Comprehensive evaluation method for phenological change trend of vegetation

Abstract

The invention relates to the field of ecological environment and climate change research, and discloses a vegetation phenology change trend comprehensive evaluation method, which comprises the following steps: acquiring climate data and tree ring data of a target area, simulating a relationship between radial growth and phenology of a tree by using a Vaganov-Shashki n model based on the acquired data, and generating a time sequence of phenology change; extracting phenological key nodes such as growing season starting time, end time and growing season length; analyzing the driving effect of climatic factors on phenological key nodes, and determining main influence factors; on the basis of the climate model and different emission scenes, predicting the vegetation phenological trend under future climate change; the response of the regional ecosystem to climate changes is evaluated based on historical and future data. Through simulation and quantitative analysis, a scientific basis is provided for long-term trend prediction and regional ecological management of vegetation phenological change, and the method has high applicability and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment and climate change research, and specifically to a comprehensive evaluation method for vegetation phenology change trend. Background Art

[0002] Vegetation phenology is an important indicator for studying the response of ecosystems to climate change, and has a significant impact on ecosystem productivity, carbon cycle and water balance. However, due to global warming, the start and end time of the vegetation growing season and the length of the growing season have changed significantly, which has a profound impact on the dynamic balance of regional ecosystems.

[0003] Existing research mostly focuses on analyzing vegetation phenology using satellite remote sensing data or ground observation data. Although these methods can provide large-scale spatial data support, they are limited by the short time span of the data, low resolution, and observation errors affected by clouds and technical conditions, resulting in obvious deficiencies in the analysis of long-term phenological change trends and the study of climate-driven mechanisms.

[0004] In addition, in the current prediction research of vegetation phenological changes, there is a lack of unified and high-precision model tools, which cannot fully quantify the dynamic impact of climate factors on phenological characteristics. Especially under the condition of considering future climate change emission scenarios, the reliability and regional applicability of prediction results still need to be further improved. At the same time, existing research lacks the organic combination of historical data and future prediction results, and cannot systematically evaluate the comprehensive impact of climate change on regional ecosystems.

[0005] Therefore, there is an urgent need for a comprehensive evaluation method for vegetation phenological changes that can combine high-precision models, historical data and future climate scenarios to provide a scientific basis for regional ecological management and climate adaptation planning. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a comprehensive evaluation method for vegetation phenological change trends, which solves the problems in the existing technology of insufficient data time span in vegetation phenological change analysis, inaccurate quantification of climate driving mechanisms, and low reliability of future climate change predictions.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for comprehensive evaluation of vegetation phenology change trends, comprising the following steps:

[0008] Collect climate data and tree ring data for the target area;

[0009] Based on the collected data, the Vaganov-Shashkin model was used to simulate the relationship between radial growth and phenology of trees and generate a time series of phenological changes;

[0010] Extract key phenological nodes, including the start and end time of the growing season and the length of the growing season;

[0011] Analyze the driving effect of climate factors on the start and end time of the growing season and the length of the growing season, and determine the main influencing factors;

[0012] Based on climate models and different emission scenarios, predict the trend of vegetation phenology under future climate change;

[0013] Assess regional ecosystem responses based on historical and future phenological change data.

[0014] Preferably, the climate data includes the daily average temperature, daily maximum temperature, daily minimum temperature and precipitation of the target area.

[0015] Preferably, the step of using the Vaganov-Shashkin model to simulate the radial growth of trees and its relationship with climate factors to generate a time series of phenological changes includes:

[0016] Climate data of the target area are used as model input;

[0017] The Vaganov-Shashkin model was used to calculate the effect of climate factors on the growth rate of tree ring width;

[0018] Based on the model output, time series of tree-ring widths were generated and correlated with climate data.

[0019] Preferably, the step of extracting candidate key nodes includes:

[0020] Based on the tree-ring width time series, calculate the changes in annual growth rate;

[0021] By setting the critical value of the growth rate, the start time, end time and length of the growing season are determined.

[0022] Preferably, the step of analyzing the driving effect of climate factors on the start time, end time and length of the growing season to determine the main influencing factors includes:

[0023] Calculate the correlation coefficient between climate factors and key phenological nodes;

[0024] The influence of climate factors is quantified through linear regression models, and the dominant climate factors and their driving intensity on changes in key phenological nodes are determined.

[0025] Preferably, the step of predicting the trend of vegetation phenology under future climate change based on the climate model and different emission scenarios comprises:

[0026] Select climate models and combine them with emission scenarios to generate future climate data;

[0027] Using future climate data as input, the Vaganov-Shashkin model is used to predict the changing trend of the start time, end time and length of the growing season.

[0028] The differences between the predicted results and historical data were compared, and the extent and rate of extension of the growing season length under different emission scenarios in the future were calculated.

[0029] Preferably, in the step of predicting the trend of vegetation phenology under future climate change, the temperature and precipitation data under different emission scenarios are corrected based on the simulation results of the climate model, and the impact of climate factors on the length of the growing season is quantified in combination with the regression analysis model to improve the prediction accuracy.

[0030] Preferably, the step of evaluating the regional ecosystem response based on historical and future phenological change data includes:

[0031] Conduct trend analysis on historical phenological data to identify the rate of change in the start and end of the growing season and the length of the growing season;

[0032] Based on the predicted phenological change trends under future climate scenarios, analyze the impact of different emission scenarios on regional vegetation phenology;

[0033] Integrate historical and future data to assess the sensitivity of vegetation to climate change and make recommendations for adaptive management of ecosystems.

[0034] Preferably, the ecosystem adaptive management recommendations include optimization of regional vegetation planting layout, water resource regulation strategies, and vegetation protection measures under extreme climatic conditions.

[0035] The present invention also provides a comprehensive evaluation device for vegetation phenology change trend, comprising:

[0036] Data acquisition module, used to obtain climate data and tree ring width data of the target area;

[0037] Data processing module, used for preprocessing climate data, including vertical lapse rate correction and data normalization;

[0038] Simulation analysis module, used to simulate radial growth and phenological changes of trees based on the Vaganov-Shashkin model;

[0039] The statistical analysis module is used to extract the time series of the start and end time of the growing season and the length of the growing season, and to determine the impact of climate factors on phenological changes;

[0040] A prediction module is used to predict the changing trend of the length of the growing season in the future based on the climate model;

[0041] The evaluation module is used to integrate historical and future phenological change trends and generate ecosystem management recommendations.

[0042] The present invention provides a comprehensive evaluation method for vegetation phenology change trend. It has the following beneficial effects:

[0043] 1. The present invention dynamically simulates the radial growth and phenological changes of trees by combining the Vaganov-Shashkin model with high-precision climate data. The data noise and inaccuracy are eliminated by using technical means such as temperature correction and data standardization, ensuring the reliability of the simulation results. The simulated phenological time series is significantly correlated with the measured data, which improves the accuracy and scientificity of vegetation phenological changes.

[0044] 2. The present invention quantifies the influence of climate factors (such as temperature and precipitation) on the start time (SOS), end time (EOS) and length of growing season (LOS) through correlation analysis and regression modeling. By constructing a multiple regression model, the leading role of different climate factors and their changing trends are clarified. This quantitative analysis provides a scientific basis for studying the response mechanism of vegetation to climate change.

[0045] 3. Based on multiple emission scenarios of the CMIP5 climate model, this paper predicts the changing trends of vegetation phenological characteristics (SOS, EOS and LOS) under future climate change. By combining historical data with model correction methods, the regional applicability and high credibility of the prediction results are guaranteed. Future prediction results provide important support for assessing the long-term impact of regional ecosystems on climate change.

[0046] 4. This paper evaluates the potential impact and adaptive capacity of climate change on regional ecosystems by comprehensively analyzing historical and future phenological change data. Management recommendations for vegetation layout optimization, water resource allocation, and response to extreme climate events are proposed for different altitude regions and emission scenarios. This method provides scientific support for adaptive planning and resource management of regional ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0048] Figure 2 It is a schematic diagram of the device structure of the present invention.

[0049] Among them, 10, data acquisition module; 20, data processing module; 30, simulation analysis module; 40, statistical analysis module; 50, prediction module; 60, evaluation module. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Please refer to the attached Figure 1 The present invention provides a comprehensive evaluation method for vegetation phenology change trends, aiming to provide a scientific basis for regional ecosystem management by simulating and predicting vegetation phenology change trends.

[0052] like Figure 1 As shown, the comprehensive evaluation method for vegetation phenology change trend may include the following steps:

[0053] S1. Collect climate data and tree ring data of the target area;

[0054] S2. Based on the collected data, the Vaganov-Shashkin model was used to simulate the relationship between radial growth and phenology of trees;

[0055] S3, extract key phenological nodes, including the start time of the growing season (SOS), the end time (EOS) and the length of the growing season (LOS);

[0056] S4. Analyze the driving effect of climate factors on SOS, EOS and LOS, and determine the main influencing factors;

[0057] S5. Predict the trend of vegetation phenology under future climate change based on climate models and different emission scenarios;

[0058] S6. Assess regional ecosystem responses based on historical and future phenological change data.

[0059] Each step of the method of the present invention is described in detail below.

[0060] Regarding step S1, in this embodiment, the collection and preprocessing of climate data and vegetation data of the target area in step S1 is a basic step of the comprehensive evaluation method of vegetation phenological change trends of the present invention, and is intended to provide high-precision and standardized data input for subsequent steps.

[0061] In the collection of climate data, this embodiment selects long-term daily meteorological data of the target area, including daily average temperature, daily maximum temperature, daily minimum temperature and daily precipitation. These meteorological data can be obtained through meteorological stations in the region, and can also be supplemented by remote sensing satellite data or reanalysis data sets (such as ERA5, etc.). In order to ensure the continuity and accuracy of meteorological data, priority is given to using meteorological station data in the region with long data collection time and complete observation records.

[0062] In this embodiment, since there is usually an altitude difference between the sampling point of the target area and the weather station, the temperature data may be affected by the altitude, so the temperature data needs to be corrected for altitude. According to the altitude difference between the target area and the weather station, the original temperature data is adjusted using the vertical lapse rate (usually 0.44°C / 100 meters). The adjustment formula is as follows:

[0063] T adjusted =T measured -(h station -h sampling )×0.44 / 100

[0064] in:

[0065] T adjusted is the corrected temperature;

[0066] T measured is the original temperature observed by the weather station;

[0067] h station and h sampling are the altitudes of the weather station and sampling point, respectively.

[0068] In the collection of vegetation data, this embodiment preferably selects the main tree species (such as Chinese pine, fir, etc.) in the target area as the research object, and collects their tree ring width data. In the specific operation, a non-destructive sampling method is adopted to extract tree core samples from the trunks of the target trees using growth cones. In order to ensure the representativeness of the samples, samples should be collected from multiple trees of different ages, and different microclimate environments in the target area should be covered as much as possible. After the samples are collected, the tree ring width is measured with high precision using a tree ring measurement system (such as the LINTAB system), with a measurement accuracy of 0.01 mm. After the sample measurement is completed, the TSAP-win software is further used to cross-dating the tree ring sequence to ensure the accuracy and time consistency of the data.

[0069] In addition, in this embodiment, in order to improve the availability of data and reduce noise interference, the original climate data and tree ring width data need to be preprocessed. The climate data needs to be standardized to remove possible seasonal trends and outliers.

[0070] For tree-ring width data, the negative exponential function method is used to remove the age effect in tree growth, ensuring that the tree-ring width sequence reflects the dynamic changes of environmental factors rather than the influence of tree age. The specific processing steps include: fitting the relationship curve between tree-ring width and age, calculating the remaining sequence, and performing arithmetic averaging to generate a standardized tree-ring width chronology.

[0071] This embodiment also proposes that after the collection and preprocessing of climate data and vegetation data are completed, the two types of data should be matched. The matching principle is to associate the tree ring width data of each year with the climate data of the corresponding year based on time, so as to provide high-quality raw data for the model input in the subsequent steps.

[0072] Regarding step S2, in this embodiment, step S2 uses the Vaganov-Shashkin (VS) model to simulate the radial growth of trees and its relationship with climate factors based on the collected data, and generates a time series of phenological changes. This step aims to achieve the association between tree ring width and climate factors through a process model, and provide data support for subsequent phenological analysis and prediction.

[0073] In this embodiment, the VS model is a tree radial growth simulation model based on the limiting effect of climate factors on the division and growth of cambium cells. The core mechanism of the model includes a "growth module" and a "cambium module", which can dynamically simulate the radial growth process of trees by inputting climate factors (including temperature, precipitation and solar radiation).

[0074] In the model operation, the pre-processed climate data is first input, including daily average temperature (T), daily precipitation (W) and daily solar radiation (E). The VS model uses these climate factors as input and combines them with the growth limit theory to calculate the daily radial growth rate of trees. The core formula of the model is as follows:

[0075] Gr(t)=Gr E (t)×min{Gr T (t),Gr W (t)}

[0076] in:

[0077] Gr(t): overall growth rate;

[0078] G E (t): growth rate controlled by solar radiation;

[0079] G T (t): growth rate controlled by air temperature;

[0080] G W (t): Growth rate controlled by soil moisture.

[0081] The model adopts the "minimum limiting factor method", that is, the total growth rate is limited by the minimum value of the growth rates of the three parts. In order to ensure the accuracy of the simulation, the growth rates of each part are further refined and modeled in this embodiment.

[0082] For the temperature-controlled growth rate Gr T (t), which is calculated as:

[0083]

[0084] in:

[0085] T(t): average temperature of the current day;

[0086] T min , T max and T opt : are the minimum, growth limit and optimum temperatures respectively.

[0087] When T(t) exceeds [T min ,T max ] range, Gr T (t)=0.

[0088] For the growth rate Gr controlled by water W (t), whose calculation depends on the dynamic changes of soil moisture and takes into account the balance between precipitation and evapotranspiration. The formula is:

[0089]

[0090] in:

[0091] W(t): current soil moisture;

[0092] W max : Maximum storage of soil moisture.

[0093] For the growth rate controlled by solar radiation Gr E (t), which is directly proportional to the daily solar radiation, and the calculation formula is:

[0094]

[0095] in:

[0096] E(t): solar radiation of the current day;

[0097] E max : Maximum radiation dose.

[0098] In this embodiment, after dynamically simulating the daily growth rate Gr(t) using the above formula, the annual tree ring width time series is generated by accumulating and summing the daily growth rate. The calculation formula is:

[0099]

[0100] in:

[0101] TRW: tree ring width;

[0102] n: Number of growing days in a year.

[0103] In addition, to verify the reliability of the model output, the model output was verified using the measured tree ring width data in this embodiment. The correlation between the model output and the actual observed data (such as the Pearson correlation coefficient) was calculated to ensure that the simulation results reached a significant correlation level (P<0.01). min ,T opt ,T max ,W max ) were gradually adjusted to ensure that the simulation results can fully reflect the region-specific climate conditions.

[0104] After the model is run, this example generates a tree-ring width time series associated with climate factors. This time series has high resolution and dynamic characteristics, providing basic data support for subsequent phenological key node extraction and climate factor analysis. Through this time series, the dynamic relationship between radial growth of trees in the target area and climate factors can be accurately reflected, providing a reliable tool for studying the long-term impact of climate change on vegetation growth.

[0105] In summary, this embodiment uses the VS model to accurately simulate the radial growth process of trees and generate a time series of phenological changes. This method has good applicability and reliability, laying a foundation for the extraction of phenological characteristics and the analysis of the impact of climate change in subsequent steps.

[0106] For step S3, in this embodiment, step S3 extracts key nodes of the growing season from the simulated phenological change time series, including the start time of the growing season (SOS), the end time of the growing season (EOS) and the length of the growing season (LOS). The implementation of this step aims to accurately identify the changing laws of vegetation phenological characteristics through time series analysis, and provide a basis for subsequent analysis and prediction.

[0107] In this embodiment, the daily radial growth rate Gr(t) is first calculated based on the time series of the radial growth rate of the tree. According to the definition of the growing season, a growth rate threshold is set. When Gr(t) exceeds the threshold, it is considered that the radial growth begins; when Gr(t) is lower than the threshold, it is considered that the radial growth ends. The specific calculation formula is as follows:

[0108] SOS=min{t|Gr(t)≥Threshold,t∈[1,365]}

[0109] EOS=max{t|Gr(t)≥Threshold,t∈[1,365]}

[0110] in:

[0111] SOS: start of growing season;

[0112] EOS: end of growing season;

[0113] t: the tth day of the year;

[0114] Threshold: The critical value of growth rate, usually 5% of the average daily growth rate.

[0115] After obtaining SOS and EOS, the length of the growing season (LOS) is calculated by subtracting SOS from EOS, where LOS represents the length of time (in days) from the start to the end of radial growth.

[0116] In order to improve the accuracy of SOS, EOS and LOS extraction, the time series is processed in segments by segmented regression analysis in this embodiment. The segmented regression analysis sets breakpoints in the time series, fits two or more trend lines, and finds the turning points (Breakpoint) where the growth rate changes significantly. These turning points correspond to the time nodes of SOS and EOS, which can effectively reduce the interference of abnormal data on the extraction results.

[0117] In addition, in this embodiment, the extracted SOS and EOS are averaged over many years to evaluate their long-term change trends. For example, the interannual change rates of SOS and EOS are evaluated by a linear regression model, and the formula is as follows:

[0118] ΔSOS=a·Year+b

[0119] ΔEOS=c·Year+d

[0120] in:

[0121] Year: year;

[0122] a,b,c,d: regression coefficients.

[0123] The above models can quantify the changing trends of SOS and EOS, thereby revealing the dynamic changes of the growing season.

[0124] In practical applications, this embodiment identifies SOS, EOS and LOS in different years by analyzing the tree ring width time series year by year, and further analyzes their interannual fluctuation characteristics. For example, in the simulated time series, 1983 and 1992 are identified as significant turning points, corresponding to the years when the change trends of SOS and EOS changed significantly.

[0125] To further analyze the regional characteristics of SOS and EOS, this embodiment combines spatial distribution data to conduct a comparative analysis of SOS and EOS in the region. Through the SOS and EOS extraction results under different climate backgrounds, it is found that the growing season starts later in high-altitude areas, while the growing season starts earlier in low-altitude areas, which verifies the applicability of the method of this embodiment.

[0126] The SOS, EOS and LOS extracted in this example lay the foundation for the subsequent analysis of the driving mechanism of climate factors. Through this step, the phenological characteristics of vegetation and its changing trends can be fully revealed, providing accurate data support for studying the vegetation response under the background of climate change.

[0127] For step S4, in this embodiment, step S4 analyzes the driving effect of climate factors on the start time (SOS), end time (EOS) and length of the growing season (LOS), and determines the main influencing factors. The implementation of this step clarifies the quantitative relationship between climate factors and vegetation phenological characteristics through data analysis and model verification in the experimental area, providing a scientific basis for the response mechanism of vegetation to climate change.

[0128] In this embodiment, the experimental area is selected as the Hasi Mountain (HSM) area, with geographical coordinates of 37.03°N, 104.47°E, and an altitude range of 2300-2460 meters. This area belongs to Jingyuan County, Baiyin City, Gansu Province, China, and is the northern edge of the Asian monsoon, with significant semi-arid to semi-humid climate characteristics. The main vegetation type is Pinus tabulaeformis, which has a high sensitivity to changes in climate factors and is an ideal object for studying the relationship between radial growth of trees and climate factors.

[0129] In the experimental area, three sampling points are set in this embodiment, and the specific distribution is as follows:

[0130] Sampling point 1: 104.45°E, 37.02°N, 2340 m above sea level;

[0131] Sampling point 2: 104.48°E, 37.05°N, 2410 m above sea level;

[0132] Sampling point 3: 104.50° east longitude, 37.08° north latitude, 2460 meters above sea level.

[0133] The distribution of sampling points covers different microclimate environments and altitude gradients, which can reflect the diverse impacts of climate factors on phenological characteristics. The meteorological data of these sampling points are from the Jingyuan Meteorological Station (104.67°E, 36.57°N, 1398.2 meters above sea level), which covers daily temperature and precipitation data from 1954 to 2011. In order to eliminate the impact of altitude differences on temperature data, the vertical lapse rate (0.44℃ / 100 meters) was used to correct the meteorological station data.

[0134] In order to quantitatively analyze the driving effect of climate factors on phenological characteristics (SOS, EOS, LOS), this embodiment adopts correlation analysis and regression analysis methods. Specifically, based on the data collected in the experimental area and the key phenological nodes extracted in step S3, an annual time series data set is constructed, and the correlation between climate factors and phenological characteristics is calculated.

[0135] For example, for SOS, this embodiment uses the Pearson correlation coefficient to calculate the relationship between the average temperature from April to June (TmeanApr-Jun) and SOS. The correlation formula is as follows:

[0136]

[0137] in:

[0138] r SOS represents the correlation between SOS and climate factors;

[0139] X i Indicates the value of a certain climate factor (such as the average temperature from April to June);

[0140] Y i Indicates the SOS of the corresponding year;

[0141] and are the means of climate factors and SOS, respectively.

[0142] The above correlation analysis found that:

[0143] The average temperature from April to June (TmeanApr-Jun) had the most significant impact on SOS (r=-0.64, P<0.01), that is, rising temperature would significantly advance SOS.

[0144] The precipitation in September of the previous year was positively correlated with SOS, but its influence was small (r=0.22, P>0.05).

[0145] For EOS, this example uses the same correlation analysis method to identify the dominant climate factor, and further verifies its driving effect in combination with the multivariate linear regression model. The results show that the maximum temperature in August (TmaxAug) is the main driving factor of EOS, and its correlation with EOS is positive (r=0.31, P<0.05). The calculation formula is:

[0146] EOS=a·Tmax Aug +b

[0147] in:

[0148] EOS is the end of the growing season;

[0149] Tmax Aug is the highest temperature in August;

[0150] a and b are regression coefficients, estimated by the least squares method.

[0151] In this example, the driving factors of LOS were analyzed, and it was found that the change of LOS was mainly controlled by the average temperature from April to August (Tmean Apr-Aug), and the correlation was significant (r=0.59, P<0.01). At the same time, by constructing a linear regression model, the effect of temperature on LOS was quantified. The model formula is as follows:

[0152] LOS=10.41·Tmean Apr-Aug -61.31

[0153] in:

[0154] LOS is the length of the growing season;

[0155] Tmean Apr-Aug This is the average temperature from April to August.

[0156] Through the regression model, it is concluded that whenever Tmean Apr-Aug With an increase of 1°C, LOS increases by about 10.41 days, which indicates that there is a significant linear relationship between the extension of the growing season and the increase in temperature.

[0157] In addition, the nonlinear effects of climate factors were analyzed in this example, and the potential effects of extreme temperature (such as the number of days above 30°C) and uneven precipitation distribution (such as the monthly precipitation standard deviation) on phenological characteristics were evaluated. The results showed that extreme temperature had limited effects on SOS and EOS, while changes in precipitation distribution had a more significant impact on LOS, but the correlation was low (r<0.3).

[0158] In order to verify the universality of the main climate factors, this example also conducted comparative analysis at different sampling points in the experimental area. For example, the SOS at low-altitude sampling points was earlier, while the SOS at high-altitude sampling points was later, but TmeanApr-Jun and TmaxAug were the main driving factors at each sampling point, verifying the applicability of the method.

[0159] For step S5, in this embodiment, step S5 predicts the trend of vegetation phenology under future climate change based on climate models and different emission scenarios. The implementation of this step quantitatively predicts the trend of key characteristics of the growing season (SOS, EOS and LOS) under different emission scenarios in the future by combining historical climate data, future climate prediction models and phenological change simulations.

[0160] In this embodiment, the future climate prediction uses the GFDL-CM3 model in CMIP5 (Coupled Model IntercomparisonProject Phase 5), and the selected emission scenarios include RCP2.6, RCP4.5, RCP6.0 and RCP8.5, which correspond to the global climate change simulation results under low, medium, high and very high carbon emission conditions, respectively. The prediction time range of the climate model is from 2012 to 2100, and the required data include the average temperature from April to August (Tmean Apr-Aug), the maximum temperature in August (Tmax Aug) and the precipitation in September of the previous year.

[0161] In the specific implementation, we first analyzed the relationship between historical climate data and phenological characteristics from 1954 to 2011, verified the correlation between the temperature data simulated by the GFDL-CM3 model and the actual observation data, and corrected the model output data. The correction method is:

[0162] T corrected =T simulated -ΔT

[0163] in:

[0164] T corrected : Corrected temperature;

[0165] T simulated : Temperature predicted by climate models;

[0166] ΔT: The average deviation between historically observed temperatures and model-simulated temperatures.

[0167] According to the corrected climate data, the above established regression model (such as LOS = 10.41 × TmeanApr-Aug-61.31) is used in this embodiment to predict the change of the length of the growing season (LOS) in the future in combination with the trend of future temperature changes under different emission scenarios. The specific calculation method is:

[0168] ΔLOS=LOS future -LOS historical

[0169] in:

[0170] LOS future : Length of growing season based on future temperature projections;

[0171] LOS historical : The mean length of the historical growing season.

[0172] The prediction results show that under the RCP2.6 scenario, TmeanApr-Aug will increase by 0.41℃ every decade on average, and the LOS will be extended by 29.6 days by the end of the 21st century; under the RCP4.5 and RCP6.0 scenarios, TmeanApr-Aug will increase by 0.7℃ and 0.65℃ every decade, respectively, and the LOS will be extended by 33.6 days and 33.7 days, respectively; and under the RCP8.5 scenario, TmeanApr-Aug will increase by 0.82℃ every decade, and the LOS will be extended by 47.4 days.

[0173] In addition, this example also analyzes the future phenological change rate under different emission scenarios in stages. For example, under the RCP4.5 scenario:

[0174] From 2012 to 2050, TmeanApr-Aug will increase by 1.8°C per decade on average, and LOS will extend by 21.6-26.4 days;

[0175] From 2051 to 2075, the increase of TmeanApr-Aug slowed down, and LOS was extended by 33.7-38.4 days;

[0176] From 2076 to 2100, the increase of TmeanApr-Aug tended to be stable, and LOS was extended to 38 days.

[0177] When predicting the change trends of SOS and EOS, this embodiment calculates the extent of SOS advance and EOS delay under different emission scenarios based on the negative correlation between the average temperature from April to June (TmeanApr-Jun) and SOS, and the positive correlation between the maximum temperature in August (TmaxAug) and EOS. For example, under the RCP8.5 scenario:

[0178] SOS is advanced by an average of 0.21 days every 10 years;

[0179] EOS is delayed by an average of 1.52 days every 10 years.

[0180] Different emission scenarios have different impacts on future phenological characteristics. Especially under the high emission scenario (RCP8.5), the rate of phenological change is significantly accelerated.

[0181] In summary, this embodiment successfully achieves the quantitative prediction of the impact of future climate change on vegetation phenological characteristics through climate model calibration, historical data verification, and quantitative analysis of regression models.

[0182] For step S6, in this embodiment, step S6 evaluates the response of the regional ecosystem to climate change based on historical and future phenological change data. This step aims to quantitatively analyze the sensitivity of the ecosystem to climate change by combining the historical change trends and future prediction results of the start time (SOS), end time (EOS) and length of the growing season (LOS), and provide a scientific basis for regional ecological management.

[0183] In this embodiment, the historical phenological characteristics (1954-2011) extracted in step S3 and the future phenological characteristics (2012-2100) predicted in step S5 are first used to construct a comprehensive data set. The data set includes the following contents:

[0184] Historical and future SOS, EOS and LOS time series;

[0185] Time series of climate factors related to phenological characteristics (such as TmeanApr-Jun, TmaxAug, TmeanApr-Aug);

[0186] Future climate prediction results under different emission scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5).

[0187] Based on the dataset, this example evaluates the response of the regional ecosystem through the following steps:

[0188] First, the interannual change rates of SOS, EOS, and LOS were calculated to assess the long-term trend of phenological characteristics. The change rate calculation formula is as follows:

[0189]

[0190] in:

[0191] SOS end , SOS start The SOS of the final and starting years respectively;

[0192] EOS end EOSstart EOS for the final year and the starting year respectively;

[0193] LOS end , LOS start are the LOS of the final year and the starting year, respectively;

[0194] t is the time span.

[0195] The results show that in the historical period (1954-2011), SOS was advanced by 0.5 days every decade, EOS was delayed by 0.26 days every decade, and LOS was extended by about 2.5 days every decade. In the future period (2012-2100), the trend of change under different emission scenarios is different, among which the LOS extension under the RCP8.5 scenario is the largest, extending by about 9.7 days every decade.

[0196] Secondly, combined with the future prediction results under different emission scenarios, this example uses a sensitivity analysis method to evaluate the potential impact of climate change on the ecosystem. The sensitivity analysis uses LOS as the key ecological indicator, analyzes the driving effect of the change of TmeanApr-Aug on LOS, and calculates the climate sensitivity coefficient (ClimateSensitivityCoefficient, CSC). The definition of CSC is as follows:

[0197]

[0198] in:

[0199] ΔLOS: the magnitude of change in the length of the growing season;

[0200] ΔTmean: The variation of average temperature from April to August.

[0201] In the experimental area, the CSC values ​​under different emission scenarios are:

[0202] RCP2.6: CSC = 10.41 days / °C;

[0203] RCP4.5: CSC = 10.44 days / °C;

[0204] RCP6.0: CSC = 10.46 days / °C;

[0205] RCP8.5: CSC = 10.52 days / °C.

[0206] The CSC value shows that with the increase of greenhouse gas emissions, the sensitivity of ecosystems to temperature changes gradually increases, which reflects that the adaptability of ecosystems to climate change may be limited under high emission scenarios.

[0207] Furthermore, this example combines spatial analysis to conduct a zoning assessment of different altitude areas within the experimental area. The analysis shows that the SOS and EOS in low-altitude areas respond faster to temperature changes, while the response in high-altitude areas is relatively slow; however, in terms of the change trend of LOS, the extension range in high-altitude areas is larger. This difference in results reveals the heterogeneous response characteristics of ecosystems in the region to climate change.

[0208] Finally, this example proposes regional ecological management recommendations based on historical and future phenological change data, including the following:

[0209] Optimize vegetation planting layout: Select suitable tree species for planting based on the climate change response characteristics of different altitude areas to maximize the adaptability of the ecosystem.

[0210] Water resource management strategy: In the context of longer growing seasons in the future, water resources in the region should be rationally allocated to alleviate potential drought stress.

[0211] Countermeasures for extreme climate events: Under high emission scenarios, extreme high temperatures and uneven precipitation distribution may exacerbate the uncertainty of vegetation growth. It is recommended to adopt ecological restoration measures based on natural processes to enhance the resilience of the ecosystem.

[0212] In summary, this example clarifies the response mechanism of regional ecosystems to climate change by quantitatively evaluating the changing trends of phenological characteristics and the driving effects of climate factors. At the same time, combined with the analysis results of historical and future data, this step provides an important scientific basis for regional ecological management and climate change adaptation planning.

[0213] In general, the present invention collects climate data and vegetation data of the target area, simulates the radial growth of trees and its relationship with climate factors in combination with the Vaganov-Shashkin model, generates a time series of phenological changes, extracts key nodes such as the start time, end time and length of the growing season, and quantifies the driving effect of climate factors on phenological characteristics through correlation analysis and regression modeling. On this basis, based on the CM IP5 climate model and different emission scenarios, the changing trend of vegetation phenology under future climate change is predicted, and combined with historical and future data, the response mechanism of regional ecosystems to climate change is evaluated. The present invention provides a scientific method for accurate simulation and long-term trend prediction of vegetation phenological changes, and provides an important reference for regional ecosystem management and climate change adaptation planning.

[0214] The comprehensive evaluation device for vegetation phenological change trends described below and the comprehensive evaluation method for vegetation phenological change trends described above can be referenced to each other.

[0215] Please refer to the attached Figure 2The present invention also provides a comprehensive evaluation device for vegetation phenology change trend, comprising:

[0216] A data acquisition module 10 is used to obtain climate data and tree ring width data of a target area;

[0217] A data processing module 20, for preprocessing the climate data, including vertical lapse rate correction and data standardization;

[0218] A simulation analysis module 30, for simulating radial growth and phenological changes of trees based on the Vaganov-Shashkin model;

[0219] The statistical analysis module 40 is used to extract the time series of the start time, end time and length of the growing season, and determine the impact of climate factors on phenological changes;

[0220] A prediction module 50 is used to predict the changing trend of the length of the future growing season based on the climate model;

[0221] Evaluation module 60 is used to integrate historical and future phenological change trends and generate ecosystem management recommendations.

[0222] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0223] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive evaluation method for vegetation phenology change trend, characterized in that: The following steps are involved: Collect climate data and tree ring data for the target area; Based on the collected data, the Vaganov-Shashkin model was used to simulate the relationship between radial growth and phenology of trees and generate a time series of phenological changes; Extract key phenological nodes, including the start and end time of the growing season and the length of the growing season; Analyze the driving effect of climate factors on the start and end time of the growing season and the length of the growing season, and determine the main influencing factors; Based on climate models and different emission scenarios, predict the trend of vegetation phenology under future climate change; Assess regional ecosystem responses based on historical and future phenological change data.

2. The comprehensive evaluation method for vegetation phenology change trend according to claim 1 is characterized in that: The climate data include the daily average temperature, daily maximum temperature, daily minimum temperature and precipitation in the target area.

3. The comprehensive evaluation method for vegetation phenology change trend according to claim 1 is characterized in that: The steps of using the Vaganov-Shashkin model to simulate the relationship between radial growth and phenology of trees and generate a time series of phenological changes include: Climate data of the target area are used as model input; The Vaganov-Shashkin model was used to calculate the effect of climate factors on the growth rate of tree ring width; Based on the model output, time series of tree-ring widths were generated and correlated with climate data.

4. The comprehensive evaluation method for vegetation phenology change trend according to claim 1 is characterized in that: The step of extracting candidate key nodes comprises: Based on the tree-ring width time series, calculate the changes in annual growth rate; By setting the critical value of the growth rate, the start time, end time and length of the growing season are determined.

5. The comprehensive evaluation method for vegetation phenology change trend according to claim 1 is characterized in that: The step of analyzing the driving effect of climate factors on the start time, end time and length of the growing season and determining the main influencing factors includes: Calculate the correlation coefficient between climate factors and key phenological nodes; The influence of climate factors is quantified through linear regression models, and the dominant climate factors and their driving intensity on changes in key phenological nodes are determined.

6. The comprehensive evaluation method for vegetation phenology change trend according to claim 1 is characterized in that: The steps of predicting the trend of vegetation phenology under future climate change based on climate models and different emission scenarios include: Select climate models and combine them with emission scenarios to generate future climate data; Using future climate data as input, the Vaganov-Shashkin model is used to predict the changing trends of the start time, end time and length of the growing season. The differences between the predicted results and historical data were compared, and the extent and rate of extension of the growing season length under different emission scenarios in the future were calculated.

7. The comprehensive evaluation method for vegetation phenology change trend according to claim 6 is characterized in that: In the step of predicting the trend of vegetation phenology under future climate change, the temperature and precipitation data under different emission scenarios are corrected based on the simulation results of the climate model, and the impact of climate factors on the length of the growing season is quantified in combination with the regression analysis model to improve the prediction accuracy.

8. The method for comprehensive evaluation of vegetation phenology change trend according to claim 1, characterized in that: The steps of assessing regional ecosystem responses based on historical and future phenological change data include: Conduct trend analysis on historical phenological data to identify the rate of change in the start and end of the growing season and the length of the growing season; Based on the predicted phenological change trends under future climate scenarios, analyze the impact of different emission scenarios on regional vegetation phenology; Integrate historical and future data to assess the sensitivity of vegetation to climate change and make recommendations for adaptive management of ecosystems.

9. The method for comprehensive evaluation of vegetation phenology change trend according to claim 8, characterized in that: The ecosystem adaptive management recommendations include optimization of regional vegetation planting layout, water resource regulation strategies, and vegetation protection measures under extreme climatic conditions.

10. A device for comprehensively evaluating vegetation phenological change trends, used to execute the method for comprehensively evaluating vegetation phenological change trends according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to obtain climate data and tree ring width data of the target area; Data processing module, used for preprocessing climate data, including vertical lapse rate correction and data normalization; Simulation analysis module, used to simulate radial growth and phenological changes of trees based on the Vaganov-Shashkin model; The statistical analysis module is used to extract the time series of the start and end time of the growing season and the length of the growing season, and to determine the impact of climate factors on phenological changes; A prediction module is used to predict the changing trend of the length of the growing season in the future based on the climate model; The evaluation module is used to integrate historical and future phenological change trends and generate ecosystem management recommendations.

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