Method for identifying dominant drought factors of vegetation loss under composite drought

Abstract

The invention discloses a method for identifying dominant drought factors of vegetation loss under composite drought, which comprises the following steps: calculating a meteorological drought index, an agricultural drought index, a temperature drought index and vegetation net primary productivity according to multi-source remote sensing data and meteorological observation data of a research area; determining the response time scale of the vegetation net primary productivity to various types of drought; calculating conditional probabilities of the vegetation net primary productivity loss in different drought combination states, and fitting by fixing two types of drought states and changing the third type of drought state to obtain a sensitivity coefficient of the vegetation net primary productivity loss probability along with the change of each type of drought state; according to the absolute value comparison result of the sensitivity coefficient, identifying the dominant drought factor, and judging the action direction of the dominant drought factor according to the positive and negative of the sensitivity coefficient. According to the method, the limitation of single drought research is broken through, and the action direction of each factor in composite drought on vegetation loss can be accurately identified.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ecological environment monitoring and climate change impact assessment, and particularly relates to a method for identifying a dominant drought factor of vegetation loss under compound drought. BACKGROUND

[0002] With the intensification of global climate change, the frequency, intensity and duration of drought events have significantly increased, causing serious impacts on terrestrial ecosystems, especially vegetation. Among the types of drought, meteorological drought caused by precipitation shortage, agricultural drought determined by soil water deficit, and temperature drought caused by high temperature often do not occur independently, but are interrelated, successive or simultaneous, forming more complex compound drought events. Vegetation net primary productivity (NPP) as a key indicator of vegetation carbon sequestration capacity is the basis of biological energy flow and nutrient cycling, and plays an important role in global carbon balance and climate change. Accurately identifying which drought factor plays a dominant role in vegetation loss in compound drought events is of great scientific significance for developing precise ecological protection and water resource management strategies. In recent years, scholars have conducted extensive research on the influencing factors of vegetation net primary productivity, but existing research has mostly focused on the assessment of vegetation loss under single drought stress, with less research on the simultaneous effects of multiple drought stresses, making it difficult to accurately assess the impact mechanism of compound drought on vegetation loss.

[0003] The existing research methods have the following technical problems: first, existing technologies mostly focus on the impact of single meteorological drought, agricultural drought or temperature drought on vegetation net primary productivity, ignoring the compound superposition effect of multiple drought factors, and cannot reflect the loss pattern of vegetation net primary productivity under real drought scenarios; second, there is a lack of standardized vegetation net primary productivity loss probability calculation model under compound drought, making it difficult to quantitatively describe the dynamic relationship between drought intensification and vegetation net primary productivity loss probability; third, existing methods cannot accurately distinguish the contribution weight of each drought factor to vegetation net primary productivity loss in compound drought, nor can they clearly determine whether each factor promotes or alleviates vegetation loss, leading to a lack of targetedness in ecological drought prevention and control. The above problems make it difficult for existing technologies to systematically and quantitatively assess the response mechanism of vegetation net primary productivity to multiple stresses of compound drought. SUMMARY

[0004] To solve the above technical problems, the application provides a method for identifying a dominant drought factor of vegetation loss under compound drought, comprising:

[0005] According to the multi-source remote sensing data and meteorological observation data of the study area, calculate the meteorological drought index, agricultural drought index, temperature drought index and vegetation net primary productivity;

[0006] According to the correlation coefficients of the meteorological drought index, the agricultural drought index, and the temperature drought index with the net primary productivity of vegetation, a response time scale of the net primary productivity of vegetation to each type of drought is determined;

[0007] According to the drought index under the response time scale, a conditional probability of loss of the net primary productivity of vegetation under different combinations of drought states is calculated, and a sensitive coefficient of the loss probability of the net primary productivity of vegetation varying with each type of drought state is fitted by fixing two types of drought states and varying a third type of drought state;

[0008] According to a comparison result of absolute values of the sensitive coefficients, a dominant drought factor affecting the loss of the net primary productivity of vegetation under compound drought is identified, and an action direction of the dominant drought factor is determined according to a positive or negative of the sensitive coefficient.

[0009] Preferably, the process of calculating the meteorological drought index, the agricultural drought index, the temperature drought index, and the net primary productivity of vegetation comprises:

[0010] After obtaining precipitation data, temperature data, a normalized vegetation index, vegetation classification data, and solar radiation data of a research area, the data is subjected to outlier rejection, spatial matching, and time scale unification processing;

[0011] A standardized precipitation index is calculated as the meteorological drought index according to the precipitation data;

[0012] A standardized soil moisture index is calculated as the agricultural drought index according to soil moisture data;

[0013] A standardized temperature index is calculated as the temperature drought index according to the temperature data;

[0014] The precipitation data, the temperature data, the vegetation classification data, the solar radiation data, and the normalized vegetation index are input into a CASA model to calculate the net primary productivity of vegetation.

[0015] Preferably, the process of determining the response time scale of the net primary productivity of vegetation to each type of drought comprises:

[0016] Correlation coefficients of the standardized precipitation index and the net primary productivity of vegetation at 1 to 12 month scales are calculated respectively to obtain a correlation coefficient sequence;

[0017] A maximum correlation coefficient is selected from the correlation coefficient sequence, and a month scale corresponding to the maximum correlation coefficient is determined as the response time scale of the net primary productivity of vegetation to meteorological drought;

[0018] By using the same method, a response time scale of the net primary productivity of vegetation to agricultural drought and a response time scale of the net primary productivity of vegetation to temperature drought are obtained respectively.

[0019] Preferably, the process of calculating the conditional probability of the loss of net primary productivity of vegetation under different combinations of drought states comprises:

[0020] determining the 40th percentile of the sequence of net primary productivity of vegetation as a loss threshold value;

[0021] defining a loss state as a state of net primary productivity of vegetation less than the 40th percentile;

[0022] calculating a loss probability according to the drought index under the response time scale by a conditional probability formula;

[0023] wherein the conditional probability formula comprises a combination condition of the agricultural drought state, the meteorological drought state and the temperature drought state.

[0024] Preferably, the process of fitting to obtain a sensitivity coefficient of the loss probability of net primary productivity of vegetation varying with each type of drought state comprises:

[0025] fixing the agricultural drought state and the temperature drought state, and decreasing the meteorological drought index from 0 to -2 in a preset interval to simulate a process of intensifying drought;

[0026] calculating a sequence of loss probabilities corresponding to different meteorological drought states according to the conditional probability formula;

[0027] performing curve fitting on the sequence of loss probabilities and the corresponding meteorological drought index by a least square method to obtain a slope coefficient of the fitted curve, and taking the slope coefficient as a sensitivity coefficient of the loss probability to the meteorological drought.

[0028] Preferably, the expression of the fitted curve obtained by the curve fitting by the least square method is a linear function;

[0029] wherein a constant term represents a basic loss probability, and a coefficient of a linear term is the sensitivity coefficient;

[0030] an absolute value of the sensitivity coefficient represents a degree of sensitivity of the loss probability to the corresponding type of drought;

[0031] a positive or negative of the sensitivity coefficient represents a direction of the effect of the type of drought on the loss probability.

[0032] Preferably, the process of identifying a dominant drought factor affecting the loss of net primary productivity of vegetation under a compound drought comprises:

[0033] respectively calculating absolute values of the meteorological drought sensitivity coefficient, the agricultural drought sensitivity coefficient and the temperature drought sensitivity coefficient;

[0034] comparing the absolute values, and determining a drought type corresponding to a maximum value as the dominant drought factor.

[0035] Preferably, the process of judging the action direction of the dominant drought factor according to the positive and negative of the sensitive coefficient comprises:

[0036] When the meteorological drought is determined as the dominant factor, if the corresponding meteorological drought sensitive coefficient is negative, it is determined that the meteorological drought aggravates the loss; if it is positive, it is determined that the meteorological drought inhibits the loss;

[0037] When the agricultural drought is determined as the dominant factor, if the corresponding agricultural drought sensitive coefficient is negative, it is determined that the agricultural drought aggravates the loss; if it is positive, it is determined that the agricultural drought inhibits the loss;

[0038] When the temperature drought is determined as the dominant factor, if the corresponding temperature drought sensitive coefficient is positive, it is determined that the temperature drought aggravates the loss; if it is negative, it is determined that the temperature drought inhibits the loss.

[0039] Preferably, the meteorological drought index adopts the standardized precipitation evapotranspiration index or the Palmer drought index instead of the standardized precipitation index;

[0040] The agricultural drought index adopts the soil relative humidity or the crop water deficit index instead of the standardized soil humidity index;

[0041] The temperature drought index adopts the heat wave duration or the extreme high temperature cumulative value instead of the standardized temperature index.

[0042] Compared with the prior art, the present application has the following advantages and technical effects:

[0043] The systematic analysis framework constructed by the present application can integrate the meteorological, agricultural and temperature drought factors into a unified analysis system, break through the limitation of single drought research, and truly reflect the loss rule of the net primary productivity of vegetation under the complex drought scenario; the sensitive coefficient is extracted through curve fitting, a standardized loss probability quantization model is established, and quantitative description of the dynamic correlation between the change of the drought state and the loss probability of the net primary productivity of vegetation is realized; the absolute value comparison mechanism of the sensitive coefficient can accurately identify the contribution weight of different factors in the complex drought, and the action direction of each factor on the vegetation loss is clear, which provides accurate basis for the prevention and control of ecological drought. BRIEF DESCRIPTION OF DRAWINGS

[0044] The drawings constituting a part of this application are used to provide further understanding of the application, the illustrative embodiments of the application and the description thereof are used to explain the application, and do not constitute improper limitation on the application. In the drawings:

[0045] Figure 1 It is a method flowchart of the embodiment of the present application;

[0046] Figure 2 It is a data processing process schematic diagram of the embodiment of the present application;

[0047] Figure 3 This is a schematic diagram illustrating the response time of vegetation net primary productivity to drought in an embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram illustrating the sensitivity of vegetation net primary productivity loss to each drought under the quantitative combined drought conditions according to an embodiment of the present invention.

[0049] Figure 5 This is a schematic diagram illustrating the dominant drought factors and their effects on vegetation loss under combined drought, according to an embodiment of the present invention. Detailed Implementation

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] like Figure 1 As shown, this embodiment provides a method for identifying the dominant drought factor for vegetation loss under combined drought, including:

[0053] Based on multi-source remote sensing data and meteorological observation data of the study area, the meteorological drought index, agricultural drought index, temperature drought index, and net primary productivity of vegetation were calculated.

[0054] Based on the correlation coefficients between the meteorological drought index, agricultural drought index, and temperature drought index and the net primary productivity of vegetation, the response time scale of net primary productivity of vegetation to various types of drought was determined.

[0055] Based on the drought index at the response time scale, the conditional probability of vegetation net primary productivity loss under different drought combinations is calculated. By fixing two types of drought states and changing the third type of drought state, the sensitivity coefficient of vegetation net primary productivity loss probability as a function of various drought states is obtained.

[0056] Based on the comparison of the absolute values ​​of the sensitivity coefficients, the dominant drought factors affecting the net primary productivity loss of vegetation under combined drought are identified, and the direction of the dominant drought factors is determined based on the positive or negative value of the sensitivity coefficients.

[0057] This embodiment constructs a full-chain analysis framework of "vegetation net primary productivity calculation - response time identification - loss probability quantification - dominant factor identification", which incorporates meteorological drought index, agricultural drought index and temperature drought index into a unified analysis system. It realizes a systematic and standardized assessment of the response mechanism of vegetation net primary productivity loss under compound drought, and solves the technical problem of existing technologies that only focus on a single drought type and ignore the compound superposition effect of multiple droughts. It can truly reflect the loss pattern of vegetation net primary productivity under actual drought scenarios.

[0058] Furthermore, such as Figure 2 As shown, the process of calculating the meteorological drought index, agricultural drought index, temperature drought index, and net primary productivity of vegetation includes:

[0059] After acquiring precipitation data, temperature data, normalized vegetation index, vegetation classification data and solar radiation data of the study area, the data were processed for outlier removal, spatial matching and time scale unification.

[0060] A standardized precipitation index is calculated based on precipitation data to serve as a meteorological drought index.

[0061] A standardized soil moisture index is calculated based on soil moisture data to serve as an agricultural drought index.

[0062] A standardized temperature index is calculated based on temperature data to serve as a temperature drought index.

[0063] Precipitation data, temperature data, vegetation classification data, solar radiation data, and normalized vegetation index are input into the CASA model to calculate the net primary productivity of vegetation.

[0064] Furthermore, this embodiment collects gridded data on precipitation, temperature, normalized vegetation index (NDI), vegetation classification, and solar radiation in the study area. Outlier removal and spatial matching are performed on the data to ensure they are processed to the same time scale and spatial resolution. This data is then used to calculate the meteorological drought index (Standardized Precipitation Index SPI), agricultural drought index (Standardized Soil Moisture Index SMI), temperature drought index (Standardized Temperature Index STI), and net primary productivity (NPP). The NPP calculation employs the CASA model, which considers physical mechanisms, incorporates remote sensing technology, and boasts high accuracy. Input data include precipitation, temperature, vegetation cover type, total solar radiation, and NDI.

[0065] This embodiment collects precipitation data, temperature data, normalized vegetation index, vegetation classification data, and solar radiation data from the study area, and performs outlier removal, spatial matching, and temporal scale unification processing. The Carnegie-Ames-Stanford model is used to calculate the net primary productivity of vegetation, while simultaneously calculating the standardized precipitation index, standardized soil moisture index, and standardized temperature index. This ensures the quality and spatiotemporal resolution consistency of the multi-source data, improves the accuracy and reliability of subsequent analysis, and solves the technical problem of calculation errors caused by diverse data sources and different scales.

[0066] Furthermore, the process of determining the timescales of vegetation net primary productivity response to various types of drought includes:

[0067] The correlation coefficients between standardized precipitation indices and net primary productivity of vegetation were calculated on 1- to 12-month timescales to obtain the correlation coefficient series.

[0068] The maximum correlation coefficient is selected from the correlation coefficient sequence, and the monthly scale corresponding to the maximum correlation coefficient is determined as the response time scale of vegetation net primary productivity to meteorological drought.

[0069] Using the same method, the response timescales of vegetation net primary productivity to agricultural drought and temperature drought were calculated respectively.

[0070] Furthermore, such as Figure 3 As shown, this embodiment identifies the response time of the National Drought Index (NPP) to three types of drought. Taking meteorological drought as an example: the correlation coefficients between the National Drought Index (SPI) and the NPP on a 1- to 12-month timescale are calculated, yielding Corr(SPI1, NPP), ..., Corr(SPI12, NPP), and the largest correlation coefficient is taken. The timescale of the meteorological drought index corresponding to the largest correlation coefficient is the response time of the NPP to meteorological drought. Similarly, the response times of the NPP to agricultural drought and temperature drought can be obtained.

[0071] This embodiment calculates the correlation coefficient sequence between the standardized precipitation index and the net primary productivity of vegetation on a 1 to 12-month scale, and selects the month scale corresponding to the largest correlation coefficient as the response time scale. The same method is used to determine the response time to agricultural drought and temperature drought. This can accurately identify the lagged response characteristics of vegetation to different drought types and solve the technical problem of inaccurate identification of response relationship caused by the lack of a time matching mechanism in the prior art.

[0072] Furthermore, the process of calculating the conditional probability of net primary productivity loss of vegetation under different drought combinations includes:

[0073] The 40th percentile of the net primary productivity sequence of vegetation was determined as the loss threshold.

[0074] A state of loss is defined as a net primary productivity of vegetation that is less than the 40th percentile.

[0075] The probability of loss is calculated using the conditional probability formula based on the drought index at the response time scale.

[0076] The conditional probability formula includes a combination of agricultural drought, meteorological drought, and temperature drought conditions.

[0077] Furthermore, this embodiment defines through extensive experimentation. (NPP state less than the 40th percentile) represents the NPP loss state studied in this embodiment. The sensitivity of NPP loss to different drought conditions under combined drought conditions is quantified. The formula for calculating the probability of NPP loss under meteorological-agricultural-temperature combined drought is:

[0078]

[0079] In the formula, The 40th percentile of the NPP sequence; , and These represent the given agricultural drought state, meteorological drought state, and temperature drought state, respectively.

[0080] The above formulas can be used to quantify the sensitivity of NPP loss to meteorological drought / agricultural drought / temperature drought under combined drought conditions, such as... Figure 4 As shown. Two drought scenarios are fixed, and the response of NPP loss to the remaining drought scenario is analyzed. Taking the calculation of the sensitivity of NPP loss to meteorological drought under combined drought as an example, as... Figure 4 The leftmost (A) represents a fixed agricultural drought state. And the temperature is arid. meteorological drought The values ​​decrease from 0 to -2 in increments of -0.05 to simulate a process of drought intensification. The calculation formula is as follows:

[0081]

[0082] In the formula, Decrease from 0 to -2 in increments of -0.05 (becoming drier).

[0083] With the changing state of meteorological drought, the probability of NPP loss... It will change. Curve fitting using least squares. The fitted curve formula is as follows:

[0084]

[0085] In the formula, It is a constant; The variation of the probability of NPP loss with meteorological drought SPI under the condition of agricultural drought and temperature drought can also be referred to as the sensitivity of NPP loss to meteorological drought under combined drought stress.

[0086] There are two situations Meteorological drought exacerbates NPP losses; Meteorological drought inhibits NPP loss. The larger the value, the more sensitive the NPP loss is to meteorological drought.

[0087] This embodiment establishes a unified standard for quantifying loss states by determining the 40th percentile of the net primary productivity sequence of vegetation as the loss threshold and calculating the loss probability under different drought combinations based on the conditional probability formula. This achieves standardized calculation of loss probability, solves the technical problem of lacking a standardized loss probability calculation model in the prior art, and can quantitatively describe the dynamic relationship between drought state changes and vegetation loss probability.

[0088] Furthermore, in this embodiment, the 30 / 50 percentile can be used instead of the 40 percentile as the loss threshold, or the loss status can be defined as "NPP anomaly value is less than 15% of the multi-year average" or "NPP interannual decrease exceeds 20%".

[0089] Furthermore, the process of fitting the sensitivity coefficient of the probability of net primary productivity loss of vegetation as a function of various drought states includes:

[0090] By fixing agricultural drought and temperature drought conditions, the meteorological drought index is decreased from 0 to -2 in a preset range to simulate the drought intensification process.

[0091] Calculate the loss probability sequence corresponding to different meteorological drought conditions based on the conditional probability formula;

[0092] The least squares method was used to fit the loss probability sequence to the corresponding meteorological drought index, and the slope coefficient of the fitted curve was obtained. The slope coefficient was then used as the sensitivity coefficient of the loss probability to meteorological drought.

[0093] Furthermore, such as Figure 5 As shown, this embodiment identifies the dominant drought factors affecting NPP loss and their direction of action under combined drought stress. The calculation is performed by calculating the maximum value of the absolute values ​​of the three sensitivities, using the following formula:

[0094]

[0095] like In this case, under the combined effects of drought, NPP loss is dominated by meteorological drought. This indicates that meteorological drought plays a dominant role in promoting NPP loss under the combined effects of drought and meteorological drought. This indicates that meteorological drought plays a dominant role in inhibiting NPP loss under the combined effects of drought and meteorological drought.

[0096] like In this case, under the combined effects of drought, NPP loss is dominated by agricultural drought. This indicates that agricultural drought, under the combined effects of drought and other factors, has a dominant role in promoting NPP loss. This indicates that agricultural drought has a dominant inhibitory effect on NPP loss under the combined effects of drought and drought.

[0097] like In this case, under the combined effects of drought, NPP loss is dominated by temperature drought. This indicates that temperature drought, under the combined effects of drought and other factors, has a dominant role in promoting NPP loss. This indicates that under the combined effects of drought and temperature drought, the inhibitory effect on NPP loss is dominant.

[0098] The method proposed in this embodiment for identifying the response mechanism of NPP loss to compound drought is simple and can effectively identify the dominant drought factors and their directions of influence in NPP loss. Specifically, by fixing two types of drought states and simulating the drought intensification process by decreasing a third type of drought index from 0 to -2 in a preset interval, the least squares method is used to fit the loss probability sequence to the corresponding drought index. The slope coefficient of the fitted curve is used as the sensitivity coefficient, which can effectively separate the independent influence of single drought factors and obtain quantitative response parameters, solving the technical problem that existing technologies cannot quantify the contribution weight of each factor.

[0099] Furthermore, the curve obtained by curve fitting using the least squares method is a linear function;

[0100] The constant term represents the basic loss probability, and the coefficient of the linear term is the sensitivity coefficient.

[0101] The absolute value of the sensitivity coefficient represents the degree to which the loss probability is sensitive to the corresponding type of drought;

[0102] The sign of the sensitivity coefficient indicates the direction of the effect of this type of drought on the probability of loss.

[0103] Furthermore, this embodiment uses a linear function as the fitting curve expression, with the constant term representing the basic loss probability and the coefficient of the linear term serving as the sensitivity coefficient. The absolute value of this coefficient represents the sensitivity of the loss probability to the corresponding type of drought, and the positive or negative sign represents the direction of the drought's effect on the loss probability. This gives the sensitivity coefficient a clear physical meaning, achieving the technical effect of simultaneously quantifying the sensitivity and direction of the effect of a single parameter, and solving the technical problem of ambiguous identification of the direction of drought effect in existing technologies.

[0104] Furthermore, the process of identifying the dominant drought factors affecting net primary productivity loss in vegetation under combined drought includes:

[0105] Calculate the absolute values ​​of the meteorological drought sensitivity coefficient, the agricultural drought sensitivity coefficient, and the temperature drought sensitivity coefficient, respectively.

[0106] After comparing the absolute values, the drought type corresponding to the maximum value is determined as the dominant drought factor.

[0107] Furthermore, this embodiment calculates the absolute values ​​of the meteorological drought sensitivity coefficient, agricultural drought sensitivity coefficient, and temperature drought sensitivity coefficient separately and compares their magnitudes. The drought type corresponding to the maximum value among the three is determined as the dominant drought factor. This establishes an objective dominant factor determination mechanism, avoids the subjectivity of manually setting thresholds, and solves the technical problems of ambiguous dominant factor identification and lack of systematic determination rules in the prior art.

[0108] Furthermore, the process of determining the direction of the dominant drought factor's effect based on the sign of the sensitivity coefficient includes:

[0109] When meteorological drought is determined to be the dominant factor, if the corresponding meteorological drought sensitivity coefficient is negative, it is determined that meteorological drought exacerbates losses; if it is positive, it is determined that meteorological drought inhibits losses.

[0110] When agricultural drought is determined to be the dominant factor, if the corresponding agricultural drought sensitivity coefficient is negative, it is determined that agricultural drought exacerbates losses; if it is positive, it is determined that agricultural drought inhibits losses.

[0111] When temperature drought is determined to be the dominant factor, if the corresponding temperature drought sensitivity coefficient is positive, it is determined that temperature drought exacerbates the loss; if it is negative, it is determined that temperature drought inhibits the loss.

[0112] Furthermore, this embodiment determines the direction of action based on the positive or negative value of the sensitivity coefficient of the dominant factor. When the coefficient is negative, it is determined that the corresponding drought type exacerbates the loss; when it is positive, it is determined that the loss is inhibited. This not only identifies the dominant factor but also clarifies its promoting or inhibiting properties, providing a direct and accurate technical basis for ecological drought prevention and control. It solves the technical problem that existing technologies cannot distinguish the direction of action, resulting in a lack of targeted prevention and control measures.

[0113] Furthermore, the meteorological drought index uses the standardized precipitation evapotranspiration index or the Palmer drought index instead of the standardized precipitation index;

[0114] The agricultural drought index uses soil relative humidity or crop water deficit index instead of the standardized soil moisture index.

[0115] The temperature drought index uses the number of days of heat waves or the cumulative value of extreme high temperatures instead of the standardized temperature index.

[0116] This embodiment calculates meteorological drought by replacing the standardized precipitation index with the standardized precipitation index using the standardized precipitation evapotranspiration index or the Palmer drought index, calculates agricultural drought by replacing the standardized soil moisture index with the soil relative humidity or crop water deficit index, and calculates temperature drought by replacing the standardized temperature index with the number of days of heat wave or the cumulative value of extreme high temperature. This allows the method to adjust the drought index type according to the climate characteristics and application needs of different study areas, thereby enhancing the versatility and adaptability of the method in different climate zones and vegetation types.

[0117] This embodiment analyzes the response mechanism of vegetation productivity to complex drought stress, and can be applied to scenarios such as drought disaster ecological risk early warning, regional ecosystem restoration planning, and dynamic assessment of carbon budget.

[0118] Specifically, this embodiment overcomes the limitations of single-drought studies by constructing a systematic analytical framework. For the first time, it achieves a full-chain, standardized analysis of NPP loss under the combined drought stress of "meteorology-agriculture-temperature".

[0119] Specifically, this embodiment can accurately identify the dominant drought factor and its direction of action. A quantitative model for the probability of NPP loss under combined drought is proposed, which can not only determine which drought factor (meteorology, agriculture, temperature) plays a dominant role, but also clarify whether its role "promotes" or "mitigates" NPP loss, providing a direct basis for precise prevention and control.

[0120] Specifically, this embodiment has good practicality and universality, and broad application prospects. It can be directly applied to drought ecological risk assessment at the national / watershed scale, providing precise technical support for ecological restoration and disaster prevention and control; it is applicable to different climate zones (arid and semi-arid zones, humid zones) and different vegetation types (grassland, farmland, forest), and can be adapted to different research needs by adjusting the drought index threshold (e.g., changing SMI≤-0.5 to SMI≤-1.0).

[0121] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of identifying a dominant drought factor of vegetation loss under compound drought, characterized in that, The method comprises the following steps: According to the multi-source remote sensing data and meteorological observation data of the study area, calculate the meteorological drought index, agricultural drought index, temperature drought index and vegetation net primary productivity; According to the correlation coefficients of the meteorological drought index, agricultural drought index and temperature drought index with vegetation net primary productivity, determine the response time scale of vegetation net primary productivity to each type of drought; According to the response time scale of the drought index, calculate the conditional probability of vegetation net primary productivity loss under different drought combination states, and through the method of fixing two types of drought states and changing the third type of drought state, fit the sensitive coefficients of vegetation net primary productivity loss probability changing with each type of drought state; According to the absolute value comparison result of the sensitive coefficients, identify the dominant drought factor affecting the loss of vegetation net primary productivity under compound drought, and judge the direction of the dominant drought factor according to the positive and negative of the sensitive coefficients.

2. The method of claim 1, wherein the process of calculating the meteorological drought index, agricultural drought index, temperature drought index and vegetation net primary productivity comprises: After obtaining the precipitation data, temperature data, normalized vegetation index, vegetation classification data and solar radiation data of the study area, the data is processed for outlier elimination, spatial matching and time scale unification; Calculate the standardized precipitation index as the meteorological drought index according to the precipitation data; Calculate the standardized soil moisture index as the agricultural drought index according to the soil moisture data; Calculate the standardized temperature index as the temperature drought index according to the temperature data; Input the precipitation data, temperature data, vegetation classification data, solar radiation data and normalized vegetation index into the CASA model to calculate the vegetation net primary productivity.

3. The method of claim 1, wherein the process of determining the response time scale of vegetation net primary productivity to each type of drought comprises: Calculate the correlation coefficients of the standardized precipitation index and vegetation net primary productivity at 1 to 12 month scales respectively to obtain a correlation coefficient sequence; Select the maximum correlation coefficient from the correlation coefficient sequence, and determine the month scale corresponding to the maximum correlation coefficient as the response time scale of vegetation net primary productivity to meteorological drought; Use the same method to calculate the response time scale of vegetation net primary productivity to agricultural drought and the response time scale of vegetation net primary productivity to temperature drought respectively.

4. The method of claim 1, wherein the process of calculating the conditional probability of vegetation net primary productivity loss under different drought combination states comprises: Determine the 40th percentile of the vegetation net primary productivity sequence as the loss threshold; Define the vegetation net primary productivity state less than the 40th percentile as the loss state; Calculate the loss probability according to the drought index under the response time scale through the conditional probability formula; The conditional probability formula includes the combination conditions of agricultural drought state, meteorological drought state and temperature drought state.

5. The method of claim 1, wherein the process of fitting the sensitive coefficients of vegetation net primary productivity loss probability changing with each type of drought state comprises: ​ ​ ​ ​ The agricultural drought state and the temperature drought state are fixed, the meteorological drought index is decreased in preset intervals from 0 to -2 to simulate the process of drought aggravation; According to the conditional probability formula, the loss probability sequence corresponding to different meteorological drought states is calculated; The least square method is used to perform curve fitting on the loss probability sequence and the corresponding meteorological drought index, to obtain the slope coefficient of the fitting curve, and the slope coefficient is taken as the sensitivity coefficient of the loss probability to the meteorological drought.

6. The method of claim 5, wherein, The fitting curve expression obtained by curve fitting using the least square method is a linear function; The constant term represents the basic loss probability, and the coefficient of the linear term is the sensitivity coefficient; The absolute value of the sensitivity coefficient represents the sensitivity degree of the loss probability to the corresponding type of drought; The positive and negative of the sensitivity coefficient represent the action direction of the type of drought on the loss probability.

7. The method of claim 1, wherein, The process of identifying the dominant drought factor affecting the loss of vegetation net primary productivity under compound drought includes: The absolute values of the meteorological drought sensitivity coefficient, the agricultural drought sensitivity coefficient, and the temperature drought sensitivity coefficient are calculated respectively; After comparing the absolute values, the drought type corresponding to the maximum value is determined as the dominant drought factor.

8. The method of claim 1, wherein, The process of determining the action direction of the dominant drought factor according to the positive and negative of the sensitivity coefficient includes: When the meteorological drought is determined as the dominant factor, if the corresponding meteorological drought sensitivity coefficient is negative, it is determined that the meteorological drought aggravates the loss; if it is positive, it is determined that the meteorological drought inhibits the loss; When the agricultural drought is determined as the dominant factor, if the corresponding agricultural drought sensitivity coefficient is negative, it is determined that the agricultural drought aggravates the loss; if it is positive, it is determined that the agricultural drought inhibits the loss; When the temperature drought is determined as the dominant factor, if the corresponding temperature drought sensitivity coefficient is positive, it is determined that the temperature drought aggravates the loss; if it is negative, it is determined that the temperature drought inhibits the loss.

9. The method of claim 1, wherein, The meteorological drought index is replaced by the standardized precipitation evapotranspiration index or the Palmer drought index instead of the standardized precipitation index; The agricultural drought index is replaced by the soil relative humidity or the crop water deficit index instead of the standardized soil moisture index; The temperature drought index is replaced by the number of heat wave days or the cumulative value of extreme high temperature instead of the standardized temperature index.

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