Carbon source and sink partition optimization method and device, and electronic device

Abstract

The application provides a carbon source and sink partition optimization method and device and electronic equipment, relates to the field of data processing, and solves the technical problem of low simulation accuracy of an existing ecological process model. The method comprises the following steps: dividing a target ecological region into a plurality of sub-ecological regions based on natural geographical features, climate types, vegetation distribution data and human activity influencing factors in the target ecological region, and determining a multi-scale and multi-type observation network based on the plurality of sub-ecological regions; reconstructing a plurality of key parameters in a WRF-VPRM model from static input into gridded parameters of dynamic grid input, to obtain an improved target WRF-VPRM model; performing partition calibration and dynamic updating of parameters for the plurality of key parameters in the target WRF-VPRM model based on observation data of a flux site by using a Markov chain Monte Carlo (MCMC) method, to obtain an ecological system carbon source and sink simulation model with linkage between climate, parameters and carbon flux.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, and electronic device for optimizing carbon source and sink zoning. Background Technology

[0002] Currently, against the backdrop of intensifying global climate change, the carbon cycle process of terrestrial ecosystems has become a core issue in Earth system science research. The global carbon flux of terrestrial ecosystems is approximately -3.4 PgC a⁻¹, while my country's is -0.44 PgC a⁻¹, offsetting about 15% of fossil fuel combustion emissions. This data highlights the crucial role of terrestrial ecosystems in buffering rising atmospheric CO₂ concentrations, and makes the accurate quantification of ecosystem carbon flux a scientific basis for predicting climate change trends and promoting energy transition. Currently, ecological process models, as the main tool for characterizing the spatiotemporal dynamics of the carbon cycle, are widely used in global and regional carbon budget assessments. However, the parameterization schemes of these ecological process models still fall short in characterizing spatial heterogeneity, especially exhibiting low simulation accuracy in complex geographical environments. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, and electronic device for optimizing carbon source and sink zones, in order to solve the technical problem of low simulation accuracy in existing ecological process models.

[0004] In a first aspect, this application provides a method for optimizing carbon source and sink zoning, the method comprising:

[0005] Obtain the target ecological region for the carbon source-sink simulation to be performed;

[0006] Based on the natural geographical features, climate type, vegetation distribution data, and human activity influencing factors of the target ecological region, the target ecological region is divided into multiple sub-ecological regions, and a multi-scale and multi-type observation network is determined based on the multiple sub-ecological regions; the observation network includes flux stations of various ecosystem types.

[0007] By reconstructing several key parameters in the WRF-VPRM (Weather Research and Forecasting - Vegetation Photosynthesis and Respiration Model) model from static inputs to gridded parameters of dynamic grid inputs, an improved target WRF-VPRM model is obtained. The multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model.

[0008] Based on the observation data from the flux stations, the MCMC (Markov Chain Monte Carlo Method) method is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux.

[0009] In one possible implementation, the process of reconstructing multiple key parameters in the WRF-VPRM model from static inputs to gridded parameters of dynamic grid inputs to obtain the improved target WRF-VPRM model includes:

[0010] Add four fields to the registry.chem file of the WRF-VPRM model; wherein, the four fields correspond to the four key parameters in the WRF-VPRM model respectively; the first field corresponds to the first parameter and is used to simulate the photosynthetic process of vegetation; the second field corresponds to the second parameter and is used to affect the ability of vegetation to convert light energy into chemical energy; the third and fourth fields correspond to the third parameter and are used to simulate the carbon dioxide flux of organisms.

[0011] The `chem_driver.F` and `module_ghg_fluxes.F` modules under the `chem` directory of the WRF-VPRM model, as well as the `real_em.F` and `ndown_em.F` modules under the `main` directory of the WRF-VPRM model, are systematically modified to obtain the modified modules. Based on the four added fields and the modified modules, multiple key parameters in the WRF-VPRM model are dynamically adjusted, reconstructing the multiple key parameters from static input to gridded data with dynamic gridded input, resulting in the improved target WRF-VPRM model. The `chem_driver.F` module and the `module_ghg_fluxes.F` module are then modified. The modules, including the real_em.F module and the ndown_em.F module, are related to the WRF-VPRM mode; the chem_driver.F module drives the entire simulation process; the module_ghg_fluxes.F module handles calculations related to greenhouse gas fluxes; the real_em.F module adapts to new parameter settings and input formats in real-world simulations; and the ndown_em.F module performs data transfer and parameter application in nested simulation scenarios.

[0012] In one possible implementation, the observational data based on the flux stations utilizes the Markov Chain Monte Carlo (MCMC) method to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux, including:

[0013] By using Bayes' theorem and the Markov chain Monte Carlo (MCMC) method, parameter partitioning optimization is performed on multiple key parameters in the target WRF-VPRM model. By integrating the observation data of the flux stations, the parameters of multiple key parameters are partitioned, calibrated, and dynamically updated to obtain an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux.

[0014] In one possible implementation, the method of using Bayes' theorem and Markov chain Monte Carlo (MCMC) optimization to partition and optimize multiple key parameters in the target WRF-VPRM model, and integrating the observation data from the flux stations to perform partition calibration and dynamic updates of the multiple key parameters, includes:

[0015] Based on the measurements from the observation data of the flux stations, determine the cost function or likelihood function for N measurements. The measured values ​​represent the photosynthetic GPP and respiration Re values ​​provided by the carbon flux network observation data.

[0016] Take the natural logarithm of both sides of the formula for L so that the cost function is expressed as a log-likelihood function LL;

[0017] The parameter values ​​of multiple key parameters are iteratively changed using the Markov Chain Monte Carlo (MCMC) method until the log-likelihood function LL reaches the global optimum. The output data of the MCMC method are the accepted parameter values ​​and the probability density function PDF. The accepted global optimum and posterior accepted parameter set represent the PDF of the most likely parameters that are consistent with the provided prior, observations, model structure, and cost function.

[0018] In one possible implementation, the expression for L is:

[0019]

[0020] in, This represents the cost function or likelihood function. This represents the i-th value among N measurements. It is the measurement estimate derived from the target WRF-VPRM model. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. The symbol represents the product of all measured values, and e represents the natural constant;

[0021] The formula for the log-likelihood function LL is as follows:

[0022]

[0023] in, This represents the i-th value among N measurements. These are the measurement estimates derived from the target WRF-VPRM model, where N represents the number of measurements. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. The symbol represents the natural logarithm.

[0024] In one possible implementation, the iterative change of the parameter values ​​of multiple key parameters using the Markov chain Monte Carlo (MCMC) method includes:

[0025] Based on the carbon flux network observation data after data quality control at the aforementioned flux stations, respiratory parameters among several key parameters were obtained using nighttime NEE observation data via the MCMC method. and ;

[0026] based on The temperature observation data from the flux stations are used to calculate the daytime ecosystem respiration parameter, one of the key parameters, through temperature-driven calculations. ;

[0027] Based on daytime NEE observation data and the daytime ecosystem respiratory parameters The relationship between these factors yields the observed values ​​of photosynthetic GPP; wherein, the daytime NEE observation data is equal to the daytime ecosystem respiration parameter. The result of subtracting the observed photosynthetic GPP value indicates that the ecosystem absorbs carbon when NEE is negative;

[0028] Based on the calculation formula of Total Ecosystem Carbon Dioxide Exchange (GEE) in the target WRF-VPRM model, the MCMC method is used to optimize the parameters among the key parameters. and parameters Wherein, the GEE is calculated by illumination-driven calculation, and the GEE is the negative value corresponding to the observed value of the photosynthetic GPP.

[0029] In one possible implementation, it also includes:

[0030] The effect function of temperature, water stress, and leaf traits on photosynthesis is calculated using the following formula based on air temperature and surface moisture index:

[0031] ;

[0032] ;

[0033] ;

[0034] in, A function representing the effect of temperature on photosynthesis. This function represents the effect of water stress on photosynthesis. The function represents the effect of leaf traits on photosynthesis; T represents air temperature. , and These represent the minimum, maximum, and optimum temperatures required for photosynthesis, respectively. When the air temperature is below... , The value is directly set to 0; LSWI Represents the surface moisture index. It is the largest during the growing season of each grid point. LSWI Value; the value corresponding to different growth stages of vegetation Different values ​​correspond to evergreen forests in the pattern. The value is set to 1.0 throughout the year; , and The function value ranges from 0.0 to 1.0.

[0035] Secondly, this application provides a carbon source and sink zoning optimization device, comprising:

[0036] The acquisition module is used to acquire the target ecological region for the carbon source-sink simulation to be performed;

[0037] The module is used to divide the target ecological region into multiple sub-ecological regions based on the natural geographical features, climate type, vegetation distribution data and human activity influencing factors in the target ecological region, and to determine a multi-scale and multi-type observation network based on the multiple sub-ecological regions; the observation network includes flux stations of various ecosystem types.

[0038] The reconstruction module is used to reconstruct multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input, so as to obtain the improved target WRF-VPRM model; the multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model.

[0039] The optimization module is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model based on the observation data of the flux stations using the Markov Chain Monte Carlo (MCMC) method, so as to obtain an ecosystem carbon source and sink simulation model that links climate, parameters and carbon flux.

[0040] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.

[0041] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.

[0042] This application brings the following beneficial effects:

[0043] This application provides a carbon source and sink zoning optimization method, apparatus, and electronic device, capable of acquiring the target ecological region to be simulated in a carbon source and sink simulation; dividing the target ecological region into multiple sub-ecological regions based on natural geographical features, climate type, vegetation distribution data, and human activity influencing factors; and determining a multi-scale and multi-type observation network based on the multiple sub-ecological regions; the observation network includes flux stations of various ecosystem types; reconstructing multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input to obtain an improved target WRF-VPRM model; the multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model; and utilizing the observation data from the flux stations to perform Markov modeling. The Markov Chain Monte Carlo (MCMC) method performs partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux. In this scheme, the target ecological region is divided into multiple sub-ecological regions based on natural geographical features, climate type, vegetation distribution data, and human activity influencing factors. The key parameters of the model are reconstructed into gridded data, and the parameters are dynamically input by modifying relevant modules of the ecological diagnostic mode. This improves the VPRM model. Combined with flux observation data, the Markov Chain Monte Carlo (MCMC) method is used for partitioned parameter calibration and dynamic updates, forming a linkage simulation mechanism between climate, parameters, and carbon flux. This significantly improves the simulation accuracy of ecological carbon flux and solves the technical problem of low simulation accuracy in existing ecological process models.

[0044] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 A schematic flowchart illustrating the carbon source and sink zoning optimization method provided in this application embodiment;

[0047] Figure 2A schematic diagram illustrating the transformation of key ecological model parameters from static input to dynamic grid input in the carbon source-sink zoning optimization method provided in this application embodiment;

[0048] Figure 3 A schematic diagram illustrating the optimization of key parameters of vegetation photosynthesis and respiration based on the MCMC method in the carbon source-sink zoning optimization method provided in the embodiments of this application;

[0049] Figure 4 A comparative analysis of NEE simulated and observed values ​​before and after the zoning optimization of flux observation stations in the Dinghushan area in 2010;

[0050] Figure 5 A comparative analysis of NEE simulated and observed values ​​before and after the optimization of flux observation station zoning in Xishuangbanna region in 2010;

[0051] Figure 6 This is a distribution map showing the differences in natural carbon flux before, after, and before and after partition optimization.

[0052] Figure 7 This is a schematic diagram of the structure of a carbon source and sink zoning optimization device provided in an embodiment of this application;

[0053] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0056] Currently, in the carbon source-sink simulation research of existing ecological models, the following technical solutions are mainly adopted for setting and optimizing key parameters of photosynthesis and respiration:

[0057] First, there is the static parameter input method: Most existing ecological models (such as the traditional VPRM model) use a fixed static input mode for key photosynthetic and respiration parameters (such as light energy utilization rate λ, photosynthetically active radiation half-saturation value PARo, and respiration parameters α and β) when simulating carbon source and sink processes. Once these parameter values ​​are set, they remain constant throughout the simulation period of the entire study area, without being adjusted over time (such as seasonal changes or interannual climate fluctuations) or spatial location (such as topographic differences or different climate zones). The model calculations are based solely on preset empirical values ​​or literature-recommended values.

[0058] Second, there is the vegetation type-oriented parameter optimization method: Some studies optimize parameters separately for different vegetation types (such as forests, grasslands, farmland, wetlands, etc.) to improve the applicability of simulations, meaning each vegetation type corresponds to a specific set of parameters. During the simulation, based on the distribution of vegetation types within the study area, different vegetation-covered areas are assigned optimization parameters corresponding to their respective types. This set of parameters applies to all distribution areas of that vegetation type within the entire study area, without considering the physiological and ecological differences of the same vegetation type under different climatic zones and topographic conditions.

[0059] The aforementioned technical solutions simplify model computational complexity by fixing parameters or dividing parameters according to vegetation type, and have been widely used in early carbon source-sink simulation studies, providing basic methodological support for the preliminary quantification of ecosystem carbon cycle processes. However, the above-mentioned existing technical solutions still have the following drawbacks:

[0060] First, most optimization efforts focus on single ecological types, lacking comprehensive optimization solutions across climate zones. Currently, many studies concentrate on individual ecosystem types such as forests, grasslands, and wetlands, considering only the environmental characteristics and ecological processes within that type during parameter optimization, while neglecting the interconnections and differences between ecosystems in different climate zones. For example, optimizing a carbon cycle model for temperate forests may only fit the temperature and precipitation patterns of that region. However, when the model is applied to subtropical forests or arid and semi-arid grasslands, parameter adaptation bias is highly likely to occur because factors such as cross-regional climate gradients and vegetation interactions are not included. This leads to a significant decrease in the consistency and accuracy of simulation results at the regional scale, making it difficult to support ecosystem management decisions across climate zones.

[0061] Secondly, existing technologies struggle to achieve dynamic grid input of parameters: In the VPRM ecological diagnostic model, land cover is divided into eight categories, and the corresponding type parameters are statically written into the WRF-VPRM model. While this static writing mechanism ensures the stability of model operation to some extent, it has significant limitations: each time parameters are modified, the entire WRF-VPRM model must be recompiled, making the process cumbersome and inefficient. For the construction of regional carbon assimilation systems (such as the my country Meteorological Administration's Carbon Source and Sink Monitoring and Verification Support System CCMVS), this deficiency presents multiple obstacles: on the one hand, it greatly increases the technical difficulty and time cost of system construction and parameter adjustment; on the other hand, the static parameter mechanism cannot flexibly respond to the ecological heterogeneity of different grid units, severely restricting the bidirectional dynamic optimization of carbon fluxes from anthropogenic and natural sources, and failing to meet the actual needs of high-precision carbon source and sink monitoring and verification.

[0062] The traditional WRF-VPRM (Weather Research and Forecasting - Vegetation Photosynthesis and Respiration Model) model adopts a "land cover category-driven static parameter input" mode, which has low simulation accuracy because it ignores the effect of climate gradient modulation and the spatial heterogeneity caused by differences in human activity areas.

[0063] Based on this, embodiments of this application provide a carbon source-sink zoning optimization method, apparatus, and electronic device, which can solve the technical problem of low simulation accuracy of existing ecological process models.

[0064] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0065] Figure 1 This is a flowchart illustrating a carbon source-sink zoning optimization method provided in an embodiment of this application. Figure 1 As shown, the method includes:

[0066] Step S110: Obtain the target ecological region for the carbon source-sink simulation to be performed.

[0067] In practical applications, the target ecological region in the embodiments of this application can be an ecological region of any region, such as ecological regions of various countries, ecological regions of various provinces, or ecological regions of various cities, etc.

[0068] Step S120: Based on the natural geographical features, climate type, vegetation distribution data and human activity influencing factors in the target ecological region, the target ecological region is divided into multiple sub-ecological regions, and a multi-scale and multi-type observation network is determined based on the multiple sub-ecological regions.

[0069] The observation network includes flux stations for various ecosystem types.

[0070] For example, my country's regions are divided into nine sub-ecological regions based on factors such as natural geographical features, climate types, vegetation distribution, and the impact of human activities:

[0071] (1) Northeastern my country Sub-Ecological Region: Covering Heilongjiang, Jilin and Liaoning provinces, geographically it is framed by the Greater Khingan Mountains, Lesser Khingan Mountains and Changbai Mountains, forming a landform pattern of mountains and rivers. This region has a temperate monsoon climate with an average annual temperature of 3-10℃ and an annual precipitation of 400-1000 mm. The simultaneous rain and heat conditions have nurtured a coniferous forest ecosystem dominated by Korean pine and larch, as well as mixed coniferous and broad-leaved forest ecosystems.

[0072] (2) Sub-ecological regions in Inner Mongolia: The Mongolian Plateau is the main body, and the climate transitions from semi-arid to arid from east to west. The average annual precipitation is 100-500 mm, and the vegetation types are meadow steppe, typical steppe and desert steppe in turn.

[0073] (3) Northwest my country’s sub-ecological region: including Xinjiang, Gansu and Ningxia. Its inland location makes it the driest region in my country, with an average annual precipitation of about 200 mm, and some areas such as the Tarim Basin even less than 50 mm. The vegetation is mainly composed of xerophytic shrubs and desert herbs.

[0074] (4) Qinghai-Tibet Plateau Ecological Region: The Qinghai-Tibet Plateau has an average altitude of over 4,000 meters and belongs to the plateau mountain climate. The average temperature decreases from 20℃ in the southeast to below -6℃ in the northwest. Precipitation decreases from the southeast to the northwest, forming unique alpine meadows, cold deserts and alpine shrub vegetation.

[0075] (5) Southwest my country’s sub-ecological region: including Yunnan, Guizhou, Sichuan and other places. The complex topography of the Hengduan Mountains and the Yunnan-Guizhou Plateau leads to significant vertical climate differentiation. The complex and diverse climate types have jointly shaped the unique vegetation distribution pattern of this region, which gradually transitions from low-altitude tropical rainforest and subtropical evergreen broad-leaved forest to high-altitude coniferous forest, alpine meadow and even sparse vegetation of glacier snow belt.

[0076] (6) Central my country sub-ecological region: including Hubei and Hunan provinces, which belong to the subtropical monsoon climate. The average annual temperature in Hubei Province is 15-20℃, while Hunan has a mild climate with four distinct seasons. The typical vegetation is mainly evergreen broad-leaved forest.

[0077] (7) Southern my country sub-ecological region: including Guangdong, Guangxi, Hainan and other places, located in the subtropical and tropical monsoon climate zone, with an average annual temperature above 25℃ and an annual precipitation of more than 1600 mm. The vegetation type is mainly subtropical humid evergreen broad-leaved forest and tropical monsoon forest and rainforest.

[0078] (8) Southeastern my country’s sub-ecological region: including Fujian, Zhejiang, Jiangsu, Anhui, Jiangxi and other places. The climate is divided by the Huai River. North of the Huai River is a temperate monsoon climate and south of the Huai River is a subtropical monsoon climate. Rainfall is concentrated in summer. The average annual temperature is 15-18℃. The vegetation is mainly composed of artificial fir forests, Masson pine forests and secondary evergreen broad-leaved forests.

[0079] (9) Northern sub-ecological region of my country: This region covers Shandong, Hebei, Henan, Shanxi and Shaanxi. Northern Shandong, most of Hebei and central and northern Henan belong to the North China Plain, while the entire Shanxi and northern Shaanxi belong to the eastern Loess Plateau. Most of the region has a temperate monsoon climate, and the natural vegetation is mainly deciduous broad-leaved forest. The North China Plain and river valley areas have a high proportion of cultivated land.

[0080] In practical applications, the carbon flux dynamic monitoring network can cover the nine ecological zones selected in this study, including the temperate forests of Northeast China, the grasslands of Inner Mongolia, and the Qinghai-Tibet Plateau. Its stations are widely distributed across a wide range of altitudes. For example, the Dangxiong alpine meadow carbon flux observation station has an altitude of only 295.7 meters, while the station located in the Nagqu alpine meadow in Tibet reaches an altitude of 4585 meters. Regarding climate conditions, there are significant differences in average annual temperature among the stations. For instance, the Changbai Mountain forest flux observation station has an average annual temperature of approximately 3.6℃, ​​while the Xishuangbanna tropical seasonal rainforest flux observation station has an average annual temperature of 21.8℃. In terms of annual precipitation, there are also significant differences between stations. For example, the Qinghai Haibei alpine grassland ecosystem national field scientific observation and research station has an average annual precipitation of only about 535.21 mm, while the Dinghushan South subtropical evergreen broad-leaved forest flux observation station is located in an area with extremely abundant precipitation, reaching 1956 mm annually. Numerous ChinaFlux stations, exhibiting significant differences in altitude, temperature, precipitation, and other factors, collectively form a multi-scale, multi-type observation network. This network encompasses stations across various ecosystem types, including forests, grasslands, and farmlands. Data from these flux stations is used for subsequent dynamic calibration of the four key parameters of the VPRM model.

[0081] Step S130: Reconstruct multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input to obtain the improved target WRF-VPRM model.

[0082] Several key parameters in the WRF-VPRM model include the first parameter related to photosynthetically active radiation, the second parameter representing vegetation light energy utilization efficiency, and the third parameter related to vegetation respiration. It should be noted that the WRF-VPRM (Weather Research and Forecasting - Vegetation Photosynthesis and Respiration Model) coupled model is a system that couples the Weather Research and Forecasting (WRF) model with the Vegetation Photosynthesis and Respiration Model (VPRM). It is primarily used to simulate the spatiotemporal variations of atmospheric CO2 biological flux and concentration, and by integrating meteorological processes and ecophysiological mechanisms, it achieves dynamic simulation of the carbon-water-air coupled cycle.

[0083] It should be noted that the VPRM model, as the ecological module within the WRF framework, is based on a "land cover category-driven static parameter input" model. This means that a fixed set of physiological and ecological parameters is preset for each vegetation type (e.g., forest, grassland, farmland) and uniformly applied in the full-region simulation. This parameter setting leads to systematic biases when the model is applied across climate zones, affecting the accuracy of regional carbon source and sink assessments. Before dynamic zoning optimization, the land cover category parameters need to be gridded.

[0084] As an optional implementation, the above-described method of reconstructing multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input to obtain the improved target WRF-VPRM model may specifically include the following steps:

[0085] Add four fields to the registry.chem file of the WRF-VPRM model; the four fields correspond to the four key parameters in the WRF-VPRM model; the first field corresponds to the first parameter, which is used to simulate the photosynthesis process of vegetation; the second field corresponds to the second parameter, which is used to affect the ability of vegetation to convert light energy into chemical energy; the third and fourth fields correspond to the third parameter, which are used to simulate the carbon dioxide flux of organisms.

[0086] The modules chem_driver.F and module_ghg_fluxes.F under the chem directory of the WRF-VPRM model, as well as the modules real_em.F and ndown_em.F under the main directory of the WRF-VPRM model, were systematically modified to obtain the modified modules. Based on the four added fields and the modified modules, several key parameters in the WRF-VPRM model were dynamically adjusted, and the gridded data of the key parameters were reconstructed from static input to dynamic gridded input to obtain the improved target WRF-VPRM model. Among them, the chem_driver.F, module_ghg_fluxes.F, real_em.F, and ndown_em.F modules are related to the WRF-VPRM model. The chem_driver.F module is used to drive the entire simulation process; the module_ghg_fluxes.F module is used to handle calculations related to greenhouse gas fluxes; the real_em.F module is used to adapt to new parameter settings and input formats in real-world simulations; and the ndown_em.F module is used to perform data transfer and parameter application in nested simulation scenarios.

[0087] like Figure 2 As shown, first, add four fields—GEE_LAMBDA, GEE_RAD, RES_ALPHA, and RES_RESP—to the registry.chem file of the WRF-VPRM model. These four fields correspond to the four key parameters in VPRM. Among them, GEE_RAD corresponds to the parameter related to photosynthetically active radiation in VPRM, and its accurate setting is crucial for simulating the photosynthetic process of vegetation; GEE_LAMBDA is related to the light energy utilization efficiency of vegetation, affecting the ability of vegetation to convert light energy into chemical energy; RES_ALPHA and RES_RESP are related to vegetation respiration and are indispensable for accurately simulating biological CO2 flux. Adding these fields provides a foundation for subsequent dynamic adjustment of key parameters of the VPRM model. Subsequently, modifications were made to modules such as chem_driver.F and module_ghg_fluxes.F in the chem directory of the WRF-VPRM model, and real_em.F and ndown_em.F in the main directory of the WRF-VPRM model. The chem_driver.F module plays a core driving role in the entire simulation process. The module_ghg_fluxes.F module is specifically used to handle greenhouse gas flux related calculations. In the main directory, the real_em.F module is responsible for better adapting to the new parameter settings and input formats in real-world simulations. The ndown_em.F module plays a crucial role in nested simulations and other scenarios, ensuring data transfer, parameter application, and the continuity and accuracy of simulation results. Through systematic modifications to these modules, the WRF-VPRM model was improved and optimized, significantly enhancing the performance and accuracy of the regional carbon assimilation system CCMVS-R, laying a solid foundation for a more in-depth and accurate study of regional carbon cycle processes.

[0088] Step S140: Based on the observation data of flux stations, the Markov chain Monte Carlo (MCMC) method is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux.

[0089] In one possible implementation, the above-mentioned observational data based on flux stations utilizes the Markov chain Monte Carlo (MCMC) method to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux. Specifically, this may include the following steps:

[0090] We used Bayes' theorem and Markov chain Monte Carlo (MCMC) method to optimize the parameters of several key parameters in the target WRF-VPRM model by partitioning them. By integrating the observation data of flux stations, we performed partitioning calibration and dynamic updates of several key parameters, and obtained an ecosystem carbon source and sink simulation model that links climate, parameters and carbon flux.

[0091] We used Bayes' theorem and Markov chain Monte Carlo (MCMC) method to optimize parameter partitioning. By integrating flux observation data, we achieved parameter calibration and dynamic updates, thereby improving the model's ability to characterize the heterogeneity of ecological partitions.

[0092] The target ecological region is divided into multiple sub-ecological regions by considering its natural geographical features, climate type, vegetation distribution data, and human activity influencing factors. The key parameters of the model are reconstructed into gridded data, and the parameters are dynamically input by modifying the relevant modules of the ecological diagnostic model. The VPRM model is improved by combining flux observation data and using the Markov Chain Monte Carlo (MCMC) method for zonal parameter calibration and dynamic updating. This forms a linkage simulation mechanism between climate, parameters, and carbon flux, which significantly improves the simulation accuracy of ecological carbon flux and solves the technical problem of low simulation accuracy in existing ecological process models.

[0093] In this embodiment, based on the my country Carbon Monitoring and Verification Support System (CCMVS) of the China Meteorological Administration, a three-level optimization framework of "ecological zoning - gridded parameters - dynamic optimization" is proposed. The embodiment divides my country into nine major ecological regions, covering the Northeast forests, Inner Mongolia grasslands, and the Qinghai-Tibet Plateau. This embodiment pioneers a dynamic input mechanism for ecological model parameters, breaking through the limitations of traditional static parameter settings and enabling real-time dynamic adjustment of parameters according to changes in environmental factors. This technical solution effectively ensures the adaptability of the model under complex spatiotemporal conditions, laying a core foundation for improving model accuracy, and is one of the core protected points of this invention. Furthermore, this embodiment proposes an innovative gridded scheme for ecological model parameters, breaking the traditional model of "one set of parameters corresponding to a single land cover category" and constructing a refined system where "each grid point has independently configured parameters." By improving the spatial resolution of parameters, the model's simulation capability is enhanced, and this technical feature has clear protective value. Furthermore, the online optimization technology for ecological zoning developed in this application aims to achieve dynamic adjustment of "differentiated parameters for the same type of land cover in different climate zones" to address the heterogeneity of different climate zones. By optimizing the zoning parameters, the applicability of the model to different regions is improved. This technological innovation constitutes an important aspect of protection.

[0094] In some embodiments, the above-mentioned parameter partitioning optimization of multiple key parameters in the target WRF-VPRM model using Bayes' theorem and Markov chain Monte Carlo (MCMC) method, and the parameter partitioning calibration and dynamic updating of multiple key parameters by integrating the observation data of flux stations, may specifically include the following steps:

[0095] Determine the cost function or likelihood function for N measurements based on the observation data from flux stations. Wherein, the measured values ​​represent the photosynthetic GPP and respiration Re values ​​provided by the carbon flux network observation data; the natural logarithm of both sides of the formula for L is taken so that the cost function is expressed as the log-likelihood function LL;

[0096] The Markov Chain Monte Carlo (MCMC) method iteratively changes the values ​​of multiple key parameters until the log-likelihood function (LL) reaches the global optimum. The output data of the MCMC method are the accepted parameter values ​​and the probability density function (PDF). The accepted global optimum and the posterior accepted parameter set represent the PDF of the most likely parameters that are consistent with the provided prior, observations, model structure, and cost function.

[0097] The formula for representing L above is:

[0098]

[0099] in, This represents the cost function or likelihood function. This represents the i-th value among N measurements. These are measurement estimates derived from the target WRF-VPRM model. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. The symbol represents the product of all measured values, and e represents the natural constant;

[0100] The formula for the log-likelihood function LL is:

[0101]

[0102] in, This represents the i-th value among N measurements. These are the measurement estimates derived from the target WRF-VPRM model, where N represents the number of measurements. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. “ " indicates the multiplication symbol.

[0103] For example, a given measurement x is the photosynthetic GPP and respiration Re values ​​provided by the ChinaFlux carbon flux network observation data. For N measurements, we define a cost function or likelihood function L:

[0104] (1)

[0105] in, This represents the i-th value among N measurements. These are measurement estimates derived from the WRF-VPRM model. This indicates the uncertainty of the data relative to the model. The symbol represents the product of all measurements, obtained under the assumption that all measurements are independent of each other. To avoid numerical precision issues and improve computational efficiency, the natural logarithm of both sides of Equation 1 can be taken, and the cost function can be expressed as the log-likelihood function LL:

[0106] (2)

[0107] For parameter estimation, the MCMC technique iteratively changes the parameter values ​​( The process continues until the log-likelihood reaches the global optimum. The output of the MCMC method is a set of accepted parameter values ​​and a probability density function (PDF). The accepted "global optimum" and posterior "accepted" parameter sets represent the PDFs of the most likely parameters consistent with the provided priors, observations, model structure, and cost function.

[0108] In some embodiments, the above-described method of using Markov chain Monte Carlo (MCMC) to iteratively change the values ​​of multiple key parameters may specifically include the following steps:

[0109] Based on carbon flux network observation data after data quality control at flux sites, respiratory parameters were obtained from several key parameters using nighttime NEE observation data via the MCMC method. and ;based on In addition, temperature observation data from flux stations were used to calculate, through temperature-driven methods, the daytime ecosystem respiration parameter, one of several key parameters, was obtained. ;

[0110] Based on daytime NEE observation data and daytime ecosystem respiration parameters The relationship between them yielded observed values ​​of photosynthetic GPP; among which, the daytime NEE observation data equaled the daytime ecosystem respiration parameter. The result of subtracting the observed photosynthetic GPP value shows that a negative NEE value indicates that the ecosystem is absorbing carbon.

[0111] Based on the formula for calculating Total Ecosystem Carbon Dioxide Exchange (GEE) in the target WRF-VPRM model, the MCMC method was used to optimize several key parameters. and parameters GEE is calculated using illumination-driven methods, and GEE is the negative value corresponding to the observed value of photosynthetic GPP.

[0112] like Figure 3 As shown, respiratory parameters were obtained using the MCMC method based on carbon flux network observation data after data quality control, using nighttime NEE observations. and , combined and And the daytime ecosystem respiration was calculated from the station's temperature observations. Then, based on the daytime NEE and The relationship yields the observed values ​​of GPP (NEE = - GPP, where NEE is negative to indicate carbon uptake by the ecosystem), and finally, the MCMC method is used to optimize the parameters based on the calculation formula of total ecosystem CO2 exchange capacity GEE (GEE=-GPP) in VPRM. and .

[0113] Figure 3 In the formula, GEE represents the total ecosystem CO2 exchange, calculated based on light intensity; Re represents the ecosystem respiration, calculated based on temperature. Where, This represents the maximum light energy utilization rate (or maximum photon efficiency). This represents the photosynthetically active radiation that corresponds to when photosynthesis reaches half-saturation. Represents photosynthetically active radiation, and EVI is the enhanced vegetation index.

[0114] In some embodiments, the method may further include the following steps:

[0115] The effect function of temperature, water stress, and leaf traits on photosynthesis is calculated using the following formula based on air temperature and surface moisture index:

[0116] ;

[0117] ;

[0118] ;

[0119] in, A function representing the effect of temperature on photosynthesis. This function represents the effect of water stress on photosynthesis. The function represents the effect of leaf traits on photosynthesis; T represents air temperature. , and These represent the minimum, maximum, and optimum temperatures required for photosynthesis, respectively. When the air temperature is below... , The value is directly set to 0; LSWI Represents the surface moisture index. It is the largest during the growing season of each grid point. LSWI Value; the value corresponding to different growth stages of vegetation Different values ​​correspond to evergreen forests in the pattern. The value is set to 1.0 throughout the year; , and The function value ranges from 0.0 to 1.0.

[0120] In practical applications, , and The function representing the influence on photosynthesis, along with the functions representing temperature, water stress, and leaf traits, is calculated as follows:

[0121] (3)

[0122] (4)

[0123] (5)

[0124] In the above formula, T represents the air temperature, and the unit is (°C). , and These represent the minimum, maximum, and optimum temperatures required for photosynthesis, respectively. When the air temperature is below... , Instead of using formula (3) above, the value is directly taken as 0. LSWI represents the surface moisture index. This represents the maximum LSWI value at each grid point during the growing season. The vegetation is in different growth stages, and its... Different values, in the model for evergreen forests The value is set to 1.0 throughout the year. , and The three function values ​​have a certain range, from 0.0 to 1.0. In the calculation of NEE during respiration... Represents the temperature over 2 meters. It can be optimized and adjusted based on station observation data.

[0125] The solution provided in this application effectively overcomes the limitations of static parameters, no longer restricted by fixed parameter settings, and can better adapt to dynamic changes under different environmental conditions, making the model more flexible and adaptable in dealing with complex situations. Moreover, it significantly improves the simulation accuracy of carbon flux in complex geographical environments. This means that the simulation results are closer to reality, providing more reliable data support for related research and applications, and helping to more accurately grasp the changing patterns of carbon flux. Furthermore, it provides a new paradigm for the optimization of global heterogeneous regional ecological models. It opens up new ideas and methods for research and practice in this field, promoting the development and improvement of global heterogeneous regional ecological models and facilitating in-depth research. The solution provided in this application also provides solid scientific support for the dynamic assessment of carbon sources and sinks in my country. Through accurate simulation and analysis, it can provide a reliable basis for the dynamic assessment of carbon sources and sinks.

[0126] The following example illustrates the verification of sites after partition optimization using the above scheme:

[0127] To verify the effectiveness of VPRM parameter zoning optimization, this scheme selects flux observation data from Dinghushan (evergreen broad-leaved forest, flux observation station location: longitude 112.53°, latitude 23.17°) and Xishuangbanna (tropical seasonal rainforest, flux observation station location: longitude 101.27°, latitude 21.93°), which did not participate in parameter calibration in 2010. Figure 4 and Figure 5 As shown, a comparative analysis of simulated and observed values ​​was conducted seasonally. From the perspective of diurnal dynamics, the optimized model accurately captures the "single-peak" diurnal variation pattern of NEE: after sunrise (6:00-8:00), with increased solar radiation, GEE (total ecosystem CO2 exchange) rapidly increases driven by PAR, and carbon absorption continues to increase; it reaches its peak at noon (around 12:00), when T is close to the optimum temperature, maximizing photosynthetic efficiency; in the afternoon (14:00-18:00), the decay of solar radiation leads to a decrease in PAR, GEE gradually weakens, and NEE approaches 0. The seasonal optimization effects at the two stations show significant heterogeneity: the root mean square error (RMSE) of the simulated and observed NEE values ​​at the Dinghushan observation station decreased from the original 3.92 μmol CO2 m -2 .s -1 4.15 umolCO2.m -2 .s -1 4.58 umol CO2.m -2 .s -1 and 3.69 umol CO2.m -2 .s -1 Optimized to 2.82 umol CO2.m-2 .s -1 3.21 umol CO2.m -2 .s -1 3.07 umol CO2.m -2 .s -1 and 2.75 umol CO2.m -2 .s -1 This resulted in reductions of approximately 28.61%, 22.65%, 32.97%, and 25.47% of the original values ​​((error before optimization - error after optimization) / error before optimization), significantly improving simulation accuracy. Similarly, the Xishuangbanna monitoring station saw an improvement from the original 5.61 umol CO2.m -2 .s -1 7.43 umol CO2.m -2 .s -1 5.64 umol CO2.m -2 .s -1 and 4.78 umol CO2.m -2 .s -1 Optimized to 4.45 umolCO2.m -2 .s -1 5.92 umol CO2.m -2 .s -1 5.03 umol CO2.m -2 .s -1 and 3.86 umol CO2.m -2 .s -1 The values ​​decreased by approximately 20.68%, 20.32%, 10.82%, and 19.25% of the original values, respectively, but also showed a significant improvement. This seasonal heterogeneity may be related to differences in biomass and water stress. By incorporating water stress factors and vegetation indices, the optimized model can better adapt to the different responses of different ecosystems to environmental factors, thereby reducing simulation bias caused by vegetation type and seasonal environmental fluctuations.

[0128] For the spatial distribution of regional carbon flux in my country, the WRF-GHG model after zoning optimization was used to simulate the natural carbon exchange process in my country at a resolution of 45 km in 2024. The results show that the estimation accuracy of natural carbon flux has been significantly improved: the maximum value of natural carbon flux in my country before zoning optimization (without deducting farmland carbon sinks and lateral fluxes) increased from -0.47 PgCa -1 Optimized to -0.97PgC a -1 After considering factors such as farmland carbon sinks, lateral fluxes, non-CO2 carbon emissions, water escape, and fires, my country's final NEE is 0.57 PgC / year. Figure 6As shown, the unoptimized model significantly underestimates several key regions, especially Xinjiang, Tibet, Qinghai, and Inner Mongolia, where the simulated carbon flux values ​​deviate significantly from the actual values: the maximum natural carbon flux (including farmland carbon sinks and without deducting lateral fluxes) in Xinjiang is 1.68 TgCa. -1 (Carbon source) corrected to -11.58TgCa -1 (Carbon sink), Tibet consists of 9.76 TgCa -1 (Carbon source) corrected to -24.11TgCa -1 (Carbon sink), Qinghai from -9.31TgCa -1 Corrected to -47.6TgC a -1 Inner Mongolia is -11.43TgCa -1 Corrected to -50.66TgC a -1 The desert steppe ecosystem in Xinjiang may be affected by drought stress, leading to an overestimation of its respiration parameters under static parameter models and resulting in misjudgment of carbon sources. However, zonal optimization (i.e., setting dynamic parameters in conjunction with local climate characteristics and the evolution of key parameters in different seasons) more accurately reflects carbon sequestration capacity under arid conditions. The alpine meadows of the Qinghai-Tibet Plateau are subjected to prolonged low-temperature environments, but traditional VPRM models use "land cover-driven static parameter settings." This parameter setting ignores the unique adaptability, physiological and ecological characteristics, and evolutionary mechanisms of alpine meadows, which may be the main reason for the systematic underestimation of the carbon sequestration capacity of the Qinghai-Tibet Plateau. This study's dynamic parameter adjustment specifically addresses these issues, ultimately improving the accuracy of carbon flux simulation across various biological communities, achieving a dual optimization of total carbon flux enhancement and precise regional characterization.

[0129] From the spatial distribution of differences before and after optimization, the underestimation of natural carbon flux before optimization has significant regional characteristics: the underestimation of carbon flux at the grid scale is most obvious in parts of South China, Southeast China, and Southwest China. This is directly related to the vegetation type and climate characteristics of these regions. South China and Southeast China are dominated by evergreen broad-leaved forests and tropical monsoon forests, with strong photosynthetic capacity and significant seasonal dynamics. Traditional static parameters do not take into account their seasonal differences, which may lead to an underestimation of carbon sink potential. In Southwest China, due to complex topography and significant vertical climate differentiation, static parameters cannot adapt to the differences in vegetation functional traits from river valleys to high mountains. However, zonal optimization achieves accurate calibration at the local scale through gridded parameters.

[0130] Overall, the optimized natural carbon flux is closer to the actual situation in terms of both total amount and spatial distribution. This verifies the effectiveness of the "ecological zoning-grid parameters-dynamic optimization" framework. By breaking through the traditional "one-size-fits-all" parameter mode, and combining observation data from ChinaFlux stations with vegetation functional traits (such as EVI and LSWI) retrieved from remote sensing, the parameterization scheme can respond to regional differences in climate gradients (such as temperature and radiation) and human activities, thus obtaining a natural carbon flux with less uncertainty in my country.

[0131] Figure 7 A schematic diagram of a carbon source-sink zoning optimization device is provided. (See diagram below.) Figure 7 As shown, the carbon source and sink zoning optimization device 700 includes:

[0132] The acquisition module 701 is used to acquire the target ecological region for the carbon source and sink simulation to be performed;

[0133] The construction module 702 is used to divide the target ecological region into multiple sub-ecological regions based on the natural geographical features, climate type, vegetation distribution data and human activity influencing factors in the target ecological region, and to determine a multi-scale and multi-type observation network based on the multiple sub-ecological regions; the observation network includes flux stations of multiple ecosystem types.

[0134] The reconstruction module 703 is used to reconstruct multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input, so as to obtain an improved target WRF-VPRM model; the multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model.

[0135] The optimization module 704 is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model based on the observation data of the flux station using the Markov chain Monte Carlo (MCMC) method, so as to obtain an ecosystem carbon source and sink simulation model that links climate, parameters and carbon flux.

[0136] The carbon source and sink partition optimization device provided in this application embodiment has the same technical features as the carbon source and sink partition optimization method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0137] An electronic device provided in this application embodiment, such as Figure 8As shown, the electronic device 800 includes a processor 802 and a memory 801. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.

[0138] See Figure 8 The electronic device also includes a bus 803 and a communication interface 804. The processor 802, the communication interface 804 and the memory 801 are connected through the bus 803. The processor 802 is used to execute executable modules, such as computer programs, stored in the memory 801.

[0139] The memory 801 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 804 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0140] Bus 803 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0141] The memory 801 is used to store programs. After receiving an execution instruction, the processor 802 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 802 or implemented by the processor 802.

[0142] The processor 802 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 802 or by instructions in software form. The processor 802 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 801, and processor 802 reads the information from memory 801 and, in conjunction with its hardware, completes the steps of the above method.

[0143] Corresponding to the above-described carbon source and sink partitioning optimization method, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above-described carbon source and sink partitioning optimization method.

[0144] The carbon source and sink zoning optimization device provided in this application embodiment can be specific hardware on a device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0145] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus 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. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0146] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0149] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, 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 a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the carbon source-sink partitioning optimization method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0151] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for optimizing carbon source and sink zoning, characterized in that, The method includes: Obtain the target ecological region for the carbon source-sink simulation to be performed; Based on the natural geographical features, climate type, vegetation distribution data, and human activity influencing factors of the target ecological region, the target ecological region is divided into multiple sub-ecological regions, and a multi-scale and multi-type observation network is determined based on the multiple sub-ecological regions; the observation network includes flux stations of various ecosystem types. The improved target WRF-VPRM model is obtained by reconstructing several key parameters from static input to gridded parameters of dynamic grid input. The multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model. Based on the observation data of the flux stations, the Markov Chain Monte Carlo (MCMC) method is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux. The process of reconstructing multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input to obtain the improved target WRF-VPRM model includes: adding four fields to the registry.chem file of the WRF-VPRM model; wherein, the four fields correspond to the four key parameters in the WRF-VPRM model; the first field corresponds to the first parameter and is used to simulate the photosynthetic process of vegetation; the second field corresponds to the second parameter and is used to affect the ability of vegetation to convert light energy into chemical energy; the third and fourth fields correspond to the third parameter and are used to simulate the carbon dioxide flux of organisms; and modifying the chem_driver.F and module_ghg_fluxes.F modules under the chem directory of the WRF-VPRM model. The modules, including the real_em.F and ndown_em.F modules in the main directory of the WRF-VPRM model, are systematically modified to obtain the modified modules. Based on the added four fields and the modified modules, multiple key parameters in the WRF-VPRM model are dynamically adjusted, reconstructing the multiple key parameters from static input to gridded data with dynamic grid input, resulting in the improved target WRF-VPRM model. The chem_driver.F, module_ghg_fluxes.F, real_em.F, and ndown_em.F modules are modules related to the WRF-VPRM mode. The chem_driver.F module drives the entire simulation process; the module_ghg_fluxes.F module handles calculations related to greenhouse gas fluxes; the real_em.F module adapts to new parameter settings and input formats in real-world simulations; and the ndown_em.F module performs data transfer and parameter application in nested simulation scenarios.

2. The method according to claim 1, characterized in that, The observational data based on the flux stations are used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model using the Markov Chain Monte Carlo (MCMC) method, resulting in an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux, including: By using Bayes' theorem and the Markov chain Monte Carlo (MCMC) method, parameter partitioning optimization is performed on multiple key parameters in the target WRF-VPRM model. By integrating the observation data of the flux stations, the parameters of multiple key parameters are partitioned, calibrated, and dynamically updated to obtain an ecosystem carbon source-sink simulation model that links climate, parameters, and carbon flux.

3. The method according to claim 2, characterized in that, The method of using Bayes' theorem and Markov chain Monte Carlo (MCMC) optimization to perform parameter partitioning on multiple key parameters in the target WRF-VPRM model, and integrating the observation data from the flux stations to perform partitioning calibration and dynamic updating of multiple key parameters, includes: Based on the measurements from the observation data of the flux stations, determine the cost function or likelihood function for N measurements. The measured values ​​represent the photosynthetic GPP and respiration Re values ​​provided by the carbon flux network observation data. Take the natural logarithm of both sides of the formula for L so that the cost function or likelihood function is expressed as a log-likelihood function LL; The parameter values ​​of multiple key parameters are iteratively changed using the Markov Chain Monte Carlo (MCMC) method until the log-likelihood function LL reaches the global optimum. The output data of the MCMC method are the accepted parameter values ​​and the probability density function PDF. The accepted global optimum and posterior accepted parameter set represent the PDF of the most likely parameters that are consistent with the provided prior, observations, model structure, and cost function.

4. The method according to claim 3, characterized in that, The formula for representing L is: in, This represents the cost function or likelihood function. This represents the i-th value among N measurements. It is the measurement estimate derived from the target WRF-VPRM model. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. The symbol represents the product of all measured values, and e represents the natural constant; The formula for the log-likelihood function LL is as follows: in, This represents the i-th value among N measurements. It is the measurement estimate derived from the target WRF-VPRM model. Indicates the number of measurements. This represents the uncertainty coefficient of the data relative to the target WRF-VPRM model. The symbol represents the natural logarithm.

5. The method according to claim 3, characterized in that, The method of iteratively changing the values ​​of multiple key parameters using the Markov chain Monte Carlo (MCMC) method includes: Based on the carbon flux network observation data after data quality control at the aforementioned flux stations, respiratory parameters among several key parameters were obtained using nighttime NEE observation data via the MCMC method. and ; Based on the respiratory parameters and The temperature observation data from the flux stations are used to calculate the daytime ecosystem respiration parameter, one of the key parameters, through temperature-driven calculations. ; Based on daytime NEE observation data and the daytime ecosystem respiratory parameters The relationship between these factors yields the observed values ​​of photosynthetic GPP; wherein, the daytime NEE observation data is equal to the daytime ecosystem respiration parameter. The result of subtracting the observed photosynthetic GPP value indicates that the ecosystem absorbs carbon when NEE is negative; Based on the calculation formula of Total Ecosystem Carbon Dioxide Exchange (GEE) in the target WRF-VPRM model, the MCMC method is used to optimize the parameters among the key parameters. and parameters Wherein, the GEE is calculated by illumination-driven calculation, and the GEE is the negative value corresponding to the observed value of the photosynthetic GPP.

6. The method according to claim 5, characterized in that, Also includes: The effect function of temperature, water stress, and leaf traits on photosynthesis is calculated using the following formula based on air temperature and surface moisture index: ； ； ； in, The function representing the effect of temperature on photosynthesis. The function representing the effect of water stress on photosynthesis. The function representing the effect of leaf traits on photosynthesis; T represents air temperature. , and These represent the minimum, maximum, and optimum temperatures required for photosynthesis, respectively. The temperature is higher than or equal to Calculate using the formula under the given conditions. The temperature is below Under the condition that it directly takes the value 0; LSWI Represents the surface moisture index. It is the largest during the growing season of each grid point. LSWI Value; the value corresponding to different growth stages of vegetation Different values ​​correspond to evergreen forests in the pattern. The value is set to 1.0 throughout the year; , and The function value ranges from 0.0 to 1.

0.

7. A carbon source-sink zoning optimization device, characterized in that, include: The acquisition module is used to acquire the target ecological region for the carbon source-sink simulation to be performed; The module is used to divide the target ecological region into multiple sub-ecological regions based on the natural geographical features, climate type, vegetation distribution data and human activity influencing factors in the target ecological region, and to determine a multi-scale and multi-type observation network based on the multiple sub-ecological regions. The observation network includes flux stations for various ecosystem types; The reconstruction module is used to reconstruct multiple key parameters in the WRF-VPRM model from static input to gridded parameters of dynamic grid input, so as to obtain the improved target WRF-VPRM model; the multiple key parameters include a first parameter related to photosynthetically active radiation, a second parameter representing vegetation light energy utilization efficiency, and a third parameter related to vegetation respiration in the WRF-VPRM model. The optimization module is used to perform partitioned calibration and dynamic updates of multiple key parameters in the target WRF-VPRM model based on the observation data of the flux stations using the Markov chain Monte Carlo (MCMC) method, so as to obtain an ecosystem carbon source and sink simulation model that links climate, parameters and carbon flux. The reconstruction module is specifically used to: add four fields to the registry.chem file of the WRF-VPRM model; wherein, the four fields correspond to the four key parameters in the WRF-VPRM model; the first field corresponds to the first parameter and is used to simulate the photosynthetic process of vegetation; the second field corresponds to the second parameter and is used to affect the ability of vegetation to convert light energy into chemical energy; the third and fourth fields correspond to the third parameter and are used to simulate the carbon dioxide flux of organisms; and modify the chem_driver.F and module_ghg_fluxes.F modules under the chem directory of the WRF-VPRM model. The modules, including the real_em.F and ndown_em.F modules in the main directory of the WRF-VPRM model, are systematically modified to obtain the modified modules. Based on the added four fields and the modified modules, multiple key parameters in the WRF-VPRM model are dynamically adjusted, reconstructing the multiple key parameters from static input to gridded data with dynamic grid input, resulting in the improved target WRF-VPRM model. The chem_driver.F, module_ghg_fluxes.F, real_em.F, and ndown_em.F modules are modules related to the WRF-VPRM mode. The chem_driver.F module drives the entire simulation process; the module_ghg_fluxes.F module handles calculations related to greenhouse gas fluxes; the real_em.F module adapts to new parameter settings and input formats in real-world simulations; and the ndown_em.F module performs data transfer and parameter application in nested simulation scenarios.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

Images