Bayesian inference based high temperature stress vegetation stomatal regulation modeling system and method

Abstract

The application discloses a high-temperature stress vegetation stomatal regulation modeling system and method based on Bayesian inference, and relates to the technical field of ecological process simulation, wherein: a data acquisition module is used for collecting and preprocessing meteorological driving data; a stomatal conductance calculation module is used for calculating basic stomatal conductance based on a Jarvis stomatal conductance empirical model; a high-temperature stress correction module is used for correcting Rubisco enzyme related parameters based on a temperature power function, and constructing a high-temperature stress stomatal conductance correction function through a Logistic function; a correction parameter inversion module is used for inversing optimal parameters in combination with measured carbon flux data, Bayesian theorem and a Monte Carlo-Markov chain algorithm; and a carbon flux simulation module is used for substituting the optimal parameters into the high-temperature stress stomatal conductance correction function to correct the basic stomatal conductance under high-temperature stress, obtaining corrected stomatal conductance, and then coupling to an ecological process model to simulate a carbon flux process. The application can improve the simulation accuracy of the model under extreme high temperature.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological process simulation, and relates to, but is not limited to, a high-temperature stress vegetation stomatal regulation modeling system and method based on Bayesian inference. BACKGROUND

[0002] Biome-BGC and other ecosystem process models are important foundations for studying carbon, water and energy exchange between vegetation and the atmosphere. The models simulate key physiological processes such as stomatal conductance of vegetation on a daily scale by being based on the Farquhar photosynthesis biochemical model and the Jarvis stomatal conductance empirical model. However, existing ecosystem process models only set response mechanisms for low-temperature stress, and lack response mechanisms for high-temperature stress, such as stomatal closure, transpiration inhibition and photosynthesis decline caused by high-temperature stress. In tropical or subtropical regions, high-temperature stress not only directly affects plant enzyme activity, but also induces plants to reduce water transpiration loss by closing stomata, and the two mechanisms together cause a decrease in photosynthetic rate. In addition, existing models are difficult to capture the process of stomatal closure caused by high temperature on an hourly scale, especially in the afternoon, under the driving of daily mean temperature, thereby causing significant deviations between simulated Gross Primary Productivity (GPP), Net Primary Productivity (NPP) and transpiration and real observations in high-temperature scenarios, and limiting the application accuracy of the models in extreme climate response research.

[0003] Therefore, it is urgent to establish a stomatal regulation modeling system that can dynamically respond to high-temperature stress to solve related problems such as significant deviations between simulated GPP, NPP and transpiration and real observations in high-temperature scenarios, and to dynamically and accurately simulate vegetation stomatal regulation behavior and its impact on carbon and water cycles in high-temperature environments, thereby improving the accuracy and reliability of the modeling results. SUMMARY

[0004] The present application provides a high-temperature stress vegetation stomatal regulation modeling system and method based on Bayesian inference.

[0005] The technical solution of the present application is implemented as follows:

[0006] In a first aspect, the present application provides a high-temperature stress vegetation stomatal regulation modeling system based on Bayesian inference, which comprises a data acquisition module, a stomatal conductance calculation module, a high-temperature stress correction module, a correction parameter inversion module and a carbon and water flux simulation module, wherein:

[0007] The data acquisition module is configured to acquire high-time-resolution meteorological driving data of a target ecological region, and perform quality control processing and preprocessing on the acquired meteorological driving data, wherein the meteorological driving data includes air temperature, solar radiation, saturated water vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration; the stomatal conductance calculation module is configured to calculate the basic stomatal conductance of a leaf based on a Jarvis stomatal conductance empirical model and the meteorological driving data; the high-temperature stress correction module is configured to correct the Michaelis-Menten constant of Rubisco enzyme and the Rubisco enzyme activity coefficient according to a temperature power function based on hourly mean temperature, and construct a high-temperature stress stomatal conductance correction function at an hourly scale based on a Logistic function; the correction parameter inversion module is configured to invert the posterior probability distribution of a first parameter and a second parameter in the high-temperature stress stomatal conductance correction function according to Bayesian theorem and a Monte Carlo-Markov chain algorithm based on the measured carbon flux data of a flux station, and determine an optimal first parameter and an optimal second parameter; the carbon flux simulation module is configured to substitute the optimal first parameter and the optimal second parameter into the high-temperature stress stomatal conductance correction function, perform high-temperature stress correction on the basic stomatal conductance to obtain a corrected stomatal conductance, couple the corrected stomatal conductance to an ecological process model, simulate a carbon flux process, and output a simulation result, and compare the simulation result with an observation result to generate a comparison evaluation result.

[0008] The technical scheme provided in the application collects high-time-resolution meteorological driving data of a target ecological region through a data acquisition module, including air temperature, solar radiation, saturated water vapor pressure difference, relative humidity and atmospheric carbon dioxide concentration, and performs quality control processing and preprocessing on the collected meteorological driving data, to provide accurate input for the model, effectively capture short-term extreme climate events such as afternoon high temperature, lay a solid data foundation for subsequent accurate simulation of plant ecological processes, and solve the shortcoming that daily average meteorological driving data cannot capture high temperature peaks; in the stomatal conductance calculation module, based on the Jarvis stomatal conductance empirical model, the influence of multiple meteorological driving data on stomatal conductance is integrated to calculate the basic stomatal conductance of leaves, to provide a reliable basis for subsequent stomatal conductance correction, and at the same time ensure the rationality and stability of the core mechanism of the model; in the high temperature stress correction module, based on the hourly average temperature, the Michaelis-Menten constant of Rubisco enzyme and the Rubisco enzyme activity coefficient are corrected according to the temperature power function, to correct the influence of high temperature stress on Rubisco enzyme activity, directly simulate the direct damage of high temperature to photosynthesis itself, i.e. non-stomatal limitation, to solve the shortcomings of existing models that only consider stomatal limitation or low temperature limitation, make the simulation of photosynthesis more accurate, construct a high-temperature stress stomatal conductance correction function on the hour scale based on the Logistic function, realize high-temperature correction of stomatal conductance, the Logistic function can depict the nonlinear closing process of stomata under high temperature, through two parameters with clear meaning in the Logistic function, flexible and accurate simulation of the differential response strategies of different ecosystems to high temperature stress is realized, and the phenomenon of stomatal closure caused by noon high temperature is effectively characterized; in the correction parameter inversion module, based on the measured carbon flux data of the flux station, the posterior probability distribution of the first parameter and the second parameter in the high-temperature stress stomatal conductance correction function is inverted according to the Bayes theorem and the Monte Carlo-Markov chain algorithm, the uncertainty in the parameter estimation process is quantified through the posterior probability distribution of the first parameter and the second parameter, to improve the scientificity and credibility of parameter estimation, avoid human subjectivity, and determine the optimal first parameter and the optimal second parameter, through defining a reasonable prior probability distribution and selecting the best sampling algorithm for parameter inversion, the efficiency and convergence of the inversion process are ensured, and finally the optimal first parameter and the optimal second parameter provide reliable and specific parameter values for subsequent simulation applications.The optimal first parameter and the optimal second parameter are substituted into the high-temperature stress stomatal conductance correction function through a carbon and water flux simulation module, and the base stomatal conductance is corrected under high-temperature stress to obtain a corrected stomatal conductance. The corrected stomatal conductance is coupled to an ecological process model to simulate a carbon and water flux process and output simulation results. The simulation results are compared with observation results to generate comparison evaluation results. All the correction and optimization results are integrated and coupled in the ecological process model to realize final simulation of key carbon and water fluxes such as GPP, NPP and transpiration, which collectively reflect the real impact of extreme high temperature on the carbon and water cycle of an ecological system. Moreover, through automatic comparison of simulation results with real observation results, a series of evaluation results such as goodness-of-fit, error analysis and confidence interval evaluation results are obtained, which can directly prove that the technical scheme provided in the application has improved precision compared with existing models, and has enhanced reliability and practical value of simulation results, thereby providing a comprehensive scientific basis for user decision-making.

[0009] Optionally, the calculation formula of the base stomatal conductance is represented by the following formula:

[0010] ;

[0011] In the formula, Gc represents the instantaneous base stomatal conductance; Gmax represents the maximum stomatal conductance; T represents the leaf temperature; Vpd represents the saturated water vapor pressure difference; Ca represents the atmospheric carbon dioxide concentration; PAR represents the light quantum; and Ψ represents the leaf water potential.

[0012] Optionally, the high-temperature stress correction module includes a high-temperature stress photosynthetic enzyme activity correction unit and a high-temperature stress stomatal conductance correction unit. The high-temperature stress photosynthetic enzyme activity correction unit is configured to correct Michaelis-Menten constants of carboxylation and oxidation reactions of Rubisco enzyme and a Rubisco enzyme activity coefficient according to a temperature power function based on hourly mean temperature, wherein the Michaelis-Menten constants include an oxygen Michaelis constant and a carbon dioxide Michaelis constant. The calculation formula of the oxygen Michaelis constant, the carbon dioxide Michaelis constant and the Rubisco enzyme activity coefficient is represented by the following formula:

[0013] ;

[0014] In the formula, Kcat represents the oxygen Michaelis constant; Kcat represents the carbon dioxide Michaelis constant; and a represents the Rubisco enzyme activity coefficient. ​​​​​​​​The Michaelis constant for oxygen in the oxidation reaction at 25 degrees Celsius. mbar; An index representing the temperature sensitivity of an oxidation reaction. ; Indicates the average temperature per hour; Represents the Michaelis constant for carbon dioxide; The Michaelis constant for carbon dioxide in the carboxylation reaction at 25 degrees Celsius. ubar; An index representing the temperature sensitivity of the carboxylation reaction. ; Indicates the Rubisco enzyme activity coefficient; This represents the Rubisco enzyme activity coefficient at 25 degrees Celsius. Indicates the temperature sensitivity index of Rubisco enzyme. The high-temperature stress stomatal conductance correction unit is used to construct the influence function of high temperature on stomatal conductance based on the Logistic function, thereby obtaining the high-temperature stress stomatal conductance correction function. The expression formula of the high-temperature stress stomatal conductance correction function is expressed by the following formula:

[0015] ;

[0016] In the formula, This represents the porosity correction function under high temperature stress. Indicates the first parameter; Indicates the second parameter; This indicates the average temperature per hour.

[0017] Optionally, the correction parameter inversion module is specifically used for: setting the prior probability distributions of the first parameter and the second parameter, wherein the prior probability distribution of the first parameter is a truncated normal distribution in the interval [0.3, 0.8], and the prior probability distribution of the second parameter is a truncated normal distribution in the interval [10, 20]; based on the prior probability distributions of the first parameter and the second parameter, sampling multiple sets of parameter candidate values ​​according to the Monte Carlo-Markov chain sampling algorithm, inputting the multiple sets of parameter candidate values ​​into an hourly-scale ecological process model, running the ecological process model, and obtaining a set of simulated carbon and water flux values ​​corresponding to each set of parameter candidate values; based on the set of simulated carbon and water flux values ​​and the measured carbon and water flux data, calculating the likelihood probability of observing the corresponding measured carbon and water flux data under each set of parameter candidate values, wherein the likelihood probability is constructed based on the error between the set of simulated carbon and water flux values ​​and the measured carbon and water flux data, and the error follows a normal distribution, and the calculation formula of the likelihood probability is expressed by the following formula:

[0018] ;

[0019] In the formula, Represents the likelihood probability; This represents the total number of data points for measured carbohydrate flux. Indicates the first Measured carbohydrate flux data for each data point; The standard deviation of measured carbohydrate flux data is represented. This represents the simulated value of carbon flux corresponding to the measured carbon flux data. Based on Bayes' theorem, and combining the prior probability distribution with the likelihood probability, the posterior probability distributions of the first and second parameters are calculated. The formulas for calculating the posterior probability distributions of the first and second parameters are expressed as follows:

[0020] ;

[0021] In the formula, Let represent the posterior probability distribution of the first and second parameters; This represents the prior probability distribution of the first and second parameters; This represents the probability distribution of measured carbohydrate flux data; This indicates the number of sampled candidate values ​​for the parameter; This represents the candidate values ​​for the i-th group of parameters; This represents the prior probability that the first and second parameters are candidate values ​​of the i-th group of parameters; This represents the likelihood probability of observing measured carbon flux data given that the first and second parameters are candidate values ​​of the i-th group of parameters. The process of determining the posterior probability distribution of the first and second parameters is performed iteratively until the posterior probability distributions of the first and second parameters converge. The optimal first and second parameters are determined based on the converged posterior probability distributions.

[0022] Optionally, the carbon flux simulation module includes a stomatal conductance correction unit and a carbon flux simulation unit, wherein: the stomatal conductance correction unit is used to substitute the optimal first parameter and the optimal second parameter into the high-temperature stress stomatal conductance correction function to perform high-temperature stress correction on the basic stomatal conductance to obtain the corrected stomatal conductance; the carbon flux simulation unit is used to couple the corrected stomatal conductance to an ecological process model, simulate the carbon flux process and output the simulation results, compare the simulation results with the observation results, and generate a comparative evaluation result, the comparative evaluation result including goodness of fit, error analysis and confidence interval evaluation results, and the simulation results including GPP, NPP and transpiration.

[0023] Secondly, embodiments of this application provide a method for modeling stomatal regulation of vegetation under high-temperature stress based on Bayesian inference. This method is applied to a system for modeling stomatal regulation of vegetation under high-temperature stress based on Bayesian inference. The system includes a data acquisition module, a stomatal conductance calculation module, a high-temperature stress correction module, a correction parameter inversion module, and a carbon-water flux simulation module. The method includes: acquiring high temporal resolution meteorological driving data of the target ecological region, and performing quality control processing and preprocessing on the acquired meteorological driving data. The meteorological driving data includes air temperature, solar radiation, saturated vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration; calculating the basic stomatal conductance of leaves based on the Jarvis stomatal conductance empirical model and the meteorological driving data; and correcting the Rubisc stomatal conductance based on hourly average temperature using a temperature power function. Based on the Michaelis-Menten constant and Rubisco enzyme activity coefficient of the enzyme, a high-temperature stress stomatal conductance correction function is constructed using the Logistic function. Based on measured carbon flux data from flux stations, the posterior probability distributions of the first and second parameters in the high-temperature stress stomatal conductance correction function are inverted using Bayes' theorem and the Monte Carlo-Markov chain algorithm, and the optimal first and second parameters are determined. These optimal parameters are then substituted into the high-temperature stress stomatal conductance correction function to correct the baseline stomatal conductance for high-temperature stress, resulting in the corrected stomatal conductance. This corrected stomatal conductance is coupled to an ecological process model to simulate the carbon flux process and output the simulation results. The simulation results are then compared with the observed results to generate a comparative evaluation result.

[0024] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps in the above-described modeling method for stomatal regulation of vegetation under high temperature stress based on Bayesian inference.

[0025] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the above-described modeling method for stomatal regulation of vegetation under high-temperature stress based on Bayesian inference.

[0026] The beneficial effects of the technical solutions provided in this application include at least the following:

[0027] This application provides a Bayesian inference-based modeling system and method for stomatal regulation of vegetation under high-temperature stress. The system collects high-temporal-resolution meteorological driving data of the target ecological region through a data acquisition module, including air temperature, solar radiation, saturated vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration. The collected meteorological driving data undergoes quality control and preprocessing to provide accurate input for the model, effectively capturing short-term extreme climate events such as afternoon high temperatures. This lays a solid data foundation for subsequent accurate simulation of plant ecological processes and overcomes the limitation of daily average meteorological driving data in capturing high-temperature peaks. In the stomatal conductance calculation module, the system... Based on the Jarvis stomatal conductance empirical model, this paper integrates the influence of multiple meteorological driving data on stomatal conductance to calculate the basic stomatal conductance of leaves, providing a reliable basis for subsequent stomatal conductance correction while ensuring the rationality and stability of the model's core mechanism. In the high-temperature stress correction module, based on hourly average temperature, the Michaelis-Menten constant and Rubisco enzyme activity coefficient are corrected according to the temperature power function to correct the effect of high-temperature stress on Rubisco enzyme activity. This directly simulates the direct damage of high temperature to photosynthesis itself, i.e., non-stomatal limitation, to address the issue that existing models only consider... The limitations of stomatal or low-temperature limitations make the simulation of photosynthesis more accurate. A stomatal conductance correction function for high-temperature stress at an hourly scale is constructed based on the Logistic function to achieve high-temperature correction of stomatal conductance. The Logistic function can characterize the nonlinear stomatal closure process under high temperatures. Through two clearly defined parameters in the Logistic function, the differentiated response strategies of different ecosystems to high-temperature stress can be flexibly and accurately simulated, achieving an effective characterization of stomatal closure caused by midday high temperatures. In the correction parameter inversion module, based on the measured carbon flux data from the flux station, and according to Bayes' theorem... The posterior probability distributions of the first and second parameters in the high-temperature stress porosity correction function were inverted using the Monte Carlo Markov chain algorithm. The uncertainty in the parameter estimation process was quantified by the posterior probability distributions of the first and second parameters, thereby improving the scientificity and credibility of the parameter estimation, avoiding human subjectivity, and determining the optimal first and second parameters. By defining a reasonable prior probability distribution and selecting the best sampling algorithm for parameter inversion, the efficiency and convergence of the inversion process were ensured. The finally determined optimal first and second parameters provide reliable and specific parameter values ​​for subsequent simulation applications.By substituting the optimal first and second parameters into the high-temperature stress stomatal conductance correction function through the carbon flux simulation module, the basic stomatal conductance is corrected for high-temperature stress, resulting in corrected stomatal conductance. This corrected stomatal conductance is then coupled to the ecological process model to simulate the carbon flux process and output simulation results. The simulation results are compared with observed results to generate comparative evaluation results. Integrating all the aforementioned correction and optimization results and coupling them into the ecological process model achieves the final simulation of key carbon fluxes such as GPP, NPP, and transpiration, comprehensively reflecting the real impact of extreme high temperatures on the ecosystem's carbon cycle. Furthermore, by automatically comparing the simulation results with actual observation results, a series of evaluation results are obtained, including goodness-of-fit, error analysis, and confidence interval assessment results. This directly demonstrates the improved accuracy of the technical solution provided in this application compared to existing models, enhancing the reliability and practical value of the simulation results and providing comprehensive scientific basis for user decision-making. The Bayesian inference-based stomatal regulation modeling system and method for vegetation under high-temperature stress provided in this application can effectively simulate stomatal closure caused by high temperatures and its impact on the carbon cycle, improving the model's prediction accuracy under extreme high-temperature scenarios. It also possesses flux station data-driven capabilities, is applicable to various ecological types, and has significant application value in ecological response simulation, climate change assessment, and carbon sink management. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0029] Figure 1 A schematic diagram of a Bayesian inference-based stomatal regulation modeling system for vegetation under high temperature stress provided in an embodiment of this application;

[0030] Figure 2 A schematic diagram of a Logistic function curve provided in an embodiment of this application;

[0031] Figure 3 A flowchart of a Bayesian inference-based stomatal regulation modeling method for vegetation under high temperature stress provided in an embodiment of this application;

[0032] Figure 4 This is a schematic diagram of the hardware entity of an electronic device provided in an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0035] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0036] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0037] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0038] In view of the current problems in the research on stomatal regulation modeling of vegetation under high temperature stress in the field of ecological process simulation technology, this application provides a system and method for stomatal regulation modeling of vegetation under high temperature stress based on Bayesian inference.

[0039] The technical solution of this application is described below, starting with the system implementation of this application.

[0040] Please refer to Figure 1 It shows a schematic diagram of a Bayesian inference-based stomatal regulation modeling system for vegetation under high temperature stress provided in an embodiment of this application, such as... Figure 1As shown, the system includes a data acquisition module 01, a stomatal conductance calculation module 02, a high-temperature stress correction module 03, a correction parameter inversion module 04, and a carbon-water flux simulation module 05. The carbon-water flux simulation module 05 is connected to the stomatal conductance calculation module 02, the high-temperature stress correction module 03, and the correction parameter inversion module 04, respectively, and the data acquisition module 01, the stomatal conductance calculation module 02, the high-temperature stress correction module 03, the correction parameter inversion module 04, and the carbon-water flux simulation module 05 are connected sequentially. The data acquisition module 01 is used to acquire high temporal resolution meteorological driving data of the target ecological area, and to perform quality control and preprocessing on the acquired meteorological driving data. The meteorological driving data includes air temperature, solar radiation, saturated vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration. The stomatal conductance calculation module 02 is used to calculate the basic stomatal conductance of the leaves based on the Jarvis stomatal conductance empirical model and the meteorological driving data. The high temperature stress correction module 03 is used to correct the Michaelis-Menten constant and Rubisco enzyme activity coefficient of Rubisco enzyme based on hourly average temperature and a temperature power function, and to construct hourly high temperature stress data based on the Logistic function. The stomatal conductance correction function; the correction parameter inversion module 04 is used to invert the posterior probability distribution of the first and second parameters in the high-temperature stress stomatal conductance correction function based on measured carbon and water flux data from flux stations, according to Bayes' theorem and Monte Carlo-Markov chain algorithm, and determine the optimal first and optimal second parameters; the carbon and water flux simulation module 05 is used to substitute the optimal first and optimal second parameters into the high-temperature stress stomatal conductance correction function, perform high-temperature stress correction on the basic stomatal conductance to obtain the corrected stomatal conductance, couple the corrected stomatal conductance to the ecological process model, simulate the carbon and water flux process and output the simulation results, compare the simulation results with the observation results, and generate a comparative evaluation result.

[0041] In this embodiment, the data acquisition module 01 is used to collect high temporal resolution meteorological driving data of the target ecological area and to perform quality control and preprocessing on the collected meteorological driving data. Specifically, meteorological driving data is read hourly in the target ecological area to obtain high temporal resolution meteorological driving data, including high temporal resolution air temperature, high temporal resolution solar radiation, high temporal resolution saturated vapor pressure difference, high temporal resolution relative humidity, and high temporal resolution atmospheric carbon dioxide concentration. By ensuring high temporal resolution (e.g., 1 hour) of the collected data, the impact of extreme climate events such as afternoon high temperatures on plant physiological processes can be quickly captured. Furthermore, the collected meteorological driving data undergoes quality control and preprocessing, such as identifying erroneous data, marking abnormal data, and filling in missing data. This process transforms the original meteorological driving data into clean, complete, and consistent data to ensure the accuracy, stability, and reliability of subsequent modeling and analysis.

[0042] In this embodiment, the stomatal conductance calculation module 02 is used to calculate the basic stomatal conductance of the blade based on the Jarvis stomatal conductance empirical model and meteorological driving data. Specifically, the Jarvis stomatal conductance empirical model is used to model the multi-factor environmental response, which includes meteorological driving data such as air temperature, solar radiation, saturated vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration. In the Jarvis stomatal conductance empirical model, the stomatal conductance response to different meteorological driving data is independent of each other. The basic stomatal conductance of the blade is calculated by multiplying the influence functions of multiple meteorological driving data on the stomatal conductance. The calculation formula for the basic stomatal conductance is expressed by the following formula:

[0043] ;

[0044] In the formula, This refers to the instantaneous basic porosity; Indicates the maximum porosity; A function representing the effect of blade temperature on stomatal conductance; A function representing the effect of saturated water vapor pressure difference on stomatal conductance; The effect function of atmospheric carbon dioxide concentration on stomatal conductance; The function representing the effect of light quanta on porosity; This represents the function that describes the effect of blade water potential on stomatal conductance. , , , , The specific values ​​are all between 0 and 1, reflecting the degree to which each meteorological driving data reduces the maximum stomatal conductance.

[0045] In this embodiment, the high-temperature stress correction module 03 includes a high-temperature stress photosynthetic enzyme activity correction unit and a high-temperature stress stomatal conductance correction unit. The high-temperature stress photosynthetic enzyme activity correction unit is used to correct the Michaelis-Menten constants and Rubisco enzyme activity coefficients for carboxylation and oxidation reactions of Rubisco enzymes based on hourly average temperature and a temperature power function, thereby correcting the effect of high-temperature stress on Rubisco enzyme activity. Specifically, high temperature limits the intensity of photosynthesis by affecting enzyme activity. The key enzyme in photosynthesis is Rubisco enzyme; therefore, by influencing Rubisco enzyme activity to adjust the intensity of photosynthesis, the Michaelis-Menten constants and Rubisco enzyme activity coefficients for carboxylation and oxidation reactions of Rubisco enzymes are corrected based on hourly average temperature and a temperature power function, thereby correcting the effect of high-temperature stress on Rubisco enzyme activity. The Michaelis-Menten constant includes the oxygen Michaelis constant and the carbon dioxide Michaelis constant. Among them, the oxygen Michaelis constant represents the affinity of Rubisco enzymes for oxygen. Rubisco enzymes catalyze the carboxylation reaction that fixes carbon dioxide and the oxidation reaction of photorespiration, and the oxygen Michaelis constant is closely related to both reactions. The carbon dioxide Michaelis constant represents the affinity of Rubisco enzymes for carbon dioxide. The higher the carbon dioxide Michaelis constant, the lower the affinity of Rubisco enzymes for carbon dioxide, meaning that a higher carbon dioxide concentration is required to reach a half-saturation rate. The Rubisco enzyme activity coefficient is a multiplier factor that directly represents the activity level of Rubisco enzymes. The formulas for calculating the oxygen Michaelis constant, carbon dioxide Michaelis constant, and Rubisco enzyme activity coefficient are expressed as follows:

[0046] ;

[0047] In the formula, This represents the Michaelis constant for oxygen. The Michaelis constant for oxygen in the oxidation reaction at 25 degrees Celsius. mbar; An index representing the temperature sensitivity of an oxidation reaction. ; Indicates the average temperature per hour; Represents the Michaelis constant for carbon dioxide, when the hourly average temperature is... When, the Michaelis constant of carbon dioxide When the hourly average temperature When, the Michaelis constant of carbon dioxide ; The Michaelis constant for carbon dioxide in the carboxylation reaction at 25 degrees Celsius. ubar; An index representing the temperature sensitivity of the carboxylation reaction. ; Indicates the Rubisco enzyme activity coefficient; when the average temperature is within hours... At that time, Rubisco enzyme activity coefficient When the hourly average temperature At that time, Rubisco enzyme activity coefficient ; This represents the Rubisco enzyme activity coefficient at 25 degrees Celsius. Indicates the temperature sensitivity index of Rubisco enzyme. Furthermore, after correcting the oxygen Michaelis constant, carbon dioxide Michaelis constant, and Rubisco enzyme activity coefficient, the effect of high temperature stress on Rubisco enzyme activity was corrected based on the corrected oxygen Michaelis constant, carbon dioxide Michaelis constant, and Rubisco enzyme activity coefficient.

[0048] In this embodiment, the high-temperature stress stomatal conductance correction unit is used to construct a function of the influence of high temperature on stomatal conductance based on the Logistic function, thereby obtaining a high-temperature stress stomatal conductance correction function and completing the high-temperature correction of stomatal conductance. Specifically, the correction of the influence of high temperature on stomatal conductance means that when the temperature is greater than a certain critical value, such as when the temperature is greater than the optimum temperature, the stomatal conductance is corrected. The Logistic function is used for high-temperature correction of stomatal conductance. Based on the Logistic function, a function of the influence of high temperature on stomatal conductance is constructed to obtain a high-temperature stress stomatal conductance correction function on an hourly scale. The expression formula of this high-temperature stress stomatal conductance correction function is expressed by the following formula:

[0049] ;

[0050] In the formula, This represents the porosity correction function under high temperature stress. Indicates the average temperature per hour; This represents the first parameter of the Logistic function; This represents the second parameter of the Logistic function, and the first parameter... Second parameter Used to control curve shape; for example, please refer to [reference needed]. Figure 2 It shows a schematic diagram of a Logistic function curve provided in an embodiment of this application. Figure 2 It includes several different first parameters. Second parameter The corresponding Logistic function curve, including and The corresponding Logistic function curve, and The corresponding Logistic function curve and its and The corresponding Logistic function curves, as shown in the graph, vary depending on the first parameter. Second parameter The temperature threshold and rate of decline at which stomatal conductance correction under high-temperature stress can be controlled; that is, the shape of the Logistic function curve presented by different parameter combinations can reflect the response process of the ecosystem to temperature stress. Specifically, the second parameter... The primary control is the location of the inflection point of the curve, i.e., the temperature threshold at which high-temperature stress porosity correction begins; the secondary parameter is... The higher the value, the higher the temperature required for the inflection point, indicating that the plant is more heat-tolerant and requires even higher temperatures to initiate heat stress stomatal conductance correction and close the stomata. (Second parameter) The smaller the value, the more sensitive the plant is to high temperatures. Even at relatively low temperatures, it will initiate heat stress stomatal conductance correction and begin closing its stomata. (First parameter) The primary control is the steepness of the curve, i.e., the rate of stomatal closure, the first parameter. The larger the value, the steeper the curve, indicating that once the temperature exceeds the threshold, the stomata will close rapidly within a very narrow temperature range. (First parameter) The smaller the value, the flatter the curve, indicating that the pore closure process spans a wider temperature range.

[0051] In this embodiment, the correction parameter inversion module 04 is used to invert the posterior probability distributions of the first and second parameters in the high-temperature stress stomatal conductance correction function based on measured carbon flux data from the flux station, according to Bayes' theorem and the Monte Carlo-Markov chain algorithm, and to determine the optimal first and second parameters. Specifically, the prior probability distributions of the first and second parameters are truncated normal distributions with a standard deviation of 5. The prior probability distributions of the first and second parameters are set, wherein the prior probability distribution of the first parameter is a truncated normal distribution in the interval [0.3, 0.8], i.e. The prior probability distribution of the second parameter is a truncated normal distribution on the interval [10, 20], i.e. Since ecological process models are typically nondifferentiable, the parameters are sampled using the Monte Carlo Markov chain sampling algorithm based on the prior probability distributions of the first and second parameters to obtain multiple sets of candidate parameter values. These candidate values ​​are then input into an hourly-scale ecological process model to simulate the carbon and water flux processes in the ecosystem. Running the ecological process model yields a set of simulated carbon and water flux values ​​corresponding to each set of candidate parameter values.

[0052] Furthermore, since there is no measured data on porosity conductance, the measured carbon flux data from the flux station is used. Based on the simulated carbon flux set and the measured carbon flux data, the likelihood probability of observing the corresponding measured carbon flux data under each set of candidate parameter values ​​is calculated. The simulated carbon flux values ​​obtained under given candidate parameter values ​​have a certain error compared to the measured carbon flux data. The likelihood probability is constructed based on the error between the simulated carbon flux set and the measured carbon flux data, and the error follows a normal distribution. The formula for calculating the likelihood probability is expressed as follows:

[0053] ;

[0054] In the formula, This represents the likelihood probability of observing the corresponding measured carbon flux data given candidate values ​​of the parameters. This represents the total number of data points for measured carbohydrate flux. Indicates the first Measured carbohydrate flux data for each data point; The standard deviation of measured carbohydrate flux data is represented. This represents the simulated value of carbon flux corresponding to the measured carbon flux data under given candidate parameter values.

[0055] Furthermore, according to Bayes' theorem, combining the prior probability distribution and the likelihood probability, the posterior probability distributions of the first and second parameters are calculated. The formulas for calculating the posterior probability distributions of the first and second parameters are expressed as follows:

[0056] ;

[0057] In the formula, Let represent the posterior probability distribution of the first and second parameters; This represents the prior probability distribution of the first and second parameters; This represents the probability distribution of measured carbohydrate flux data; This indicates the number of sampled candidate values ​​for the parameter; This represents the candidate values ​​for the i-th group of parameters; This represents the prior probability that the first and second parameters are candidate values ​​of the i-th group of parameters; This represents the likelihood probability of observing measured carbon flux data given that the first and second parameters are candidate values ​​from the i-th group of parameters. The process of determining the posterior probability distributions of the first and second parameters is iteratively executed until these distributions converge. The optimal first and second parameters can then be determined based on the converged posterior probability distributions.

[0058] In this embodiment, the carbon flux simulation module 05 includes a stomatal conductance correction unit and a carbon flux simulation unit. The stomatal conductance correction unit substitutes the optimal first parameter and the optimal second parameter into a high-temperature stress stomatal conductance correction function, and performs high-temperature stress correction on the basic stomatal conductance based on the high-temperature stress stomatal conductance correction function after substituting the optimal first parameter and the optimal second parameter, thus obtaining the corrected stomatal conductance. The carbon flux simulation unit couples the corrected stomatal conductance to an ecological process model, simulates the carbon flux process, and outputs simulation results, including GPP, NPP, and transpiration. The simulation results are compared with observed results to generate a comparative evaluation result, which includes goodness-of-fit, error analysis, and confidence interval evaluation results.

[0059] In summary, the Bayesian inference-based stomatal regulation modeling system for high-temperature stress vegetation provided in this application collects high temporal resolution meteorological driving data of the target ecological area through a data acquisition module, including air temperature, solar radiation, saturated vapor pressure difference, relative humidity, and atmospheric carbon dioxide concentration. The collected meteorological driving data undergoes quality control and preprocessing to provide accurate input for the model, effectively capturing short-term extreme climate events such as afternoon high temperatures. This lays a solid data foundation for subsequent accurate simulation of plant ecological processes and overcomes the limitation of daily average meteorological driving data in capturing high-temperature peaks. Furthermore, the system is effective in stomatal conductance calculation. In this module, based on the Jarvis stomatal conductance empirical model, the influence of multiple meteorological driving data on stomatal conductance is integrated to calculate the basic stomatal conductance of the leaf, providing a reliable basis for subsequent stomatal conductance correction while ensuring the rationality and stability of the model's core mechanism. In the high-temperature stress correction module, based on hourly average temperature, the Michaelis-Menten constant and Rubisco enzyme activity coefficient are corrected according to a temperature power function to correct the effect of high-temperature stress on Rubisco enzyme activity, directly simulating the direct damage of high temperature to photosynthesis itself, i.e., non-stomatal limitation, to address the shortcomings of existing models. To address the limitations of considering only stomatal or low-temperature limitations and improve the accuracy of photosynthesis simulations, a stomatal conductance correction function for high-temperature stress at an hourly scale is constructed based on the Logistic function. This achieves high-temperature correction of stomatal conductance. The Logistic function can characterize the nonlinear stomatal closure process under high temperatures. By using two clearly defined parameters in the Logistic function, the differentiated response strategies of different ecosystems to high-temperature stress can be flexibly and accurately simulated, effectively characterizing the stomatal closure phenomenon caused by midday high temperatures. In the correction parameter inversion module, based on measured carbon flux data from flux stations, and according to Bayesian methods... The posterior probability distributions of the first and second parameters in the high-temperature stress porosity correction function were inverted using the Monte Carlo-Markov chain algorithm. These posterior probability distributions quantified the uncertainty in the parameter estimation process, improving the scientific rigor and reliability of the parameter estimation, avoiding human subjectivity, and determining the optimal first and second parameters. By defining a reasonable prior probability distribution and selecting the best sampling algorithm for parameter inversion, the efficiency and convergence of the inversion process were ensured. The final determined optimal first and second parameters provide reliable and specific parameter values ​​for subsequent simulation applications.By substituting the optimal first and second parameters into the high-temperature stress stomatal conductance correction function through the carbon flux simulation module, the basic stomatal conductance is corrected for high-temperature stress, resulting in corrected stomatal conductance. This corrected stomatal conductance is then coupled to the ecological process model to simulate the carbon flux process and output simulation results. The simulation results are compared with observed results to generate comparative evaluation results. Integrating all the aforementioned correction and optimization results and coupling them into the ecological process model achieves the final simulation of key carbon fluxes such as GPP, NPP, and transpiration, comprehensively reflecting the real impact of extreme high temperatures on the ecosystem's carbon cycle. Furthermore, by automatically comparing the simulation results with actual observation results, a series of evaluation results are obtained, including goodness-of-fit, error analysis, and confidence interval assessment results. This directly demonstrates the improved accuracy of the technical solution provided in this application compared to existing models, enhancing the reliability and practical value of the simulation results and providing comprehensive scientific basis for user decision-making. The Bayesian inference-based stomatal regulation modeling system for vegetation under high-temperature stress provided in this application can effectively simulate stomatal closure caused by high temperatures and its impact on the carbon cycle, improving the model's prediction accuracy under extreme high-temperature scenarios. It also possesses flux station data-driven capabilities, is applicable to various ecological types, and has significant application value in ecological response simulation, climate change assessment, and carbon sink management.

[0060] The above is a description of the system embodiments of this application. Based on the foregoing embodiments, the method embodiments of this application are described below.

[0061] Please refer to Figure 3 It shows a flowchart of a Bayesian inference-based stomatal regulation modeling method for vegetation under high temperature stress, provided in an embodiment of this application. This method is applied to, for example... Figure 1 The Bayesian inference-based stomatal regulation modeling system for vegetation under high-temperature stress illustrated here is described in the system embodiment for details not disclosed in the method embodiment. The system includes a data acquisition module, a stomatal conductance calculation module, a high-temperature stress correction module, a correction parameter inversion module, and a carbon-water flux simulation module. The carbon-water flux simulation module is connected to the stomatal conductance calculation module, the high-temperature stress correction module, and the correction parameter inversion module, respectively, and these modules are connected sequentially. Figure 3 As shown, the method includes the following steps S310 to S350.

[0062] Step S310: Collect high temporal resolution meteorological driving data of the target ecological area, and perform quality control processing and preprocessing on the collected meteorological driving data. The meteorological driving data includes air temperature, solar radiation, saturated water vapor pressure difference, relative humidity and atmospheric carbon dioxide concentration.

[0063] Step S320: Based on the Jarvis stomatal conductance empirical model, calculate the basic stomatal conductance of the blade according to the meteorological driving data.

[0064] In this embodiment of the application, the formula for calculating the basic porosity is expressed by the following formula:

[0065] ;

[0066] In the formula, This refers to the instantaneous basic porosity; Indicates the maximum porosity; A function representing the effect of blade temperature on stomatal conductance; A function representing the effect of saturated water vapor pressure difference on stomatal conductance; The effect function of atmospheric carbon dioxide concentration on stomatal conductance; The function representing the effect of light quanta on porosity; This represents the function that describes the effect of blade water potential on stomatal conductance.

[0067] Step S330: Based on the hourly average temperature, the Michaelis-Menten constant and Rubisco enzyme activity coefficient are corrected according to the temperature power function, and a high-temperature stress stomatal conductance correction function is constructed based on the Logistic function.

[0068] In this embodiment, based on the hourly average temperature, the Michaelis-Menten constants and Rubisco enzyme activity coefficients for the carboxylation and oxidation reactions are corrected according to a temperature power function. The Michaelis-Menten constants include the oxygen Michaelis constant and the carbon dioxide Michaelis constant. The calculation formulas for the oxygen Michaelis constant, the carbon dioxide Michaelis constant, and the Rubisco enzyme activity coefficient are expressed by the following formulas:

[0069] ;

[0070] In the formula, This represents the Michaelis constant for oxygen. The Michaelis constant for oxygen in the oxidation reaction at 25 degrees Celsius. mbar; An index representing the temperature sensitivity of an oxidation reaction. ; Indicates the average temperature per hour; Represents the Michaelis constant for carbon dioxide; The Michaelis constant for carbon dioxide in the carboxylation reaction at 25 degrees Celsius. ubar; An index representing the temperature sensitivity of the carboxylation reaction. ; Indicates the Rubisco enzyme activity coefficient; This represents the Rubisco enzyme activity coefficient at 25 degrees Celsius. Indicates the temperature sensitivity index of Rubisco enzyme. Based on the Logistic function, a function to assess the influence of high temperature on stomatal conductance is constructed, resulting in a high-temperature stress stomatal conductance correction function. The formula for this high-temperature stress stomatal conductance correction function is expressed as follows:

[0071] ;

[0072] In the formula, This represents the porosity correction function under high temperature stress. Indicates the first parameter; Indicates the second parameter; This indicates the average temperature per hour.

[0073] Step S340: Based on the measured carbon flux data of the flux station, the posterior probability distributions of the first and second parameters in the high-temperature stress stomatal conductance correction function are inverted according to Bayes' theorem and Monte Carlo-Markov chain algorithm, and the optimal first and second parameters are determined.

[0074] In this embodiment, prior probability distributions for a first parameter and a second parameter are set. The prior probability distribution of the first parameter is a truncated normal distribution in the interval [0.3, 0.8], and the prior probability distribution of the second parameter is a truncated normal distribution in the interval [10, 20]. Based on the prior probability distributions of the first and second parameters, multiple sets of candidate parameter values ​​are sampled according to the Monte Carlo-Markov chain sampling algorithm. These multiple sets of candidate parameter values ​​are input into an hourly-scale ecological process model, and the ecological process model is run to obtain a set of simulated carbon and water flux values ​​corresponding to each set of candidate parameter values. Based on the set of simulated carbon and water flux values ​​and the measured carbon and water flux data, the likelihood probability of observing the corresponding measured carbon and water flux data under each set of candidate parameter values ​​is calculated. The likelihood probability is constructed based on the error between the set of simulated carbon and water flux values ​​and the measured carbon and water flux data, and the error follows a normal distribution. The formula for calculating the likelihood probability is expressed by the following formula:

[0075] ;

[0076] In the formula, Represents the likelihood probability; This represents the total number of data points for measured carbohydrate flux. Indicates the first Measured carbohydrate flux data for each data point; The standard deviation of measured carbohydrate flux data is represented. This represents the simulated value of carbon flux corresponding to the measured carbon flux data. Based on Bayes' theorem, and combining the prior probability distribution with the likelihood probability, the posterior probability distributions of the first and second parameters are calculated. The formulas for calculating the posterior probability distributions of the first and second parameters are expressed as follows:

[0077] ;

[0078] In the formula, Let represent the posterior probability distribution of the first and second parameters; This represents the prior probability distribution of the first and second parameters; This represents the probability distribution of measured carbohydrate flux data; This indicates the number of sampled candidate values ​​for the parameter; This represents the candidate values ​​for the i-th group of parameters; This represents the prior probability that the first and second parameters are candidate values ​​of the i-th group of parameters; This represents the likelihood probability of observing measured carbon flux data given that the first and second parameters are candidate values ​​of the i-th group of parameters. The process of determining the posterior probability distribution of the first and second parameters is performed iteratively until the posterior probability distributions of the first and second parameters converge. The optimal first and second parameters are determined based on the converged posterior probability distributions.

[0079] Step S350: Substitute the optimal first parameter and the optimal second parameter into the high temperature stress stomatal conductance correction function to perform high temperature stress correction on the basic stomatal conductance to obtain the corrected stomatal conductance. Couple the corrected stomatal conductance to the ecological process model to simulate the carbon-water flux process and output the simulation results. Compare the simulation results with the observation results to generate a comparative evaluation result.

[0080] In this embodiment, the optimal first parameter and the optimal second parameter are substituted into the high-temperature stress stomatal conductance correction function to correct the basic stomatal conductance for high-temperature stress, thereby obtaining the corrected stomatal conductance. The corrected stomatal conductance is coupled to an ecological process model to simulate the carbon-water flux process and output the simulation results. The simulation results are compared with the observation results to generate a comparative evaluation result, which includes goodness of fit, error analysis, and confidence interval evaluation results. The simulation results include GPP, NPP, and transpiration.

[0081] It should be noted that, in the embodiments of this application, if the above-mentioned modeling method for stomatal regulation of vegetation under high-temperature stress based on Bayesian inference is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a magnetic disk, or an optical disk. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0082] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon. When executed by a processor, this computer program implements the steps in any of the Bayesian inference-based stomatal regulation modeling methods for high-temperature stress vegetation described in the above embodiments. Correspondingly, embodiments of this application also provide a computer program product. When executed by a processor of an electronic device, this computer program product is used to implement the steps in any of the Bayesian inference-based stomatal regulation modeling methods for high-temperature stress vegetation described in the above embodiments.

[0083] Based on the same technical concept, this application provides an electronic device for implementing the Bayesian inference-based stomatal regulation modeling method for vegetation under high temperature stress described in the above method embodiments. Figure 4 This is a hardware entity diagram of an electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device 400 includes a memory 410 and a processor 420. The memory 410 stores a computer program that can run on the processor 420. When the processor 420 executes the program, it implements the steps in any of the Bayesian inference-based stomatal regulation modeling methods for vegetation under high temperature stress described in the embodiments of this application.

[0084] The memory 410 is configured to store instructions and applications executable by the processor 420, and can also cache data to be processed or already processed by the processor 420 and various modules in the electronic device (e.g., image data, audio data, voice communication data and video communication data), which can be implemented by flash memory or random access memory (RAM).

[0085] When processor 420 executes a program, it implements any of the steps in the Bayesian inference-based modeling method for stomatal regulation of vegetation under high-temperature stress described above. Processor 420 typically controls the overall operation of electronic equipment 400.

[0086] The aforementioned processor can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that other electronic devices can also implement the functions of the aforementioned processor, and this application does not specifically limit the specific implementation.

[0087] The aforementioned computer storage media / memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0088] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0089] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0092] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0093] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0094] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause the device automatic test line to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0095] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0096] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0097] The above description is merely an embodiment 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 modeling system for stomatal regulation of vegetation under high-temperature stress based on Bayesian inference, characterized in that, The system includes: The data acquisition module is used to collect high temporal resolution meteorological driving data of the target ecological area, and to perform quality control processing and preprocessing on the collected meteorological driving data. The meteorological driving data includes air temperature, solar radiation, saturated water vapor pressure difference, relative humidity and atmospheric carbon dioxide concentration. The stomatal conductance calculation module is used to calculate the basic stomatal conductance of the blade based on the Jarvis stomatal conductance empirical model and the meteorological driving data. The high-temperature stress correction module includes a high-temperature stress photosynthetic enzyme activity correction unit and a high-temperature stress stomatal conductance correction unit, wherein: The high-temperature stress photosynthetic enzyme activity correction unit is used to correct the Michaelis-Menten constants and Rubisco enzyme activity coefficients for the carboxylation and oxidation reactions of Rubisco enzymes based on hourly average temperature and according to a temperature power function. The Michaelis-Menten constants include the oxygen Michaelis constant and the carbon dioxide Michaelis constant. The calculation formulas for the oxygen Michaelis constant, the carbon dioxide Michaelis constant, and the Rubisco enzyme activity coefficient are expressed by the following formulas: ； In the formula, This represents the Michaelis constant for oxygen. The Michaelis constant for oxygen in the oxidation reaction at 25 degrees Celsius. mbar; An index representing the temperature sensitivity of an oxidation reaction. ; Indicates the average temperature per hour; Represents the Michaelis constant for carbon dioxide; The Michaelis constant for carbon dioxide in the carboxylation reaction at 25 degrees Celsius. ubar; An index representing the temperature sensitivity of the carboxylation reaction. ; Indicates the Rubisco enzyme activity coefficient; This represents the Rubisco enzyme activity coefficient at 25 degrees Celsius. Indicates the temperature sensitivity index of Rubisco enzyme. The high-temperature stress stomatal conductance correction unit is used to construct the influence function of high temperature on stomatal conductance based on the Logistic function, thereby obtaining the high-temperature stress stomatal conductance correction function. The expression formula of the high-temperature stress stomatal conductance correction function is expressed by the following formula: ； In the formula, This represents the porosity correction function under high temperature stress. Indicates the first parameter; Indicates the second parameter; Indicates the average temperature per hour; The correction parameter inversion module is used to invert the posterior probability distribution of the first and second parameters in the high-temperature stress stomatal conductance correction function based on the measured carbon flux data of the flux station, according to Bayes' theorem and Monte Carlo-Markov chain algorithm, and to determine the optimal first and optimal second parameters. The carbon flux simulation module is used to substitute the optimal first parameter and the optimal second parameter into the high-temperature stress stomatal conductance correction function, perform high-temperature stress correction on the basic stomatal conductance to obtain the corrected stomatal conductance, couple the corrected stomatal conductance to the ecological process model, simulate the carbon flux process and output the simulation results, compare the simulation results with the observation results, and generate a comparative evaluation result.

2. The system according to claim 1, characterized in that, The formula for calculating the basic porosity conductivity is expressed by the following formula: ； In the formula, This refers to the instantaneous basic porosity; Indicates the maximum porosity; A function representing the effect of blade temperature on stomatal conductance; A function representing the effect of saturated water vapor pressure difference on stomatal conductance; The effect function of atmospheric carbon dioxide concentration on stomatal conductance; The function representing the effect of light quanta on porosity; This represents the function that describes the effect of blade water potential on stomatal conductance.

3. The system according to claim 1, characterized in that, The correction parameter inversion module is specifically used for: Set the prior probability distributions of the first parameter and the second parameter, wherein the prior probability distribution of the first parameter is a truncated normal distribution on the interval [0.3, 0.8], and the prior probability distribution of the second parameter is a truncated normal distribution on the interval [10, 20]. Based on the prior probability distribution of the first parameter and the prior probability distribution of the second parameter, multiple sets of parameter candidate values ​​are sampled according to the Monte Carlo-Markov chain sampling algorithm. The multiple sets of parameter candidate values ​​are input into an hourly-scale ecological process model, and the ecological process model is run to obtain a set of simulated carbon and water flux values ​​corresponding to each set of parameter candidate values. Based on the set of simulated carbohydrate flux values ​​and the measured carbohydrate flux data, the likelihood probability of observing the corresponding measured carbohydrate flux data under each set of candidate parameter values ​​is calculated. The likelihood probability is constructed based on the error between the set of simulated carbohydrate flux values ​​and the measured carbohydrate flux data, and the error follows a normal distribution. The formula for calculating the likelihood probability is expressed as follows: ； In the formula, Represents the likelihood probability; This represents the total number of data points for measured carbohydrate flux. Indicates the first Measured carbohydrate flux data for each data point; The standard deviation of measured carbohydrate flux data is represented. This represents the simulated value of carbon flux corresponding to the measured carbon flux data. According to Bayes' theorem, combining the prior probability distribution and the likelihood probability, the posterior probability distributions of the first and second parameters are calculated. The formulas for calculating the posterior probability distributions of the first and second parameters are expressed as follows: ； In the formula, Let represent the posterior probability distribution of the first and second parameters; This represents the prior probability distribution of the first and second parameters; This represents the probability distribution of measured carbohydrate flux data; This indicates the number of sampled candidate values ​​for the parameter; This represents the candidate values ​​for the i-th group of parameters; This represents the prior probability that the first and second parameters are candidate values ​​of the i-th group of parameters; It represents the likelihood probability of observed measured carbon flux data under the condition that the first and second parameters are candidate values ​​of the i-th group of parameters; The process of determining the posterior probability distributions of the first and second parameters is iteratively executed until the posterior probability distributions of the first and second parameters converge. The optimal first and second parameters are then determined based on the converged posterior probability distributions.

4. The system according to claim 1, characterized in that, The carbon flux simulation module includes a porosity correction unit and a carbon flux simulation unit, wherein: The porosity correction unit is used to substitute the optimal first parameter and the optimal second parameter into the high-temperature stress porosity correction function to perform high-temperature stress correction on the basic porosity to obtain the corrected porosity. The carbon flux simulation unit is used to couple the corrected stomatal conductance to the ecological process model, simulate the carbon flux process and output simulation results, compare the simulation results with the observation results, and generate comparative evaluation results. The comparative evaluation results include goodness of fit, error analysis and confidence interval evaluation results. The simulation results include gross primary productivity, net primary productivity and evapotranspiration.

5. A method for modeling stomatal regulation in vegetation under high-temperature stress based on Bayesian inference, characterized in that, A system for modeling stomatal regulation in vegetation under high-temperature stress based on Bayesian inference is provided. The system includes a data acquisition module, a stomatal conductance calculation module, a high-temperature stress correction module, a correction parameter inversion module, and a carbon flux simulation module. The method includes: High temporal resolution meteorological driving data of the target ecological area is collected, and the collected meteorological driving data is subjected to quality control processing and preprocessing. The meteorological driving data includes air temperature, solar radiation, saturated water vapor pressure difference, relative humidity and atmospheric carbon dioxide concentration. Based on the Jarvis stomatal conductance empirical model, the basic stomatal conductance of the blade is calculated according to the meteorological driving data. Based on the hourly average temperature, the Michaelis-Menten constants and Rubisco enzyme activity coefficients for carboxylation and oxidation reactions are corrected according to a temperature power function. The Michaelis-Menten constants include the oxygen Michaelis constant and the carbon dioxide Michaelis constant. The calculation formulas for the oxygen Michaelis constant, the carbon dioxide Michaelis constant, and the Rubisco enzyme activity coefficient are expressed by the following formulas: ； In the formula, This represents the Michaelis constant for oxygen. The Michaelis constant for oxygen in the oxidation reaction at 25 degrees Celsius. mbar; An index representing the temperature sensitivity of an oxidation reaction. ; Indicates the average temperature per hour; Represents the Michaelis constant for carbon dioxide; The Michaelis constant for carbon dioxide in the carboxylation reaction at 25 degrees Celsius. ubar; An index representing the temperature sensitivity of the carboxylation reaction. ; Indicates the Rubisco enzyme activity coefficient; This represents the Rubisco enzyme activity coefficient at 25 degrees Celsius. Indicates the temperature sensitivity index of Rubisco enzyme. Based on the Logistic function, a function to assess the influence of high temperature on stomatal conductance is constructed, resulting in a high-temperature stress stomatal conductance correction function. The formula for this high-temperature stress stomatal conductance correction function is expressed as follows: ； In the formula, This represents the porosity correction function under high temperature stress. Indicates the first parameter; Indicates the second parameter; Indicates the average temperature per hour; Based on the measured carbon flux data from the flux station, the posterior probability distributions of the first and second parameters in the high-temperature stress stomatal conductance correction function are inverted according to Bayes' theorem and the Monte Carlo-Markov chain algorithm, and the optimal first and second parameters are determined. Substitute the optimal first parameter and the optimal second parameter into the high-temperature stress stomatal conductance correction function to perform high-temperature stress correction on the basic stomatal conductance to obtain the corrected stomatal conductance. Couple the corrected stomatal conductance to the ecological process model to simulate the carbon-water flux process and output the simulation results. Compare the simulation results with the observation results to generate a comparative evaluation result.

6. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 5.

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