Land surface process simulation method and system coupled with multi-source algorithm

Abstract

This application discloses a method and system for simulating land surface processes using a coupled multi-source algorithm. The method includes: acquiring current atmospheric driving data and the results of the previous round of land surface process simulation, the previous round's simulation results including the leaf area index (LAI); performing physical process simulation based on the current atmospheric driving data and the previous round's simulation results to obtain the current round's physical process results; performing carbon allocation operations according to a preset vegetation carbon allocation algorithm, based on the previous round's land surface process simulation results and the current round's physical process results to obtain vegetation carbon data, including a leaf carbon pool; determining the current round's LAI based on the leaf carbon pool and the current atmospheric driving data; and combining the vegetation carbon data and the current round's LAI to determine the current round's biological process results, ultimately obtaining the current round's land surface process simulation results. This achieves real-time closed-loop coupling of physical and biological processes through the LAI, improving the reliability of the land surface process simulation results.

Description

Technical Field

[0001] This application relates to the field of ecological science and technology, and in particular to a method and system for simulating land surface processes using coupled multi-source algorithms. Background Technology

[0002] Land Surface Models (LSMs) are numerical models implemented using computer programs to simulate the entire land system. They aim to construct an atmosphere-vegetation-soil continuum, focusing on energy conversion and material flow. Current trends in LSM development increasingly emphasize the mechanistic description of vegetation physiological and ecological processes, particularly biogeochemical processes such as photosynthesis, carbon allocation, and soil carbon cycling.

[0003] While internationally mainstream LSMs have well-developed mechanisms, they are large in scale and complex in parameters, making them difficult to efficiently adapt to typical terrestrial ecosystems in China. In contrast, the Atmosphere–Vegetation Interaction Model (AVIM), an LSM applicable to China's terrestrial ecological environment, has a relatively simple framework and good regional applicability. AVIM includes two major processes: physical and biological. The Leaf Area Index (LAI), as a key vegetation structure parameter, participates in both physical process simulations (such as radiative transfer and turbulent exchange) and biological process simulations (such as the calculation of total primary productivity at the canopy scale).

[0004] However, in the aforementioned AVIM model, although LAI can be output from biological processes and act on physical processes, its calculation in AVIM relies on a simplified empirical carbon allocation scheme, making it difficult to accurately reflect the dynamic regulation of vegetation growth by environmental stress. In other words, the generation mechanism of leaf area index (LAI) is not strongly coupled with vegetation physiological processes. This results in a weak interaction between physical and biological processes, making it difficult for AVIM to accurately simulate the dynamic response of vegetation to environmental changes, thus affecting the reliability of land surface simulation results. Summary of the Invention

[0005] The main objective of this application is to provide a land surface process simulation method and system that couples multiple source algorithms, aiming to solve the technical problem that existing LSMs have weak interaction between physical and biological simulation processes, making it difficult for AVIM to accurately simulate the dynamic response of vegetation to environmental changes, thus affecting the reliability of land surface simulation results.

[0006] To achieve the above objectives, this application proposes a land surface process simulation method coupled with a multi-source algorithm, the method comprising: Acquire current atmospheric driving data and the simulation results of the previous land surface process, wherein the simulation results of the previous land surface process include at least the leaf area index of the previous round; Based on the current atmospheric driving data and the simulation results of the previous round of land surface processes, physical process simulation is performed to obtain the results of the current round of physical processes. According to the preset vegetation carbon allocation algorithm, carbon allocation is performed based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes to obtain vegetation carbon data, which includes leaf carbon pools. The current blade area index is determined based on the blade carbon pool and the current atmospheric driving data. The results of the current biological process are determined by combining the vegetation carbon data and the current leaf area index, and the results of the current physical process and the current biological process are determined as the simulation results of the current land surface process.

[0007] In one embodiment, the simulation results of the previous round of land surface processes include: vegetation canopy state variables and soil surface state variables, wherein the previous round leaf area index belongs to the vegetation canopy state variables; The step of performing physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical processes includes: The current atmospheric driving data and the simulation results of the previous land surface process are input into the preset physical process simulation model, which includes a radiation transfer module, a water and heat flux calculation module, and a temperature and humidity calculation module. The total radiation budget is determined by the radiation transfer module based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables. The water and heat flux calculation module calculates the water and heat flux based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables, and obtains the water and heat flux calculation results. The temperature and humidity calculation module calculates the canopy temperature and soil temperature and humidity results based on the water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables, according to the preset canopy temperature algorithm and the preset soil temperature and humidity algorithm. The total radiation budget, the calculated results of the water and heat flux, the calculated results of the canopy temperature, and the calculated results of the soil temperature and humidity are determined as the results of this round of physical processes.

[0008] In one embodiment, the step of calculating water and heat flux based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables to obtain the water and heat flux calculation results includes: Substitute the current atmospheric driving data, the vegetation canopy state variables and the soil surface state variables into the preset aerodynamic impedance algorithm to obtain the current round of aerodynamic impedance. The preset aerodynamic impedance algorithm is obtained by introducing the Moning-Obukhov similarity theory correction function into the aerodynamic impedance algorithm in the original AVIM. The current aerodynamic impedance, the current atmospheric driving data, the vegetation canopy state variables and the soil surface state variables are substituted into the preset water and heat flux algorithm to obtain the current water vapor flux and the current sensible heat flux. The preset water and heat flux algorithm includes a vegetation underlying surface algorithm and a bare soil underlying surface algorithm, which are respectively improved based on the community land surface model framework. The current aerodynamic impedance, current water vapor flux, and current sensible heat flux are determined as the results of the water heat flux calculation.

[0009] In one embodiment, the step of calculating the canopy temperature and soil temperature and humidity results based on the water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables, according to a preset canopy temperature algorithm and a preset soil temperature and humidity algorithm, includes: The water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables are substituted into the preset canopy temperature algorithm to obtain the canopy temperature for this round, and determined as the canopy temperature calculation result. The preset canopy temperature algorithm is obtained by introducing a partial derivative term to characterize the canopy air temperature on the canopy temperature in the original AVIM canopy temperature algorithm. The calculation results of the water and heat flux, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables are substituted into the preset soil temperature and humidity algorithm to obtain the soil temperature and humidity of each layer, and determined as the soil temperature and humidity calculation results. The preset soil temperature and humidity algorithm includes a soil temperature algorithm and a soil humidity algorithm that use the same number of soil layers.

[0010] In one embodiment, the results of the current physical process include: canopy temperature, soil temperature, and current aerodynamic impedance; the simulation results of the previous land surface process also include: previous vegetation carbon content; The step of obtaining vegetation carbon data by performing carbon allocation operations based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes according to a preset vegetation carbon allocation algorithm includes: The leaf area index of the previous round, the canopy temperature, the soil temperature, and the aerodynamic impedance of the current round are substituted into the preset photosynthesis algorithm to obtain the total primary productivity of the canopy in the current round. The preset photosynthesis algorithm includes the Faqual photosynthesis algorithm suitable for C3 plants and the Kolatz photosynthesis algorithm suitable for C4 plants. The number of phenologically relevant days is determined based on the current atmospheric driving data. The total primary productivity of the current canopy, the number of phenologically relevant days, the vegetation carbon content of the previous cycle, the canopy temperature, and the soil temperature are then substituted into the preset vegetation carbon allocation algorithm to obtain vegetation carbon data. The preset vegetation carbon allocation algorithm is the vegetation carbon allocation algorithm in the standard biological community biogeochemical cycle model.

[0011] In one embodiment, the step of substituting the previous leaf area index, the canopy temperature, the soil temperature, and the current aerodynamic impedance into a preset photosynthesis algorithm to obtain the total primary productivity of the canopy in the current cycle further includes: Substituting the previous leaf area index, the canopy temperature, the soil temperature, and the current aerodynamic impedance into a preset photosynthesis algorithm, a net photosynthetic rate expression is obtained, which includes a canopy stomatal conductance term. A stomatal conductance expression for the canopy is constructed based on the Ball-Berry model, and the stomatal conductance expression for the canopy includes a net photosynthetic rate term; The net photosynthetic rate expression and the canopy stomatal conductance expression are iteratively solved to obtain the current round of canopy stomatal impedance and the current round of canopy total primary productivity. The current round of canopy stomatal impedance is a result of the current round of biological processes.

[0012] In one embodiment, the step of substituting the current round of total primary productivity of the canopy, the number of phenologically relevant days, the vegetation carbon content of the previous round, the canopy temperature, and the soil temperature into the preset vegetation carbon allocation algorithm to obtain vegetation carbon data includes: The vegetation phenological distribution flux is calculated based on the phenological-related days and the previous round of vegetation carbon content. Vegetation sustaining respiration is calculated based on the canopy temperature, the soil temperature, and the carbon content of the previous vegetation cycle. The vegetation carbon growth distribution flux is calculated based on the total primary productivity of the current canopy, the vegetation sustaining respiration, and the vegetation carbon content of the previous cycle, and the vegetation growth respiration is calculated based on the vegetation carbon growth distribution flux. The previous round of vegetation carbon content is updated based on the vegetation maintenance respiration, the vegetation growth respiration, and the vegetation carbon growth distribution flux to obtain the current round of vegetation carbon content, which includes the leaf carbon pool. The current vegetation carbon content, vegetation maintenance respiration, vegetation growth respiration, vegetation carbon growth distribution flux, and current canopy total primary productivity are defined as vegetation carbon data.

[0013] In one embodiment, the current atmospheric driving data includes a specific leaf area constant; The step of determining the current leaf area index based on the blade carbon pool and the current atmospheric driving data includes: The leaf carbon pool and the specific leaf area constant are multiplied to calculate the current leaf area index.

[0014] In one embodiment, the results of the current physical process include: soil temperature and soil moisture; the vegetation carbon data further includes: vegetation litter. Before the step of determining the results of the current biological process by combining the vegetation carbon data and the current leaf area index, the method further includes: The soil temperature, soil moisture and vegetation litter are substituted into a preset soil carbon conversion decomposition algorithm to obtain soil carbon data. The preset soil carbon conversion decomposition algorithm is the soil carbon conversion decomposition algorithm in the CENTURY soil organic matter turnover model. Accordingly, the step of determining the results of the current biological process by combining the vegetation carbon data and the current leaf area index includes: The results of this biological process are determined by combining the vegetation carbon data, the soil carbon data, and the current leaf area index.

[0015] Furthermore, to achieve the above objectives, this application also proposes a land surface process simulation system coupled with a multi-source algorithm, the system comprising: The data preparation module is used to acquire current atmospheric driving data and the simulation results of the previous round of land surface processes, wherein the simulation results of the previous round of land surface processes include at least the leaf area index of the previous round. The physical process simulation module is used to perform physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical processes. The biological process simulation module is used to perform carbon allocation operations according to a preset vegetation carbon allocation algorithm, based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes, to obtain vegetation carbon data, which includes a leaf carbon pool; determine the leaf area index of the current round based on the leaf carbon pool and the current atmospheric driving data; and determine the results of the current round of biological processes by combining the vegetation carbon data and the leaf area index of the current round. The results output module is used to determine the results of the current round of physical processes and the results of the current round of biological processes as the simulation results of the current round of land surface processes.

[0016] This application discloses a land surface process simulation method coupled with a multi-source algorithm. The method includes: acquiring current atmospheric driving data and the simulation results of the previous round of land surface processes, wherein the simulation results of the previous round of land surface processes include at least the leaf area index of the previous round; performing physical process simulation based on the current atmospheric driving data and the simulation results of the previous round of land surface processes to obtain the physical process results of the current round; performing carbon allocation operation according to a preset vegetation carbon allocation algorithm based on the simulation results of the previous round of land surface processes and the physical process results of the current round to obtain vegetation carbon data, wherein the vegetation carbon data includes a leaf carbon pool; determining the leaf area index of the current round based on the leaf carbon pool and the current atmospheric driving data; determining the biological process results of the current round by combining the vegetation carbon data and the leaf area index of the current round, and determining the physical process results and biological process results of the current round as the simulation results of the current round of land surface processes.

[0017] This application, based on previous simulation results and current atmospheric driving data, dynamically calculates the leaf area index (LAI) of the current simulation using a vegetation carbon allocation algorithm, and simultaneously applies it to the calculation of biological processes and the feedback of subsequent physical processes. This achieves real-time, closed-loop coupling of physical and biological processes through the LAI. Consequently, it effectively overcomes the problem in traditional AVIM simulations where the LAI relies on a simplified empirical carbon allocation scheme, resulting in weak coupling between physical and biological processes. This significantly improves the reliability of land surface process simulation results in reflecting vegetation dynamic physiological responses and environmental stress feedback. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the land surface process simulation method coupled with multi-source algorithms of this application. Figure 2 This is a flowchart illustrating the second embodiment of the land surface process simulation method coupled with multi-source algorithms in this application; Figure 3 This is a flowchart illustrating the third embodiment of the land surface process simulation method coupled with multi-source algorithms in this application; Figure 4 A schematic diagram illustrating the coupling mechanism between physical and biological processes in AEIM; Figure 5This is a schematic diagram of the module structure of the land surface process simulation system coupled with multi-source algorithms in this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] This application provides a land surface process simulation method coupled with a multi-source algorithm, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the land surface process simulation method coupled with multi-source algorithms of this application. In this embodiment, the method includes steps S10 to S50: Step S10: Obtain current atmospheric driving data and the simulation results of the previous round of land surface processes, wherein the simulation results of the previous round of land surface processes include at least the leaf area index of the previous round.

[0025] It should be noted that the executing entity in this embodiment can be a computing electronic device with data processing, network communication, and program execution functions, such as a desktop computer, a mainframe computer, or a server cluster. It can also be other electronic devices that have installed the land surface process simulation program coupled with the multi-source algorithm of this embodiment. This embodiment does not limit this; the land surface process simulation device coupled with the multi-source algorithm (hereinafter referred to as "device") can be used as an example to illustrate each embodiment.

[0026] Understandably, current atmospheric driving data can be data that can be directly obtained from the external environment at the current moment for the terrestrial system region to be simulated. Since terrestrial ecosystems do not exist in isolation, their energy and material exchange processes are constantly driven by the atmospheric environment. Therefore, current atmospheric driving data can be used as input to the Atmosphere-Ecosystem Interaction Model (AEIM) constructed by this method, thereby driving the current round of land surface process simulation.

[0027] The current atmospheric driving data can primarily include meteorological data, such as solar radiation, temperature, precipitation, wind speed, and humidity. This current atmospheric driving data can also include: climatological data, carbon dioxide data, geographic location data, soil texture data, vegetation classification data, soil parameters, and vegetation parameters.

[0028] The results of the previous land surface process simulation can be considered as a complete set of data representing the state of the entire land system, output by AEIM at the end of the previous calculation time. Because terrestrial ecosystems possess continuity and memory, AEIM can maintain the latest land surface process simulation results, thus providing the initial field for the current land surface process simulation.

[0029] It is understandable that AEIM can be built on AVIM, and therefore it can also be divided into physical processes and biological processes. This complete set of data can include simulation data of the previous round of physical processes (such as canopy temperature, soil temperature, soil moisture, etc. obtained at the previous calculation time) and simulation data of the previous round of biological processes (such as vegetation carbon data, soil carbon data, etc. obtained at the previous calculation time).

[0030] It should also be noted that since the leaf area index is a key dynamic variable involved in physical and biological processes, the updated output of the leaf area index can also be achieved in the land surface process simulation based on AEIM. That is, the above land surface process simulation results can include at least the leaf area index of the previous round.

[0031] Furthermore, if classified according to the type of output data, the simulation results of the previous round of land surface processes can include: carbon storage, carbon flux, water vapor flux, sensible heat flux, radiation flux, aerodynamic impedance, non-turbulent impedance, vegetation canopy state variables, soil and surface state variables, etc.

[0032] In practice, the device can acquire current atmospheric driving data and the results of the previous process simulation as input data to drive the current process simulation. The current atmospheric driving data and the results of the previous process simulation together constitute the starting point for the model's iterative evolution, thereby ensuring that the current land surface process simulation can truly reflect the dynamic response process of the terrestrial ecosystem under the combined influence of external driving forces and its own state.

[0033] Step S20: Perform physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical process simulation.

[0034] It should be noted that the physical process models in AEIM can be built based on existing AVIM physical process algorithms, specifically including: aerodynamic impedance algorithm, hydrothermal flux algorithm, soil temperature and humidity algorithm, canopy temperature algorithm, etc.

[0035] Therefore, in the physical process, AEIM can input the current atmospheric driving data and the state variables output at the previous moment into the physical process model. The physical process model can then calculate the simulated physical process quantities such as water vapor flux, sensible heat flux, canopy temperature, soil temperature, and soil moisture based on the aforementioned physical process algorithms, and use these as the results of the current round of physical processes.

[0036] In its implementation, the device extracts phase values ​​from current atmospheric driving data and the results of the previous land surface process simulation. For example, it can obtain boundary conditions such as radiation, precipitation, and turbulent dynamics from the current atmospheric driving data, and variables such as soil temperature, soil moisture, canopy temperature, canopy stomatal impedance, and leaf area index from the previous land surface process simulation results. These values ​​are then substituted into the aforementioned physical process algorithm to obtain the results of the current round of physical process simulation. The results of this round of physical process simulation can be used to characterize the physical state of the terrestrial ecosystem at the current moment and can also provide the latest environmental field data for the current round of biological process simulation.

[0037] Step S30: According to the preset vegetation carbon allocation algorithm, carbon allocation operation is performed based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes to obtain vegetation carbon data, which includes leaf carbon pools.

[0038] It should be noted that AEIM can perform biological process simulations based on the physical process results output in this round, and the biological process models in AEIM can be constructed by coupling several specific open-source biophysical and chemical algorithms available on the market. These algorithms can include: photosynthesis algorithm, vegetation carbon allocation algorithm, soil carbon conversion algorithm, etc.

[0039] It should be understood that the preset vegetation carbon allocation algorithm can be based on the vegetation carbon allocation algorithm in a well-defined, internationally accepted mechanistic model (such as the standard biogeochemical cycle model BIOME-BGC), which can specify how plants allocate photosynthetic products to different organs (leaves, stems, roots).

[0040] In the specific implementation, according to the input parameter requirements of the preset vegetation carbon allocation algorithm, the corresponding specific parameter values ​​can be extracted from the simulation results of the previous round of land surface processes and the results of the current round of physical processes, and then substituted into the preset vegetation carbon allocation algorithm for calculation, so as to obtain the carbon storage of each part of the vegetation at the current moment.

[0041] The carbon storage in the leaves of vegetation is called the leaf carbon pool. This carbon pool is produced by photosynthesis, consumed through respiration, and allocated through various processes before being stored in the leaves. It serves as key data for calculating and updating the leaf area index.

[0042] Step S40: Determine the current blade area index based on the blade carbon pool and the current atmospheric driving data.

[0043] Understandably, determining the leaf area index (LAI) based on leaf carbon pools and current atmospheric driving data allows vegetation carbon accumulation to be transformed into physically meaningful canopy structure parameters. Leaf carbon pools reflect the material input of vegetation to photosynthetic organs, while current atmospheric driving data can be used to environmentally regulate leaf formation efficiency.

[0044] It should be understood that current atmospheric driving data may also include a pre-defined specific leaf area (SLA) constant, which is a fixed conversion factor that can be used to convert abstract carbon content into leaf area with physical and ecological significance.

[0045] It should be noted that the formula for calculating leaf area index can be expressed as: LAI = Leaf carbon pool SLA. As can be seen from this calculation formula, the dynamics of LAI originate from the dynamics of the leaf carbon pool, while SLA, as a constant in the conversion formula, ensures the consistency of leaf construction strategies for the same vegetation type. This allows the biological process model to maintain ecological rationality while possessing dynamic response capabilities.

[0046] In practice, the leaf carbon pool and specific leaf area constant can be multiplied to obtain the current leaf area index. This current leaf area index can then be used as input data for the next round of land surface process simulations, participating in the next round of physical process simulations.

[0047] Step S50: Combine the vegetation carbon data and the current leaf area index to determine the results of the current biological process, and determine the results of the current physical process and the current biological process as the simulation results of the current land surface process.

[0048] It should be noted that combining vegetation carbon data and the current leaf area index to determine the results of the current biological process can integrate the vegetation carbon pool status and canopy structure information to form a complete ecosystem carbon cycle output.

[0049] The vegetation carbon data includes carbon storage and respiratory flux in various organs, while the leaf area index is used to calculate material exchange at the canopy scale. Combining the results of the current round of physical and biological processes into the overall simulation results of the current round of land surface processes helps to achieve synchronous updates and outputs of physical and biological states within each calculation step.

[0050] It should also be noted that the simulation results of this round of land surface processes have the same data composition as the previous round of land surface process simulation results, but with different specific values, thus providing a continuous and self-consistent initial field for the next round of land surface process simulation.

[0051] Furthermore, while calculating the carbon storage of each part of the vegetation at the current moment using a preset vegetation carbon allocation algorithm, the vegetation litter at the current moment can also be calculated simultaneously. That is, the aforementioned vegetation carbon data also includes vegetation litter. Therefore, since the initial source of soil carbon is vegetation carbon, and its decomposition rate is affected by soil temperature and soil moisture, soil carbon data can also be determined based on vegetation litter. That is, before step S50, the following steps are also included: Step S01: Substitute the soil temperature, soil moisture, and vegetation litter into a preset soil carbon conversion decomposition algorithm to obtain soil carbon data.

[0052] It should be understood that the preset soil carbon conversion and decomposition algorithm is also a biophysical and chemical algorithm coupled to the biological process simulation model in AEIM, and the soil carbon conversion and decomposition algorithm in the CENTURY soil organic matter turnover model can be specifically selected.

[0053] It should be noted that CENTURY includes five types of soil carbon pools: structural pool, metabolic pool, active pool, slow-decomposing pool, and inert pool. The structural, metabolic, and active pools are further divided into aboveground and underground pools, while the slow-decomposing and inert pools are only underground, resulting in a total of eight soil carbon pools. Before the decomposition of aboveground and underground litter, the model classifies them: portions with higher lignin content are assigned to the structural pool, while portions with higher nitrogen content are assigned to the metabolic pool. The sum of carbon dioxide fluxes produced by the decomposition of soil organic carbon constitutes heterotrophic respiration, and solid waste products (SCF) circulate between different soil carbon pools. Specifically: SCF from the metabolic pool only enters the active pool, while SCF from the structural pool enters both the active and slow-decomposing pools; the slow-decomposing pool is the sole destination for SCF from the aboveground active pool; and soil organic carbon can circulate among the underground active, slow-decomposing, and inert pools. Based on the soil carbon conversion algorithms in CENTURY, soil carbon data such as the decomposition rate of each soil carbon pool, the carbon content of the soil carbon pool, and soil heterotrophic respiration can be calculated.

[0054] It should also be noted that the original calculation step size of BIOME-BGC, on which the aforementioned vegetation carbon allocation algorithm is based, is daily, while the original calculation step size of CENTURY is monthly. Therefore, to achieve a unified step size, the calculation step size of the soil carbon conversion and decomposition algorithm in CENTURY can be directly modified to be calculated daily to obtain the preset soil carbon conversion and decomposition algorithm of this embodiment. That is, in this embodiment, the vegetation carbon allocation algorithm and the soil carbon conversion and decomposition algorithm can be set to the same calculation step size.

[0055] In a specific implementation, the input parameter values ​​of a preset soil carbon conversion and decomposition algorithm can be obtained based on the soil temperature and soil moisture obtained from the aforementioned physical process, as well as the vegetation litter extracted according to the aforementioned vegetation carbon allocation algorithm. Then, the algorithm can calculate soil carbon data including soil organic carbon and soil heterotrophic respiration.

[0056] Accordingly, step S50 includes: Step S500: Combine the vegetation carbon data, the soil carbon data, and the current leaf area index to determine the results of the current biological process, and determine the results of the current physical process and the results of the current biological process as the simulation results of the current land surface process.

[0057] It should be noted that the results of this round of biological processes may include at least the aforementioned vegetation carbon data, soil carbon data, and leaf area index.

[0058] Among them, vegetation carbon data may include: the total amount of each carbon pool in vegetation (which belongs to carbon storage in the classification of output data types); total primary productivity of vegetation, maintenance respiration of each pool of vegetation, growth respiration of each pool of vegetation, and net primary productivity of vegetation (which belongs to carbon flux in the classification of output data types).

[0059] Soil carbon data can include: the total amount of each carbon pool in the soil (which belongs to the carbon storage in the output data type classification) and heterotrophic respiration in each pool of the soil (which belongs to the carbon flux in the output data type classification).

[0060] In addition, the leaf area index can be considered one of the vegetation canopy state variables in the aforementioned classification based on output data type.

[0061] Therefore, the results of this round of biological processes may include: (1) carbon storage: carbon pools and total amount in vegetation, carbon pools and total amount in soil. (2) carbon flux: total primary productivity of vegetation, maintenance respiration of each pool of vegetation, growth respiration of each pool of vegetation, net primary productivity of vegetation, heterotrophic respiration of each pool of soil, net ecosystem productivity; (3) vegetation canopy state variables: leaf area index.

[0062] In practice, the results of the current round of physical processes and the results of the current round of biological processes obtained from the aforementioned physical process simulation are combined to obtain the current round of land surface process simulation results, which can be used as the output of AEIM at the current moment. The data in the current round of land surface process simulation results can also be used as the input data of AEIM at the next moment. Thus, the synchronous simulation of the dynamic changes of water / heat / carbon in the terrestrial ecosystem is realized through AEIM.

[0063] This embodiment dynamically calculates the leaf area index (LAI) for the current simulation based on the previous simulation results and current atmospheric driving data using a vegetation carbon allocation algorithm. This LAI is then simultaneously applied to the calculation of biological processes and the feedback from subsequent physical processes, thereby achieving real-time, closed-loop coupling between physical and biological processes through the LAI. This effectively overcomes the problem in traditional AVIM simulations where the LAI relies on a simplified empirical carbon allocation scheme, resulting in weak coupling between physical and biological processes. It significantly improves the reliability of land surface process simulation results in reflecting vegetation dynamic physiological responses and environmental stress feedback.

[0064] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the land surface process simulation method coupled with multi-source algorithms in this application.

[0065] In this embodiment, to specifically illustrate the physical simulation process of AEIM, step S20 specifically includes: steps S201~S205: Step S201: Input the current atmospheric driving data and the simulation results of the previous land surface process into the preset physical process simulation model. The preset physical process simulation model includes a radiation transfer module, a water and heat flux calculation module, and a temperature and humidity calculation module.

[0066] It should be noted that the physical process simulation model in AEIM can be based on the physical process algorithm in AVIM, and improved upon. For ease of description, this physical process simulation model can be divided into modules such as radiation transfer module, water and heat flux calculation module, and temperature and humidity calculation module. In each module, the corresponding improved physical process algorithm can be applied for calculation.

[0067] It should also be noted that the current atmospheric driving data may include: the solar zenith angle at the current moment and vegetation morphology parameters; the simulation results of the previous round of land surface processes may include simulated quantities such as vegetation canopy state variables, soil surface state variables, and non-turbulent impedance. Furthermore, the simulated quantities obtained from the previous round of calculations can be substituted as specific parameter values ​​into the corresponding physical process algorithms based on the algorithm input requirements of different modules to obtain the simulated quantities output in this round.

[0068] The specific variables of vegetation canopy state include: leaf area index, canopy leaf temperature, canopy snow cover, canopy water cover, canopy reflectivity / absorption / transmittance, canopy zero-plane displacement, canopy roughness, etc.; the specific variables of soil surface state include: surface snow cover, surface reflectivity, soil temperature / humidity, etc.; non-turbulent impedance can include: canopy stomatal impedance, etc.

[0069] Step S202: The total radiation budget is determined by the radiation transfer module based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables.

[0070] It should be noted that the radiation transmission module can directly use the original radiation algorithm and related reflectivity, absorptivity and transmissivity algorithms in AVIM, which will not be elaborated in this embodiment.

[0071] In its implementation, the radiation transfer module can first calculate the soil surface reflectance using the solar zenith angle, soil moisture, and ground snow cover; then calculate the canopy reflectance, canopy absorptivity, and canopy transmittance using the solar zenith angle, leaf area index, canopy snow cover, and ground reflectance; and finally calculate the radiation budget of the soil and canopy using ground reflectance, canopy reflectance / absorptivity / transmittance, soil surface temperature, and canopy temperature.

[0072] Step S203: The water and heat flux calculation module calculates the water and heat flux based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables to obtain the water and heat flux calculation results.

[0073] It should be understood that the water and heat flux calculation module can be further divided into an aerodynamic impedance calculation unit and a water and heat flux calculation unit. Then, the current atmospheric driving data, vegetation canopy state variables, and soil surface state variables can be substituted into the preset aerodynamic impedance algorithm to obtain the current aerodynamic impedance; and the current aerodynamic impedance, current atmospheric driving data, vegetation canopy state variables, and soil surface state variables can be substituted into the preset water and heat flux algorithm to obtain the current water vapor flux and the current sensible heat flux.

[0074] The preset aerodynamic impedance algorithm in the aerodynamic impedance calculation unit can be an improvement on the original aerodynamic impedance algorithm in AVIM. In this embodiment, considering that the aerodynamic impedance algorithm of AVIM is only applicable to neutral stratified atmospheres, a correction function based on the Monin-Obukhov similarity theory is introduced. This allows the calculation of momentum, sensible heat and latent heat fluxes under four atmospheric boundary layer states: very unstable, unstable, stable and very stable. In turn, the aerodynamic impedance under different atmospheric boundary layer stability can be calculated.

[0075] The hydrothermal flux calculation unit involves the calculation process of hydrothermal flux under vegetation and bare soil surfaces. The theoretical expressions of hydrothermal flux under vegetation and bare soil surfaces are not the same. In AVIM, the original hydrothermal flux algorithm only sets the leaf area index to a minimum value when calculating the hydrothermal flux under bare soil surfaces, and still uses the algorithm for vegetation surfaces, which leads to low physical accuracy of the calculation results.

[0076] Therefore, in the preset hydrothermal flux algorithm of this embodiment, considering that the hydrothermal sources of the vegetation underlying surface are the canopy and the ground surface, while the hydrothermal source of the bare soil underlying surface is only the ground surface, different hydrothermal flux algorithms (vegetation underlying surface algorithm and bare soil underlying surface algorithm) are adopted for the two underlying surfaces. The brief representation of these different hydrothermal flux algorithms is as follows: Algorithm for sensible heat flux under vegetation:

[0077] Calculation of sensible heat flux of bare soil underlying surface:

[0078] Calculation of water vapor flux under vegetation surface:

[0079] Calculation of water vapor flux over bare soil surface:

[0080] In the formula, To provide canopy heat flux to the underlying vegetation surface, To provide surface heat flux to the vegetation underlying surface; To provide the canopy heat flux to the bare soil underlying surface; To provide canopy water vapor flux to the vegetation underlying surface, To provide surface water vapor flux to the vegetation underlying surface; and All of these are the canopy water vapor flux provided to the bare soil underlying surface.

[0081] It should be understood that the different sensible heat fluxes and water vapor fluxes mentioned above can be calculated using aerodynamic impedance. This calculation process can be directly referenced from the water heat flux algorithm in AVIM, and will not be elaborated upon in this embodiment.

[0082] It should also be noted that, considering that when calculating water and heat fluxes, although the aforementioned aerodynamic impedance calculation unit can calculate aerodynamic impedances under different atmospheric boundary layer stability based on the correction function, the obtained aerodynamic impedances are coarse expressions. Therefore, the Community Land Model (CLM) framework, especially the iterative solution idea in CLM4.5 (hereinafter referred to as CLM4.5), can be introduced to further improve the aforementioned vegetated underlying surface algorithms (vegetated underlying surface sensible heat flux algorithm, vegetated underlying surface water vapor flux algorithm) and bare soil underlying surface algorithms (vegetated underlying surface water vapor flux algorithm, bare soil underlying surface water vapor flux algorithm): the stable value is gradually approximated in a cyclic iterative manner, and outliers are redistributed when there is an energy budget imbalance, so as to finally obtain the water vapor flux and sensible heat flux that satisfy the energy budget balance.

[0083] In addition, the calculation process of related simulation quantities such as zero plane displacement and roughness of the canopy, water vapor flux impedance of the soil surface, water / snow accumulation in the canopy and snow accumulation on the ground are also involved in the aerodynamic impedance calculation unit and the water and heat flux calculation unit. These can be directly referred to the relevant algorithms in AVIM, and will not be elaborated in this embodiment.

[0084] In its implementation, the water and heat flux calculation module can first calculate the zero-plane displacement and roughness of the canopy from vegetation morphology parameters and ground roughness; then calculate the water vapor flux impedance of the soil surface from soil surface moisture; and finally calculate the aerodynamic impedance, water vapor / heat flux, canopy water / snow accumulation, and ground snow accumulation under different atmospheric boundary layer stability conditions from ground net shortwave radiation, soil surface temperature, soil surface water vapor flux impedance, canopy net shortwave radiation, canopy temperature, canopy stomatal impedance, leaf area index, canopy zero-plane displacement, and roughness.

[0085] Step S204: The temperature and humidity calculation module calculates the canopy temperature and soil temperature and humidity results based on the water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables, according to the preset canopy temperature algorithm and the preset soil temperature and humidity algorithm.

[0086] It should be understood that the temperature and humidity calculation module can be further divided into a canopy temperature calculation unit and a soil temperature and humidity calculation unit. Then, the results of water and heat flux calculations, total radiation budget, vegetation canopy state variables, and soil surface state variables can be substituted into a preset canopy temperature algorithm to obtain the current round of canopy temperature, which is then determined as the canopy temperature calculation result. Similarly, the results of water and heat flux calculations, total radiation budget, vegetation canopy state variables, and soil surface state variables can be substituted into a preset soil temperature and humidity algorithm to obtain the soil temperature and humidity of each layer, which are then determined as the soil temperature and humidity calculation results.

[0087] The preset canopy temperature algorithm in the canopy temperature calculation unit can be an improvement on the original canopy temperature algorithm in AVIM. In this embodiment, considering the influence of canopy air temperature and canopy air humidity on canopy temperature, a partial derivative term of canopy air temperature / humidity with respect to canopy temperature is introduced into the original canopy temperature algorithm to strengthen the coupling relationship between the canopy and the atmosphere / ground.

[0088] The soil temperature and humidity calculation unit involves calculating both soil temperature and soil humidity. Since the soil temperature algorithm in AVIM divides the soil into 3 layers, while the soil humidity algorithm divides it into 11 layers, this embodiment pre-defines both the soil temperature and humidity algorithms into the same 11 layers, thereby achieving coordinated changes in soil hydrothermal processes and more refined vertical resolution.

[0089] It should also be noted that the specific formulas and required parameter types involved in the above algorithms can be directly referred to AVIM, and will not be elaborated on in this embodiment.

[0090] In practice, since canopy temperature depends on net canopy radiation, canopy sensible heat flux, canopy evaporation, and canopy transpiration, canopy temperature can be calculated from leaf area index, canopy snow / water accumulation, ground snow accumulation, canopy temperature, net shortwave radiation of the canopy, aerodynamic impedance, canopy stomatal impedance, soil surface water vapor flux impedance, canopy water vapor / heat flux, and soil surface moisture. Since soil temperature depends on soil heat flux, soil volumetric specific heat capacity, and soil thermal conductivity, and soil heat flux includes net ground radiation, ground sensible heat flux, and ground evaporation, soil can be divided into 11 layers. Soil temperature can be calculated from ground snow accumulation, canopy temperature, canopy snow / water accumulation, ground net shortwave radiation, aerodynamic impedance, canopy stomatal impedance, soil surface water vapor flux impedance, ground water vapor / heat flux, soil moisture, and soil temperature. Soil moisture depends on surface water flux, canopy transpiration, soil water diffusivity, and soil water conductivity. Therefore, the soil can also be divided into 11 layers, and the soil moisture for the next time period can be calculated based on ground snow accumulation, canopy precipitation, surface runoff, soil moisture evaporation, canopy transpiration, and soil moisture.

[0091] Step S205: The total radiation budget, the calculated results of the water and heat flux, the calculated results of the canopy temperature, and the calculated results of the soil temperature and humidity are determined as the results of this round of physical processes.

[0092] It should be noted that by integrating the above calculation results of total radiation budget, water and heat flux, canopy temperature, and soil temperature and humidity, the results of this round of physical processes can be obtained.

[0093] Specifically, according to the aforementioned output data types, the results of this round of physical processes can include: (1) Water vapor flux: sublimation of surface snow, evaporation of soil moisture, sublimation of canopy snow, evaporation of canopy soil water, and evaporation of canopy intercepted water. (2) Sensitive heat flux: sensitive heat exchange between canopy surface and canopy air, sensitive heat exchange between soil surface and canopy air, sensitive heat exchange between canopy air and boundary layer atmosphere, and sensitive heat exchange between soil surface and boundary layer atmosphere. (3) Radiation flux: long / shortwave radiation from soil surface and long / shortwave radiation from canopy surface. (4) Aerodynamic impedance: sensitive heat / water vapor impedance between canopy surface and canopy air, sensitive heat / water vapor impedance between soil surface and canopy air, and momentum / sensitive heat / water vapor impedance between canopy air and boundary layer atmosphere. (5) Non-turbulent impedance: water vapor impedance of soil surface. (6) Vegetation canopy state variables: canopy leaf temperature, canopy snow cover, canopy water cover, canopy reflectivity / absorption / transmittance, canopy zero-plane displacement, canopy roughness. (7) Soil and surface state variables: surface snow cover, surface reflectivity, soil temperature / humidity.

[0094] The physical process simulation model of AEIM in this embodiment is built on the basis of AVIM: a correction function is added to the aerodynamic impedance, which improves the applicability of the algorithm to atmospheric conditions; when calculating water and heat fluxes, the algorithms for sensible heat and water vapor fluxes under vegetated and bare soil surfaces are clearly distinguished, and the stable values ​​are gradually approximated by iterative loops in CLM4.5, thereby improving the theoretical expression of water and heat transfer under different atmospheric boundary layer stability; when calculating soil temperature / humidity, the number of soil layers is unified to 11, realizing the coordinated change of soil temperature / humidity, thus improving the reliability of the physical process results obtained from AEIM simulation. Furthermore, since the leaf area index and canopy stomatal impedance, etc., output from the previous round of land surface simulation (the previous round of biological processes), are used as key input parameters in the calculation process of each unit of the physical process simulation model, dynamic coupling between physical and biological processes is achieved.

[0095] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the land surface process simulation method coupled with multi-source algorithms in this application.

[0096] In this embodiment, to specifically illustrate the biosimulation process of AEIM, step S30 specifically includes: steps S301~S302: Step S301: Substitute the previous leaf area index, the canopy temperature, the soil temperature, and the current aerodynamic impedance into the preset photosynthesis algorithm to obtain the total primary productivity of the canopy in this round.

[0097] Understandably, in the biological simulation process, the simulation quantities obtained from the previous round of calculation and the simulation quantities obtained from the current round of calculation can be substituted as specific parameter values ​​into the corresponding biological process algorithm based on different algorithm input requirements to obtain the simulation quantities output in the current round.

[0098] It should be noted that the preset photosynthesis algorithm in the AEIM biological process model can be based on the photosynthesis model in CLM4.5, and the C3 and C4 photosynthesis algorithms used at the leaf scale adopt the modeling schemes proposed by Farquhar and Collatz, respectively. The Farquhar (C3) and Collatz (C4) models are constructed based on enzyme kinetics and can realistically reflect the biochemical limitations of carbon dioxide concentration, light, and temperature on photosynthesis.

[0099] In addition to photosynthesis modeling, AEIM can also use the Ball-Berry model to calculate canopy stomatal impedance. Canopy stomatal impedance and canopy stomatal conductance are reciprocals of each other. The Ball-Berry model can directly correlate stomatal conductance with net photosynthetic rate, leaf surface humidity, and carbon dioxide concentration, constructing an expression for canopy stomatal conductance that includes a net photosynthetic rate term.

[0100] It should be understood that the stomatal conductance in the Ball-Berry model is a function of the net photosynthetic rate in the preset photosynthesis algorithm, and the net photosynthetic rate depends on the intercellular carbon dioxide concentration. The carbon dioxide concentration itself is controlled by the stomatal conductance. That is, the expression of the net photosynthetic rate constructed based on the aforementioned photosynthesis model in CLM4.5 includes a canopy stomatal conductance term.

[0101] Therefore, the net photosynthetic rate term and the canopy stomatal conductance term are interdependent in the above two expressions. AEIM can achieve synergistic optimization of the two through iterative solution: simultaneously solving for a self-consistent solution that satisfies both the canopy stomatal conductance expression and the net photosynthetic rate expression (the specific values ​​of the net photosynthetic rate term and the canopy stomatal conductance term), thereby obtaining the total primary productivity of the canopy and the canopy stomatal impedance in this round.

[0102] In practical implementation, the biological process model can calculate the total primary productivity of the canopy and the stomatal impedance of the canopy from the canopy temperature, soil moisture, aerodynamic impedance, and leaf area index. The stomatal impedance of the canopy can also be used as a type of output data in the results of this round of biological processes.

[0103] Step S302: Determine the number of phenologically relevant days based on the current atmospheric driving data, and substitute the total primary productivity of the current canopy, the number of phenologically relevant days, the vegetation carbon content of the previous round, the canopy temperature, and the soil temperature into the preset vegetation carbon allocation algorithm to obtain vegetation carbon data.

[0104] It should be noted that the current atmospheric driving data may include the current date and the preset reference growing season start date. The previous round of vegetation carbon content refers to the various carbon pools and total amount of vegetation in the aforementioned vegetation carbon data.

[0105] Phenologically relevant days can be used to characterize plant growth stages. Specifically, the model can start counting from a preset reference growing season start date and accumulate the effective temperature (such as temperature above a certain base temperature) of each day to the current date to obtain the phenologically relevant days.

[0106] It should be understood that in the AEIM biological process model, both the vegetation carbon allocation algorithm and the vegetation carbon loss algorithm can be built based on the modeling algorithms in the Standard Biogeochemical Cycling Model of Biological Communities (BIOME-BGC): The vegetation is divided into six tissues: leaves, living trunk, dead trunk, living coarse roots, dead coarse roots, and fine roots. Each tissue contains not only the structural carbon / nitrogen pool for the current growing season but also the storage and turnover carbon / nitrogen pool for the next growing season. To ensure the smooth and stable distribution of carbon / nitrogen in the vegetation, a temporary carbon / nitrogen pool is pre-set as a buffer. This temporary carbon / nitrogen pool also provides the respiratory substrate for the survival and growth of the vegetation. The carbon consumed when carbon / nitrogen migrates from the storage pool to the structural pool is provided by a separately established growth and respiration storage carbon pool and a growth and respiration turnover carbon pool. When a part of the vegetation withers and falls, nitrogen is released into the redistribution nitrogen pool and then recycled by the plant.

[0107] To illustrate in detail how to perform vegetation carbon allocation calculations based on the obtained phenological-related days, the steps for obtaining vegetation carbon data through a preset vegetation carbon allocation algorithm may include: steps S3021~S3025: Step S3021: Calculate the vegetation phenological distribution flux based on the phenological related days and the previous round of vegetation carbon content.

[0108] It should be noted that vegetation phenological distribution flux can be defined as the rate at which plants reallocate existing carbon and nitrogen among their different organs (leaves, stems, roots) based on the current season (phenological stage) and their own reserves in the absence of new photosynthetic products (GPP) input.

[0109] In practice, the life cycle stage of the plant on the current date can be determined based on the number of phenologically relevant days, and then the vegetation carbon phenological distribution flux can be calculated based on the carbon allocation parameters corresponding to the life cycle stage (which are a type of input data in the current atmospheric driving data).

[0110] Step S3022: Calculate vegetation sustaining respiration based on the canopy temperature, the soil temperature, and the carbon content of the previous vegetation cycle.

[0111] Step S3023: Calculate the vegetation carbon growth distribution flux based on the total primary productivity of the current canopy, the vegetation maintenance respiration, and the vegetation carbon content of the previous cycle, and calculate the vegetation growth respiration based on the vegetation carbon growth distribution flux.

[0112] Step S3024: Update the previous round of vegetation carbon content based on the vegetation maintenance respiration, the vegetation growth respiration, and the vegetation carbon growth distribution flux to obtain the current round of vegetation carbon content, wherein the current round of vegetation carbon content includes leaf carbon pool.

[0113] In practice, vegetation sustaining respiration can be calculated first from canopy temperature, soil temperature, and vegetation carbon content; then, vegetation carbon growth distribution flux can be calculated from total primary productivity of the canopy, vegetation sustaining respiration, and vegetation carbon content; vegetation growth respiration can be calculated from vegetation carbon distribution flux; and finally, vegetation carbon content can be updated from vegetation sustaining respiration, vegetation growth respiration, and vegetation carbon distribution flux to obtain the current vegetation carbon content.

[0114] Step S3025: The current vegetation carbon content, the vegetation maintenance respiration, the vegetation growth respiration, the vegetation carbon growth distribution flux, and the current canopy total primary productivity are determined as vegetation carbon data.

[0115] In practice, the simulated quantities calculated in the above stages are integrated to obtain vegetation carbon data, which is a part of the results of biological processes.

[0116] Furthermore, by employing the vegetation carbon littering algorithm in the AEIM biological process model, vegetation litter can be obtained, and then soil carbon data can be calculated using the soil carbon conversion and decomposition algorithm in CENTURY.

[0117] Furthermore, since nitrogen changes passively in a fixed ratio when vegetation carbon and soil carbon are transferred, and does not interact with the outside world, the corresponding vegetation nitrogen data and soil nitrogen data can be determined directly based on the carbon-nitrogen ratio.

[0118] Therefore, the current-cycle biological process results obtained from the AEIM-based biosimulation process can include vegetation carbon / nitrogen data and soil carbon / nitrogen data. Furthermore, it can also include canopy stomatal impedance calculated at the stage applying the photosynthesis algorithm and leaf area index calculated at the stage applying the photosynthesis algorithm.

[0119] The biological process model of AEIM in this embodiment abandons the original vegetation physiological process algorithm in AVIM and introduces and modifies several biological process simulation schemes in this field: the photosynthesis algorithm is selected from the Farquhar and Collatz enzyme kinetic model; the vegetation carbon allocation / fallenness algorithm is selected from BIOME-BGC; and the soil carbon conversion and decomposition algorithm is selected from CENTURY. This constructs a complete and mechanistic simulation model of vegetation and soil carbon and nitrogen cycles. By unifying the step size of the biological process model of this embodiment with the aforementioned physical process model, AEIM can achieve a close coupling between physical and biological processes. It can synchronously simulate the dynamic changes of water, heat, and carbon in the "atmosphere-vegetation-soil" ecosystem with high spatiotemporal resolution, obtaining reliable land surface simulation results that reflect real vegetation physiological activities.

[0120] Furthermore, you can also refer to this section. Figure 4 This paper explains the improvements of the AEIM constructed in the land surface process simulation method coupled with multi-source algorithms in this application compared to AVIM. Figure 4 This is a schematic diagram illustrating the coupling mechanism between physical and biological processes in AEIM.

[0121] Depend on Figure 4 It can be seen that, Figure 4 The area inside the dashed box represents the coupling of physical processes, while the area outside the dashed box represents the coupling of biological processes. The physical and biological processes are also coupled with each other, with leaf area index and other parameters participating in the two processes as key simulation quantities.

[0122] Key simulation quantities in the physical process can include radiative transfer, canopy temperature, soil temperature, and soil moisture.

[0123] The physical process model of AEIM is based on and improved from AVIM: the simulated quantities related to radiation transfer (such as surface reflectivity, canopy reflectivity, canopy absorptivity, and canopy transmissivity) are obtained using the original algorithm in AVIM; the aerodynamic impedance required to calculate canopy temperature is calculated by an algorithm improved by introducing the Monin-Obukhov correction function into the original aerodynamic impedance algorithm; the hydrothermal flux required to calculate canopy temperature, soil temperature, and soil moisture is calculated by the vegetation underlay algorithm and the bare soil underlay algorithm improved by the CLM4.5 framework; the canopy temperature is calculated by an algorithm obtained by introducing a number of terms representing the partial derivative of canopy air temperature with respect to canopy temperature into the original algorithm in AVIM; the soil temperature and soil moisture are calculated by an algorithm obtained by improving the original soil temperature algorithm and soil moisture algorithm in AVIM by performing the same number of soil layer divisions.

[0124] Key analog quantities in biological processes can include photosynthesis, vegetation carbon, soil carbon, and leaf area index.

[0125] The biological process model of AEIM can be constructed based on the biological process simulation scheme in this field: the photosynthetic stage adopts the C3 / C4 leaf photosynthesis model proposed by Farquhar and Collatz; the vegetation carbon allocation / fall algorithm is selected from BIOME-BGC; and the soil carbon conversion and decomposition algorithm is selected from CENTURY.

[0126] Furthermore, after obtaining vegetation carbon data, leaf carbon pools can be identified, and then leaf area index can be calculated by multiplying the leaf carbon pools by the specific leaf area.

[0127] AEIM uses leaf area index as a key pivot, dynamically linking the physical processes (radiative transfer, canopy / soil temperature, soil moisture) within the dashed box with the biological processes (photosynthesis, vegetation carbon, soil carbon) outside the dashed box into an organic whole. The physical processes, based on the improved AVIM framework, provide a high-precision real-time environmental driving field for the biological processes by introducing a fully stable turbulence scheme, differentiating underlying surface flux algorithms, and unifying soil stratification solutions. The biological processes fully integrate mainstream mechanistic models in fields such as Farquhar / Collatz photosynthesis, BIOME-BGC vegetation carbon allocation, and CENTURY soil carbon decomposition. The output vegetation carbon data is used to generate leaf area index in real time, and along with canopy stomatal impedance calculated synchronously during the photosynthetic stage, it is immediately fed back to the physical processes, altering canopy structure and surface properties.

[0128] This application proposes a land surface process simulation method that couples multiple sources of algorithms. By constructing AEIM, physical and biological processes are coupled in a high-frequency, two-way, closed-loop manner through key simulation quantities such as leaf area index and canopy stomatal impedance. This overcomes the shortcomings of AVIM, such as the disconnect between the two and the lag in vegetation response. AEIM can synchronously and coordinately simulate the dynamic interaction and adaptation processes of terrestrial ecosystems in water, heat, and carbon cycles, significantly improving the reliability and predictive ability of simulating ecosystem functions and climate feedback under changing environments.

[0129] This application also provides a land surface process simulation system coupled with a multi-source algorithm; please refer to [reference needed]. Figure 5 , Figure 5 This is a schematic diagram of the modular structure of the land surface process simulation system coupled with multi-source algorithms in this application. The system includes: The data preparation module 501 is used to acquire current atmospheric driving data and the simulation results of the previous round of land surface processes, wherein the simulation results of the previous round of land surface processes include at least the leaf area index of the previous round. The physical process simulation module 502 is used to perform physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical processes. The biological process simulation module 503 is used to perform carbon allocation operations according to a preset vegetation carbon allocation algorithm, based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes, to obtain vegetation carbon data, which includes a leaf carbon pool; determine the leaf area index of the current round based on the leaf carbon pool and the current atmospheric driving data; and determine the results of the current round of biological processes by combining the vegetation carbon data and the leaf area index of the current round. The result output module 504 is used to determine the results of the current round of physical processes and the results of the current round of biological processes as the simulation results of the current round of land surface processes.

[0130] This embodiment dynamically calculates the leaf area index (LAI) for the current simulation based on the previous simulation results and current atmospheric driving data using a vegetation carbon allocation algorithm. This LAI is then simultaneously applied to the calculation of biological processes and the feedback from subsequent physical processes, thus achieving real-time, closed-loop coupling between physical and biological processes through the LAI. This effectively overcomes the problem of weak coupling between physical and biological processes in traditional AVIM simulations, which rely on simplified empirical carbon allocation schemes due to the LAI's dependence on the leaf area index. This significantly improves the reliability of land surface process simulation results in reflecting vegetation dynamic physiological responses and environmental stress feedback.

[0131] 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 system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other elements in the process, method, article, or system that includes that element.

[0132] The above embodiment numbers are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. They are only some embodiments of this application and do not limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A method for simulating land surface processes using coupled multi-source algorithms, characterized in that, The method includes: Acquire current atmospheric driving data and the simulation results of the previous land surface process, wherein the simulation results of the previous land surface process include at least the leaf area index of the previous round; Based on the current atmospheric driving data and the simulation results of the previous round of land surface processes, physical process simulation is performed to obtain the results of the current round of physical processes. According to the preset vegetation carbon allocation algorithm, carbon allocation is performed based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes to obtain vegetation carbon data, which includes leaf carbon pools. The current blade area index is determined based on the blade carbon pool and the current atmospheric driving data. The results of the current biological process are determined by combining the vegetation carbon data and the current leaf area index, and the results of the current physical process and the current biological process are determined as the simulation results of the current land surface process.

2. The method as described in claim 1, characterized in that, The simulation results of the previous land surface process include: vegetation canopy state variables and soil surface state variables, and the leaf area index of the previous round belongs to the vegetation canopy state variables; The step of performing physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical processes includes: The current atmospheric driving data and the simulation results of the previous land surface process are input into the preset physical process simulation model, which includes a radiation transfer module, a water and heat flux calculation module, and a temperature and humidity calculation module. The total radiation budget is determined by the radiation transfer module based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables. The water and heat flux calculation module calculates the water and heat flux based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables, and obtains the water and heat flux calculation results. The temperature and humidity calculation module calculates the canopy temperature and soil temperature and humidity results based on the water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables, according to the preset canopy temperature algorithm and the preset soil temperature and humidity algorithm. The total radiation budget, the calculated results of the water and heat flux, the calculated results of the canopy temperature, and the calculated results of the soil temperature and humidity are determined as the results of this round of physical processes.

3. The method as described in claim 2, characterized in that, The step of calculating water and heat flux based on the current atmospheric driving data, the vegetation canopy state variables, and the soil surface state variables to obtain the water and heat flux calculation results includes: Substitute the current atmospheric driving data, the vegetation canopy state variables and the soil surface state variables into the preset aerodynamic impedance algorithm to obtain the current round of aerodynamic impedance. The preset aerodynamic impedance algorithm is obtained by introducing the Moning-Obukhov similarity theory correction function into the aerodynamic impedance algorithm in the original AVIM. The current aerodynamic impedance, the current atmospheric driving data, the vegetation canopy state variables and the soil surface state variables are substituted into the preset water and heat flux algorithm to obtain the current water vapor flux and the current sensible heat flux. The preset water and heat flux algorithm includes a vegetation underlying surface algorithm and a bare soil underlying surface algorithm, which are respectively improved based on the community land surface model framework. The current aerodynamic impedance, current water vapor flux, and current sensible heat flux are determined as the results of the water heat flux calculation.

4. The method as described in claim 2, characterized in that, The step of calculating the canopy temperature and soil temperature and humidity results based on the water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables, according to a preset canopy temperature algorithm and a preset soil temperature and humidity algorithm, includes: The water and heat flux calculation results, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables are substituted into the preset canopy temperature algorithm to obtain the canopy temperature for this round, and determined as the canopy temperature calculation result. The preset canopy temperature algorithm is obtained by introducing a partial derivative term to characterize the canopy air temperature on the canopy temperature in the original AVIM canopy temperature algorithm. The calculation results of the water and heat flux, the total radiation budget, the vegetation canopy state variables, and the soil surface state variables are substituted into the preset soil temperature and humidity algorithm to obtain the soil temperature and humidity of each layer, and determined as the soil temperature and humidity calculation results. The preset soil temperature and humidity algorithm includes a soil temperature algorithm and a soil humidity algorithm that use the same number of soil layers.

5. The method as described in claim 1, characterized in that, The results of this round of physical processes include: canopy temperature, soil temperature, and aerodynamic impedance; the results of the previous round of land surface process simulation also include: vegetation carbon content in the previous round. The step of obtaining vegetation carbon data by performing carbon allocation operations based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes according to a preset vegetation carbon allocation algorithm includes: The leaf area index of the previous round, the canopy temperature, the soil temperature, and the aerodynamic impedance of the current round are substituted into the preset photosynthesis algorithm to obtain the total primary productivity of the canopy in the current round. The preset photosynthesis algorithm includes the Faqual photosynthesis algorithm suitable for C3 plants and the Kolatz photosynthesis algorithm suitable for C4 plants. The number of phenologically relevant days is determined based on the current atmospheric driving data. The total primary productivity of the current canopy, the number of phenologically relevant days, the vegetation carbon content of the previous cycle, the canopy temperature, and the soil temperature are then substituted into the preset vegetation carbon allocation algorithm to obtain vegetation carbon data. The preset vegetation carbon allocation algorithm is the vegetation carbon allocation algorithm in the standard biological community biogeochemical cycle model.

6. The method as described in claim 5, characterized in that, The step of obtaining the total primary productivity of the canopy in the current cycle by substituting the leaf area index of the previous cycle, the canopy temperature, the soil temperature, and the aerodynamic impedance of the current cycle into a preset photosynthesis algorithm further includes: Substituting the previous leaf area index, the canopy temperature, the soil temperature, and the current aerodynamic impedance into a preset photosynthesis algorithm, a net photosynthetic rate expression is obtained, which includes a canopy stomatal conductance term. A stomatal conductance expression for the canopy is constructed based on the Ball-Berry model, and the stomatal conductance expression for the canopy includes a net photosynthetic rate term; The net photosynthetic rate expression and the canopy stomatal conductance expression are iteratively solved to obtain the current round of canopy stomatal impedance and the current round of canopy total primary productivity. The current round of canopy stomatal impedance is a result of the current round of biological processes.

7. The method as described in claim 5, characterized in that, The step of substituting the total primary productivity of the current canopy, the number of phenologically relevant days, the vegetation carbon content of the previous cycle, the canopy temperature, and the soil temperature into the preset vegetation carbon allocation algorithm to obtain vegetation carbon data includes: The vegetation phenological distribution flux is calculated based on the phenological-related days and the previous round of vegetation carbon content. Vegetation sustaining respiration is calculated based on the canopy temperature, the soil temperature, and the carbon content of the previous vegetation cycle. The vegetation carbon growth distribution flux is calculated based on the total primary productivity of the current canopy, the vegetation sustaining respiration, and the vegetation carbon content of the previous cycle, and the vegetation growth respiration is calculated based on the vegetation carbon growth distribution flux. The previous round of vegetation carbon content is updated based on the vegetation maintenance respiration, the vegetation growth respiration, and the vegetation carbon growth distribution flux to obtain the current round of vegetation carbon content, which includes the leaf carbon pool. The current vegetation carbon content, vegetation maintenance respiration, vegetation growth respiration, vegetation carbon growth distribution flux, and current canopy total primary productivity are defined as vegetation carbon data.

8. The method as described in claim 1, characterized in that, The current atmospheric driving data includes the specific leaf area constant; The step of determining the current leaf area index based on the blade carbon pool and the current atmospheric driving data includes: The leaf carbon pool and the specific leaf area constant are multiplied to calculate the current leaf area index.

9. The method as described in claim 1, characterized in that, The results of this round of physical processes include: soil temperature and soil moisture; the vegetation carbon data also include: vegetation litter. Before the step of determining the results of the current biological process by combining the vegetation carbon data and the current leaf area index, the method further includes: The soil temperature, soil moisture and vegetation litter are substituted into a preset soil carbon conversion decomposition algorithm to obtain soil carbon data. The preset soil carbon conversion decomposition algorithm is the soil carbon conversion decomposition algorithm in the CENTURY soil organic matter turnover model. Accordingly, the step of determining the results of the current biological process by combining the vegetation carbon data and the current leaf area index includes: The results of this biological process are determined by combining the vegetation carbon data, the soil carbon data, and the current leaf area index.

10. A land surface process simulation system coupled with a multi-source algorithm, characterized in that, The system includes: The data preparation module is used to acquire current atmospheric driving data and the simulation results of the previous round of land surface processes, wherein the simulation results of the previous round of land surface processes include at least the leaf area index of the previous round. The physical process simulation module is used to perform physical process simulation based on the current atmospheric driving data and the previous round of land surface process simulation results to obtain the results of the current round of physical processes. The biological process simulation module is used to perform carbon allocation operations according to a preset vegetation carbon allocation algorithm, based on the simulation results of the previous round of land surface processes and the results of the current round of physical processes, to obtain vegetation carbon data, which includes a leaf carbon pool; determine the leaf area index of the current round based on the leaf carbon pool and the current atmospheric driving data; and determine the results of the current round of biological processes by combining the vegetation carbon data and the leaf area index of the current round. The results output module is used to determine the results of the current round of physical processes and the results of the current round of biological processes as the simulation results of the current round of land surface processes.

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