Numerical mode simulation method for CO2 flux of high-resolution rice, wheat and corn

Abstract

The invention relates to a numerical mode simulation method for CO2 flux of high-resolution rice, wheat and corn, which comprises the following steps: calculating a vegetation index and a land surface water index, and carrying out inversion to obtain key vegetation parameters of a land ecosystem; the meteorological data and the topographic data are processed, meteorological field initial and boundary condition files are generated, and CO2 chemical initial and boundary condition files are generated; integrating the gridded artificial source CO2 emission data file; applying the optimized photosynthetic and respiratory action parameters to a photosynthetic and respiratory action calculation formula of the terrestrial ecosystem, and constructing a CO2 flux simulation system of the crop system; outputting the CO2 flux of the rice, the wheat and the corn in the simulation area. The method has the advantages that CO2 flux seasonal change characteristics in the key phenological period in the growth process of rice, wheat and corn are simulated, carbon cycle characteristics in the growth process of crops can be better reflected, and more reliable data support is provided for ecological meteorological research.

Description

Technical Field

[0001] The present invention belongs to the field of numerical simulation, and in particular relates to a numerical model simulation method for high-resolution CO2 flux of rice, wheat and corn. Background Art

[0002] Carbon dioxide (CO2), a key greenhouse gas in the atmosphere, is widely considered the dominant force driving climate fluctuations and change. Rising CO2 concentrations can warm the troposphere and cool the stratosphere, intensifying the equator-to-pole circulation and potentially reshaping the future chemical composition of the atmosphere. Globally, despite significant annual variability in global carbon sinks, the ocean and terrestrial biosphere collectively absorb approximately 45% of the annual anthropogenic CO2 emissions. Uncertainties in regional CO2 sources and sinks are even more significant than in global simulations. The complex mechanisms and interactions governing CO2 uptake and emissions by terrestrial ecosystems remain largely unresolved. Therefore, accurately quantifying the CO2 uptake capacity of terrestrial ecosystems is of great scientific and practical significance for a deeper understanding of the carbon cycle and its equilibrium state, as well as the actual effects of anthropogenic emissions on the growth of atmospheric CO2 concentrations.

[0003] To quantify terrestrial CO2 fluxes at regional and global scales, a range of top-down and bottom-up methodologies have been developed. Atmospheric inversion techniques use atmospheric transport models to infer the CO2 exchange flux between the surface and the atmosphere based on the spatiotemporal distribution gradients of CO2 obtained from ground-based observations or satellite remote sensing. However, there are significant inconsistencies between the simulated terrestrial CO2 flux values ​​derived from different atmospheric inversion methods. Bottom-up approaches simulate terrestrial CO2 fluxes by integrating biochemical principles with physical processes using process-based or data-driven ecosystem models. The LUE model is widely used to simulate GEE and NEE, but its results still have significant uncertainty and variability due to differences in model structure and different choices of model parameters. Both top-down and bottom-up simulation methods show significant differences at all spatiotemporal scales.

[0004] To address the limitations of bottom-up CO2 flux estimation, the WRF-Chem model optimizes the simulation of biogenic CO2 concentrations and fluxes based on the VPRM model. While existing models have made significant progress in simulating atmospheric CO2 concentrations and fluxes, they still face challenges and deficiencies in simulating crop CO2 fluxes. Currently, the application and evaluation of WRF-VPRM are primarily focused on North America, while model performance in Asia, including China, remains unknown. While it demonstrates reasonable accuracy in simulating CO2 fluxes from single crops like wheat or maize, existing model parameterization schemes underperform in simulating the CO2 fluxes during crop growth. For rice, a key crop, existing parameterization schemes fail to fully account for its unique physiological characteristics and growth habits, lacking targeted parameter optimization, resulting in significant discrepancies between simulated results and actual observations. Therefore, a current research focus and urgent need is to develop a high-precision CO2 flux simulation method for rice, wheat, and maize in China that can simultaneously account for the physiological characteristics and phenological variations of multiple crops to meet the needs of regional carbon cycle research. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn.

[0006] First, a high-resolution numerical model simulation method for CO2 fluxes from rice, wheat, and corn is provided, including:

[0007] Step 1: Calculate the vegetation index and land surface water index based on satellite remote sensing data, and invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data;

[0008] Step 2: Process meteorological data and terrain data to generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration dataset;

[0009] Step 3: Integrate the gridded anthropogenic CO2 emission data files;

[0010] Step 4: extract the daily distribution data files of the three major crops, use the optimized photosynthetic and respiratory parameters in the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β' and the photosynthetic active radiation half-saturation value PAR0';

[0011] Step 5: Input the key vegetation parameters of the terrestrial ecosystem, the meteorological field initial and boundary condition files, the CO2 chemical initial and boundary condition files, the anthropogenic CO2 emission data files, and the daily distribution data files of the three major crops into the optimized WRF-VPRM model and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

[0012] Preferably, in step 4, extracting the daily distribution data files of the three major crops includes:

[0013] (a) Read the grid area data file and geographic information file, and extract the grid area, longitude and latitude information, and their dimension parameters;

[0014] (b) Read the crop phenology data file, obtain the longitude, latitude and annual cumulative day information, and perform NaN processing on abnormal values;

[0015] (c) Initialize the crop area array and phenological date array, calculate the boundary longitude and latitude by traversing the grid points, determine the number of grid phenological data index ranges, extract the effective grid area and phenological period annual cumulative daily values, and calculate the daily crop area distribution;

[0016] (d) Set the crop area determination threshold, filter the valid phenological period data, create a standardized output file, define the dimension and variable structure, and write the processed phenological data.

[0017] Preferably, in step 1, the calculation formulas for the vegetation index and the land surface water index are:

[0018]

[0019] Where: EVI is the enhanced vegetation index; G is the gain coefficient; ρ NIR is the reflectivity in the near-infrared band; ρ Red is the reflectivity of red light band; ρ Blue is the reflectance of the blue light band; C1 and C2 are the correction coefficients of atmospheric correction for red light and blue light; L is the soil background adjustment factor; LSWI is the land surface water index; ρ SWIR is the reflectivity in the shortwave infrared band.

[0020] Preferably, in step 4, the calculation formula for photosynthetic respiration of the terrestrial ecosystem is:

[0021]

[0022] Where: T scale is the temperature scale, W scale is the water stress scale, P scale is the phenological scale, PAR is photosynthetically active radiation, and PAR0′ is its half-saturation value, in μmol m -2 s-1 ; EVI is the vegetation enhancement index, λ' is the maximum light energy utilization efficiency, unit is μmol CO2 / μmol PAR m -2 s -1 , is an empirical parameter; T is the temperature at 2 meters above sea level; α' and β' are two empirical parameters, with units of μmol CO2 m -2 s -1 K -1 and μmol CO2 m -2 s -1 ; GEE is the total ecosystem CO2 exchange that is dependent on light; RESP is the ecosystem respiration that is independent of light; NEE is the net exchange of CO2 between ecosystems, in μmol m -2 s -1 .

[0023] As a preference, in step 4, the temperature scale T scale , water stress scale W scale , phenological scale P scale The calculation formula is:

[0024]

[0025] Where: T min 、T max 、T opt Represent the lowest, highest and optimal temperatures for photosynthesis (℃). If the air temperature is lower than T min , then T scale Set to 0; LSWI max is the maximum LSWI in each grid vegetation growing season; for evergreen category, the annual P scale Set to 1, for deciduous vegetation and grassland, P scale is the vegetation phenology scale, which is a linear function of LSWI.

[0026] Preferably, in step 1, the enhanced vegetation index EVI, land surface water index LSWI and vegetation coverage VEGFRA and the extreme values ​​of the two indices are inverted by the VPRM preprocessor, mapped to the model grid, and the VPRM vegetation data input file is generated.

[0027] In a second aspect, a high-resolution numerical model simulation system for CO2 fluxes of rice, wheat, and corn is provided, which is used to perform any of the methods described in the first aspect, including:

[0028] The inversion module is used to calculate the vegetation index and land surface water index based on satellite remote sensing data, and to invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data;

[0029] The processing module is used to process meteorological data and terrain data, generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration data set;

[0030] Integration module, used to integrate gridded anthropogenic CO2 emission data files;

[0031] An extraction module is used to extract daily distribution data files of the three major crops, apply the optimized photosynthetic and respiratory parameters to the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β', and the photosynthetically active radiation half-saturation value PAR0';

[0032] The simulation and output module is used to input key vegetation parameters of terrestrial ecosystems, meteorological field initial and boundary condition files, CO2 chemical initial and boundary condition files, anthropogenic CO2 emission data files, and daily distribution data files of the three major crops into the optimized WRF-VPRM model to run and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

[0033] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any one of the methods described in the first aspect.

[0034] In a fourth aspect, an electronic device is provided, including:

[0035] Memory, used to store computer programs;

[0036] A processor is used to execute the computer program to implement any method as described in the first aspect.

[0037] The beneficial effects of the present invention are:

[0038] 1. This paper introduces the ChinaCropPhen1km high-resolution crop phenology dataset to provide the model with nationwide planting distribution information and phenological timing information for the three major crops. By integrating this precise crop phenology information with the WRF-Chem model, the seasonal variation characteristics of CO2 flux during the key phenological periods of the rice, wheat, and corn growth processes are simulated, which can better reflect the carbon cycle characteristics during crop growth and provide more reliable data support for ecological meteorological research.

[0039] 2. Based on the parameter values ​​of rice, wheat and corn, the present invention uses the water and carbon flux observation data of Changling Farmland Ecological Station, combined with the EVI and LSWI inversion results, to fit the four rice parameters. The optimized rice, wheat and corn parameter values ​​λ′, α′, β′ and PAR0′ are used in the model. Compared with the original parameters, the present invention can well reflect the CO2 flux during the rice growth process and achieve high-precision simulation of the CO2 flux of rice, wheat and corn in the selected area. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flowchart of the numerical model simulation method for high-resolution CO2 flux of rice, wheat and corn provided by the present invention;

[0041] Figure 2 Schematic diagram of the seasonal changes in CO2 flux over China's land during the growth of different crop types simulated in 2020 by the present invention; the GEE curve in the figure represents the CO2 flux absorbed by crop photosynthesis, the RESP curve represents the CO2 flux released by the ecosystem, and the NEE curve is the difference between the two, representing the net assimilation capacity; (a) is the time series of CO2 flux changes for all crops in total, (b) is the time series of CO2 flux changes for rice-growing areas, (c) is the time series of CO2 flux changes for winter wheat-growing areas, and (d) is the time series of CO2 flux changes for corn-growing areas;

[0042] Figure 3 Schematic diagram of the comparison between the hourly photosynthetically active radiation (PAR) of different seasons in 2020 simulated by the present invention and the observed data; the dark curve represents the observed data, the light curve represents the simulation results, and the error bars represent the uncertainty of the data; (a) is the comparison between the hourly PAR observation and simulation in winter, (b) is the comparison between the hourly PAR observation and simulation in spring, (c) is the comparison between the hourly PAR observation and simulation in summer, and (d) is the comparison between the hourly PAR observation and simulation in autumn. The four sub-graphs (e)-(h) below are the scatter point fitting relationship between the simulated PAR and the observed PAR in different seasons. (e) is winter, (f) is spring, (g) is summer, and (h) is autumn;

[0043] Figure 4 This is a schematic diagram comparing the simulated results of atmospheric CO2 concentration in 2020 under different observation methods simulated by the present invention with the observed data; the oblique dotted line in the figure represents the 1:1 line under ideal conditions, and the solid line is the linear regression fitting result; (a) is the comparison between the near-surface atmospheric CO2 observation and simulation results at Lin'an Station, (b) is the comparison between the near-surface atmospheric CO2 observation and simulation results at Waliguan, (c) and (d) are the comparisons between the total carbon column observations and model simulation results at the TCCON stations in Hefei and Xianghe, and (e) is the comparison between the average atmospheric CO2 column concentration inverted by the OCO-2 satellite and the model simulation results. DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to the following examples. The following examples are provided only to facilitate understanding of the present invention. It should be noted that, without departing from the principles of the present invention, it is possible for a person skilled in the art to make various modifications to the present invention, and such improvements and modifications fall within the scope of the claims of the present invention.

[0045] Example 1:

[0046] To solve the problems of the prior art, Example 1 of the present application provides a numerical model simulation method for high-resolution CO2 flux of rice, wheat, and corn, including:

[0047] Step 1: Calculate the vegetation index and land surface water index based on satellite remote sensing data, and invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data.

[0048] Specifically, the vegetation index and land surface water index are calculated based on MODIS satellite remote sensing data, and the calculated data are inverted with SYNMAP vegetation type data through the VPRM preprocessor to obtain the key vegetation parameters of the terrestrial ecosystem.

[0049] For example, the VPRM preprocessor uses data calculated from MODIS satellite remote sensing and data on different vegetation types to obtain vegetation type data covering the EVI and LSWI data area, generate grid cell areas, interpolate EVI and LSWI data onto the SYNMAP grid, calculate intermediate data, aggregate EVI and LSWI data onto the WRF grid, smooth the results and exclude winter outliers, and calculate vegetation index extremes and vegetation cover fractions.

[0050] The data of different vegetation types come from SYNMAP, a global geographic and vegetation type information dataset with a horizontal resolution of 1 km.

[0051] The model grid uses the Lambert projection, set to a single nested layer, with a central longitude of 116°E and a latitude of 39°N. The grid resolution is 9 km × 9 km, the grid is set to 360 (east-west) × 360 (north-south), and the number of vertical layers is 34. The spatial resolution can be further increased as needed.

[0052] The key vegetation index data of terrestrial ecosystems, such as the enhanced vegetation index (EVI), land surface water index (LSWI), vegetation cover fraction (VEGFRA), and the extreme values ​​of the two indices during the simulation period (EVIMAX, EVIMIN, LSWIMAX, LSWIMIN), obtained by inversion of the VPRM preprocessor, are used as the input data of the VPRM model.

[0053] In addition, in step 1, the MOD09A1 product is processed into satellite data observed every 8 days. The calculation formulas for vegetation index and land surface water index are:

[0054]

[0055] Where: EVI is the enhanced vegetation index; G is the gain coefficient, which is 2.5; ρ NIR is the reflectivity in the near-infrared band; ρ Red is the reflectivity of red light band; ρ Blue is the reflectance of the blue light band; C1 and C2 are the correction coefficients for atmospheric correction of red light and blue light, which are 6 and 7.5 respectively; L is the soil background adjustment factor, which is 1; LSWI is the land surface water index; ρ SWIR is the reflectivity in the shortwave infrared band.

[0056] Step 2: Process meteorological data and terrain data to generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration dataset.

[0057] For example, based on the 1°×1°FNL reanalysis data and terrain data from the National Centers for Environmental Prediction (NCEP) at intervals of 6 hours from the United States, the FNL initial field data are horizontally interpolated to the simulation area grid by running the geogrid, ungrib, and metgrid program modules to obtain the meteorological field data file, and the real.exe program module is executed to generate the meteorological field initial and boundary condition files; the s10oc_v2020 from the Jena CarboScope running at intervals of 6 hours is interpolated to the WRF grid to generate the chemical initial and boundary condition files of CO2.

[0058] Step 3: Integrate the gridded anthropogenic CO2 emission data files.

[0059] Specifically, the MEIC CO2 emission inventory of Tsinghua University is used as the anthropogenic CO2 emission data. The data of agriculture, industry, electricity, residents and transportation are first converted into gridded NetCDF files and the unit is converted into g / km 2 / s, and integrated by time dimension, and then interpolated to the WRF grid through bilinear interpolation to generate the processed anthropogenic CO2 emission data file.

[0060] Step 4: Extract the daily distribution data files of the three major crops, apply the optimized photosynthetic and respiratory parameters to the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β', and the photosynthetically active radiation half-saturation value PAR0'. Step 5: Input the key vegetation parameters of the terrestrial ecosystem, the meteorological field initial and boundary condition files, the CO2 chemical initial and boundary condition files, the anthropogenic CO2 emission data files, and the daily distribution data files of the three major crops into the optimized WRF-VPRM model and run it, outputting the CO2 flux of rice, wheat, and corn in the simulation area.

[0061] Example 2:

[0062] Based on Example 1, Example 2 of the present application provides a more specific numerical model simulation method for high-resolution CO2 flux of rice, wheat, and corn, including:

[0063] Step 1: Calculate the vegetation index and land surface water index based on satellite remote sensing data, and invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data.

[0064] Step 2: Process meteorological data and terrain data to generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration dataset.

[0065] Step 3: Integrate the gridded anthropogenic CO2 emission data files.

[0066] Step 4: Extract the daily distribution data files of the three major crops, use the optimized photosynthetic and respiratory parameters in the photosynthetic and respiratory calculation formula of the terrestrial ecosystem, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β' and the photosynthetic active radiation half-saturation value PAR0'.

[0067] In step 4, the extraction of daily distribution data files of the three major crops includes:

[0068] (a) Read the grid area data file (gridarea.nc) and the geographic information file (geo_em.d01.nc) and extract the grid area, longitude and latitude information, and their dimension parameters.

[0069] (b) Read the ChinaCropPhen1km crop phenology data file, obtain the longitude, latitude, and days-of-year (DOY) information, and perform NaN processing on outliers.

[0070] (c) Initialize the crop area array and phenological date array, calculate the boundary longitude and latitude by traversing the grid points, determine the number of grid phenological data index ranges (for example, how many 1km grid phenological data index ranges are there in a 9km grid), extract the effective grid area and the annual cumulative daily value of the phenological period, and calculate the daily crop area distribution.

[0071] (d) Set the crop area determination threshold, filter the valid phenological period data, create a standardized output file, define the dimension and variable structure, and write the processed phenological data.

[0072] For example, according to the planting area data of the three major crops in 2020 from the National Bureau of Statistics (rice 30.08 thousand hectares, wheat 23.38 thousand hectares, corn 41.26 thousand hectares), the crop area determination threshold is set (rice threshold1 = 8.9e6 m 2 , wheat threshold2=24.9e6 m 2 , corn threshold3=14.7e6 m 2 ), filter valid phenological period data, create a standardized NetCDF output file (Regrid3.crop phenological data file name), define the dimension and variable structure, and write the processed phenological data.

[0073] Specifically, the phenological dataset read in the present invention is the 2015 ChinaCropPhen1km phenological remote sensing product dataset of the three major grain crops in China.

[0074] In this paper, the 1km grid phenological data is re-gridded to a 9km WRF grid, the area-weighted sum is calculated for each phenological date, a threshold is applied and the final phenological date is determined, and combined with the planting area data, the planting distribution information of the three major crops across the country and their phenological time information can be obtained.

[0075] After extracting the daily distribution data of the three major crops, the annual cumulative days (DOY) of wheat sowing (w1) and harvesting (w2), corn sowing (m1) and harvesting (m2), single-season rice / early rice sowing (r1) and harvesting (r2), and late rice sowing (r3) and harvesting (r4) were read, and the maturity period data was corrected by 10 days to the harvest period.

[0076] Each grid point determines the crop type on a daily basis. If the current day falls within the sowing-harvesting interval of a crop, it is marked as the corresponding category (1: rice; 2: winter wheat; 3: corn), and invalid areas with a growing period of less than 50 days are excluded.

[0077] The smoothed key vegetation parameter data generated in step 1 are combined with the processed phenological period data to dynamically determine crop types, perform land use type correction, expand the temporal resolution to the entire year, and create a time-related file (VPRM_input_timedep.nc) as the input file of the WRF model to achieve spatiotemporal matching between crop types and vegetation data.

[0078] In addition, in step 4, the calculation formula for the photosynthetic respiration of the terrestrial ecosystem is:

[0079]

[0080] Where: T scale is the temperature scale, W scale is the water stress scale, P scale is the phenological scale, PAR is photosynthetically active radiation, and PAR0′ is its half-saturation value, in μmolm -2 s -1 ; EVI is the vegetation enhancement index, λ' is the maximum light energy utilization efficiency, unit is μmol CO2 / μmol PAR m -2 s -1 , is an empirical parameter; T is the temperature at 2 meters above sea level; α' and β' are two empirical parameters, with units of μmol CO2 m -2 s -1 K -1 and μmol CO2 m -2 s -1 ; GEE is the total ecosystem CO2 exchange that is dependent on light; RESP is the ecosystem respiration that is independent of light; NEE is the net exchange of CO2 between ecosystems, in μmol m -2 s -1 .

[0081] Furthermore, in step 4, the temperature scale T scale , water stress scale W scale , phenological scale P scale The calculation formula is:

[0082]

[0083] Where: T min 、T max 、T opt Represent the lowest, highest and optimal temperatures for photosynthesis (℃). If the air temperature is lower than T min , then T scale Set to 0; LSWI max is the maximum LSWI in each grid vegetation growing season; for evergreen category, the annual P scaleSet to 1, for deciduous vegetation and grassland, P scale is the vegetation phenology scale, which is a linear function of LSWI.

[0084] The present invention uses optimized parameter values ​​that have good simulation effects in China, calibrates and adjusts the four parameters of rice crops based on eddy observation data, with the aim of reflecting the CO2 flux during the rice growth process with higher accuracy.

[0085] Based on the original parameters, the present invention uses the water and carbon flux observation data of Changling Farmland Ecological Station, combined with the EVI and LSWI inversion results, to fit the optimized rice λ′, α′, β′ and PAR0′ parameters, as shown in Table 1:

[0086] Table 1 Calculation parameters of photosynthesis and respiration of rice, wheat and corn crops

[0087]

[0088] Step 5: Input the key vegetation parameters of the terrestrial ecosystem, the meteorological field initial and boundary condition files, the CO2 chemical initial and boundary condition files, the anthropogenic CO2 emission data files, and the daily distribution data files of the three major crops into the optimized WRF-VPRM model and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

[0089] The present invention sets up a verification test of CO2 flux distribution and seasonal changes during crop growth and multiple groups of comparative verification tests, simulates the atmospheric CO2 concentration and the CO2 flux of rice, wheat, and corn crops in Northeast China in 2020, and verifies the optimization effect of the optimized WRF-VPRM model simulation. The specific verification test design is shown in Table 2:

[0090] Table 2 Verification and data table

[0091]

[0092]

[0093] The verification simulation time is all for the whole year of 2020, and the models used are all simulated after adding rice, wheat, and corn phenological data and optimizing parameters. The present invention uses the WRF V3.9.1 version coupled with the VPRM model for simulation. The initial and boundary conditions of the meteorological field are derived from the 1°×1°FNL reanalysis data of the NCEP with a time interval of 6 hours. The chemical initial and boundary conditions of CO2 are obtained according to the s10oc_v2022 interpolation of the Jena CarboScope running at a 6-hour interval. The model is set to single-layer nesting, with a central longitude of 116°E, a latitude of 39°N, a grid spacing of 9km×9km, and a total grid of 360 (east-west) × 360 (north-south) in the simulation area, and 34 vertical stretching layers are set. The model physical process and parameterization scheme selection are shown in Table 3:

[0094] Table 3 Model physical processes and parameterization scheme selection table

[0095]

[0096] It should be noted that the parts in this embodiment that are the same or similar to those in Example 1 can be referenced to each other and will not be described in detail in this application.

[0097] Example 3:

[0098] Based on Example 2, Example 3 of the present application provides a numerical model simulation system for high-resolution CO2 flux of rice, wheat, and corn, including:

[0099] The inversion module is used to calculate the vegetation index and land surface water index based on satellite remote sensing data, and to invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data;

[0100] The processing module is used to process meteorological data and terrain data, generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration data set;

[0101] Integration module, used to integrate gridded anthropogenic CO2 emission data files;

[0102] An extraction module is used to extract daily distribution data files of the three major crops, apply the optimized photosynthetic and respiratory parameters to the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β', and the photosynthetically active radiation half-saturation value PAR0';

[0103] The simulation and output module is used to input key vegetation parameters of terrestrial ecosystems, meteorological field initial and boundary condition files, CO2 chemical initial and boundary condition files, anthropogenic CO2 emission data files, and daily distribution data files of the three major crops into the optimized WRF-VPRM model to run and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

[0104] Estimating CO2 fluxes in farmland ecosystems is crucial for assessing regional carbon budgets. The figure shows the seasonal variations in carbon fluxes for major crops in China, as simulated and observed. The results indicate significant differences in carbon uptake capacity among different crops. Regarding seasonal variations, the optimized model accurately simulated carbon flux variations for rice, winter wheat, and corn ( Figure 2 a, b, c, d). Winter wheat's net ecosystem exchange (NEE) peaks between DOY 100 and 200, while maize and rice's primary carbon sequestration period occurs between DOY 150 and 250. This model more accurately depicts the seasonal variations in carbon flux across different crop types, significantly improving the model's ability to simulate crop carbon sequestration capacity.

[0105] PAR is a key parameter that affects photosynthetic and respiratory fluxes, and the model simulation results need to be adjusted. Due to the deviations in the simulation of the four seasons, the model simulation values ​​of PAR were artificially corrected to make them consistent with the observation data. By comparing with the data of 10 ground observation stations, the daily variation characteristics of PAR observed and simulated ( Figure 3 ad) and the correlation analysis between the two ( Figure 3 eh). From the perspective of diurnal variation characteristics, the optimization experiment can well reproduce the diurnal variation trend of PAR in each season. The peak value appears at noon with a small error, indicating that the model can well describe the variation law of PAR in different seasons. Further analysis of the linear fitting relationship between observation and simulation ( Figure 3 eh), the correlation coefficient r and the slope are close to 1.00, indicating that the simulation results after optimizing PAR are highly consistent with the observation data with small errors, especially in spring and summer (slp. = 1.084, 1.002).

[0106] In order to verify the simulation accuracy of atmospheric CO2 concentration of the present invention, the near-surface atmospheric CO2 concentration ( Figure 4 ab) and atmospheric XCO2 column concentration ( Figure 4 The simulated and measured values ​​of ce). Figure 4 b) Located in the high altitude area of ​​the Qinghai-Tibet Plateau, it has a relatively clean atmospheric background and is less affected by human factors. The consistency index IOA is 0.81, the mean deviation MB is -0.91 μmol mol-1, and the simulation error is relatively small compared with Lin'an Station ( Figure 4 a) Smaller. Hefei Station ( Figure 4c) and Xianghe Station ( Figure 4 The MB of the two sites are 0.76 μmol mol-1 and 0.87 μmol mol-1, respectively. The correlation coefficient r and IOA of the two sites are both over 0.70. OCO-2 satellite inversion ( Figure 4 e) has a MB of 0.39 μmolmol-1 and an IOA of 0.91, indicating that the reliability of the optimized model in simulating atmospheric XCO2 column concentrations is better than that of near-surface CO2 concentrations, and that ground observation data may be greatly affected by local factors.

[0107] It should be noted that the system provided in this embodiment is a system corresponding to the method provided in Example 2. Therefore, the parts in this embodiment that are the same or similar to those in Example 2 can be referenced to each other and will not be repeated in this application.

[0108] In summary, the present invention improves the simulation accuracy of carbon flux in farmland ecosystems and improves the simulation effect of the spatial distribution of atmospheric CO2 concentration by optimizing model parameters and incorporating daily distribution data of rice, wheat and corn crop phenology.

Claims

1. A high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn, characterized by: include: Step 1: Calculate the vegetation index and land surface water index based on satellite remote sensing data, and invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data; Step 2: Process meteorological data and terrain data to generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration dataset; Step 3: Integrate the gridded anthropogenic CO2 emission data files; Step 4: extract the daily distribution data files of the three major crops, use the optimized photosynthetic and respiratory parameters in the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β' and the photosynthetic active radiation half-saturation value PAR0'; Step 5: Input the key vegetation parameters of the terrestrial ecosystem, the meteorological field initial and boundary condition files, the CO2 chemical initial and boundary condition files, the anthropogenic CO2 emission data files, and the daily distribution data files of the three major crops into the optimized WRF-VPRM model and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

2. The high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn according to claim 1 is characterized in that: In step 4, the extraction of daily distribution data files of the three major crops includes: (a) Read the grid area data file and geographic information file, and extract the grid area, longitude and latitude information, and their dimension parameters; (b) Read the crop phenology data file, obtain the longitude, latitude and annual cumulative day information, and perform NaN processing on abnormal values; (c) Initialize the crop area array and phenological date array, calculate the boundary longitude and latitude by traversing the grid points, determine the number of grid phenological data index ranges, extract the effective grid area and phenological period annual cumulative daily values, and calculate the daily crop area distribution; (d) Set the crop area determination threshold, filter the valid phenological period data, create a standardized output file, define the dimension and variable structure, and write the processed phenological data.

3. The high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn according to claim 2 is characterized in that: In step 1, the calculation formulas for vegetation index and land surface water index are: Where: EVI is the enhanced vegetation index; G is the gain coefficient; ρ NIR is the reflectivity in the near-infrared band; ρ Red is the reflectivity of red light band; ρ Blue is the reflectance of the blue light band; C1 and C2 are the correction coefficients of atmospheric correction for red light and blue light; L is the soil background adjustment factor; LSWI is the land surface water index; ρ SWIR is the reflectivity in the shortwave infrared band.

4. The high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn according to claim 3 is characterized in that: In step 4, the calculation formula for the photosynthetic respiration of the terrestrial ecosystem is: Where: T scale is the temperature scale, W scale is the water stress scale, P scale is the phenological scale, PAR is photosynthetically active radiation, and PAR0′ is its half-saturation value, in μmol m -2 s -1 ; EVI is the vegetation enhancement index, λ' is the maximum light energy utilization efficiency, unit is μmolCO2 / μmol PAR m -2 s -1 , is an empirical parameter; T is the temperature at 2 meters above sea level; α' and β' are two empirical parameters, with units of μmol CO2 m -2 s -1 K -1 and μmol CO2 m -2 s -1 ; GEE is the total ecosystem CO2 exchange that is dependent on light; RESP is the ecosystem respiration that is independent of light; NEE is the net exchange of CO2 between ecosystems, in μmol m -2 s -1 .

5. The high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn according to claim 4 is characterized in that: In step 4, the temperature scale T scale , water stress scale W scale , phenological scale P scale The calculation formula is: Where: T min 、T max 、T opt Represent the minimum, maximum and optimal temperatures for photosynthesis respectively. If the air temperature is lower than T min , then T scale Set to 0; LSWI max is the maximum LSWI in each grid vegetation growing season; for evergreen category, the annual P scale Set to 1, for deciduous vegetation and grassland, P scale is the vegetation phenology scale, which is a linear function of LSWI.

6. The high-resolution numerical model simulation method for CO2 flux of rice, wheat and corn according to claim 4, characterized in that: In step 1, the enhanced vegetation index (EVI), land surface water index (LSWI), and vegetation cover (VEGFRA) and the extreme values ​​of the two indices are inverted through the VPRM preprocessor and mapped onto the model grid to generate the VPRM vegetation data input file.

7. High-resolution numerical model simulation system for CO2 flux of rice, wheat and corn, characterized by: Used to perform the method according to any one of claims 1 to 6, comprising: The inversion module is used to calculate the vegetation index and land surface water index based on satellite remote sensing data, and to invert the key vegetation parameters of the terrestrial ecosystem based on the calculated data; The processing module is used to process meteorological data and terrain data, generate meteorological field initial and boundary condition files, and generate CO2 chemical initial and boundary condition files based on the CO2 concentration data set; Integration module, used to integrate gridded anthropogenic CO2 emission data files; An extraction module is used to extract daily distribution data files of the three major crops, apply the optimized photosynthetic and respiratory parameters to the terrestrial ecosystem photosynthetic and respiratory calculation formula, and construct a crop system CO2 flux simulation system; the photosynthetic and respiratory parameters include the maximum light energy utilization rate λ', the temperature response coefficient α', the basal respiratory coefficient β', and the photosynthetically active radiation half-saturation value PAR0'; The simulation and output module is used to input key vegetation parameters of terrestrial ecosystems, meteorological field initial and boundary condition files, CO2 chemical initial and boundary condition files, anthropogenic CO2 emission data files, and daily distribution data files of the three major crops into the optimized WRF-VPRM model, run the numerical model, and output the CO2 fluxes of rice, wheat, and corn in the simulation area.

8. A computer storage medium, characterized in that The computer storage medium stores a computer program; when the computer program is run on a computer, the computer executes the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 6.

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