A carbon and water flux simulation method and system based on vegetation hydraulic transport

Abstract

The present application relates to a carbon-water flux simulation method and system based on vegetation hydraulic transmission. The method includes: constructing a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module, obtaining model driving data and model parameters and calculating vegetation photosynthesis, stomatal conductance, vegetation transpiration and soil evaporation respectively; calculating the water absorption rate of vegetation based on the model driving data and model parameters; calculating the soil moisture of each preset layer based on the water absorption rate and model parameters; iterating the corresponding process variables of the model by updating the vegetation water stress factor within a preset time, until the number of iterations reaches a preset critical number, and then outputting the carbon-water flux simulation results. This method takes into account the flow of water between the soil-vegetation-atmosphere continuum, is more mechanistic, and strengthens the water-carbon coupling relationship in the model; using the vegetation water stress factor to adjust the stomatal conductance, the simulation accuracy of the model during drought is better than the original model.

Description

Technical Field

[0001] The present application relates to the technical field of terrestrial ecosystems, and in particular to a carbon and water flux simulation method and system based on vegetation hydraulic transport. Background Art

[0002] As global climate change intensifies, terrestrial ecosystems play a key role in the global carbon and water cycle. Terrestrial ecosystem models have important scientific significance and application value in studying water and carbon cycles, drought, global change, and carbon neutrality. By simulating the dynamic changes of vegetation, biomass, soil organic carbon, etc., terrestrial ecosystem models can help us deeply understand the distribution and flow of carbon and water in ecosystems, as well as their response to climate change; terrestrial ecosystem models can predict ecosystem feedback under different climate scenarios, providing a scientific basis for the formulation of adaptive management strategies.

[0003] In the related art, for example, Chinese patent CN109800971B provides a model system for evaluating the impact of ozone on vegetation productivity in a region, including a BEPS module, a WRF module and a CMAQ module. The BEPS module includes ozone input, data transmission, and ozone impact modules on photosynthesis and transpiration. The ozone input, data transmission and ozone impact modules on photosynthesis and transpiration are added to the model. Most existing models respond to drought by using simplified, linear relationships to characterize the unidirectional response of vegetation physiological processes to soil moisture or atmospheric moisture deficit. The current BEPS (Biosphere-atmosphere Exchange Process Simulator) model divides the vegetation canopy into shade leaf and sun leaf components through a hierarchical design, calculates photosynthesis and transpiration respectively, and performs scale conversion from leaves to canopies; when the current BEPS model considers the impact of drought, it uses soil moisture stress factors to regulate stomatal conductance, and organically combines the carbon and water cycle processes of vegetation through stomatal conductance, thereby simulating the carbon and water flux of the ecosystem.

[0004] However, the current BEPS model does not take into account vegetation hydraulic processes and lacks a description of the mechanism of water along the soil-vegetation-atmosphere continuum. As a result, the BEPS model cannot fully consider the dynamic response of vegetation to water changes in the water and carbon cycle, resulting in low simulation accuracy of the BEPS model under drought conditions. Summary of the invention

[0005] Based on this, it is necessary to provide a carbon and water flux simulation method and system based on vegetation hydraulic transport that can improve the simulation accuracy of the BEPS model under drought conditions in response to the above technical problems.

[0006] In a first aspect, the present application provides a carbon and water flux simulation method based on vegetation hydraulic transport. The method comprises:

[0007] Construct a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module, obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively;

[0008] Calculate the water uptake rate of vegetation based on model-driven data and model parameters;

[0009] Calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0010] The corresponding process variables of the model are iterated within a preset time by updating the vegetation water stress factor until the number of iterations reaches a preset critical number, and then the carbon and water flux simulation results are output. The process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0011] In one embodiment, calculating the water absorption rate of vegetation based on model-driven data and model parameters includes:

[0012] Based on the model driving data and the model parameters, the preset first water absorption rate, the second water absorption rate and the third water absorption rate are calculated respectively; the first water absorption rate, the second water absorption rate and the third water absorption rate are compared and the minimum value is taken, and the minimum value is set as the water absorption rate of the current soil layer; the water absorption rates of different soil layers are calculated and summed, and the sum is set as the total water absorption rate of the vegetation; the formula is as follows:

[0013] ;

[0014] in, is the total water absorption rate, For the The water absorption rate corresponding to the layer of soil is For the The first water absorption rate corresponding to the soil layer, For the The second water absorption rate corresponding to the layer of soil, For the The third water absorption rate corresponding to the soil layer.

[0015] In one embodiment, the first water absorption rate includes:

[0016] The first water absorption rate is calculated based on the root density distribution of each layer, leaf water potential, soil water potential of each layer, vegetation height and soil layer depth; the formula is as follows:

[0017] ;

[0018] in, For the The first water absorption rate corresponding to the soil layer, For the The root density distribution corresponding to the soil layer, For leaf water potential, For the The soil water potential corresponding to the soil layer, is the vegetation canopy height, For the The soil depth corresponding to the soil layer, To be assigned to The vegetation resistance corresponding to the soil layer, For the Soil root resistance corresponding to the soil layer.

[0019] In one embodiment, the second water absorption rate includes:

[0020] The second water absorption rate is calculated based on the potential transpiration rate of vegetation, the parameters controlling the potential transpiration demand, and the root density distribution of each layer; the formula is as follows:

[0021] ;

[0022] in, For the The second water absorption rate corresponding to the layer of soil, For the The root density distribution corresponding to the soil layer, is the potential transpiration rate of vegetation, Parameters for controlling potential transpiration demand.

[0023] In one embodiment, the third water absorption rate includes:

[0024] The third water absorption rate is calculated based on the maximum water storage capacity of vegetation, actual water storage capacity, time step and root density distribution of each layer; the formula is as follows:

[0025] ;

[0026] in, For the The third water absorption rate corresponding to the soil layer is, For the The root density distribution corresponding to the soil layer, is the maximum water storage capacity of vegetation, is the actual water storage capacity, is the time step.

[0027] In one embodiment, calculating the soil moisture of a preset number of layers based on the water absorption rate and the model parameters comprises:

[0028] Calculate the corresponding soil moisture based on the number of soil layers at different depths; the formula is as follows:

[0029] ;

[0030] in, Indicates Soil moisture corresponding to the soil layer, Indicates The soil thickness corresponding to the soil layer, is the net rainfall rate reaching the surface, is the soil evaporation rate, represents the surface runoff, Indicates The soil layer and the The vertical flow of water between soil layers, Indicates The soil layer and the The vertical flow of water between the soil layers; in the fifth layer, represents the saturated groundwater flow;

[0031] The vertical flow of water between adjacent soil layers is calculated based on the model parameters; the formula is as follows:

[0032] ;

[0033] ;

[0034] ;

[0035] in, Indicates Soil hydraulic conductivity corresponding to the soil layer, Indicates The soil hydraulic conductivity corresponding to the soil layer, Indicates The soil water potential corresponding to the soil layer, Indicates The soil water potential corresponding to the soil layer, Indicates The soil thickness corresponding to the soil layer, Indicates The soil thickness corresponding to the soil layer, Indicates The saturated hydraulic conductivity of the soil layer is For the The porosity of the soil layer is is the Campbell coefficient, which is related to soil texture. For the Layer air inlet suction.

[0036] In the second aspect, the present application also provides a carbon and water flux simulation system based on vegetation hydraulic transport. The system includes:

[0037] Model parameter preparation module, used to build the biosphere-atmosphere exchange process simulator model coupled with the vegetation hydraulic module (BEPS-EcoHydro), obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration and soil evaporation respectively;

[0038] A water absorption rate calculation module, used to calculate the water absorption rate of vegetation based on model driving data and model parameters;

[0039] A soil moisture calculation module, used to calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0040] The cyclic simulation output module is used to iterate the corresponding process variables of the model by updating the vegetation water stress factor within a preset time until the number of iterations reaches a preset critical number, and then output the carbon and water flux simulation results, wherein the process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0041] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0042] Construct a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module (BEPS-EcoHydro), obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively;

[0043] Calculate the water uptake rate of vegetation based on model-driven data and model parameters;

[0044] Calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0045] The corresponding process variables of the model are iterated within a preset time by updating the vegetation water stress factor until the number of iterations reaches a preset critical number, and then the carbon and water flux simulation results are output. The process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Construct a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module (BEPS-EcoHydro), obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively;

[0048] Calculate the water uptake rate of vegetation based on model-driven data and model parameters;

[0049] Calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0050] The corresponding process variables of the model are iterated within a preset time by updating the vegetation water stress factor until the number of iterations reaches a preset critical number, and then the carbon and water flux simulation results are output. The process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0051] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0052] Construct a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module (BEPS-EcoHydro), obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively;

[0053] Calculate the water uptake rate of vegetation based on model-driven data and model parameters;

[0054] Calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0055] The corresponding process variables of the model are iterated within a preset time by updating the vegetation water stress factor until the number of iterations reaches a preset critical number, and then the carbon and water flux simulation results are output. The process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0056] This application adopts the above method, taking into account the flow of water between the soil-vegetation-atmosphere continuum, which is more mechanistic and strengthens the water-carbon coupling relationship in the model; the vegetation water stress factor is used to regulate stomatal conductance, so that the simulation accuracy of the model during drought is better than the original model. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of a carbon and water flux simulation method based on vegetation hydraulic transport in one embodiment;

[0058] Figure 2 A flowchart of carbon and water flux calculation based on vegetation hydraulic transport in one embodiment;

[0059] Figure 3 A simulation diagram of leaf water potential in one embodiment;

[0060] Figure 4 A schematic diagram showing a comparison of soil moisture results in one embodiment;

[0061] Figure 5 A comparison diagram of simulation results of evapotranspiration (ET) and gross primary productivity (GPP) during drought and non-drought periods in one embodiment;

[0062] Figure 6 It is a structural block diagram of a carbon and water flux simulation system based on vegetation hydraulic transport in one embodiment;

[0063] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] like Figure 1-Figure 2 As shown, the carbon and water flux simulation method based on vegetation hydraulic transport provided in the embodiment of the present application comprises the following steps:

[0066] Step 202, construct a biosphere-atmosphere exchange process simulator (BEPS-EcoHydro) model coupled with a vegetation hydraulic module, obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively.

[0067] Model-driven data include meteorological data and leaf area index (LAI) data. Meteorological data include temperature, rainfall, downwelling shortwave radiation, relative humidity, wind speed, and are hourly scale data. Meteorological data can be obtained from ground observations or extracted from meteorological model data. Leaf area index data are daily scale data and can be obtained through ground observations or remote sensing products.

[0068] It is worth mentioning that the boundary conditions of the model also need to be set, including soil carbon pool, aggregation index, soil texture, average annual soil moisture content and average annual temperature, average annual snow depth, and vegetation type in the area; at the same time, according to literature research or reference to other scientific research, set model parameters under different vegetation types, preset model parameters, and set initial vegetation hydraulic stress factor .

[0069] Step 204 , calculating the water absorption rate of the vegetation based on the model driving data and the model parameters.

[0070] Among them, taking into account the mechanism of water transmission in the soil-vegetation-atmosphere continuum, that is, the core mechanism of vegetation hydraulic transmission, this application considers the calculation of three water absorption rates: the water absorption rate driven by the water potential difference between leaves and soil is set as the first water absorption rate; the water absorption rate caused by potential transpiration is set to the second water absorption rate; the water absorption rate caused by the internal water storage of vegetation is set to the third water absorption rate.

[0071] Step 206: Calculate the soil moisture of preset layers based on the water absorption rate and model parameters.

[0072] Among them, this application considers five soil layers and calculates the soil moisture corresponding to soil depths of 0.05 m, 0.15 m, 0.35 m, 0.75 m and 2 m respectively.

[0073] Step 208, iterating the model-related process variables by updating the vegetation water stress factor within a preset time, until the number of iterations reaches a preset critical number, and then outputting the carbon and water flux simulation results.

[0074] Among them, the present application performs simulation calculations on an hourly scale, and sets a maximum number of iterations per hour to ensure that the carbon and water flux simulation is more accurate. After the first round of steps 202-206 is calculated, the current number of iterations is judged. If the maximum number of iterative calculations has been reached, the final carbon and water flux simulation results are output, including soil moisture, leaf water potential, evapotranspiration (ET) and gross primary productivity (GPP). The process variables include at least the photosynthesis rate of vegetation, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer.

[0075] The above-mentioned carbon and water flux simulation method based on vegetation hydraulic transport takes into account the flow of water between the soil-vegetation-atmosphere continuum, which is more mechanistic and strengthens the water-carbon coupling relationship in the model; the vegetation water stress factor is used to regulate stomatal conductance, so that the simulation accuracy of the model during drought is better than the original model.

[0076] In one embodiment, a specific formula for calculating the water absorption rate of vegetation based on model-driven data and model parameters is as follows:

[0077] ;

[0078] in, is the total water absorption rate (m / s), For the The water absorption rate corresponding to the layer of soil (m / s). In the formula, The first The first water absorption rate corresponding to the layer of soil, that is, the water absorption driven by the water potential difference between leaves and soil, is: For the The root density distribution corresponding to the soil layer, is the leaf water potential (m, equivalent to 1 / 101 MPa), For the Soil water potential corresponding to the soil layer (m), is the vegetation canopy height (m), is the depth of each soil layer (m), To be assigned to The vegetation resistance corresponding to the soil layer (s), For the Soil root resistance corresponding to the soil layer (s); The first The second water absorption rate corresponding to the layer of soil, that is, the water absorption caused by potential transpiration, is is the potential transpiration rate of vegetation (m / s), Parameter for controlling potential transpiration demand (m / s); The first The third water absorption rate corresponding to the layer of soil is the water absorption caused by the internal water storage of vegetation. is the maximum water storage capacity of vegetation (m), is the actual water storage capacity (m), is the time step (s).

[0079] In this embodiment, three water absorption schemes of vegetation in the ecosystem are considered, namely, water absorption driven by water potential gradient, water absorption caused by potential transpiration demand, and water absorption generated by the internal water storage of vegetation. Based on the synergy between them, the minimum value of the three schemes is taken as the actual water absorption of the model, and the balance between different water demands and supplies is comprehensively considered, thereby reducing the simulation risk of hydraulic failure of the model.

[0080] In one embodiment, the calculation of soil moisture specifically includes:

[0081] Soil moisture is calculated in layers. In this embodiment, five soil layers are preset, and the soil depths are 0.05 m, 0.15 m, 0.35 m, 0.75 m, and 2 m respectively. The soil moisture simulation of each layer adopts the following formula:

[0082] ;

[0083] in, Indicates Soil moisture corresponding to the soil layer (m 3 / m 3 ), Indicates Soil thickness corresponding to the soil layer (m), represents the net rainfall rate reaching the surface (m / s), is the soil evaporation rate, represents the surface runoff, Indicates The soil layer and the The vertical flow of water between soil layers, Indicates The soil layer and the The vertical flow of water between the soil layers; in the fifth layer, represents the saturated groundwater flow rate. The vertical flow rate of water between adjacent soil layers is calculated as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] in, Indicates Soil hydraulic conductivity corresponding to the soil layer (m / s), Indicates Soil hydraulic conductivity corresponding to the soil layer, Indicates Soil water potential corresponding to the soil layer (m), Indicates Soil water potential corresponding to the soil layer (m), Indicates The saturated hydraulic conductivity of the soil layer is For the The porosity of the soil layer is is the Campbell coefficient, which is related to soil texture. For the Layer air inlet suction.

[0088] In one embodiment, the model parameters are iterated by updating the vegetation water stress factor within a preset time, specifically including:

[0089] This embodiment performs simulation calculations on an hourly scale, and sets a maximum number of iterations per hour to ensure that the carbon and water flux simulation is more accurate. After the first round of calculations is completed, the current number of iterations is judged. If the maximum number of iterations has been reached, the final carbon and water flux simulation results are output. The carbon and water flux simulation results include soil moisture, leaf water potential, evapotranspiration (ET) and gross primary productivity (GPP). GPP (μmol / m 2 / s) is calculated based on the leaf area index and net photosynthetic rate of shade leaves and sun leaves:

[0090] ;

[0091] in, is the gross primary productivity, is the leaf area index of the sun leaf component, is the leaf area index of the shade leaf component, The net photosynthetic rate of the sun leaf (μmol / m 2 / s), represents the net photosynthetic rate of shade leaves (μmol / m 2 / s).

[0092] If the maximum number of iterations is not exceeded, the leaf water potential is calculated based on the change in water storage under the current number of iterations and the actual water storage. The calculation formula is as follows:

[0093]

[0094]

[0095] in, is the actual water storage capacity under the current iteration number (m), is the vegetation water storage capacity in the previous iteration (m), is the change in water storage capacity (m), is the total water absorption rate of vegetation (m / s), is the total transpiration rate of the vegetation canopy (m / s), is the time step (s), is the maximum water storage capacity of vegetation (m), is the minimum leaf water potential (m), is the vegetation canopy height (m), Critical water potential threshold for stomatal opening (m).

[0096] After obtaining the leaf water potential, the vegetation hydraulic stress factor ( ):

[0097] ;

[0098] in, To simulate leaf water potential, is the critical water potential threshold for stomatal opening (m), is the minimum leaf water potential (m); when the vegetation hydraulic stress factor ( ) is calculated, and then the vegetation hydraulic stress factor is substituted back into step 202. The update is performed, and steps 202 to 206 are repeated to continue determining the number of iterations.

[0099] In one embodiment, the specific process of calculating the photosynthesis and stomatal conductance of vegetation includes:

[0100] The Yin-Yang leaf components are separated according to the leaf area index and aggregation index, and the calculation formula is as follows:

[0101] ;

[0102] ;

[0103] in, is the leaf area index of the sun leaf component (m 2 / m 2 ), is the solar zenith angle, is the aggregation index, is the total leaf area index (m 2 / m 2 ), is the leaf area index of shade leaf components (m 2 / m 2).

[0104] It is worth mentioning that for yin-yang leaves, photosynthesis simulation was performed on each component based on the Farquhar photochemical model at the leaf scale, and the photosynthetic rate of the leaves was calculated as follows:

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] in, represents the net photosynthetic rate of leaves (μmol / m 2 / s), Indicates the sunlit or shaded component, and represents the photosynthetic rate limited by carboxylase activity and electron transport, respectively (μmol / m 2 / s), represents the dark respiration rate of leaves during the day (μmol / m 2 / s), represents the maximum carboxylation rate (μmol / m 2 / s), and are the carbon dioxide concentration and oxygen concentration in the mesophyll cells (mol / mol), is the carbon dioxide compensation point during dark respiration, and are the Michaelis constants, which are set to 274.6 and 419.8, respectively.

[0110] The stomatal conductance of the shade leaf and sun leaf components was calculated using the Ball-Woodrow-Berry empirical model:

[0111] ;

[0112] in, is the stomatal conductance of each component of the shade leaf or sun leaf; Indicates shade leaf or sun leaf component; is the intercept term of the stomatal conductance model (mol / m 2 / s); is the slope parameter of the stomatal conductance model; is the relative humidity of the leaf surface; Indicates the carbon dioxide concentration for leaves; It is the vegetation hydraulic stress factor, with a value between 0 and 1, and is set to 1 in the initial calculation.

[0113] In one embodiment, the specific process of calculating vegetation transpiration and soil evaporation includes:

[0114] Vegetation transpiration is calculated using the Penman-Monteith equation as follows:

[0115] ;

[0116] in, represents the transpiration rate of each component of the shade leaf or sun leaf (m / s), is the air density, is the specific heat of air at constant temperature, is the wet / dry constant, is the atmospheric vapor pressure difference, is the rate of change of saturated vapor pressure with temperature, is the leaf surface temperature of the shade or sun leaf component (°C), is the aerodynamic impedance (s / m), is the boundary layer impedance (s / m), is the stomatal conductance of shade leaves or sun leaves. The total transpiration rate of the vegetation canopy is equal to the leaf area weighted sum of the evaporation rates of shade leaves and sun leaves:

[0117] ;

[0118] in, is the total transpiration rate of the canopy (m / s), and is the transpiration rate of the sun leaf and the transpiration rate of the shade leaf (m / s), and is the leaf area of ​​sun leaf and shade leaf components (m 2 / m 2 ).

[0119] Soil evaporation is also calculated using the Penman-Monteith equation, except that the impedance mainly comes from the soil. The formula is as follows:

[0120] ;

[0121] in, is the evaporation coefficient of latent hot water, is the net surface radiation, is the surface vapor pressure difference, is the surface aerodynamic impedance, is the soil evaporation resistance, which can be calculated based on soil moisture:

[0122] ;

[0123] in, is the soil evaporation resistance, is the first layer of soil moisture, is the soil porosity.

[0124] This embodiment adopts the above method. This application introduces a vegetation hydraulic module (BEPS-EcoHydro) into the BEPS model and realizes the leaf water potential simulation of the BEPS model; this application comprehensively considers three water absorption schemes of vegetation in the ecosystem, namely, water absorption driven by water potential gradient, water absorption caused by potential transpiration demand, and water absorption generated by the internal water storage of vegetation, and based on the synergy between them, takes the minimum of the three schemes as the actual water absorption of the model, comprehensively considers the balance between different water demands and supplies, and reduces the simulation risk of hydraulic failure of the model; this application considers the flow of water between the soil-vegetation-atmosphere continuum, is more mechanistic, and strengthens the water-carbon coupling relationship in the model; this application uses vegetation water stress factors to adjust stomatal conductance, so that the simulation accuracy of the model's evapotranspiration (ET) and gross primary productivity (GPP) during drought is better than that of the original model, which proves that this method can effectively improve the model's response ability to drought.

[0125] It is worth mentioning that the specific preparations for obtaining model-driven data and model parameters include the preparation of meteorological data, the preparation of leaf area index (LAI) data, the setting of model boundary conditions, and the setting of model parameters. These data and parameter settings are the prerequisites for the successful implementation of this method.

[0126] Meteorological data include temperature (°C), rainfall (m / h), downwelling shortwave radiation (W / m 2 ), relative humidity (%), wind speed (m / s), and hourly scale data, meteorological data need to be made into NetCDF format. Taking the US-MOz station (39.7441°N, 92.2000°W) as an example, the meteorological data of this station can be obtained from the US flux observation network, and the temperature, rainfall, downlink shortwave radiation, relative humidity and wind speed are stored as variables T, PRCP, SSRD, RH, WS respectively.

[0127] Since this model method requires continuously changing daily-scale leaf area index data as driving data, such data can be obtained from existing public remote sensing data. Taking the US-MOz station as an example, the LAI data used by this station is extracted from the GLOBMAP data product. Since the temporal resolution of the remote sensing data is 8 days, it is also necessary to use a spline function to interpolate to the daily scale. The LAI data also needs to be made into NetCDF format.

[0128] Setting of model boundary conditions. The boundary conditions of the model include soil carbon pool, aggregation index, soil texture, annual soil moisture content and annual temperature, annual snow depth, and vegetation type in the area. These boundary conditions are mainly used to initialize various state variables in the simulation process of this method, and can be obtained from the attribute information recorded by the site. Taking the US-MOz site as an example, the boundary condition data preparation is also in NetCDF format. Among them, ccd, cfmd, cfsd, cm, cp, cs, csm, csmd, and cssd are soil carbon pool related parameters. CCD is the stem and coarse root litter carbon pool, cfmd is the underground litter easily decomposed part carbon pool, cfsd is the underground litter structure part carbon pool, cm is the soil microbial carbon pool, cp is the inert carbon pool, cs is the slowly decomposed carbon pool, cs is the surface microbial carbon pool, csmd is the surface litter easily decomposed part carbon pool, and cssd is the surface litter structure part carbon pool. ci is the aggregation index, Hp is the canopy height, laiyr is the annual average leaf area index, and nppyr is the annual average net primary productivity. latitude is latitude, longitude is longitude, lcno is the vegetation type defined in the BEPS model, PCT_PFT is the percentage of vegetation type; sdp is the annual average snow depth, st is the annual average soil temperature, and sw is the annual average soil moisture. stext is the soil texture type of the site. tt_veg is the accumulated temperature from germination to flowering, tt_rep is the accumulated temperature from flowering to harvesting, phot_type is the photoperiod option, emer_doy is the germination date, and har_doy is the harvesting date; tt_veg, tt_rep, phot_type, emer_doy, and har_doy are crop module related parameters.

[0129] Model parameter setting. The setting of model parameters is to ensure the accuracy and rationality of the simulation results of this method. The model parameters included in this method are mainly divided into parameters related to vegetation photosynthesis process, parameters related to vegetation and soil respiration process, simulation parameters of sunlight-induced chlorophyll fluorescence (SIF) and vegetation optical thickness (VOD), parameters related to soil hydraulics, and parameters related to vegetation hydraulic process. These parameters can be obtained from literature research or optimized for specific sites. Among them, p_Vcmax, p_VJ_slope, p_VN_slope, p_b_h2o, p_m_h2o, p_f_leaf, p_kc25, p_ko25, and p_tau25 are parameters related to vegetation photosynthesis. p_Vcmax is the maximum carboxylation rate (Vcmax) of Rubisco enzyme at 25 ℃; p_VJ_slope is used to represent the slope between Vcmax and Jmax (maximum photosynthetic electron transfer rate); p_VN_slope represents the slope between Vcmax and leaf nitrogen content; p_b_h2o represents the intercept term in the Ball-Berry stomatal conductance model; p_m_h2o is the slope term in the Ball-Berry stomatal conductance model; p_f_leaf represents the ratio of photosynthetically active radiation to shortwave radiation; p_kc25 represents the Michaelis constant for CO2; p_ko25 represents the Michaelis constant for O2; p_tau25 represents the specificity factor of Rubisco enzyme for CO2.

[0130] p_q10 is a parameter related to vegetation respiration process; p_f_resp is a parameter related to soil respiration; p_sif_alpha and p_sif_beta are parameters related to SIF simulation; p_vod_a, p_vod_b and p_vod_c are parameters related to VOD simulation.

[0131] p_f_decay, p_Ksat_scalar, p_b_scalar, p_porosity_scalar, p_vfc_scalar, p_vwp_scalar, p_psisat_scalar, p_drainage_scalar are soil hydraulics related parameters, among which p_f_decay is the root density distribution; p_Ksat_scalar is the soil saturated hydraulic conductivity scale factor, p_b_scalar is the Campbell parameter scale factor, p_porosity_scalar is the soil porosity scale factor, p_vfc_scalar is the field water holding capacity scale factor, p_vwp_scalar is the wilting point scale factor, p_psisat_scalar is the saturated soil matrix potential scale factor; p_drainage_scalar is the drainage coefficient scale factor.

[0132] p_theta_Amin, p_pox, p_fei_c, p_spac_p1, p_spac_p2, p_tWA, p_tWB, p_Ttrig, p_r_xylem, p_r_r, p_Lr, p_deltal_min, p_deltal_max, p_p_delta, p_ppslh, p_fei_min, p_fei_th, p_p_excess are parameters related to vegetation hydraulics, among which p_theta_Amin is the maximum. minimum air content, p_pox is the air change rate coefficient, p_fei_c is the critical pressure when the potential water absorption decreases, p_spac_p1 and p_spac_p2 are coefficients in the water potential response function, p_tWA and p_tWB are coefficients in the temperature response function, p_Ttrig is the trigger temperature, p_r_xylem is the xylem resistance coefficient, p_r_r is the root resistance coefficient, p_Lr is the root length index, p_deltal_min is the minimum characteristic distance, p_deltal_max is the maximum characteristic distance, p_p_delta is the change rate coefficient of the characteristic distance, p_ppslh is the proportional factor of the maximum plant water storage, p_fei_min is the minimum leaf water potential of the vegetation, p_fei_th is the leaf water absorption threshold, and p_p_excess is the maximum flow rate that exceeds the rate corresponding to the atmospheric potential demand rate.

[0133] The simulation time is 2005-2019, and the start time is set to January 1, 2005. The model program is called according to the prepared driving input data and boundary condition files, as well as the set parameters. The various calculation modules in the previous flowchart are already embedded in it, so they are not described in detail. The simulation results of this method are marked as BEPS-EcoHydro, which means that the simulation results of vegetation hydraulic processes are considered; at the same time, the carbon and water flux simulation of the original BEPS model is also carried out at this site to compare with the simulation results of BEPS-EcoHydro.

[0134] The advantages of this method are explained below from the results of simulating leaf water potential, soil moisture, evapotranspiration (ET) and gross primary productivity (GPP). After considering the vegetation hydraulic module, the BEPS model has the function of simulating leaf water potential, which is the model of this method BEPS-EcoHydro. Figure 3 As shown in the figure, the interannual variation of the simulated pre-dawn leaf water potential is consistent with the observed value. In drought years, the water potential drops sharply, which is consistent with the observed value. From the performance index, the correlation of the simulated leaf water potential is at a high level (R=0.733), and the root mean square error is at a low level (RMSE=0.587 MPa).

[0135] Reference Figure 4 , Figure 4 The simulation results of this method (BEPS-EcoHydro) and the original BEPS model are compared in terms of soil moisture. It can be seen from the figure that the soil moisture simulated by this method has a lower determination coefficient (R 2 ) is higher, the root mean square error (RMSE) is smaller, and the interannual variations are closer to the observed values.

[0136] Reference Figure 5 , Figure 5 The figure shows the comparison of evapotranspiration (ET) and gross primary productivity (GPP) simulated by the BEPS-EcoHydro model of this method with the original BEPS model in drought and non-drought periods. As can be seen from the figure, in non-drought periods, the difference between this method (BEPS-EcoHydro) and the original BEPS model is not much, but in drought periods, the ET and GPP simulated by this method have been greatly improved in both the coefficient of determination and the root mean square error, with a higher R 2 and smaller RMSE, indicating that this method has better performance in drought periods and can simulate the ecosystem response to drought more accurately.

[0137] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a carbon-water flux simulation device based on vegetation hydraulic transmission for implementing the carbon-water flux simulation method based on vegetation hydraulic transmission involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the carbon-water flux simulation device based on vegetation hydraulic transmission provided below can refer to the limitations of the carbon-water flux simulation method based on vegetation hydraulic transmission above, and will not be repeated here.

[0139] In one embodiment, Figure 6As shown, a carbon and water flux simulation system based on vegetation hydraulic transport is provided, including: a model parameter preparation module, a water absorption rate calculation module, a soil moisture calculation module and a cycle simulation output module, wherein:

[0140] Model parameter preparation module, used to build the BEPS-EcoHydro model coupled with the vegetation hydraulic module, obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration and soil evaporation respectively;

[0141] A water absorption rate calculation module, used to calculate the water absorption rate of vegetation based on model driving data and model parameters;

[0142] A soil moisture calculation module, used to calculate the soil moisture of each preset layer based on the water absorption rate and model parameters;

[0143] The cyclic simulation output module is used to iterate the model-related process variables within a preset time until the number of iterations reaches a preset critical number, and then output the carbon and water flux simulation results, wherein the process variables at least include the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results at least include soil moisture, leaf water potential, evapotranspiration and total primary productivity.

[0144] In one embodiment, the water absorption rate calculation module is further used to calculate the preset first water absorption rate, second water absorption rate and third water absorption rate respectively based on the model driving data and the model parameters; compare the first water absorption rate, the second water absorption rate and the third water absorption rate and take the minimum value, and set the minimum value as the water absorption rate of the current soil layer; calculate the water absorption rates of different soil layers and sum them, and set the sum value as the total water absorption rate of the vegetation; the formula is as follows: };in, is the total water absorption rate, For the The water absorption rate corresponding to the layer of soil is For the The first water absorption rate corresponds to the layer of soil, For the The soil layer corresponds to the second water absorption rate, For the The layer of soil corresponds to the third water absorption rate.

[0145] In one embodiment, the water absorption rate calculation module is further used to calculate the first water absorption rate based on the root density distribution of each layer, the leaf water potential, the soil water potential of each layer, the vegetation height and the soil layer depth; the formula is as follows: ;in, For the The first water absorption rate corresponds to the layer of soil, For the The root density distribution corresponding to the soil layer, For leaf water potential, For the The soil water potential corresponding to the soil layer, is the vegetation canopy height, For the The soil depth corresponding to the soil layer, To be assigned to The vegetation resistance corresponding to the soil layer, For the Soil root resistance corresponding to the soil layer.

[0146] In one embodiment, the water absorption rate calculation module is further used to calculate a second water absorption rate based on the potential transpiration rate of vegetation, the parameter controlling the potential transpiration demand, and the root density distribution of each layer; the formula is as follows: ;in, For the The soil layer corresponds to the second water absorption rate, For the The root density distribution corresponding to the soil layer, is the potential transpiration rate of vegetation, Parameters for controlling potential transpiration demand.

[0147] In one embodiment, the water absorption rate calculation module is further used to calculate a third water absorption rate based on the maximum water storage capacity of the vegetation, the actual water storage capacity, the time step, and the root density distribution of each layer; the formula is as follows: ;in, For the The third water absorption rate corresponds to the soil layer. For the The root density distribution corresponding to the soil layer, is the maximum water storage capacity of vegetation, is the actual water storage capacity, is the time step.

[0148] In one embodiment, the soil moisture calculation module is further used to calculate the corresponding soil moisture based on the number of soil layers at different depths; the formula is as follows: ;in, Indicates Soil moisture corresponding to the soil layer, Indicates Layer of soil corresponds to soil thickness, is the net rainfall rate reaching the surface, represents soil evaporation rate, RF represents surface runoff, Indicates The soil layer and the The vertical flow of water between soil layers, Indicates The soil layer and the The vertical flow of water between the soil layers; in the fifth layer, Represents the saturated groundwater flow; calculates the vertical flow of water between adjacent soil layers based on the model parameters; the formula is as follows: ; ; ;in, Indicates Soil hydraulic conductivity corresponding to the soil layer, Indicates Soil hydraulic conductivity corresponding to the soil layer, Indicates The soil water potential corresponding to the soil layer, Indicates The soil water potential corresponding to the soil layer, Indicates The soil thickness corresponding to the soil layer, Indicates The soil thickness corresponding to the soil layer, represents the saturated hydraulic conductivity, is the soil porosity, is the Campbell coefficient, which is related to soil texture. For the Layer air inlet suction.

[0149] Each module in the above-mentioned carbon and water flux simulation system based on vegetation hydraulic transport can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0150] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a carbon and water flux simulation method based on vegetation hydraulic transmission is implemented.

[0151] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0153] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0154] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0156] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0157] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A carbon and water flux simulation method based on vegetation hydraulic transport, characterized in that: The method comprises: Construct a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module, obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively; Calculate the water uptake rate of vegetation based on model-driven data and model parameters; Calculate the soil moisture of each preset layer based on the water absorption rate and model parameters; Calculate the corresponding soil moisture based on the number of soil layers at different depths; the formula is as follows: Among them, θ i represents the soil moisture corresponding to the i-th layer of soil, d i represents the soil thickness corresponding to the i-th soil layer, P gs is the net rainfall rate reaching the surface, E g represents soil evaporation rate, RF represents surface runoff, Q i，i+1 represents the vertical flow of water between the i-th soil layer and the i+1-th soil layer, Q i-1，i represents the vertical flow of water between the i-1th soil layer and the i-th soil layer; in the fifth layer, Q5 represents the saturated groundwater flow; The vertical flow of water between adjacent soil layers is calculated based on the model parameters; the formula is as follows: Among them, k i represents the soil hydraulic conductivity corresponding to the i-th layer of soil, k i+1 represents the soil hydraulic conductivity corresponding to the i+1th layer of soil, ψ soil，i represents the soil water potential corresponding to the i-th soil layer, ψ soil，i+1 represents the soil water potential corresponding to the i+1th layer of soil, d i represents the soil thickness corresponding to the i-th soil layer, d i+1 represents the soil thickness corresponding to the i+1th layer of soil, K sat，i represents the saturated hydraulic conductivity corresponding to the i-th layer of soil, θ s，i is the porosity corresponding to the i-th layer of soil, b is the Campbell coefficient, which is related to the soil texture, ψ s，i is the air inlet suction of the i-th layer; The corresponding process variables of the model are iterated within a preset time by updating the vegetation water stress factor until the number of iterations reaches a preset critical number, and then the carbon and water flux simulation results are output. The process variables include at least the vegetation photosynthesis rate, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results include at least soil moisture, leaf water potential, evapotranspiration and total primary productivity.

2. The carbon and water flux simulation method based on vegetation hydraulic transport according to claim 1 is characterized in that: The method of calculating the water absorption rate of vegetation based on the model-driven data and the model parameters includes: Based on the model driving data and the model parameters, the preset first water absorption rate, the second water absorption rate and the third water absorption rate are calculated respectively; the first water absorption rate, the second water absorption rate and the third water absorption rate are compared and the minimum value is taken, and the minimum value is set as the water absorption rate of the current soil layer; the water absorption rates of different soil layers are calculated and summed, and the sum value is set as the total water absorption rate of the vegetation; the formula is as follows: Among them, Q upt is the total water absorption rate, q upt，i is the water absorption rate corresponding to the i-th layer of soil, q1(i) is the first water absorption rate corresponding to the i-th layer of soil, q2(i) is the second water absorption rate corresponding to the i-th layer of soil, and q3(i) is the third water absorption rate corresponding to the i-th layer of soil.

3. The carbon and water flux simulation method based on vegetation hydraulic transport according to claim 2 is characterized in that: The first water absorption rate includes: The first water absorption rate is calculated based on the root density distribution of each layer, leaf water potential, soil water potential of each layer, vegetation height and soil layer depth; the formula is as follows: Where q1(i) is the first water absorption rate corresponding to the i-th layer of soil, r i is the root density distribution corresponding to the i-th layer of soil, ψ leaf is the leaf water potential, ψ soil，i is the soil water potential corresponding to the i-th soil layer, H p is the vegetation canopy height, z i is the soil depth corresponding to the i-th soil layer, R p，i is the vegetation resistance assigned to the i-th soil layer, R soil，i is the soil root resistance corresponding to the i-th soil layer.

4. The carbon and water flux simulation method based on vegetation hydraulic transport according to claim 2 is characterized in that: The second water absorption rate includes: The second water absorption rate is calculated based on the potential transpiration rate of vegetation, the parameters controlling the potential transpiration demand, and the root density distribution of each layer; the formula is as follows: q2(i)=r i (TR p +p excess ); Where q2(i) is the second water absorption rate corresponding to the i-th layer of soil, r i is the root density distribution corresponding to the i-th soil layer, TR p is the potential transpiration rate of vegetation, p excess Parameters for controlling potential transpiration demand.

5. The carbon and water flux simulation method based on vegetation hydraulic transport according to claim 2 is characterized in that: The third water absorption rate includes: The third water absorption rate is calculated based on the maximum water storage capacity of vegetation, actual water storage capacity, time step and root density distribution of each layer; the formula is as follows: Where q3(i) is the third water absorption rate corresponding to the i-th layer of soil, r i is the root density distribution corresponding to the i-th soil layer, PWS max is the maximum water storage capacity of vegetation, PWS is the actual water storage capacity, and Δt is the time step.

6. A carbon and water flux simulation system based on vegetation hydraulic transport, characterized in that: The system comprises: Model parameter preparation module, used to build a biosphere-atmosphere exchange process simulator model coupled with a vegetation hydraulic module, obtain model driving data and model parameters, and calculate vegetation photosynthesis, stomatal conductance, vegetation transpiration, and soil evaporation respectively; A water absorption rate calculation module, used to calculate the water absorption rate of vegetation based on model driving data and model parameters; A soil moisture calculation module, used to calculate the soil moisture of each preset layer based on the water absorption rate and model parameters; Calculate the corresponding soil moisture based on the number of soil layers at different depths; the formula is as follows: Among them, θ i represents the soil moisture corresponding to the i-th layer of soil, d i represents the soil thickness corresponding to the i-th soil layer, P gs is the net rainfall rate reaching the surface, E g represents soil evaporation rate, RF represents surface runoff, Q i，i+1 represents the vertical flow of water between the i-th soil layer and the i+1-th soil layer, Q i-1，i represents the vertical flow of water between the i-1th soil layer and the i-th soil layer; in the fifth layer, Q5 represents the saturated groundwater flow; The vertical flow of water between adjacent soil layers is calculated based on the model parameters; the formula is as follows: Among them, k i represents the soil hydraulic conductivity corresponding to the i-th layer of soil, k i+1 represents the soil hydraulic conductivity corresponding to the i+1th layer of soil, ψ soil，i represents the soil water potential corresponding to the i-th soil layer, ψ soil，i+1 represents the soil water potential corresponding to the i+1th layer of soil, d i represents the soil thickness corresponding to the i-th soil layer, d i+1 represents the soil thickness corresponding to the i+1th layer of soil, K sat，i represents the saturated hydraulic conductivity corresponding to the i-th layer of soil, θ s，i is the porosity corresponding to the i-th layer of soil, b is the Campbell coefficient, which is related to the soil texture, ψ s，i is the air inlet suction of the i-th layer; The cyclic simulation output module is used to iterate the corresponding process variables of the model within a preset time until the number of iterations reaches a preset critical number, and then output the carbon and water flux simulation results, wherein the process variables at least include the photosynthesis rate of vegetation, stomatal conductance, vegetation transpiration rate, soil evaporation rate, water absorption rate and soil moisture of each preset layer, and the carbon and water flux simulation results at least include soil moisture, leaf water potential, evapotranspiration and total primary productivity.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images