Method for evaluating resource utilization coupling response strategy of vegetation to drought

Abstract

The invention belongs to the technical field of drought risk prediction and vegetation influence evaluation, and particularly discloses a vegetation-to-drought resource utilization coupling response strategy evaluation method, which comprises the following steps: acquiring target data; constructing a drought index according to the target data, and extracting a drought event; extracting vegetation total productivity, vegetation net level productivity and evapotranspiration abnormal data before and after the drought event, and calculating response intensity of vegetation variables; calculating the average variation of the light energy utilization efficiency, the carbon utilization efficiency and the moisture utilization efficiency; comprehensively considering the multivariable resistance-recovery state of the vegetation physiological process and the regulating variable of the resource utilization efficiency of the vegetation in response to drought stress, and analyzing the drought event resistance-recovery response capability of the vegetation. The method can more accurately and objectively understand the physiological mechanism difference of the vegetation to the drought stress response.

Description

Technical Field

[0001] This invention relates to the field of drought risk prediction and vegetation impact assessment technology, and in particular to a method for assessing the coupled resource utilization response strategy of vegetation to drought. Background Technology

[0002] Vegetation interacts with the climate system and serves as a sensitive indicator of ecosystem responses to climate change, playing a crucial role in global hydrological cycles, land-atmosphere energy exchange, and climate regulation. With intensifying global warming, the frequency and intensity of extreme weather events are increasing, and drought events are becoming more frequent, placing greater pressure and challenges on vegetation ecological functions and terrestrial ecosystem carbon sinks. In-depth research into vegetation's response strategies to drought is essential for further understanding the mechanisms by which ecosystems and carbon cycle processes respond to climate change and for developing effective mitigation and adaptation strategies.

[0003] According to literature review, current analyses of vegetation responses to drought mainly focus on response characteristics. Studies show that the impact of drought on various vegetation indices exhibits widespread spatiotemporal heterogeneity, with these differences closely related to the severity of drought events, climatic zones, vegetation types, and time scales. The essence of this spatiotemporal heterogeneity lies in how vegetation regulates water, light, and carbon resource utilization at the ecosystem level to cope with drought stress. This is closely related to vegetation physiological processes such as photosynthesis, respiration, and transpiration. However, current research largely focuses on changes in vegetation index responses under drought conditions, neglecting the trade-offs and synergistic effects of vegetation physiological processes. There is a lack of research on vegetation response strategies to drought, making it difficult to comprehensively reflect the differences in vegetation response characteristics to drought.

[0004] Therefore, it is still necessary to further explore vegetation physiological processes and accurately characterize vegetation response strategies to drought at the event scale. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method for evaluating the coupled response strategy of vegetation to drought resource utilization, which fully considers the trade-off and synergistic regulation of vegetation's light energy, carbon and water resource utilization, reveals the response strategy mechanism of vegetation to drought from resistance to recovery, and constructs an evaluation scheme for the coupled response strategy of vegetation to drought resource utilization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for evaluating the coupled response strategies of vegetation to drought resource utilization, the method comprising: Acquire target data; wherein, the target data includes radiation data, meteorological data, vegetation productivity data, evapotranspiration data, soil moisture content data, and hydrological data within the target area; Based on the target data, a drought index is constructed, and drought events are extracted; Starting from the physiological processes of vegetation, such as photosynthesis, respiration, and transpiration, we extracted abnormal data on total vegetation productivity, net vegetation productivity, and evapotranspiration before and after the occurrence of drought events, and calculated the response intensity of vegetation variables. Based on the response intensity of vegetation variables and the light energy use efficiency, carbon use efficiency and water use efficiency of vegetation physiological characteristic parameters, the average changes in light energy use efficiency, carbon use efficiency and water use efficiency are calculated. Based on the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, and considering the resistance-recovery status of multiple variables in vegetation physiological processes and the moderating effect of vegetation resource use efficiency in response to drought stress, the resistance-recovery response capacity of vegetation to drought events is analyzed.

[0007] Furthermore, the radiation data includes long-series data on photosynthetically active radiation; the meteorological data includes long-series data on precipitation, temperature, wind speed, and humidity; the vegetation productivity data includes long-series data on total primary productivity and net primary productivity of vegetation; the evapotranspiration data includes long-series data on transpiration, actual evapotranspiration, and potential evapotranspiration; the soil moisture content data includes long-series data on soil moisture content in different strata; and the hydrological data includes long-series data on total runoff.

[0008] Furthermore, based on the target data, a drought index is constructed, and drought events are extracted, including: Construct a regional water surplus sequence, a soil moisture content sequence, and a daily or pentad-scale time series of total runoff. Fit the three sequences according to the selected marginal distribution function to obtain three fitted sequences. The three fitted sequences were input into the C-Vine copula model to construct the joint distribution of regional water surplus, soil moisture content and total runoff. The joint distribution function was then converted into the cumulative probability value of percentiles through parametric statistics to obtain the drought index. Based on the drought index, the data are recombined in chronological order to form a continuous long-term series. By setting a drought threshold, all continuous periods with index values ​​lower than the drought threshold are identified from the long-term series, and each continuous period is defined as a drought event. The identified drought events are merged, including: determining whether the interval between two adjacent drought events is shorter than a preset time window; if the above time condition is met, determining whether the water status has not recovered significantly during the interval, based on the drought index always being lower than a preset recovery threshold; if both of the above conditions are met, the two adjacent drought events are merged.

[0009] Furthermore, the marginal distribution function is selected as follows: multiple marginal distributions are fitted to the sequences of regional water surplus, soil moisture content, and total runoff, respectively, and the optimal marginal distribution function is selected for each sequence based on a predetermined statistical goodness-of-fit criterion.

[0010] Furthermore, the response intensity of vegetation variables was calculated using a formula. RI X : (1) In the formula, For duration, These represent the start time and end time of vegetation anomalies, respectively. This represents the index of vegetation physiological process variables at the onset of vegetation anomalies. Variables for vegetation physiological processes X The function that changes with time t It is a time variable.

[0011] Furthermore, based on the response intensity of vegetation variables and the light energy use efficiency, carbon use efficiency, and water use efficiency of vegetation physiological characteristic parameters, the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency are calculated using the following formulas: (2) In the formula, This represents the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency during the drought resistance phase of vegetation response. This represents the change in photosynthetically active radiation. These represent the response intensity of vegetation in the drought resistance stage, specifically the total primary productivity, net primary productivity, and evapotranspiration of vegetation. PAR Photosynthetically active radiation, GPP Total primary productivity of vegetation, ET It is due to evaporation.

[0012] Furthermore, after calculating the average changes in light energy utilization efficiency, carbon utilization efficiency, and water use efficiency, the method further includes: The trade-off synergy degree and trade-off synergy index are used to measure the response strategies of vegetation to the trade-off or synergy of light-carbon-water utilization in response to drought events; wherein the formulas for calculating the trade-off synergy degree and trade-off synergy index are as follows: (3) In the formula, Let and represent the degree of synergy and the synergy index of the trade-off in the efficiency of any two resource utilization methods, respectively. When the value is negative, it indicates that the two resource utilization strategies are trade-offs. When the value is positive, it indicates that it belongs to a cooperative strategy. A higher value indicates a stronger trade-off or synergistic effect. and These represent the average changes in resource utilization efficiency for the i-th and j-th types, respectively.

[0013] Furthermore, based on the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, and comprehensively considering the resistance-recovery status of multivariate vegetation physiological processes and the moderating effect of vegetation resource use efficiency in response to drought stress, the resistance-recovery response capacity of vegetation to drought events is analyzed, including: Considering the entire event-scale process from resistance to recovery under drought stress, including the resistance phase and the recovery phase, the resistance phase includes the rate at which vegetation resists drought. Maximum amplitude and average performance variation The recovery phase includes the recovery speed. Amplitude and average performance variation ; For any vegetation variable X Constructing resistance-recovery state indicators under drought stress By combining the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, the drought resistance-recovery response capacity of vegetation was calculated. .

[0014] Furthermore, the speed at which vegetation resists drought Maximum amplitude Average performance change The recovery phase includes the recovery speed. Amplitude and average performance variation The calculation formula is as follows: (4) (5) In the formula, The duration of the resistance phase, The time when vegetation anomalies reach their extreme point. This is an extreme point of vegetation index anomaly. This is the derivative of the change in vegetation physiological process variables over time. The recovery phase lasted for a period of time. The end time of vegetation anomaly. This is an index of vegetation physiological process variables at the end of vegetation abnormality.

[0015] Furthermore, the resistance-recovery state index under drought stress and vegetation's drought resistance-recovery response The calculation formula is: (6) (7) In the formula, These are indicators of vegetation's total primary productivity, net primary productivity, and evapotranspiration resistance-recovery status under drought stress. This refers to the overall changes in light energy use efficiency, carbon use efficiency, and water use efficiency in response to drought events. This represents an extreme value point of the vegetation index.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention starts from the physiological activities of plant photosynthesis, respiration and transpiration, and quantitatively analyzes the "light-carbon-water" resource utilization trade-off and synergistic response strategy of vegetation to drought resistance-recovery based on changes in light energy use efficiency, carbon use efficiency and water use efficiency. Previous studies have mostly focused on the response of vegetation indices to drought stress, which is difficult to effectively reveal the mechanism of the difference in drought response among different types of vegetation. The method for assessing the whole process of vegetation resistance-recovery response capacity to drought proposed in this invention can more accurately and objectively understand the differences in the physiological mechanisms of vegetation response to drought stress. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for evaluating a vegetation resource utilization coupled response strategy in response to drought, provided as an embodiment of the present invention; Figure 2 Spatial distribution map of vegetation's synergistic response strategy to drought in 2022, considering the trade-offs of light, carbon, and water resources, provided for embodiments of the present invention; Figure 3 A diagram showing the results of the synergistic response strategy for the trade-off of "light-carbon-water" resource utilization in drought for different vegetation types, as provided in an embodiment of the present invention. Figure 4 The diagram shows the results of the resistance-recovery response capabilities of different vegetation types to drought events, as provided in the embodiments of the present invention. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] This invention provides a method for evaluating the coupled response strategies of vegetation to drought resource utilization, such as... Figure 1 The diagram shows a flowchart of a method for evaluating the coupled response strategy of vegetation to drought resource utilization provided in an embodiment of the present invention. This method can be implemented through the following steps S10 to S50.

[0022] S10: Acquire target data, including radiation data, meteorological data, vegetation productivity data, evapotranspiration data, soil moisture content data, and hydrological data within the target area.

[0023] Step S10 is the data preparation step. During implementation, radiation data, meteorological data, vegetation productivity data, evapotranspiration data, soil moisture content data, and hydrological data are collected and organized within the target area. In this embodiment, the target area is the region to be studied, specifically taking the Yangtze River Basin as an example. Various types of data within the study area are collected and organized. Radiation data includes long-series data on photosynthetically active radiation; meteorological data includes long-series data on precipitation, temperature, wind speed, and humidity; vegetation productivity data includes long-series data on total primary productivity and net primary productivity; evapotranspiration data includes long-series data on transpiration, actual evapotranspiration, and potential evapotranspiration; soil moisture content data includes long-series data on soil moisture content at different strata; and hydrological data includes long-series data on total runoff.

[0024] This embodiment takes the Yangtze River Basin as the study area and collects and organizes ERA5-Land datasets from 2001 onwards, including photosynthetically active radiation, precipitation, temperature, wind speed, humidity, transpiration, actual evapotranspiration, potential evapotranspiration, soil water, and runoff. It also collects vegetation productivity datasets from 2001 onwards retrieved from remote sensing products, including process-based gross primary productivity and net primary productivity of vegetation.

[0025] S20: Based on the target data, construct a drought index and extract drought events.

[0026] In step S20, drought indicators are constructed based on hydrological, meteorological, and soil data in the study area, including precipitation, potential evapotranspiration, soil water, and runoff. Drought events are extracted and the occurrence and development patterns of drought are analyzed.

[0027] Specifically, time series analysis was conducted on daily or pentad scales for regional water surplus (difference between precipitation and potential evapotranspiration), soil moisture content, and total runoff. First, univariate probability distribution types, such as the Generalized Extreme Value (GEV) distribution and the three-parameter log-logistic distribution, were selected as marginal distribution functions. Marginal distribution functions were fitted to the three series respectively, and the Kolmogorov-Smirnov (KS) test and the Akaike Information Criterion (AIC) were used as evaluation criteria to select the most suitable marginal distribution function. Furthermore, the C-Vine Copula was used to construct the joint distribution of regional water surplus, soil moisture content, and total runoff. The joint distribution function was then further transformed into cumulative percentile probability values ​​through parametric statistics to obtain the drought index.

[0028] Based on the constructed drought index, the data are recombined chronologically to form a continuous long-term series for identifying drought events. A minimum time period is set; when the interval between two adjacent drought events is short (less than the set minimum time period) and there is no significant water recovery during the interval, the two drought events are considered to be related. These two drought events are then merged to analyze drought characteristics, including the frequency, duration, and timing of drought occurrence.

[0029] It should be noted that the method for identifying drought events is as follows: based on the drought index, the data is recombined chronologically to form a continuous long-term series. By setting a drought threshold, all consecutive periods in the long-term series where the index value is lower than the drought threshold are identified, and each consecutive period is defined as a drought event. The drought threshold is a preset fixed value or a statistical percentile value, the selection of which depends on the climatic background of the study area and the conventional definition of drought. For example, the 20th percentile of the comprehensive drought index series can be selected as the drought threshold. In specific operation, the entire comprehensive drought index time series is traversed. When the index value first falls below the drought threshold, it is marked as the start time of the drought event; when the index value rises and stabilizes above the drought threshold after being continuously below it, it is marked as the end time of the drought event. The entire period from the start time to the end time constitutes a complete drought event, and its duration, average intensity, and minimum intensity can be used as characteristic quantities for subsequent analysis. To further accurately characterize independent drought processes, identified events can be merged: a minimum interval is set, and if the interval between two adjacent drought events is shorter than this minimum interval, and the peak value of the composite drought index fails to recover to a preset recovery threshold (e.g., the 50th percentile) within this interval, then the two drought events are considered to belong to the same drought process and should be merged into a single composite drought event.

[0030] In this embodiment, marginal distribution functions were fitted to the regional water surplus, soil moisture content, and total runoff sequences at eight-day timescales for each grid cell in the study area from 2001 to 2023. Using KS and AIC tests, the three-parameter log-logistic, generalized extreme value distribution, and log-normal distribution were selected for fitting the regional water surplus, soil moisture content, and total runoff sequences, respectively. A C-Vine copula model was chosen to simulate the complex dependency structure among the three sequences, simplifying the estimation of the high-dimensional copula function into a series of two-dimensional copula function estimates to obtain the comprehensive drought index sequence. Further drought merging was performed, and based on this, long-term results of short-timescale drought events in the study area were obtained.

[0031] S30: Starting from the physiological processes of photosynthesis, respiration and transpiration of vegetation, extract the abnormal data of total vegetation productivity, net vegetation productivity and evapotranspiration before and after the occurrence of drought events, and calculate the response intensity of vegetation variables.

[0032] Step S30 starts from the physiological processes of vegetation, such as photosynthesis, respiration, and transpiration, and extracts abnormal data on total vegetation productivity, net vegetation productivity, and evapotranspiration before and after the occurrence of drought events to analyze the differences in the physiological response characteristics of vegetation to drought.

[0033] Specifically, based on the drought events extracted in step S20, and starting from the vegetation physiological processes of photosynthesis, respiration, and transpiration, key variables including total primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) were selected for time series matching. The changes in the initial point of vegetation anomaly, the extreme point of vegetation anomaly occurrence, and the point of vegetation recovery after the drought event were considered, dividing the process into two stages: a resistance stage and a recovery stage. This response state includes a positive response, i.e., the vegetation index shows a trend of first increasing and then decreasing after the drought, and a negative response, i.e., the vegetation index shows a trend of first decreasing and then increasing after the drought. Based on this, the differences in the response states of different vegetation physiological process variables (GPP, NPP, ET) were studied, and the response intensity of the vegetation variables was analyzed. The calculation formula is as follows: (1) In the formula, For duration, These represent the start time and end time of vegetation anomalies, respectively. This represents the index of vegetation physiological process variables at the onset of vegetation anomalies. Variables for vegetation physiological processes X (GPP, NPP, ET) are functions that change over time.

[0034] In this embodiment, the entire time series of vegetation data (including GPP, NPP, and ET) for each grid point in the study area from 2001 to 2023, collected every eight days, was first processed to remove linear trends and eliminate the influence of atmospheric CO2 concentration and other factors on vegetation growth. Then, the entire time series was divided into 46 subsequences according to the same eight-day period each year, and standardized outlier calculations were performed on each subsequence. This allowed for the extraction of relative anomalous signals under the same seasonal background, avoiding interference from seasonal variations. The first appearance of a negative anomaly after drought was defined as the initial response stage; the period from the initial response to the maximum negative anomaly was defined as the vegetation resistance stage; and the period from the extreme response point to the recovery of a positive anomaly was defined as the end of the vegetation recovery stage.

[0035] S40: Based on the response intensity of vegetation variables and the light energy use efficiency, carbon use efficiency and water use efficiency of vegetation physiological characteristic parameters, calculate the average changes in light energy use efficiency, carbon use efficiency and water use efficiency.

[0036] In step S40, based on the vegetation physiological characteristic parameters of light energy utilization efficiency, carbon utilization efficiency and water utilization efficiency, the collaborative response strategy of vegetation in the "light-carbon-water" resource utilization trade-off during the entire drought event is analyzed.

[0037] Specifically, based on the resource utilization efficiency theory, this study analyzes the changes in light energy use efficiency (LUE=GPP / PAR, where PAR is photosynthetically active radiation), carbon use efficiency (CUE=NPP / GPP), and water use efficiency (WUE=GPP / ET), which characterize the physiological characteristics of vegetation in terrestrial ecosystems, to examine the coordinated response strategy of vegetation in the "light-carbon-water" resource utilization trade-off during the entire drought process, specifically in the resistance and recovery phases. These three indicators reveal the resource allocation strategies and physiological adaptation mechanisms of plants under drought and heat stress from the perspectives of water, energy, and carbon metabolism, respectively. Their coupled analysis can more systematically explain the trade-offs and adaptation processes of vegetation during drought events. Taking the resistance phase as an example, the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency during the drought resistance phase are calculated using the following formulas: (2) In the formula, This represents the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency during the drought resistance phase of vegetation response. This represents the intensity of vegetation response during the drought resistance phase, i.e., the average change relative to the initial state of vegetation anomaly. This represents the change in photosynthetically active radiation.

[0038] The trade-off synergy degree is used to measure the response strategy of vegetation to the trade-off or synergy of "light-carbon-water" utilization in response to drought events. The calculation formula is as follows: (3) In the formula, Let and represent the degree of synergy and the synergy index of the trade-off in the efficiency of any two resource utilization methods, respectively. When the value is negative, it indicates that the two resource utilization strategies are trade-offs. When the value is positive, it indicates that it belongs to a cooperative strategy. A higher value indicates a stronger trade-off or synergistic effect. This represents the change in the efficiency of light energy, carbon, or water utilization.

[0039] like Figure 2 The figure shows the spatial distribution of vegetation's synergistic response strategies to drought in 2022, focusing on the trade-offs in light, carbon, and water resources. This figure, based on the trade-off synergy degree and index calculated in the preceding steps, visually demonstrates the application results of the method of this invention at the regional scale. Specifically, Figure 2 Neutron plots (a), (c), and (e) show the spatial distribution of the trade-off synergy (TDS) between light energy utilization efficiency and carbon utilization efficiency (LUE-CUE), water utilization efficiency and carbon utilization efficiency (WUE-CUE), and water utilization efficiency and light energy utilization efficiency (WUE-LUE), respectively. Figure 2The study clearly shows that vegetation response strategies to drought exhibit significant spatial heterogeneity across different geographical regions. For example, in the southern region with better water conditions, the TDS values ​​for water use efficiency and carbon use efficiency are mostly positive (primarily a synergistic strategy), indicating that vegetation can coordinate and improve the efficiency of multiple resource utilization to cope with drought. In contrast, in the northwestern region with severe drought stress, the TDS values ​​for water use efficiency and carbon use efficiency are mostly negative (primarily a trade-off strategy), indicating that when resources are limited, vegetation tends to sacrifice one efficiency (such as carbon energy use efficiency) to maintain the stability of another key efficiency (such as water use efficiency).

[0040] Accordingly, Figure 2 Neutron maps (b), (d), and (f) illustrate the spatial distribution of the Trade-Off Synergy Index (TSI) corresponding to the aforementioned resource efficiency combinations. The level of the TSI reflects the strength of the trade-off or synergy relationship, and its spatial pattern reveals the differences in the flexibility of vegetation physiological regulation strategies in different ecosystems. Therefore, the assessment method provided by this invention can not only quantitatively analyze the physiological and ecological strategies of vegetation in response to drought at the pixel scale, but also visualize the spatial patterns at the regional and even global scales, thereby revealing the differentiated adaptation mechanisms of vegetation to drought stress in a macroscopic and profound way—a result that traditional methods focusing only on changes in vegetation indices cannot achieve.

[0041] like Figure 3 The figure shows the results of the coordinated response strategies of different vegetation types in the "light-carbon-water" resource utilization trade-offs in response to drought. Through statistical analysis of the calculation results of five typical vegetation types—woodland, shrubland, grassland, wetland, and cultivated land—in the study area, this figure clearly demonstrates the differences in resource utilization strategies adopted by different vegetation types in response to drought stress.

[0042] Specifically, the results illustrated are based on the trade-off synergy degree and trade-off synergy index calculated by formula (3), and systematically compare the response characteristics of different vegetation types in three sets of relationships: water use efficiency versus light use efficiency (WUE-LUE), water use efficiency versus carbon use efficiency (WUE-CUE), and light use efficiency versus carbon use efficiency (LUE-CUE). Figure 3The results show that wetlands exhibit positive TDS values ​​in most combinations, tending to adopt synergistic strategies, indicating a strong resource coordination capacity. Grasslands, however, show more negative TDS values ​​under drought stress, demonstrating a clear trade-off strategy and reflecting the limitations of their physiological regulation under resource-constrained conditions. Furthermore, comparing TSI values ​​among different combinations reveals that water use efficiency and light use efficiency exhibit high TSI values, indicating a strong coupling effect. Therefore, the method of this invention not only enables spatial pattern analysis but also quantitatively reveals the differentiated physiological adaptation strategies of different ecosystems to drought stress from the perspective of vegetation functional types. This result deepens the understanding of vegetation drought response mechanisms and provides a precise scientific basis for zoned and classified adaptive management of ecosystems.

[0043] S50: Based on the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, and taking into account the resistance-recovery status of multiple variables in vegetation physiological processes, the regulation of vegetation resource use efficiency in response to drought stress, the resistance-recovery response capacity of vegetation to drought events is analyzed.

[0044] In step S50, the entire event-scale process from resistance to recovery under drought stress is considered, with the resistance phase including the rate of vegetation resistance to drought. Maximum amplitude and average performance variation The recovery phase includes the recovery speed. Amplitude Average performance change The calculation formula is as follows: (4) (5) In the formula, The duration of the resistance phase, The time when vegetation anomalies reach their extreme point. This is an extreme point of vegetation index anomaly. This is the derivative of the change in vegetation physiological process variables over time. The recovery phase lasted for a period of time. The end time of vegetation anomaly. This is an index of vegetation physiological process variables at the end of vegetation abnormality.

[0045] Based on this, for any vegetation variable X Constructing resistance-recovery state indicators under drought stress This study uses the Euclidean norm to comprehensively consider the resistance-recovery state of multivariates in vegetation physiological processes. It also considers the moderating effects of light use efficiency, carbon use efficiency, and water use efficiency in response to drought stress to characterize the vegetation's work strategies in resisting drought events, thus assessing the vegetation's resistance-recovery response capacity to drought. The calculation is as follows: (6) (7) In the formula, These are indicators of GPP, NPP, and ET's resistance-recovery status under drought stress. CI represents the overall change in light energy use efficiency, carbon use efficiency, and water use efficiency in response to drought events. A higher CI value indicates a stronger drought resistance-recovery response capability of the vegetation.

[0046] like Figure 4 The figure shows the results of the resistance-recovery response capabilities of different vegetation types to drought events. Based on the resistance-recovery response capability CI values ​​calculated using formula (7), this figure statistically analyzes five typical vegetation types—forest, shrubland, grassland, wetland, and cultivated land—to visually compare the overall resilience of different vegetation types to drought stress. Specifically, Figure 4 The results showed that the CI value of wetlands was significantly higher than that of other vegetation types, indicating that they exhibited the strongest resistance-recovery capacity during drought events, which is attributed to their water conditions. The CI values ​​of shrubs and grasslands were moderate, reflecting a certain degree of environmental adaptability, but relatively limited resistance-recovery capacity. The CI value of cultivated land was low, indicating its vulnerability to drought stress. This embodiment... Figure 4 This invention confirms that the assessment method provided can effectively quantify the comprehensive response capabilities of different vegetation types to drought events and accurately identify vulnerable links and dominant types in ecosystems. This result overcomes the limitations of traditional methods that only describe response phenomena, revealing differences in drought adaptability of vegetation throughout the entire resistance-recovery process. It provides crucial quantitative decision-making support for regional ecosystem protection, vegetation restoration planning, and drought-resistant crop breeding.

[0047] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating the coupled response strategies of vegetation to drought resource utilization, characterized in that, The method includes: Acquire target data; wherein, the target data includes radiation data, meteorological data, vegetation productivity data, evapotranspiration data, soil moisture content data, and hydrological data within the target area; Based on the target data, a drought index is constructed, and drought events are extracted; Starting from the physiological processes of vegetation, such as photosynthesis, respiration, and transpiration, we extracted abnormal data on total vegetation productivity, net vegetation productivity, and evapotranspiration before and after the occurrence of drought events, and calculated the response intensity of vegetation variables. Based on the response intensity of vegetation variables and the light energy use efficiency, carbon use efficiency and water use efficiency of vegetation physiological characteristic parameters, the average changes in light energy use efficiency, carbon use efficiency and water use efficiency are calculated. Based on the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, and considering the resistance-recovery status of multiple variables in vegetation physiological processes and the moderating effect of vegetation resource use efficiency in response to drought stress, the resistance-recovery response capacity of vegetation to drought events is analyzed.

2. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, The radiation data includes long-series data on photosynthetically active radiation; the meteorological data includes long-series data on precipitation, temperature, wind speed, and humidity; the vegetation productivity data includes long-series data on total primary productivity and net primary productivity; the evapotranspiration data includes long-series data on transpiration, actual evapotranspiration, and potential evapotranspiration; the soil moisture content data includes long-series data on soil moisture content at different strata; and the hydrological data includes long-series data on total runoff.

3. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, Based on the target data, a drought index is constructed, and drought events are extracted, including: Construct a regional water surplus sequence, a soil moisture content sequence, and a daily or pentad-scale time series of total runoff. Fit the three sequences according to the selected marginal distribution function to obtain three fitted sequences. The three fitted sequences were input into the C-Vine copula model to construct the joint distribution of regional water surplus, soil moisture content and total runoff. The joint distribution function was then converted into the cumulative probability value of percentiles through parametric statistics to obtain the drought index. Based on the drought index, the data are recombined in chronological order to form a continuous long-term series. By setting a drought threshold, all continuous periods with index values ​​lower than the drought threshold are identified from the long-term series, and each continuous period is defined as a drought event. The identified drought events are merged, including: determining whether the interval between two adjacent drought events is shorter than a preset time window; if the above time condition is met, determining whether the water status has not recovered significantly during the interval, based on the drought index always being lower than a preset recovery threshold; if both of the above conditions are met, the two adjacent drought events are merged.

4. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 3, characterized in that, The marginal distribution function is selected by fitting multiple marginal distributions to the sequences of regional water surplus, soil moisture content, and total runoff, and selecting the optimal marginal distribution function for each sequence based on a predetermined statistical goodness-of-fit criterion.

5. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, The response intensity of vegetation variables is calculated using a formula. RI X : （1） In the formula, For duration, These represent the start time and end time of vegetation anomalies, respectively. This represents the index of vegetation physiological process variables at the onset of vegetation anomalies. Variables for vegetation physiological processes X The function that changes with time t It is a time variable.

6. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, Based on the response intensity of vegetation variables and the light energy use efficiency, carbon use efficiency, and water use efficiency of vegetation physiological characteristic parameters, the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency are calculated using the following formulas: （2） In the formula, This represents the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency during the drought resistance phase of vegetation response. This represents the change in photosynthetically active radiation. These represent the response intensity of vegetation in the drought resistance stage, specifically the total primary productivity, net primary productivity, and evapotranspiration of vegetation. PAR Photosynthetically active radiation, GPP Total primary productivity of vegetation, ET It is due to evaporation.

7. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, After calculating the average changes in light energy utilization efficiency, carbon utilization efficiency, and water use efficiency, the method further includes: The trade-off synergy degree and trade-off synergy index are used to measure the response strategies of vegetation to the trade-off or synergy of light-carbon-water utilization in response to drought events; wherein the formulas for calculating the trade-off synergy degree and trade-off synergy index are as follows: （3） In the formula, Let and represent the degree of synergy and the synergy index of the trade-off in the efficiency of any two resource utilization methods, respectively. When the value is negative, it indicates that the two resource utilization strategies are trade-offs. When the value is positive, it indicates that it belongs to a cooperative strategy. A higher value indicates a stronger trade-off or synergistic effect. and These represent the average changes in resource utilization efficiency for the i-th and j-th types, respectively.

8. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 1, characterized in that, Based on the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, and comprehensively considering the resistance-recovery status of multivariate vegetation physiological processes, the moderating effect of vegetation resource use efficiency in response to drought stress is analyzed to assess the vegetation's resistance-recovery response to drought events, including: Considering the entire event-scale process from resistance to recovery under drought stress, including the resistance phase and the recovery phase, the resistance phase includes the rate at which vegetation resists drought. Maximum amplitude and average performance variation The recovery phase includes the recovery speed. Amplitude and average performance variation ; For any vegetation variable X Constructing a resistance-recovery state index under drought stress By combining the average changes in light energy use efficiency, carbon use efficiency, and water use efficiency, the drought resistance-recovery response capacity of vegetation was calculated. .

9. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 8, characterized in that, The speed at which vegetation resists drought Maximum amplitude Average performance change The recovery phase includes the recovery speed. Amplitude and average performance variation The calculation formula is as follows: （4） （5） In the formula, The duration of the resistance phase, The time when vegetation anomalies reach their extreme point. This is an extreme point of vegetation index anomaly. This is the derivative of the change in vegetation physiological process variables over time. The recovery phase lasted for a period of time. The end time of vegetation anomaly. This represents the vegetation physiological process variable index at the end of the vegetation abnormality.

10. The method for evaluating the coupled response strategy of vegetation to drought resource utilization according to claim 8, characterized in that, The resistance-recovery status index under drought stress and vegetation's drought resistance-recovery response The calculation formula is: （6） （7） In the formula, These are indicators of vegetation's total primary productivity, net primary productivity, and evapotranspiration resistance-recovery status under drought stress. This refers to the overall changes in light energy use efficiency, carbon use efficiency, and water use efficiency in response to drought events. This represents an extreme value point of the vegetation index.

Images