sensitivity and stability of the evaluation.

[0064] Taking a certain grid cell as an example, its soil stress intensity is 0.6 and its crop vulnerability index is 0.5. Using nonlinear coupling calculation, the comprehensive impact index is approximately 0.56.

[0065] If a simple linear weighting is used, the result is only 0.56 (0.6×0.6+0.5×0.4=0.56). Although the results of the two methods are close in this example, the advantages of the nonlinear method are more obvious in extreme cases.

[0066] Risk level classification provides an intuitive reference for decision-making. Low-risk areas can carry out agricultural production activities normally, while medium-risk areas need to closely monitor weather changes and prepare for drainage. High-risk areas should take preventive measures in advance, such as digging drainage ditches and repairing drainage facilities. Extremely high-risk areas require the activation of emergency response, and if necessary, take measures such as draining accumulated water and relocating crops to minimize disaster losses.

[0067] By calculating 2,000 grid cells across the entire study area, a spatial distribution map of crop disaster risk can be created. High-risk areas are often located in low-lying areas with poor drainage, where sensitive crops are grown, and in critical growth stages. This refined risk assessment provides a scientific basis for agricultural management departments to formulate differentiated disaster prevention strategies, contributing to precise disaster risk control.

[0068] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the impact of extreme precipitation on crops based on multi-factor coupling, characterized in that, The method includes the following steps: Step S1: Obtain extreme precipitation intensity, soil type data, crop type distribution data and topographic data of the study area, divide the study area into regular grid units, and calculate the precipitation input and soil moisture content of each grid unit. Step S2: Calculate the soil waterlogging stress index of each grid cell based on soil moisture content, and obtain the soil stress intensity driven by waterlogging considering the effects of drainage capacity and topographic slope. Step S3: Determine the precipitation sensitivity coefficient based on the current growth stage of the crop and the soil waterlogging stress index of each grid unit to obtain the crop vulnerability index; Step S4: Calculate the comprehensive impact index of each grid unit based on the soil stress intensity driven by waterlogging and the crop vulnerability index. Construct a crop disaster risk level classification based on the comprehensive impact index and assess the crop disaster risk level of each grid unit.

2. The method according to claim 1, characterized in that, The soil moisture content is determined by precipitation input, evapotranspiration output, surface runoff, and deep infiltration. The calculation method for the soil moisture content is as follows: for grid cells... At any moment Precipitation input Based on extreme precipitation intensity calculate: ;in, Extreme precipitation intensity, For grid cells area, For the calculation period; Grid cells At any moment Soil moisture content : ;in, This represents the soil moisture content at the previous moment. For a moment The actual evaporation rate For a moment Surface runoff, For a moment The amount of deep leakage, This refers to the depth of the crop root system.

3. The method according to claim 2, characterized in that, Grid cells Actual evaporation and surface runoff The calculation method is as follows: based on the mesh element Crop type determines crop coefficient Reference evaporation rate is ,time Actual evaporation for: ;in, Soil moisture stress coefficient: ;in, Field holding capacity; For the wilting point, take 0.05 for sandy soil, 0.10 for loam, and 0.15 for clay. Surface runoff The calculation method is as follows: ;in, For the maximum potential retention, , For grid cells The number of curves is determined by soil type and land use type, and is obtained by consulting the SCS runoff curve number table.

4. The method according to claim 3, characterized in that, The soil waterlogging stress index The calculation method is as follows: calculate the grid cell. Deviation from field capacity soil moisture content: ;when When the soil is waterlogged, it indicates the presence of soil waterlogging stress; the soil waterlogging stress index. ,when hour: ;in, For grid cells The number of days of continuous flooding This is the time accumulation factor, and its value range is... Rice: 0.3%, corn: 0.2%, wheat: 0.15%, soybean: 0.2%; when hour, .

5. The method according to claim 4, characterized in that, According to grid cells Soil texture type determines saturated hydraulic conductivity Field water holding capacity saturated water content When the soil texture is sandy, , , When the soil texture type is loam, , , When the soil texture is clay, , , .

6. The method according to claim 5, characterized in that, The deep leakage Calculated based on saturated hydraulic conductivity and soil moisture content: ;in, The infiltration index is used to characterize the control effect of soil texture on the infiltration process. It is determined according to the soil texture, with 2 for sandy soil, 3 for loam, and 4 for clay.

7. The method according to claim 6, characterized in that, The method for calculating soil stress intensity driven by waterlogging is as follows: considering the effects of drainage capacity and topographic slope, an effective drainage coefficient is defined. : ;in, For reference hydraulic conductivity, a value of 50 mm / h is used. The slope of the ground. The slope enhancement coefficient is denoted by [value]. According to the slope of the terrain Confirmed: When When the value is 0.2, When the value is 0.35, Take 0.5 at a time. This is a correction factor for drainage facilities; natural drainage is taken as... Artificial drainage system The soil stress intensity driven by waterlogging. : , Used to characterize the waterlogging stress intensity of each grid cell under the current soil moisture conditions.

8. The method according to claim 7, characterized in that, The dynamic vulnerability index is calculated as follows: Obtain the sowing date of crops in each grid unit and divide the crop growth period into seedling stage.

1. Vegetative growth period Reproductive growth period and maturity According to the number of days after sowing Determine the current reproductive stage ; Define grid cells Crop precipitation sensitivity coefficient : ;in Based on the sensitivity coefficient, rice Corn wheat harvest Soybean Extract ; Crop vulnerability index for: ;in, This refers to the total number of days in the crop's entire growth period. The fertility process regulation index is set at 0.

3. For crop type coefficients, rice is taken as follows: Corn wheat harvest Soybean Extract ; This characterizes the sensitivity of crops in each grid unit to waterlogging stress at the current growth stage.

9. The method according to claim 8, characterized in that, The comprehensive impact index is constructed by coupling waterlogging-driven soil stress intensity. and crop vulnerability index For grid cells Calculate its comprehensive impact index : ;in, The coupling index is set to 2.

5.

10. The method according to claim 9, characterized in that, A four-level classification of crop disaster risk levels is constructed based on the comprehensive impact index. : 。

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