Method for evaluating vegetation loss risk under drought and flood sudden turning stress
By combining Copula functions and Bayesian frameworks, and based on precipitation and NDVI data, the probability of wet-dry combined events and the risk of vegetation loss are quantified. This solves the problem of underestimating vegetation loss risk in traditional assessment methods, and enables accurate assessment of vegetation loss risk under wet-dry combined events and spatial targeting of ecological management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
- Filing Date
- 2025-08-19
- Publication Date
- 2026-05-08
AI Technical Summary
Traditional disaster risk assessment methods cannot accurately assess the risk of vegetation loss under combined dry and wet events, ignore the cumulative effect of hydrological stress during the transition between dry and wet periods, and fail to consider the dynamic response of disaster-bearing bodies under combined events, resulting in an underestimation of ecological loss risk.
By combining Copula functions and Bayesian frameworks, and based on precipitation and NDVI data, the probability of wet-dry composite events and the risk of vegetation loss are quantified. By identifying wet-dry composite events between adjacent seasons, moderate and severe scenarios are divided, and the probability of vegetation loss and exposure are calculated to construct a vegetation loss risk assessment model.
It enables accurate assessment of vegetation loss risk under combined wet and dry events, quantifies the interdependence of combined events, identifies high-risk vegetation areas, and provides spatially targeted measures for ecological management, which has important reference value for ecological risk prevention and control under climate change.
Smart Images
Figure CN121998402A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of ecological disaster risk assessment, and more specifically, relates to a method for assessing the risk of vegetation loss under the stress of sudden shifts between drought and flood. Background Technology
[0002] With the intensification of global climate change, the frequency and intensity of combined dry and wet events, such as rapid shifts between drought and flood, are constantly increasing, causing serious stress and damage to ecosystems, especially vegetation.
[0003] However, traditional disaster risk assessment methods mostly focus on single disasters (such as drought or flood), and have many limitations. These include a lack of quantification of the synergistic effects of combined dry and wet events, neglecting the cumulative effects of hydrological stress during the transition between dry and wet periods, potentially leading to an underestimation of ecological loss risk, and existing vulnerability assessments are largely based on static indicators, failing to consider the dynamic response of affected bodies under combined events, and single-hazard models cannot capture the cascading effects between various events. These problems prevent traditional disaster risk assessment methods from accurately assessing ecological loss risk.
[0004] Therefore, there is an urgent need for a method to assess the risk of vegetation loss under the stress of rapid shifts between drought and flood, so as to more accurately assess the risk of vegetation loss and provide a scientific basis for ecological protection and disaster management. Summary of the Invention
[0005] In view of the shortcomings of the existing technology, the purpose of this application is to provide a method for assessing the risk of vegetation loss under the stress of sudden shifts between drought and flood, which can more accurately assess the risk of vegetation loss and provide a scientific basis for ecological protection and disaster management.
[0006] To achieve the above objectives, in a first aspect, this application provides a method for assessing the risk of vegetation loss under sudden shifts in stress from drought to flood, comprising the following steps:
[0007] S10: Acquire and process the monthly precipitation data and NDVI data of the target area to obtain the seasonal-scale sequence of gridded precipitation data and NDVI data. S20, based on the seasonal sequence of precipitation data, identifies dry-wet composite events between adjacent seasons and classifies them into moderate and severe scenarios. Then, the Copula function is used to calculate the probability of occurrence of dry-wet composite events between adjacent seasons under different scenarios. At the same time, the Bayesian framework is combined with the Copula function to calculate the probability of vegetation loss under the stress of dry-wet composite events between adjacent seasons under different scenarios. S30, based on the seasonal-scale sequence of NDVI data, uses the seasonal average normalized vegetation index to calculate the vegetation system's exposure to dry-wet combined events. S40. Calculate the vegetation loss risk under the stress of dry-wet compound events based on the occurrence probability and loss probability calculated in step S20 and the exposure calculated in step S30.
[0008] The evaluation method of vegetation loss risk under the stress of rapid alternation of drought and flood in this application has the following effects: (1) Construct the joint distribution of compound events based on the compound event probability modeling of the Copula function, quantify the dependence of compound events, and is more in line with the characteristics of actual disaster chains compared to the superposition of single-disaster probabilities; (2) Compared with exploring risks mainly based on the human system (population, GDP), prioritizing the exposure of the natural system can better identify high-risk vegetation areas (such as the wetland-grassland transition zone), making ecological management more targeted, and at the same time having important reference value for ecological risk prevention and control under climate change.
[0009] As a further preference, in step S20, the steps of identifying dry-wet compound events between adjacent seasons and dividing moderate and severe scenarios based on the seasonal-scale sequence of precipitation data are specifically as follows: Calculate the SPI index according to the seasonal-scale sequence of precipitation data; Extract drought events and wet events using the SPI index through the drought level division standard, and divide moderate and severe scenarios; Identify dry-wet compound events between adjacent seasons according to the seasons when drought events and wet events occur. The compound events include four of the following: changing from dry to wet in spring and summer, changing from wet to dry in spring and summer, continuous drought in spring and summer, continuous wetness in spring and summer, changing from dry to wet in summer and autumn, changing from wet to dry in summer and autumn, continuous drought in summer and autumn, continuous wetness in summer and autumn, changing from dry to wet in autumn and winter, changing from wet to dry in autumn and winter, continuous drought in autumn and winter, continuous wetness in autumn and winter, changing from dry to wet in winter and spring, changing from wet to dry in winter and spring, continuous drought in winter and spring, and continuous wetness in winter and spring.
[0010] As a further preference, the drought level division standard is: -2 < SPI ≤ -1.5 is severe drought, -1.5 < SPI ≤ -1 is moderate drought, 1 < SPI ≤ 1.5 is moderate flood, and 1.5 < SPI ≤ 2 is severe flood.
[0011] As a further preference, in step S20, the steps of calculating the occurrence probability of dry-wet compound events between adjacent seasons in different scenarios using the Copula function are specifically as follows: Use the normal distribution and the generalized extreme value distribution to fit the marginal distributions of the SPI index of adjacent seasons, and select the optimal marginal distribution using the Akaike information criterion; The optimal joint distribution function of SPI sequences between adjacent seasons is selected from Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula and t-Copula functions using the root mean square error and AIC criterion. Based on the optimal joint distribution function, the probability of occurrence of dry-wet combined events between adjacent seasons under different scenarios is calculated.
[0012] As a further preferred option, the probability calculation formulas for the occurrence of dry-to-wet, wet-to-dry, consecutive drought, and consecutive wet periods between adjacent seasons under the moderate scenario are as follows:
[0013]
[0014]
[0015]
[0016] The probabilities of alternating dry and wet seasons, alternating wet and dry seasons, consecutive droughts, and consecutive wet seasons under severe scenarios are calculated as follows:
[0017]
[0018]
[0019]
[0020] In the formula, ( ) represents the joint distribution function of the SPI sequences between adjacent seasons; X and Y These represent the SPI values for the two types of events, respectively. X Represents the rainfall situation for the current season. Y This indicates the rainfall situation for the next season; 、 These represent the probability of a change from dry to wet seasons between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of a change from wet to dry between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive droughts between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive wet seasons occurring under moderate and severe scenarios, respectively.
[0021] As a further preferred step, in step S20, based on the SPI index of the seasonal precipitation data sequence and the monthly NDVI data, the Bayesian framework is combined with the Copula function to calculate the probability of vegetation loss under the combined dry and wet events stress between adjacent seasons under different scenarios.
[0022] As a further preferred option, step S20, which involves using a Bayesian framework combined with Copula functions to calculate the probability of vegetation loss under combined dry and wet events between adjacent seasons under different scenarios, specifically includes: The candidate marginal distributions are normal distribution, generalized extreme value distribution and Gumbel distribution, and the candidate joint distribution functions are Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula and t-Copula. The optimal distribution of NDVI is selected by KS test and Akaike information criterion. The joint distribution function is selected using the root mean square error and the AIC criterion. Then, based on the joint distribution function, the probability of vegetation loss under combined dry and wet stress between adjacent seasons is calculated under different scenarios.
[0023] As a further preferred embodiment, in step S20, the expression for the probability of vegetation loss under the stress of a dry-to-wet transition event between adjacent seasons under a moderate scenario is as follows:
[0024]
[0025]
[0026] The expression for the probability of vegetation loss under the stress of a wet-to-dry transition between adjacent seasons in a moderate scenario is as follows:
[0027]
[0028]
[0029] The expression for the probability of vegetation loss under the stress of consecutive drought events between adjacent seasons under moderate conditions is as follows:
[0030]
[0031]
[0032] The expression for the probability of vegetation loss under the stress of consecutive wet events between adjacent seasons in a moderate scenario is as follows:
[0033]
[0034]
[0035] The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from dry to wet seasons between adjacent seasons:
[0036]
[0037]
[0038] The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from wet to dry periods between adjacent seasons:
[0039]
[0040]
[0041] The expression for the probability of vegetation loss under severe drought stress between adjacent seasons:
[0042]
[0043]
[0044] The expression for the probability of vegetation loss under severe stress conditions of consecutive wet events between adjacent seasons:
[0045]
[0046]
[0047] In the formula, Indicates NDVI order; Indicates SPI sequence and NDVI sequence; The marginal distribution function representing the NDVI data sequence; Indicates NDVI <NDVI 40th Scenario of vegetation loss; and Represents the marginal distribution function of two seasonal SPI sequences; Represents the joint distribution function of the two seasonal SPI sequences; This represents the joint distribution function of the SPI and NDVI sequences for two seasons; 、 These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from dry to wet between adjacent seasons. , These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from wet to dry between adjacent seasons. , These represent the probability of vegetation loss under consecutive drought stress between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of vegetation loss under continuous wet stress between adjacent seasons in moderate and severe scenarios, respectively.
[0048] As a further preferred option, in step S40, the risk of vegetation loss under the combined stress of dry and wet events is calculated by multiplying the occurrence probability and loss probability in step S20 and the exposure degree in step S30.
[0049] Secondly, this application provides a vegetation loss risk assessment device under combined drought and wet stress, including a processor and a storage medium. The processor loads and executes instructions and data in the storage medium to implement the vegetation loss risk assessment method under the above-described drought-flood stress.
[0050] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description
[0051] Figure 1 This is a flowchart of the vegetation loss risk assessment method provided in this application under the stress of sudden shift from drought to flood; Figure 2 This is a spatial distribution pattern map of terrestrial vegetation exposure in China from 1982 to 2022, provided in an embodiment of this application. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0053] The following is a description of the technical terms used in this application: The disaster-bearing body is an assessment element in a disaster event, referring to the object that may be damaged or affected in a disaster event. It can be population, roads, animals and plants, buildings, etc., and in this application, it refers to vegetation.
[0054] The vulnerability of a disaster-bearing body refers to the likelihood or extent to which a disaster-bearing body will suffer losses when affected by a disaster. It reflects the sensitivity and adaptability of the disaster-bearing body to external disturbances (such as natural disasters, environmental changes, etc.) and is used to measure the "vulnerability" or "fragility" of a disaster-bearing body when a disaster occurs.
[0055] Exposure of a disaster-bearing body refers to the degree or extent to which a disaster-bearing body may be threatened when a disaster occurs. It is used to measure the distribution and quantity of disaster-bearing bodies within the scope of disaster impact.
[0056] This application, through research, has found the following shortcomings in traditional disaster risk assessment: (1) Limitations of independent event analysis Previous studies have mostly focused on single drought or flood events, neglecting the synergistic stress effects of abrupt shifts between drought and flood (such as sudden heavy rainfall following a drought) on vegetation. For example, prolonged drought reduces soil permeability, while subsequent torrential rains may trigger more severe soil erosion and root damage, but single-hazard models cannot quantify this nonlinear superposition effect.
[0057] (2) Deficiencies of static vulnerability assessment Existing studies often use fixed vulnerability indicators (such as vegetation type and soil water holding capacity), but under conditions of rapid drought-flood transitions, the adaptability of vegetation changes dynamically with the duration of stress. For example, short-term drought may only affect photosynthesis, but if superimposed with flooding, it may lead to root hypoxia and death, and traditional methods are difficult to reflect this dynamic response process.
[0058] (3) Uniqueness of exposure characterization Studies on human system exposure (such as population and GDP) are relatively mature, but the exposure of natural systems (such as key ecoregions and vegetation functional groups) is often weakened.
[0059] To address the aforementioned issues, this application provides a method for assessing vegetation loss risk under the stress of a sudden shift from drought to flood (dry-wet combined event). This method comprehensively considers the probability of occurrence of the dry-wet combined event, the vulnerability (loss probability) of vegetation, and the exposure level. It utilizes data such as the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI), combined with Copula functions and a Bayesian framework, to quantitatively assess the risk of vegetation loss.
[0060] like Figure 1As shown, the method for assessing vegetation loss risk under sudden drought-flood stress provided in this application includes steps S10 to S40, which are detailed below: S10: Acquire and process monthly precipitation data and normalized vegetation index (NDVI) data of the target area to obtain a gridded seasonal sequence of precipitation and NDVI data.
[0061] In one embodiment, step S10 provided in this application may specifically include: acquiring specified precipitation data and NDVI data; processing the precipitation data and NDVI data into a seasonal scale sequence; in this embodiment, the seasonal division criteria are: spring (March-May), summer (June-August), autumn (September-November), and winter (December-February of the following year); and then processing the precipitation data and NDVI data using a spatial interpolation method to obtain gridded precipitation data and NDVI data with high spatial resolution. Preferably, the spatial resolution here can be selected as 0.5°.
[0062] S20 identifies inter-seasonal wet-dry composite events based on seasonal precipitation data sequences, classifying them into moderate and severe scenarios. It also calculates the probability of occurrence of inter-seasonal wet-dry composite events and the probability of vegetation loss under the stress of inter-seasonal wet-dry composite events in both scenarios.
[0063] In one embodiment, the steps provided in this application for calculating the probability of occurrence of a combined dry and wet event between adjacent seasons under two scenarios can specifically be as follows: (1) After obtaining the precipitation sequence through step S10, the SPI index is calculated.
[0064] (2) Then, based on the drought level classification criteria in Table 1, drought events and wet events are extracted using the SPI index. Different composite events are combined according to the season in which the drought and wet events occur: dry to wet in spring-summer, wet to dry in spring-summer, continuous drought in spring-summer, continuous wet in spring-summer; dry to wet in summer-autumn, wet to dry in summer-autumn, continuous drought in summer-autumn, continuous wet in summer-autumn; dry to wet in autumn-winter, wet to dry in autumn-winter, continuous drought in autumn-winter, continuous wet in autumn-winter; dry to wet in winter-spring, wet to dry in winter-spring, continuous drought in winter-spring, continuous wet in winter-spring.
[0065] Table 1. Standards for Classifying Drought and Flood Levels
[0066] (3) Based on the severity of drought and wet events, discuss the probability of occurrence of combined dry and wet events between adjacent seasons under moderate and severe scenarios.
[0067] Specifically, the process of calculating the probability of a wet-dry combination event using a two-dimensional Copula function is as follows: First, the marginal distributions of the SPI indexes between adjacent seasons are fitted using a normal distribution and a generalized extreme value (GEV) distribution. The optimal marginal distribution is then selected using the Akaike Information Criterion (AIC). Finally, the optimal joint distribution function for the SPI sequences between adjacent seasons is selected from Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula, and t-Copula functions using the root mean square error (RMSE) and AIC criteria. Based on the selected optimal joint distribution function, the probability of a wet-dry combination event between adjacent seasons is calculated. The calculation formula is as follows: The probabilities of alternating dry and wet seasons, alternating wet and dry seasons, consecutive droughts, and consecutive wet seasons under a moderate scenario are calculated as follows:
[0068]
[0069]
[0070]
[0071] The probabilities of alternating dry and wet seasons, alternating wet and dry seasons, consecutive droughts, and consecutive wet seasons under severe scenarios are calculated as follows:
[0072]
[0073]
[0074]
[0075] In the formula, ( ) represents the joint distribution function of the SPI sequences between adjacent seasons; X and Y These represent the SPI values for the two types of events, respectively. X Represents the rainfall situation for the current season. Y This indicates the rainfall situation for the next season; 、 These represent the probability of a change from dry to wet seasons between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of a change from wet to dry between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive droughts between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive wet seasons occurring under moderate and severe scenarios, respectively.
[0076] In one embodiment, the steps provided in this application for calculating the probability of vegetation loss (vulnerability) under combined dry and wet events between adjacent seasons under two scenarios can specifically be as follows: Based on the SPI sequence and monthly NDVI sequences, a Bayesian framework combined with the Copula function was used to calculate the probability of vegetation loss under combined dry and wet stress. The specific process is as follows: The candidate marginal distributions were the normal distribution, the generalized extreme value distribution, and the Gumbel distribution; the candidate joint distribution functions were Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula, and t-Copula. The optimal distribution of NDVI was selected using the KS test and the Akaike information criterion, and the joint distribution function was selected using the root mean square error and the AIC criterion. Based on the selected optimal joint distribution function, the probability of vegetation loss under combined dry and wet stress was calculated. The calculation formula is as follows: The expression for the probability of vegetation loss under the stress of a dry-to-wet transition between adjacent seasons in a moderate scenario is as follows:
[0077]
[0078]
[0079] The expression for the probability of vegetation loss under the stress of a wet-to-dry transition between adjacent seasons in a moderate scenario is as follows:
[0080]
[0081]
[0082] The expression for the probability of vegetation loss under the stress of consecutive drought events between adjacent seasons under moderate conditions is as follows:
[0083]
[0084]
[0085] The expression for the probability of vegetation loss under the stress of consecutive wet events between adjacent seasons in a moderate scenario is as follows:
[0086]
[0087]
[0088] The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from dry to wet seasons between adjacent seasons:
[0089]
[0090]
[0091] The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from wet to dry periods between adjacent seasons:
[0092]
[0093]
[0094] The expression for the probability of vegetation loss under severe drought stress between adjacent seasons:
[0095]
[0096]
[0097] The expression for the probability of vegetation loss under severe stress conditions of consecutive wet events between adjacent seasons:
[0098]
[0099]
[0100] In the formula, Indicates NDVI order; Indicates SPI sequence and NDVI sequence; The marginal distribution function representing the NDVI data sequence; Indicates NDVI <NDVI 40th Scenario of vegetation loss; and Represents the marginal distribution function of two seasonal SPI sequences; Represents the joint distribution function of the two seasonal SPI sequences; This represents the joint distribution function of the SPI and NDVI sequences for two seasons; 、 These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from dry to wet between adjacent seasons. , These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from wet to dry between adjacent seasons. , These represent the probability of vegetation loss under consecutive drought stress between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of vegetation loss under continuous wet stress between adjacent seasons in moderate and severe scenarios, respectively.
[0101] S30 treats the vegetation system as the disaster-bearing body, and uses the surface vegetation cover status to characterize the vegetation system's exposure to combined dry and wet events. Specifically, based on the seasonal-scale series of NDVI data, the seasonally averaged normalized vegetation index is used to calculate the vegetation system's exposure to combined dry and wet events.
[0102] S40. Based on the occurrence probability and loss probability in step S20 and the exposure degree in step S30, according to the calculation formula "loss risk = occurrence probability of dry-wet combined event × vulnerability (loss probability) × exposure degree", that is, by multiplying the occurrence probability and loss probability in step S20 and the exposure degree in step S30, the vegetation loss risk under the stress of dry-wet combined event is calculated.
[0103] The assessment method for vegetation loss risk under the stress of sudden shift from drought to flood in this application has the following effects: (1) Based on the probability modeling of compound events using the Copula function, the joint distribution of compound events is constructed, and the interdependence of compound events is quantified. Compared with the probability superposition of single disasters, it is more in line with the actual disaster chain characteristics; (2) Compared with exploring risks based on human systems (population, GDP), prioritizing the exposure of natural systems can better identify high-risk vegetation areas (such as wetland-grassland transition zones), making ecological management more spatially targeted, and at the same time, it has important reference value for ecological risk prevention and control under climate change.
[0104] The following section uses nine major river basins in China as examples to provide a detailed explanation of the vegetation loss risk assessment method provided in this application under the stress of sudden shifts between drought and flood.
[0105] This specific embodiment provides a method for assessing the risk of vegetation loss under the stress of a sudden shift from drought to flood. The method involves multiplying the probability of occurrence of a combined drought-wet event and the probability of vegetation loss under such stress, calculated in step S20, with the vegetation system exposure calculated in step S30, by the probability of occurrence of the combined drought-wet event. P), the vulnerability of vegetation ( Vul ) and exposure ( Exp The risk of vegetation loss under combined dry and wet stress is estimated using the following expression:
[0106] In the formula, j —The j-th pixel.
[0107] (1) Vegetation exposure analysis Based on monthly NDVI data from 1982 to 2022, the seasonal average NDVI value for each pixel was calculated to characterize vegetation exposure under the influence of wet-dry events. A higher NDVI value indicates more vigorous vegetation growth and higher cover, corresponding to a higher degree of exposure of the vegetation system to wet-dry events; conversely, a lower NDVI value indicates poorer vegetation growth and lower cover, corresponding to a lower degree of exposure of the vegetation system to wet-dry events. Figure 2 This paper presents the spatial distribution pattern of seasonal vegetation exposure across China from 1982 to 2022. Temporally, summer has the highest vegetation cover (NDVI value of 0.45), followed by autumn with good vegetation growth (NDVI value of 0.36). Influenced by factors such as temperature and sunlight, vegetation cover is lower in spring and winter, with NDVI values of 0.31 and 0.25, respectively. Spatially, NDVI values gradually increase from northwest to southeast. In the northwest, NDVI values are concentrated between 0 and 0.4, while in the southeast, they are mainly distributed between 0.4 and 1.0. Table 2 shows that the Pearl River Basin and the Southeast River Basins have good vegetation growth in all four seasons, while the Yellow River Basin and inland river basins have poor vegetation growth. Therefore, the spatial differentiation of vegetation exposure among different river basins in China is quite significant.
[0108] Table 2. Average vegetation exposure in different seasons in the nine major river basins
[0109] (2) Average risk of vegetation loss under combined dry and wet stress The average risk of vegetation loss under the stress of a wet-dry combined event is calculated based on the formula "risk = hazard of disaster-causing factor × vulnerability × exposure". The probability of occurrence of the wet-dry combined event calculated in step S20 is used as the hazard of the disaster-causing factor, the probability of vegetation loss under the stress of the wet-dry combined event is used as the vulnerability, and the vegetation exposure is calculated in step S30.
[0110] (2.1) Average risk of summer vegetation loss under spring-summer dry-wet complex stress Under the moderate scenario, as shown in Table 3, spatially, the Songliao River Basin has the highest average risk of summer vegetation loss under the stress of spring-summer dry-wet combined events (3.35%), followed by the Southeast River Basins (3.3%), the Yangtze River Basin (3.08%), the Pearl River Basin (2.95%), the Huai River Basin (2.71%), the Hai River Basin (2.67%), the Yellow River Basin (2.15%), and the Southwest River Basins (2.15%). The lowest average risk is found in inland river basins (0.79%). Analyzing different events, the average risk of consecutive drought events is 0.83%, the average risk of consecutive wet events is 0.52%, and the average risk of dry-to-wet and wet-to-dry events are 0.41% and 0.32%, respectively. Therefore, the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different combined dry and wet events under moderate scenarios, the high-risk area for dry-to-wet events is located in the northern part of the Songliao River Basin, while the high-risk area for wet-to-dry events is distributed in the southern part of the southeastern river basins. The high-risk areas for consecutive drought and consecutive wet events are basically the same, concentrated in the northwestern border areas of the southeastern river basins and inland river basins of my country. It is worth noting that the high-risk areas for dry-to-wet events and consecutive drought events have basically opposite distributions.
[0111] Under severe scenarios, as shown in Table 3, spatially, the Southeast River Basin has the highest average risk of summer vegetation loss under the stress of spring-summer dry-wet combined events (0.71%), followed by the Songliao River Basin (0.7%), the Yangtze River Basin (0.68%), the Hai River Basin (0.58%), the Pearl River Basin (0.56%), the Huai River Basin (0.54%), the Yellow River Basin (0.5%), and the Southwest River Basin (0.48%). The lowest average risk is found in inland river basins (0.18%). Analyzing different events, the average risk of consecutive drought events is 0.21%, the average risk of consecutive wet events is 0.11%, and the average risk of dry-to-wet and wet-to-dry events are 0.08% and 0.05%, respectively. Therefore, the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different dry-wet combined events under severe scenarios, there are no obvious high-risk areas for events transitioning from dry to wet or from wet to dry. The high-risk areas for consecutive drought and consecutive wet events are basically the same, both concentrated in the western part of the Yangtze River Basin and the Yellow River Basin in my country.
[0112] Table 3. Average risk of summer vegetation loss in different regions under spring-summer combined dry and wet events.
[0113] (2.2) Average risk of autumn vegetation loss under spring-summer dry-wet combined event stress Under the moderate scenario, as shown in Table 4, spatially, the Southeast River Basin has the highest average risk of autumn vegetation loss under the stress of spring-summer dry-wet combined events (3.24%), followed by the Pearl River Basin (3.14%), the Yangtze River Basin (2.62%), the Huai River Basin (1.99%), the Southwest River Basin (1.96%), the Songliao River Basin (1.92%), the Hai River Basin (1.69%), and the Yellow River Basin (1.48%). The lowest average risk is found in inland river basins (0.55%). Analyzing different events, the average risk of consecutive drought events is 0.63%, the average risk of consecutive wet events is 0.39%, and the average risks of dry-to-wet and wet-to-dry events are 0.29% and 0.28%, respectively. Therefore, the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different dry-wet combined events under moderate scenarios, the high-risk area for dry-to-wet events is located in the northern part of the Songliao River Basin, the high-risk area for wet-to-dry events is located in the southern part of the Southeast River Basin and the lower reaches of the Pearl River Basin, the high-risk area for continuous drought events is concentrated in the northwestern border areas of the Southeast River Basin and the inland river basins, and the high-risk area for continuous wet events is concentrated in the upper and lower reaches of the Yangtze River Basin, the lower reaches of the Southwest River Basin and the northern part of the Southeast River Basin.
[0114] Under severe scenarios, as shown in Table 4, spatially, the Southeast River Basin has the highest average risk of summer vegetation loss under the stress of spring-summer dry-wet combined events (0.73%), followed by the Pearl River Basin (0.6%), the Yangtze River Basin (0.58%), the Southwest River Basin (0.43%), the Songliao River Basin (0.42%), the Huai River Basin (0.41%), the Hai River Basin (0.38%), and the Yellow River Basin (0.34%). The lowest average risk is found in inland river basins (0.13%). Analyzing different events, the average risk of consecutive drought events is 0.16%, the average risk of consecutive wet events is 0.08%, and the average risk of dry-to-wet and wet-to-dry events is 0.05%. This indicates that the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different dry-wet combined events under severe scenarios, there are no obvious high-risk areas for events transitioning from dry to wet, from wet to dry, and continuous wet events. The high-risk areas for continuous drought events are distributed in the western part of the Yangtze River Basin and the Yellow River Basin in my country.
[0115] Table 4. Average risk of autumn vegetation loss in different regions under spring-summer combined dry and wet stress events.
[0116] Comparing the average risk of summer vegetation loss with the average risk of autumn vegetation loss under spring-summer combined wet-dry stress events, it can be seen that the average risk of summer vegetation loss is higher than that of autumn vegetation loss under both moderate and severe scenarios. Looking at different regions, the inland river basin, Songliao River basin, Yellow River basin, Hai River basin, and Huai River basin all show a higher average risk of summer vegetation loss than autumn vegetation loss. However, the Yangtze River basin experiencing severe wet-to-dry transition, the Pearl River basin experiencing moderate wet-to-dry transition and continuous drought, the Pearl River basin experiencing severe continuous drought, the southwestern river basins experiencing moderate to severe wet-to-dry transition, and the southeastern river basins experiencing moderate to severe wet-to-dry transition and continuous wetness all show a higher average risk of autumn vegetation loss than summer vegetation loss. This indicates that the river basins with a higher average risk of autumn vegetation loss than summer vegetation loss are all in the south and are concentrated in wet-to-dry transition events.
[0117] (2.3) Average risk of autumn vegetation loss under summer-autumn dry-wet combined event stress Under the moderate scenario, as shown in Table 5, spatially, the Pearl River Basin has the highest average risk of autumn vegetation loss under the stress of summer-autumn dry-wet complex events (3.2%), followed by the Southeast River Basins (3.13%), the Yangtze River Basin (2.66%), the Huai River Basin (2.08%), the Southwest River Basins (2.04%), the Songliao River Basin (1.90%), the Hai River Basin (1.69%), and the Yellow River Basin (1.49%). The lowest average risk is found in inland river basins (0.55%). Analyzing different events, the average risk of consecutive drought events is 0.61%, the average risk of consecutive wet events is 0.49%, and the average risks of dry-to-wet and wet-to-dry events are 0.28% and 0.22%, respectively. Therefore, the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different dry-wet combined events under moderate scenarios, the high-risk areas for dry-to-wet events are located in the lower reaches of the Yangtze River Basin, the lower reaches of the Pearl River Basin, and the southern part of the Southeast River Basins. There are no obvious high-risk areas for wet-to-dry events. The high-risk areas for consecutive drought and consecutive wet events are basically the same in the Yangtze River Basin, the Southeast River Basins, the Southwest River Basins, and the Pearl River Basin, while the high-risk areas in the Songliao River Basin, the Hai River Basin, and the Huai River Basin are basically opposite.
[0118] Under severe scenarios, as shown in Table 5, spatially, the Pearl River Basin has the highest average risk of autumn vegetation loss under the stress of summer-autumn dry-wet complex events (0.64%), followed by the Southeast River Basins (0.61%), the Yangtze River Basin (0.60%), the Southwest River Basins (0.47%), the Huai River Basin (0.43%), the Songliao River Basin (0.43%), the Hai River Basin (0.35%), and the Yellow River Basin (0.35%). The lowest average risk is found in inland river basins (0.12%). Analyzing different events, the average risk of consecutive drought events is 0.16%, the average risk of consecutive wet events is 0.11%, and the average risk of dry-to-wet and wet-to-dry events are 0.05% and 0.03%, respectively. Therefore, the average risk of consecutive drought and consecutive wet events is higher than that of dry-to-wet transition events. Observing the distribution of high-risk areas for different dry-wet combined events under severe scenarios, there are no obvious high-risk areas for events transitioning from dry to wet, from wet to dry, and continuous wet events. The high-risk areas for continuous drought events are distributed in the western part of the Yangtze River Basin and the Yellow River Basin in my country.
[0119] Table 5. Average risk of autumn vegetation loss in different regions under summer-autumn combined dry and wet stress events.
[0120] This specific embodiment assesses the risk of vegetation loss under combined dry and wet stress events based on the definition of vegetation loss risk. The following conclusions are drawn: (1) Under moderate spring-summer drought-to-wet stress, the high-risk area for summer vegetation loss is located in the northern part of the Songliao River Basin, while the high-risk area for wet-to-dry stress is located in the southern part of the southeastern river basins. The high-risk areas for consecutive drought and consecutive wet stress are basically the same, both concentrated in the northwestern border areas of the southeastern river basins and inland river basins of my country. It is worth noting that the high-risk areas for drought-to-wet stress and consecutive drought have basically opposite distributions. Under severe scenarios, the high-risk areas for consecutive drought and consecutive wet stress are basically the same, both concentrated in the western part of the Yangtze River Basin and the Yellow River Basin of my country.
[0121] (2) Under moderate spring-summer drought-to-wet stress, the high-risk area for autumn vegetation loss is located in the northern part of the Songliao River Basin. The high-risk area for wet-to-dry stress is distributed in the southern part of the Southeast River Basin and the lower reaches of the Pearl River Basin. The high-risk area for consecutive drought stress is concentrated in the northwestern border region of the Southeast River Basin and the inland river basin. The high-risk area for consecutive wet stress is concentrated in the upper and lower reaches of the Yangtze River Basin, the lower reaches of the Southwest River Basin, and the northern part of the Southeast River Basin. Under severe stress, the high-risk area for consecutive drought stress is distributed in the western part of the Yangtze River Basin and the Yellow River Basin. Comparing the average risk of summer vegetation loss and the average risk of autumn vegetation loss under spring-summer drought-wet composite stress, it can be seen that the average risk of summer vegetation loss is higher than that of autumn vegetation loss under both moderate and severe stress.
[0122] (3) Under moderate summer-autumn drought-to-wet stress, the high-risk areas for average risk of autumn vegetation loss are located in the lower reaches of the Yangtze River Basin, the lower reaches of the Pearl River Basin, and the southern parts of the Southeast River Basin. There are no obvious high-risk areas for wet-to-dry events. The high-risk areas for consecutive drought and consecutive wet events are basically the same in the Yangtze River Basin, the Southeast River Basin, the Southwest River Basin, and the Pearl River Basin, while the high-risk areas in the Songliao River Basin, the Hai River Basin, and the Huai River Basin are basically opposite. Under severe scenarios, the high-risk areas for consecutive drought events are distributed in the western parts of the Yangtze River Basin and the Yellow River Basin in my country.
[0123] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for assessing the risk of vegetation loss under the stress of sudden shifts between drought and flood, characterized in that, It includes the following steps: S10. Obtain and process the monthly precipitation data and NDVI data of the target area to obtain the precipitation data and NDVI data seasonal scale sequences after gridification; S20. Based on the precipitation data seasonal scale sequence, identify the dry-wet compound events between adjacent seasons, divide the moderate and severe scenarios, then use the Copula function to calculate the occurrence probabilities of the dry-wet compound events between adjacent seasons under different scenarios, and at the same time use the Bayesian framework combined with the Copula function to calculate the loss probabilities of vegetation under the stress of the dry-wet compound events between adjacent seasons; S30. Based on the NDVI data seasonal scale sequence, use the seasonal average normalized vegetation index to calculate the exposure of the vegetation system to the dry-wet compound events; S40. According to the occurrence probabilities and loss probabilities calculated in step S20 and the exposure calculated in step S30, calculate the vegetation loss risk under the stress of the dry-wet compound events.
2. The method for assessing the risk of vegetation loss under the stress of sudden shift from drought to flood as described in claim 1, characterized in that, In step S20, the steps of identifying the dry-wet compound events between adjacent seasons and dividing the moderate and severe scenarios based on the precipitation data seasonal scale sequence are specifically as follows: Calculate the SPI index according to the precipitation data seasonal scale sequence; Through the drought level division standard, use the SPI index to extract drought events and wet events, and divide the moderate and severe scenarios; According to the seasons when the drought events and wet events occur, identify the dry-wet compound events between adjacent seasons, and the compound events include four of the following: changing from dry to wet in spring and summer, changing from wet to dry in spring and summer, continuous drought in spring and summer, continuous wetness in spring and summer, changing from dry to wet in summer and autumn, changing from wet to dry in summer and autumn, continuous drought in summer and autumn, continuous wetness in summer and autumn, changing from dry to wet in autumn and winter, changing from wet to dry in autumn and winter, continuous drought in autumn and winter, continuous wetness in autumn and winter, changing from dry to wet in winter and spring, changing from wet to dry in winter and spring, continuous drought in winter and spring, continuous wetness in winter and spring.
3. The method for assessing the risk of vegetation loss under sudden drought-flood stress as described in claim 2, characterized in that, The drought level division standard is: -2 < SPI ≤ -1.5 is severe drought, -1.5 < SPI ≤ -1 is moderate drought, 1 < SPI ≤ 1.5 is moderate flood, 1.5 < SPI ≤ 2 is severe flood.
4. The method for assessing the risk of vegetation loss under the stress of sudden shift from drought to flood as described in claim 1, characterized in that, In step S20, the steps of using the Copula function to calculate the occurrence probabilities of the dry-wet compound events between adjacent seasons under different scenarios are specifically as follows: Use the normal distribution and the generalized extreme value distribution to fit the marginal distributions of the SPI index of adjacent seasons, and use the Akaike information criterion to screen out the optimal marginal distributions; Use the root mean square error and the AIC criterion to select the optimal joint distribution function of the SPI sequences between adjacent seasons from the Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula and t-Copula functions; According to the optimal joint distribution function, calculate the occurrence probabilities of the dry-wet compound events between adjacent seasons under different scenarios.
5. The method for assessing the risk of vegetation loss under sudden drought-flood stress as described in claim 1 or 4, characterized in that, The calculation formulas for the occurrence probabilities of changing from dry to wet, changing from wet to dry, continuous drought, and continuous wetness between adjacent seasons in the moderate scenario are respectively: The calculation formulas for the occurrence probabilities of changing from dry to wet, changing from wet to dry, continuous drought, and continuous wetness between adjacent seasons in the severe scenario are respectively: In the formula, ( ) represents the joint distribution function of the SPI sequences between adjacent seasons; X and Y These represent the SPI values for the two types of events, respectively. X Represents the rainfall situation for the current season. Y This indicates the rainfall situation for the next season; 、 These represent the probability of a change from dry to wet seasons between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of a change from wet to dry between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive droughts between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of consecutive wet seasons occurring under moderate and severe scenarios, respectively.
6. The method for assessing the risk of vegetation loss under the stress of sudden shift from drought to flood as described in claim 1, characterized in that, In step S20, based on the SPI index of the seasonal precipitation data sequence and the monthly NDVI data, the Bayesian framework is combined with the Copula function to calculate the probability of vegetation loss under the combined dry and wet events stress between adjacent seasons under different scenarios.
7. The method for assessing the risk of vegetation loss under sudden drought-flood stress as described in claim 1, characterized in that, Step S20, which involves using a Bayesian framework and Copula functions to calculate the probability of vegetation loss under combined dry and wet stress events between adjacent seasons under different scenarios, specifically includes: The candidate marginal distributions are normal distribution, generalized extreme value distribution and Gumbel distribution, and the candidate joint distribution functions are Clayton-Copula, Frank-Copula, Gumbel-Copula, Gaussian-Copula and t-Copula. The optimal distribution of NDVI is selected by KS test and Akaike information criterion. The joint distribution function is selected using the root mean square error and the AIC criterion. Then, based on the joint distribution function, the probability of vegetation loss under combined dry and wet stress between adjacent seasons is calculated under different scenarios.
8. The method for assessing the risk of vegetation loss under sudden drought-flood stress as described in claim 1, characterized in that, In step S20, the expression for the probability of vegetation loss under the stress of a dry-to-wet transition between adjacent seasons under the moderate scenario is as follows: The expression for the probability of vegetation loss under the stress of a wet-to-dry transition between adjacent seasons in a moderate scenario is as follows: The expression for the probability of vegetation loss under the stress of consecutive drought events between adjacent seasons under moderate conditions is as follows: The expression for the probability of vegetation loss under the stress of consecutive wet events between adjacent seasons in a moderate scenario is as follows: The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from dry to wet seasons between adjacent seasons: The expression for the probability of vegetation loss under severe stress scenarios, specifically the stress of a transition from wet to dry periods between adjacent seasons: The expression for the probability of vegetation loss under severe drought stress between adjacent seasons: The expression for the probability of vegetation loss under severe stress conditions of consecutive wet events between adjacent seasons: In the formula, Indicates an NDVI sequence; Indicates SPI sequence and NDVI sequence; The marginal distribution function representing the NDVI data sequence; Indicates NDVI <NDVI 40th Scenario of vegetation loss; and Represents the marginal distribution function of two seasonal SPI sequences; Represents the joint distribution function of the two seasonal SPI sequences; This represents the joint distribution function of the SPI and NDVI sequences for two seasons; 、 These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from dry to wet between adjacent seasons. , These represent the probability of vegetation loss under moderate and severe stress scenarios, where vegetation transitions from wet to dry between adjacent seasons. , These represent the probability of vegetation loss under consecutive drought stress between adjacent seasons under moderate and severe scenarios, respectively. , These represent the probability of vegetation loss under continuous wet stress between adjacent seasons in moderate and severe scenarios, respectively.
9. The method for assessing the risk of vegetation loss under the stress of sudden shift from drought to flood as described in claim 1, characterized in that, In step S40, the vegetation loss risk under the combined stress of dry and wet events is calculated by multiplying the occurrence probability and loss probability in step S20 and the exposure degree in step S30.
10. A vegetation loss risk assessment device under combined dry and wet stress events, characterized in that, The system includes a processor and a storage medium, wherein the processor loads and executes instructions and data in the storage medium to implement the vegetation loss risk assessment method under the stress of sudden shift from drought to flood as described in any one of claims 1 to 9.