A method and system for simulating future scenarios of carbon sinks in forest ecosystems

Abstract

The present application provides a method and system for simulating future scenarios of forest ecosystem carbon sinks, belonging to the field of information technology applicable to administrative management purposes. Based on the High-accuracy Surface Modeling (HASM) method, climate data and soil data are processed respectively, and the pixel values at the locations of forest plots in the climate raster data and soil raster data are extracted into the forest plot data to obtain a forest plot environmental information dataset; multiple future scenarios of the forest ecosystem under different management measures are designed; the Forest-DNDC model is used to simulate the carbon density of each future scenario, and the random forest model is used to predict the carbon density of other areas outside the forest plots. The prediction results are used as the initial field, and the simulation results output by the Forest-DNDC model are used as the control conditions for HASM simulation to obtain the forest and soil carbon density raster data under future scenarios. This method can effectively improve the simulation accuracy and provide a scientific basis for the formulation of future county-scale forest management and climate mitigation policies.

Description

Technical Field

[0001] This application relates to the field of information technology applicable to administrative management purposes, and particularly relates to a method and system for simulating future scenarios of forest ecosystem carbon sinks. Background Art

[0002] Forests not only play a key role in the process of achieving carbon neutrality by fixing carbon dioxide in the atmosphere, but also have a profound impact on the global carbon balance and the long-term control of climate change.

[0003] Although a large number of studies have explored the potential of forest carbon sinks and their importance in mitigating climate change, it is still an unsolved problem on how to obtain data for simulating future scenarios of forest ecosystem carbon sinks at the county scale to support the research on the evolution of forest carbon density at the subtropical county scale under different climate change scenarios, especially the systematic analysis of the long-term impact of mild and extreme climate scenarios on forest carbon density, and at the same time support the complexity analysis of the effectiveness of different management policies in the spatial and temporal dimensions under different climate scenarios at the subtropical county scale.

[0004] Therefore, there is a need to provide an improved technical solution to address the above deficiencies in the prior art. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for simulating future scenarios of forest ecosystem carbon sinks to alleviate the problems existing in the above prior art.

[0006] To achieve the above purpose, this application provides the following technical solutions:

[0007] This application provides a method for simulating future scenarios of forest ecosystem carbon sinks. The method uses the Forest-DNDC model as the scenario simulation model, and includes the following steps:

[0008] Step S1: Obtain climate data, soil data, forest plot data, and management measures of the study area;

[0009] Step S2: Based on the high-accuracy surface modeling (HASM) method, process the climate data and soil data respectively to obtain climate raster data and soil raster data with a specified resolution, and extract the pixel values at the locations of the forest plots in the climate raster data and soil raster data into the forest plot data to obtain a forest plot environmental information dataset;

[0010] Step S3: Design multiple future scenarios of the forest ecosystem under different management measures according to the management measures actually recorded in the historical forest management of the study area;

[0011] Step S4: According to the forest plot environmental information dataset, considering multiple future climate change scenarios, use the Forest-DNDC model to simulate the carbon density of each future scenario, and obtain the simulation results of the forest plot soil and forest carbon density year by year for all forest plots under different climate changes and different scenarios;

[0012] Step S5: Input the simulation results of the forest plot soil and forest carbon density into a pre-trained random forest model, and combine with the forest plot environmental information dataset to predict the carbon density of other areas outside the forest plot, and obtain the spatial distribution raster data of the soil carbon density and forest carbon density covering the entire study area under future scenarios;

[0013] Step S6: Use this spatial distribution raster data as the initial field, and use the simulation results of the forest plot soil and forest carbon density output by the Forest-DNDC model as the control conditions to conduct HASM simulation on the forest ecosystem carbon sink under future scenarios, and obtain the high-precision forest and soil future scenario raster data at the specified resolution.

[0014] In a possible implementation manner, in step S2, based on the HASM method, the climate data is processed, including the following steps:

[0015] Perform regression simulation on the historical data of all meteorological stations in the climate data, and then use the inverse distance weighting method for interpolation to obtain the daily-scale climate trend surface at the specified resolution;

[0016] Use the pixel predicted value at the location of each meteorological station in the daily-scale climate trend surface minus the actual observed value of the station to obtain the residual of each meteorological station;

[0017] Select multiple meteorological stations in the study area and its surrounding areas, and crop them in the daily-scale climate trend surface with the spatial range of the selected meteorological stations;

[0018] Use the cropped result as the climate initial field of the HASM method, and use the residuals of the selected multiple meteorological stations as the climate control conditions of the HASM method to carry out HASM simulation to obtain the daily-scale residual surface at the specified resolution;

[0019] Sum the daily-scale climate trend surface and the residual surface to obtain the climate raster data at the specified resolution.

[0020] In a possible implementation manner, in step S2, based on the HASM method, the soil data is processed to obtain the soil raster data at the specified resolution, including:

[0021] Calculate the organic carbon density at different soil depths for each soil sampling point;

[0022] Taking the calculated soil organic carbon density as the dependent variable and multiple soil properties and environmental factors of soil sampling point data as the independent variables, a random forest model is used to predict the spatial distribution of soil organic carbon at different soil depths;

[0023] Taking the spatial distribution of soil organic carbon at different soil depths as the initial field and the soil sampling point data as the control conditions, HASM simulation is carried out on soil organic carbon to obtain a soil organic carbon raster surface with a specified resolution;

[0024] The trend surface data of other soil parameters are obtained by using the ordinary linear regression method, and taking the second national soil survey data and soil sampling point data as the control conditions, HASM simulation is carried out on other soil parameters to obtain a raster surface of other soil parameters with a specified resolution.

[0025] In a possible implementation manner, in step S4, before using the Forest-DNDC model to simulate the carbon density change in each future scenario, it further includes: the step of improving the management measure module of the Forest-DNDC model, specifically as follows:

[0026] Judgment conditions are added to each management measure in the management measure module of the Forest-DNDC model, and the management measure time is set as a cycle, and the judgment conditions are used to judge whether the management measure should be executed.

[0027] In a possible implementation manner, in step S4, before using the Forest-DNDC model to simulate the carbon density in each future scenario, it further includes: the step of performing high-precision inversion on the forest physiological phenology parameters of the base year, specifically as follows:

[0028] For each forest plot in the forest plot environmental information dataset, according to the measured data of the forest plot, the calculated value of forest carbon density is calculated by using the empirical formula;

[0029] Determine the inversion parameter dataset of the current forest plot;

[0030] Traverse the inversion parameter dataset in order, and perform the following steps for each set of parameters:

[0031] Set the current set of parameters as the physiological phenology parameters of the Forest-DNDC model, and perform simulation based on the Forest-DNDC model to obtain the simulated value of forest carbon density;

[0032] Calculate the error between the simulated value of forest carbon density and the calculated value of forest carbon density. If the error does not meet the preset threshold requirement, set the next set of parameters as the physiological phenological parameters of the Forest-DNDC model, and use the Forest-DNDC model to simulate again to obtain a new simulated value of forest carbon density. Calculate the error between the new simulated value of forest carbon density and the calculated value of forest carbon density until the error meets the preset threshold requirement, and end the loop.

[0033] Take the physiological phenological parameters corresponding to the end of the loop as the final physiological phenological parameters of the current forest plot.

[0034] In a possible implementation, the determination of the inversion parameter dataset of the current forest plot includes:

[0035] Input the dominant tree species and forest age of the current forest plot into the Forest-DNDC model to obtain the default physiological phenological parameters generated by the Forest-DNDC model based on the empirical model, and then generate a simulated value of forest carbon density based on the default physiological phenological parameters.

[0036] Compare the magnitude relationship between the simulated value of forest carbon density and the calculated value of forest carbon density. According to this magnitude relationship, record the value of the current default physiological phenological parameter as the upper limit or lower limit of each parameter value. At the same time, change the value of the forest age according to this magnitude relationship, and re-input the changed forest age into the Forest-DNDC model to obtain new default physiological phenological parameters. Use these new default physiological phenological parameters for simulation to obtain a new simulated value of forest carbon density.

[0037] Compare the magnitude of the new simulated value of forest carbon density and the calculated value of forest carbon density again. If the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, record the new default physiological phenological parameter as the lower limit or upper limit of each parameter value, and then obtain the value range of each physiological phenological parameter according to the upper and lower limits of each parameter; otherwise, continue to change the forest age and re-simulate until the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, and stop the loop.

[0038] According to the value range of each physiological phenological parameter, determine multiple possible values of each physiological phenological parameter in an equal-step manner starting from the lower limit, and form the inversion parameter dataset of the current forest plot with all possible values of all physiological phenological parameters.

[0039] In a possible implementation, before inputting the simulation result into the pre-trained random forest model in step S5, it further includes:

[0040] Select the annual simulation results of forest plot soil and forest carbon density of all forest plots under different climate changes and different scenarios at fixed-year intervals, and perform data formatting to obtain a forest plot simulation dataset;

[0041] Input the formatted forest plot simulation dataset into a pre-trained random forest model.

[0042] This embodiment provides a simulation system for future scenarios of forest ecosystem carbon sinks. The system uses the Forest-DNDC model as a scenario simulation model, including:

[0043] A data acquisition module for acquiring climate data, soil data, forest plot data, and management measures of the study area;

[0044] A data processing module for processing climate data and soil data respectively based on the high-accuracy surface modeling (HASM) method to obtain climate grid data and soil grid data with a specified resolution, and extracting the pixel values at the locations of forest plots in the climate grid data and soil grid data into the forest plot data to obtain a forest plot environmental information dataset;

[0045] A scenario setting module for designing multiple future scenarios of the forest ecosystem under different management measures according to the management measures actually recorded in the historical forest management of the study area;

[0046] A plot carbon density simulation module for simulating the carbon density of each future scenario using the Forest-DNDC model considering various future climate change situations according to the forest plot environmental information dataset to obtain the annual simulation results of forest plot soil and forest carbon density of all forest plots under different climate changes and different scenarios;

[0047] A carbon density initial field prediction module for inputting the simulation results of forest plot soil and forest carbon density into a pre-trained random forest model, and combining with the forest plot environmental information dataset to predict the carbon density of other areas outside the forest plots to obtain spatial distribution grid data of soil carbon density and forest carbon density covering the entire study area under future scenarios;

[0048] A carbon density downscaling module for performing HASM simulation on the carbon sink of the forest ecosystem under future scenarios with this spatial distribution grid data as the initial field and the simulation results of forest plot soil and forest carbon density output by the Forest-DNDC model as the control conditions to obtain high-precision forest and soil future scenario grid data with a specified resolution.

[0049] The technical solution of the embodiment of the present application has the following beneficial effects:

[0050] First, since the Forest-DNDC model is specifically optimized for forest ecosystems, providing multi-process coupling simulation capabilities, flexible input and management settings, and high-precision microscopic process descriptions, it can better simulate tree species characteristics, phenological changes, and forest physiological processes. Selecting this model for forest carbon sink simulation has relatively high accuracy.

[0051] Second, the HASM method can interpolate climate and soil data more precisely, making the data more continuous and smooth in space. Compared with traditional interpolation methods, HASM is more suitable for processing data in complex terrain areas, and can improve the spatial resolution and accuracy of the data. By obtaining accurate climate, soil, and forest plot data, the uncertainty of input data in the model simulation process can be significantly reduced.

[0052] Third, extracting the pixel values at the locations of forest plots in climate raster data and soil raster data into the forest plot data to achieve spatial alignment of the data can ensure the consistency of the forest plot data with the data of its surrounding environment, guarantee the applicability of the model under different regional conditions, and improve the reliability of the model simulation results.

[0053] Fourth, the scenario design based on real data and historical management experience can make the scenario design more in line with the actual situation. At the same time, by designing multiple management measure scenarios, the impact of each measure on the forest carbon sink capacity can be quantified.

[0054] Fifth, using the forest plot environmental information dataset (including data such as soil, climate, and tree species) enables the Forest-DNDC model to perform precise simulations based on the conditions of specific plots, capture the subtle differences of different plots under future climate change conditions, and improve the accuracy of the simulation results.

[0055] Sixth, inputting the simulation results of forest plot soil and forest carbon density into a pre-trained random forest model, and combining with the forest plot environmental information dataset, to predict the carbon density of areas outside the forest plots. Utilizing the processing ability of the random forest model for non-linear and complex relationships to effectively capture the impact of environmental factors on carbon density, the carbon density simulation results based on limited plots can be extended to the entire study area, and then used as the initial field to perform simulations using the HASM method to generate future scenario raster data of forest and soil carbon density with more refined spatial patterns.

[0056] In summary, the method provided in this application improves the spatial resolution and accuracy of input data and the spatial alignment of input data through the HASM method, providing reliable and high-quality input data for the Forest-DNDC model. On this basis, using Forest-DNDC as a scenario simulation model, the future scenarios of each plot are simulated by combining multi-scenario design and different climate change situations, and the simulation results of Forest-DNDC are extended from the plot location to the entire study area using the random forest model and the HASM method. Finally, high-precision future scenario raster data of forest and soil carbon density under different climate change situations in the study area are obtained, providing scientific support and basis for decision-making departments to respond to climate change and formulate carbon management strategies. At the same time, this method provides technical support for revealing the comprehensive impact of management measures on forest and soil carbon density under different climate change scenarios (such as SSP2-4.5 and SSP5-8.5) at the typical subtropical county scale, helping to comprehensively evaluate the dynamic changes of forest ecosystem carbon density at the typical subtropical county scale under different climate scenarios by combining the spatial heterogeneity of different altitudes, tree species structures and management strategies, providing a scientific basis for future forest management and climate mitigation policies at the subtropical county scale, and contributing to the realization of the global carbon neutrality goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 FIG. is a schematic flowchart of a method for simulating future scenarios of forest ecosystem carbon sinks according to some embodiments of the present application.

[0058] Figure 2 FIG. is a schematic diagram of the spatial distribution of forest plots in the study area.

[0059] Figure 3 FIG. is a schematic diagram of the spatial distribution of soil organic carbon at different soil depths.

[0060] Figure 4 FIG. is a schematic flowchart of the process of inverting forest physiological phenological parameters by Forest-DNDC.

[0061] Figure 5 FIG. is a spatio-temporal distribution map of forest carbon density under the natural development scenario under SSP2-4.5 climate conditions.

[0062] Figure 6 FIG. is a spatio-temporal distribution map of soil carbon density under the natural development scenario under SSP2-4.5 climate conditions.

[0063] Figure 7 FIG. is a trend chart of the impact of different climate conditions in the natural development scenario on forest carbon density.

[0064] Figure 8 FIG. is a trend chart of the impact of different climate conditions in the natural development scenario on soil carbon density Detailed implementation manners

[0065] For ease of understanding, some terms involved in this embodiment are described below. These descriptions are merely exemplary and should not be construed as limiting the technical solutions of this application.

[0066] Forest ecosystem carbon sink refers to the process by which forests absorb carbon dioxide (CO2) from the atmosphere through photosynthesis and fix it in plant bodies and soil.

[0067] Forest carbon density refers to the sum of the carbon density of the aboveground part of arbors, the carbon density of the underground part of arbors, the carbon density of the understory vegetation layer, and the carbon density of the litter layer.

[0068] Soil carbon density mainly includes soil organic carbon, which is the carbon existing in the soil in an organic form and is mainly transformed from plant residues, litter, root decomposition products, etc. After these organic substances are decomposed by soil microorganisms, stable organic carbon is formed, which can be stored in the soil for a long time.

[0069] Referring to "embodiment" in this article means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0070] The embodiments of this application are described below with reference to the accompanying drawings.

[0071] Embodiment 1:

[0072] This embodiment provides a method for simulating future scenarios of forest ecosystem carbon sinks, as Figure 1 shown. This method includes:

[0073] Step S1: Obtain the climate data, soil data, forest plot data, and management measures of the study area.

[0074] In this embodiment, the study area can be any geographical area. For example, it can be an area at the global, national, or provincial scale. Preferably, the study area in this embodiment is an area at the county scale. Areas at the global, national, and provincial scales can usually obtain more comprehensive and standardized data, and their models often use larger spatial units (i.e., lower spatial resolutions, such as low resolutions of 5 km and below), which are suitable for rough estimation. Compared with the global, national, and provincial scales, there is a lack of high-resolution (e.g., high resolutions of 100 m and above) spatial data in the simulation of forest ecosystem carbon sinks at the county scale, and the data lacks systematicness and consistency, resulting in insufficient simulation accuracy and inability to provide effective decision-making support and scientific research basis.

[0075] In this embodiment, taking County Z in Province J located in the subtropical zone as an example, the technical solution will be described in detail.

[0076] In this embodiment, the climate data includes historical climate station data and future climate scenario data.

[0077] The historical climate station data can be sourced from the historical observation data of various meteorological stations in Province J. The information recorded at each station can include daily-scale temperature, daily-scale precipitation, the station number, the longitude and latitude coordinates of the location, and the date of the recorded data. Exemplarily, the historical climate station data in Province J includes the observation data of 173 meteorological stations from 1957 to 2019.

[0078] The future climate scenario data comes from the official website of CMIP6, which provides multiple climate scenarios for simulating future climate change. Among them, SSP1-2.6 is a low-emission pathway, mainly considering relatively optimistic global emission reduction efforts. SSP2-4.5 represents the "middle development route", also known as the "sustainable but not extreme scenario", and SSP5-8.5 is the "fossil fuel-driven economic development scenario", representing a high-emission scenario of "economy prior to environmental protection". As a low-emission pathway, SSP1-2.6 is relatively idealized, while SSP2-4.5 and SSP5-8.5 cover medium and extreme climate change scenarios. These two scenarios can provide more diverse results for research and can simultaneously show milder and more severe climate change impacts. Therefore, the future climate scenario data in this embodiment includes two future climate scenarios, SSP2-4.5 and SSP5-8.5, which can cover the realistic scenarios from medium emission reduction to no emission reduction, making the simulation of future change trends more practical and reflecting a wider range of possibilities.

[0079] The soil data includes soil sampling measured data and soil profile data. Among them, the soil sampling measured data comes from the actual forest plot soil sampling measured data in 2023 and 2024 in the study area, including multiple measured sample points (such as 37 sample points). The soil measurement indicators in each measured sample point include soil profile thickness, pH value, electrical conductivity, water content, organic carbon, total soil nitrogen, soil bulk density, and soil mechanical composition. The soil profile data comes from the second national soil census data and also includes multiple soil slope sample points. The total number of all soil profile data in County Z of Province J is 74 sample points.

[0080] The data of forest plots (hereinafter referred to as plots or sample plots) come from multiple fixed-plot forest resource inventories in the study area over the years (from 1977 to 2019), and their attribute information is forest information, such as dominant tree species, tree age, etc. Specifically, all plots in the forest plot data are square, with an area of 0.08 hectares, and are marked by three positioning trees at the southwest corner. For each sample plot, the sample collection team needs to fill in two main tables (i.e., inventory tables). The first table is the "Environmental Factor Record Table", which records information including plot ID, longitude, latitude, plot type, attribute, land type, geomorphic type, elevation, slope aspect, slope position, soil name, soil thickness, forest species, source, dominant tree species, crown density, age group, average age, etc. The second table is the "Diameter at Breast Height (DBH) Measurement Table", which records the ID of the sample tree, the tree species, and the diameter at breast height (DBH). If there are special tree species in the study area in a specific plot, such as bamboo forests, the sample collection team will also record the number of full-size moso bamboos and the number of mixed bamboos. Exemplarily, the spatial distribution of the forest plots in the study area is as Figure 2 shown.

[0081] The management measures come from the actual records of forest management in the study area over the years, including the cutting methods, cutting areas, planting types, planting proportions, etc. for different tree species. Specifically, when conducting a statistical analysis of the dominant tree species of all forest plots in the study area in 2019, taking the characteristics of the subtropical climate into account and taking County Z in Province J as an example, the main targets considered are Chinese fir, pine, and moso bamboo. Therefore, when classifying tree species, only these three tree species are considered, and other tree species are uniformly classified as 'other tree species'. There are 61 plots with Chinese fir as the dominant tree species, 5 plots with pine, 62 plots with moso bamboo, and 117 plots with other forest species as the dominant tree species (specifically, there is 1 plot with chestnut as the dominant tree species, 1 plot with oak, 1 plot with paulownia, 1 plot with arbor, 2 plots with liquidambar, 3 plots with schima, 4 plots with alniphyllum fortunei, 7 plots with castanopsis fargesii, 8 plots with castanopsis sclerophylla, 51 plots with alnus cremastogyne, 13 plots with other soft broad-leaved trees, and 71 plots with other hard broad-leaved trees). There are corresponding planting and cutting management measures for Chinese fir and other tree species. Moso bamboo and pine only have an annual cutting policy, but no tree planting. The actual recorded management measures in this county are that Chinese fir is cut by 10% annually and planted by 9.3%; pine is cut by 12% annually and not planted; moso bamboo is cut by 0.5% annually and not planted; other tree species are cut by 2.5% annually on average and planted by 2%.

[0082] Step S2: Based on the HASM method, process the climate data and soil data respectively to obtain climate raster data and soil raster data with a specified resolution, and extract the pixel values at the locations of the forest plots in the climate raster data and soil raster data into the forest plot data to obtain the forest plot environmental information dataset.

[0083] In this embodiment, the purpose of step S2 is to use the HASM method to perform high-precision processing on the input data (climate data, soil data) of the model, so as to improve the quality of the input data, and then improve the accuracy of model simulation, and achieve the optimal simulation result.

[0084] Among them, the specified resolution refers to clearly setting the spatial resolution of climate grid data and soil grid data, that is, specifying the level of detail of the data in space, usually expressed as the actual area covered by each pixel (or each grid cell). For example, in climate grid data, if the specified resolution is 1 km, it means that each grid cell represents an area of 1 km by 1 km. In the field of geographic information processing, the higher the spatial resolution (such as 1 km × 1 km), the more details the data can reflect, and the more subtle changes can be captured; on the contrary, a lower resolution (such as 25 km × 25 km) may not be able to effectively reflect local features and changes.

[0085] It should be noted that in this embodiment, the spatial resolution of the future climate scenario data provided by CMIP6 is 25 km, which cannot be applied to the simulation of forest ecosystem carbon sinks at the county scale. Moreover, meteorological stations and soil data are all point data, with uneven spatial distribution and limited quantity, making it difficult to meet the high-precision simulation requirements covering the entire region. Therefore, in this embodiment, the HASM method is used to process climate data and soil data respectively to obtain climate grid data and soil grid data with the specified resolution (high resolution) to improve data quality.

[0086] Specifically, first, high-precision simulation of climate elements is carried out, including the following steps:

[0087] Step S211: Perform regression simulation on the historical data of all meteorological stations in the climate data, and then use the Inverse Distance Weighting (IDW) method for interpolation to obtain the daily-scale climate trend surface with the specified resolution.

[0088] It should be noted that in this embodiment, the range covered by all meteorological stations can be larger than the coverage range of the study area. For example, if the study area is a certain county, all meteorological stations can cover the entire province where the county is located.

[0089] By using the historical data of meteorological stations for regression simulation, the long-term change trend of climate variables can be better captured, reducing the impact of short-term fluctuations and local drastic changes on the results. The IDW method can generate a relatively accurate and continuous climate trend surface by considering the information of multiple surrounding stations, helping to eliminate the limitations of only considering the data of some meteorological stations and providing more consistent spatial distribution information.

[0090] In this embodiment, a specified resolution, for example, can be a spatial resolution of 90m.

[0091] To improve the calculation efficiency, a spatial resolution of an intermediate scale can be set first. For example, a spatial resolution of 1km is set, and the daily-scale temperature and precipitation data of Province J and its surrounding areas are regressively simulated using all stations (i.e., 173 meteorological stations) to obtain daily-scale temperature and precipitation data with a resolution of 1km. Then, the HASM method is used to downscale the daily-scale temperature and precipitation data with the spatial resolution of the intermediate scale to the specified resolution (high resolution).

[0092] Exemplarily, the equations for regressing the daily-scale temperature and precipitation data are as follows:

[0093] pr = a pr ×X + b pr ×Y + D pr (1)

[0094] tas = a tas ×X + b tas ×Y + c tas ×DEM + D tas (2)

[0095] Wherein, X represents longitude, Y represents latitude, pr represents the precipitation prediction value, a pr represents the longitude coefficient in the precipitation regression equation, b pr represents the latitude coefficient in the precipitation regression equation, D pr represents the intercept in the precipitation regression equation; tas represents the temperature prediction value, a tas represents the longitude coefficient in the regression temperature equation, b tas represents the latitude coefficient in the temperature regression equation, c tas represents the coefficient of DEM in the temperature regression equation, D tas represents the intercept in the temperature regression equation.

[0096] Based on the above equations, the coefficients of the regression equation are obtained using least squares regression, and according to this regression equation, the daily-scale climate trend surface with the specified resolution is obtained by the inverse distance weighting method.

[0097] Step S212: Subtract the actual observed value of each meteorological station from the pixel prediction value at the location of each meteorological station in the daily-scale climate trend surface to obtain the residuals of each meteorological station.

[0098] Step S213: Select multiple meteorological stations in and around the study area (for example, 38 meteorological stations around the study area can be selected), and crop the daily-scale climate trend surface within the spatial range of the selected meteorological stations. To improve efficiency, the cropping can be performed on the daily-scale climate trend surface with a medium-scale spatial resolution to ensure that the climate data used in the analysis matches the actual conditions of the study area, while ensuring focus on a specific area, that is, the cropping process enables the researcher to focus on the climate characteristics of a specific area, avoiding unnecessary data processing and improving the efficiency of HASM calculation.

[0099] Step S214: Use the cropping result as the initial climate field of the HASM method and the residuals of the selected multiple meteorological stations as the climate control conditions of the HASM method to carry out HASM simulation to obtain the daily-scale residual surface with the specified resolution.

[0100] Step S215: Sum the daily-scale climate trend surface and the residual surface to obtain the climate grid data with the specified resolution.

[0101] Specifically, the climate grid data (daily-scale high-precision climate surface data) with the specified resolution can be calculated from the following equation:

[0102] Surface = Surf IDW +Surf HASM (3)

[0103] where Surface represents the climate grid data with the specified resolution, that is, the grid surface data of high-precision temperature or precipitation on a daily scale, Surf IDW represents the daily-scale climate trend surface interpolated using the IDW method, that is, the trend surface of daily temperature or precipitation, and Surf HASM represents the daily-scale residual surface with the specified resolution interpolated using the HASM method, that is, the residual surface of daily temperature or precipitation.

[0104] The above is the process of high-precision simulation of climate elements. This process combines the ideas of historical data analysis, spatial interpolation and regional cropping, the HASM method, and surface summation to form a complete set of data integration methods, making the climate data closer to the research objectives and actual needs.

[0105] Secondly, based on the HASM method, the soil data is processed to obtain the soil grid data with the specified resolution, which specifically includes:

[0106] Step S221: Calculate the organic carbon density at different soil layer depths for each soil sampling point.

[0107] Among them, the organic carbon density at each soil depth is calculated using the following formula:

[0108] SOC stocks = SOC × BD × d × (1 - G) (4)

[0109] Where SOC stocks is the soil organic carbon density, with the unit of kg / m 2 , SOC is the organic carbon content, with the unit of g / kg, BD is the soil bulk density, with the unit of g / cm 3 , d is the soil thickness, with the unit of cm, and G is the volume proportion (%) of gravel with a diameter > 2 mm. This formula is also known as the Rodríguez - Murillo formula. By integrating multiple key soil parameters, it can more accurately reflect the organic carbon storage in the soil, reduce the impact of single - parameter fluctuations on the calculation of soil organic carbon density, and its structure is simple and easy to understand, without the need for complex models and a large amount of input data, making it easy to apply in actual operations.

[0110] Step S222: Using the calculated soil organic carbon density as the dependent variable and multiple soil properties and environmental factors of the soil sampling point data as independent variables, a random forest model is used to predict the spatial distribution of soil organic carbon at different soil layer depths.

[0111] Among them, the different soil layer depths include the soil layer depths in two cases: 0 - 5 cm and 0 - 100 cm.

[0112] Use the random forest combined with plot information to predict the spatial distribution of soil organic carbon at 0 - 5 cm and 0 - 100 cm. The random forest takes 74 soil organic carbon (SOC) sample points as the dependent variable and a total of 23 independent variables are input, which are: hillshade, slope, aspect, slope length, valley depth, elevation, convergence index, clay content (0 - 5 cm, 0 - 100 cm), sand content (0 - 5 cm, 0 - 100 cm), pH (0 - 5 cm, 0 - 100 cm), soil type, soil thickness, land use type, forest type, canopy density, average forest age, plan curvature, profile curvature, annual average rainfall, topographic wetness index. Their sources are shown in Table 1 as follows:

[0113] Table 1 Sources of Random Forest Input Data

[0114]

[0115]

[0116] Input the above - mentioned data into the random forest model. The final prediction result is the spatial distribution of soil organic carbon at different soil layer depths with high precision, and its spatial resolution is the specified resolution, that is, 90 m. The soil organic carbon raster data includes two different soil layer depths: 0 - 5 cm and 0 - 100 cm, as Figure 3 shown.

[0117] In this embodiment, aiming at the problem that it is difficult for ordinary regression models to express the non-linear relationship between soil organic carbon density and soil properties and environmental factors, a random forest is used as a prediction model to perform predictions by integrating multiple decision trees. The input data uses multiple soil properties (such as soil type, texture, pH value, organic matter content) and environmental factors (such as temperature, precipitation, terrain, vegetation type) as independent variables, enabling the model to comprehensively consider the interactions of these factors, thereby more accurately estimating the spatial distribution of soil organic carbon.

[0118] Furthermore, the present application also verified the accuracy of the model through a ten-fold cross-validation method. The results showed that for the SOC in the 0-5 cm soil layer: MSE: 35.516, MAE: 4.468, R2: 0.561, RMSE: 5.960; for the total SOC of all soil layers: MSE: 9.518, MAE: 2.221, R2: 0.530, RMSE: 3.085, indicating that the prediction results of the random forest model can meet the requirements of HASM simulation.

[0119] Step S223: Using the spatial distribution of soil organic carbon at different soil depths as the initial field and the soil sampling point data as the control condition, perform HASM simulation on soil organic carbon to obtain a soil organic carbon raster surface with a specified resolution.

[0120] Using the spatial distribution of soil organic carbon at different soil depths as the initial field ensures that the starting point of the simulation is the best estimate based on existing data, thereby improving the accuracy of the model. The soil sampling point data as the control condition enables the HASM simulation to perform interpolation and extension closely around the observed data, reducing the generation of deviations during the simulation process. At the same time, HASM can well handle the non-uniformity in spatial data, making the generated soil organic carbon raster surface more realistically reflect the distribution characteristics of soil carbon in different regions and at different depths.

[0121] Step S224: Use the ordinary linear regression method to obtain the trend surface data of other soil parameters. Using the second national soil survey data and soil sampling point data as the control condition, perform HASM simulation on other soil parameters to obtain a raster surface of other soil parameters with a specified resolution.

[0122] Among them, the ordinary linear regression method can provide trend surface data for other soil parameters as an initial estimate. This trend surface can capture the overall change trend of soil parameters and provide a reasonable starting point for subsequent HASM simulation. Using the second national soil survey data and soil sampling point data as the control condition can ensure that the simulation process closely depends on real observed data, thereby improving the accuracy of the simulation results and ensuring that the raster surface of other soil parameters also meets the requirements of the forest ecosystem carbon sink in the study area.

[0123] Finally, extract the pixel values at the locations of forest plots from the climate raster data (including air temperature and precipitation) and soil raster data (including soil organic carbon and other soil parameters) obtained in the above steps into the forest plot data, so that the environmental information of all forest plots is consistent in terms of spatial location and at a relatively high spatial resolution, and a dataset of forest plot environmental information is obtained.

[0124] Step S3: Design multiple future scenarios of the forest ecosystem under different management measures according to the management measures actually recorded in the historical forest management in the study area.

[0125] The purpose of designing multiple scenarios is to better simulate and depict the future scenarios of the carbon density of the forest ecosystem in the study area under different development paths driven by the coupling of climate change and human activity interference. Since the management measures actually recorded in the historical forest management are derived from real data, scenario design based on real data and using the historical management records of the study area can make the scenario design more in line with the actual situation, and thus enable the Forest-DNDC model to more accurately predict the carbon sequestration capacity and change trends of the forest ecosystem under different future management strategies.

[0126] Exemplarily, three scenarios can be designed: the natural development scenario, the economic development priority scenario, and the management optimization scenario (the maximum species diversity scenario).

[0127] Among them, the natural development scenario: refers to the future scenario of the change in the carbon density of the forest ecosystem in the study area under the current actual management measures in the study area. Taking County Z in Province J as an example, in this scenario, the felling volume of moso bamboo is extremely small, with an average annual felling volume of no more than 0.5% in the whole county. However, its moso bamboo is widely distributed. Through on-site visits and field analysis, the following reasons lead to this phenomenon: 1) The local bread industry is developed, and most people are engaged in bread-related industries, resulting in a shortage of labor for moso bamboo felling; 2) The transportation is inconvenient. Most moso bamboo forests are distributed in relatively steep and dangerous terrains, and at the same time, the felling difficulty is relatively large and the risk coefficient is relatively high; 3) The profit is low. At present, the felling cost of moso bamboo is high, and the purchase price of moso bamboo has declined compared with previous years, resulting in a small profit margin. For the above reasons, although the county has a large amount of moso bamboo, the felling volume is small. In addition, no tree planting is carried out for moso bamboo because the natural reproduction speed of moso bamboo itself is relatively fast and there is no need to consume additional labor costs for planting. In this scenario, the felling volume of pine trees is slightly higher, but no tree planting is carried out. The overall average planting / felling ratio of plots with dominant tree species of Chinese fir forests and other forest types is approximately 0.9.

[0128] Economic development priority scenario: It refers to the future change scenario of the carbon density of the forest ecosystem driven by the future development strategic plan that focuses on economic development in the study area. Under the economic priority development scenario, based on the actual management measures of the forest ecosystem in the natural development scenario, the annual felling volume of forest land with Chinese fir as the dominant tree species will be increased by 20%, and the planting volume will be increased by 7%. The annual felling volume of forest land with masson pine as the dominant tree species will be reduced by 58%. The annual felling volume of forest land with moso bamboo as the dominant tree species will be increased by 100%. The annual felling volume of forest land with other forest types as the dominant tree species will be increased by 40% while the planting volume remains unchanged. The annual felling volume of all forest types will be increased by 48%, and the planting volume will be increased by 1.74%. In this scenario, the planting and felling ratio of the sample plots is about 1:1.2. For the masson pine forest, reducing its felling volume is to ensure that the study area can maintain a richer ecological species as much as possible and prevent it from being completely cut down. Doubling the felling volume of moso bamboo will not only bring higher economic benefits but also prevent moso bamboo from over-reproducing and occupying the reproduction areas of other tree species, resulting in the reduction of other forest types. Currently, this situation is set relatively conservatively. Through on-site visits and investigations, the natural breeding rate of local moso bamboo exceeds 20% per year, so the future felling volume of moso bamboo can be higher.

[0129] Management optimization scenario (maximum species diversity scenario): It refers to the development management optimization scenario that prioritizes ecological conservation and species diversity under the coupled action of natural climate and human activity disturbances. In this scenario, the development goal is to protect and achieve the maximum species diversity, and the optimal management model of the forest ecosystem for biodiversity conservation is designed, so that under the combined action of future climate change and human activities, the forest ecosystem in the study area will develop towards the direction of maximizing species diversity.

[0130] Based on these three scenarios, the impacts of two climate change situations, SSP2-4.5 and SSP5-8.5, on the changes in the carbon density of the forest ecosystem in the study area under the management policies of the same scenario are further analyzed and considered respectively.

[0131] Step S4: According to the forest sample plot environmental information dataset, considering various future climate change situations, use the Forest-DNDC model to simulate the carbon density of each future scenario, and obtain the simulation results of the soil and forest carbon density of all forest sample plots year by year under different climate changes and different scenarios.

[0132] Specifically, based on the forest plot environmental information dataset, considering various future climate change scenarios, the Forest-DNDC model is used to conduct simulations at the locations where each forest plot is located. The parameters input into the model include: climate data (daily-scale temperature, precipitation) at the location, soil data (soil type, forest land type, soil layer thickness, number of soil layers, pH, surface soil organic carbon content (0-5 cm, kg C / kg), total soil organic carbon content (kg carbon / hectare), stone content, bulk density, clay content, hydrologic conductivity, soil porosity, field capacity, crop wilting point, etc.), forest data (forest type, forest age, tree physiological phenological parameters, etc.), and management measures (planting, cutting, burning, drainage, fertilization).

[0133] In this step, using the forest plot environmental information dataset as the input data enables the model to perform accurate simulations based on the conditions of the forest plots in the study area. Combining different climate change scenarios (such as temperature and precipitation changes), the simulations can better reflect the dynamic changes of forest ecosystems under different future climate change paths and different management measures, which helps to predict the impact of climate change on forest carbon density. At the same time, the simulation results provide annual changes in soil and forest carbon density information, which can quantify the interannual changes in forest carbon sinks under future scenarios and evaluate the long-term effects of different forest management measures. Long-term simulations are carried out separately for each plot (for example, a total of 42 years from 2019 to 2060), and finally the annual simulation results of all plots are obtained. By separately simulating all forest plots according to their different environmental factors, the environment of each plot can be accurately considered.

[0134] It should be noted that in the existing Forest-DNDC model, the management measures can only be set once during the simulation process, and this management measure can only be executed at a specific time point. However, in the simulation of long-term time series of future scenarios (such as the study of the evolution of forest ecosystems over decades or even centuries), the dynamic changes of the ecosystem often require multiple management measures to be fully described and understood. The above-mentioned way of setting management measures in the existing Forest-DNDC model results in the inability of the existing Forest-DNDC model to reflect long-term management strategies and lack of simulation of responses to multiple interventions, and it cannot meet the needs of simulating the dynamic changes of the ecosystem.

[0135] In view of this, in step S4, before using the Forest-DNDC model to simulate the carbon density change in each future scenario, it further includes: the step of improving the management measure module of the Forest-DNDC model, specifically as follows: adding a judgment condition to each management measure in the management measure module of the Forest-DNDC model, and setting the management measure time as a cycle, where the judgment condition is used to determine whether the management measure should be executed.

[0136] Since the Forest-DNDC model adopts a modular design structure, in this embodiment, the management module is improved by using modular design to support the simulation of multiple management measures. Specifically, a judgment condition (logical condition) is added to determine whether the management measure should be executed. By adding the judgment condition, the model can check whether the condition for executing the management measure is met at each time step (such as year, month or day). If the condition is met, the preset management measure is executed at that time point. Among them, the judgment condition can be set according to the time step or other environmental variables. For example, the judgment condition can be set as "fertilize when the soil nutrient drops to a certain threshold".

[0137] Set the time of the management measure as a fixed cycle, for example, execute the management measure once a year, once every five years or once every ten years. This means that the management operation is no longer a one-time operation, but is regularly repeated throughout the simulation process, that is, the model makes a conditional judgment in each management cycle to determine whether to implement a management measure.

[0138] It should also be noted that in traditional simulations, the forest physiological phenology parameters are usually set by the Forest-DNDC model based on empirical models. Such a setting has little deviation for large-scale simulations, but when applied to county-scale simulations, it cannot reflect the local forest ecological characteristics, resulting in insufficient simulation accuracy. To solve the problem of forest physiological phenology parameters, some existing technologies use the method of actually measuring the physiological phenology parameters of all forest plots to ensure the accuracy of the physiological phenology parameters. However, such a method has a very high engineering difficulty and a very high cost.

[0139] In view of this, in this application, in step S4, before using the Forest-DNDC model to simulate the carbon density in each future scenario, it further includes: the step of using the Forest-DNDC model to perform high-precision inversion of the forest physiological phenology parameters in the base year (such as 2019).

[0140] The high-precision inversion of the forest physiological phenology parameters in the base year (such as 2019) using Forest-DNDC is based on the following assumption: assume that the growth parameters of local tree species are consistent with the growth parameters of this tree species defaulted by the model. Refer to Figure 4 , including the following steps:

[0141] For each forest plot in the forest plot environmental information dataset, according to the measured data of the forest plot (i.e., the measured data in the forest plot inventory form), the calculated value of forest carbon density (i.e., the actual carbon density) is calculated using an empirical formula.

[0142] Determine the inversion parameter dataset of the current forest plot; among them, the inversion parameter dataset includes multiple sets of values of the physiological phenological parameters of the current forest plot, and these parameter combinations will be used as the candidate parameter groups for the simulation of the Forest-DNDC model in this study area. The optimal parameters of the current forest plot are found by traversing. For example, assume that the forest physiological phenological parameters include parameters A, B, and C. Then, one set of parameters includes {A = 9; B = 15; C = 30}, and another set of parameters can be {A = 12; B = 18; C = 33}, and so on.

[0143] Traverse the inversion parameter dataset in order, and perform the following steps for each set of parameters:

[0144] Set the current set of parameters as the physiological phenological parameters of the Forest-DNDC model, and perform simulations based on the Forest-DNDC model to obtain the simulated value of forest carbon density under the current set of parameters.

[0145] Calculate the error between the simulated value of forest carbon density and the calculated value of forest carbon density. If the error does not meet the preset threshold requirement (for example, the threshold requirement is that the error < 5%), then perform parameter screening, that is, exclude the current set of parameters from the candidate parameter values, and set the next set of parameters as the physiological phenological parameters of the Forest-DNDC model, and re-perform simulations using the Forest-DNDC model to obtain a new simulated value of forest carbon density, and calculate the error between the new simulated value of forest carbon density and the calculated value of forest carbon density until the error meets the preset threshold requirement (i.e., the error < 5%), and end the loop.

[0146] Take the physiological phenological parameters corresponding to the end of the loop as the final physiological phenological parameters of the current forest plot.

[0147] Furthermore, determine the inversion parameter dataset of the current forest plot, including:

[0148] Input the dominant forest species and forest age of the current forest plot into the Forest-DNDC model to obtain the default physiological phenological parameters generated by the Forest-DNDC model based on the empirical model, and then generate the simulated value of forest carbon density based on the default physiological phenological parameters; the dominant forest species and forest age of the current forest plot can be extracted from the forest plot data.

[0149] Compare the simulated forest carbon density with the calculated forest carbon density (e.g., greater than or less than relationship), and record the values of the current default physiological and phenological parameters as the upper limits (when the simulated forest carbon density is greater than the calculated forest carbon density) or lower limits (when the simulated forest carbon density is less than the calculated forest carbon density) of each parameter according to this size relationship. At the same time, change the value of the forest age according to this size relationship (if the simulated forest carbon density is greater than the calculated forest carbon density, the forest age is reduced; if the simulated forest carbon density is less than the calculated forest carbon density, the forest age is increased), and re-enter the changed forest age into the Forest-DNDC model to obtain new default physiological and phenological parameters. Use these new default physiological and phenological parameters for simulation to obtain a new simulated forest carbon density;

[0150] Compare the new simulated forest carbon density with the calculated forest carbon density again. If the size relationship between the new simulated forest carbon density and the calculated forest carbon density changes (for example, the original greater than relationship changes to a less than relationship), record the new default physiological and phenological parameters as the lower or upper limits of each parameter, and then obtain the value range of each physiological and phenological parameter according to the upper and lower limits of each parameter; otherwise, continue to change the forest age and re-simulate until the size relationship between the new simulated forest carbon density and the calculated forest carbon density changes, and stop the loop;

[0151] Through the above steps, it can be ensured that the physiological and phenological parameters of the simulated forest carbon density output by the Forest-DNDC model that are closest to the calculated forest carbon density are included in the value range.

[0152] According to the value range of each physiological and phenological parameter, determine multiple possible values of each physiological and phenological parameter in an equal step size manner starting from the lower limit, and form the inversion parameter dataset of the current forest plot with all possible values of all physiological and phenological parameters.

[0153] After performing the above steps for each forest plot, a high-precision dataset of forest physiological and phenological parameters for all plots in the base year can be inverted.

[0154] To facilitate understanding of the above process of inverting forest physiological and phenological parameters, an example is given below.

[0155] Suppose there are 245 plots in all forest plot data, and each plot executes the following inversion logic:

[0156] 1. Calculate the forest carbon density based on the existing empirical equation. Assume that the actually calculated carbon density is 8 T C / ha;

[0157] 2. Input the dominant tree species and stand age of the sample plot into the Forest-DNDC model. Assuming the input dominant tree species is Chinese fir and the stand age is 15 years, the model will generate a set of default physiological phenological parameters based on the model experience. Suppose the generated default physiological phenological parameters are {A: 9, B: 15, C: 30}. Based on this set of parameters, execute the Forest-DNDC model. Suppose the model simulation result is 5 T C / ha. Compare it with the actual calculated carbon density of 8 T C / ha. The former is less than the latter. At this time, record {A: 9, B: 15, C: 30} as the lower limits of parameters A, B, and C. Then increase the stand age, for example, set it to 20 years, and simulate again. The Forest-DNDC model generates a new set of physiological phenological parameters. Suppose the parameters are {A: 12, B: 18, C: 33}. The new simulated value of forest carbon density is 10 T C / ha. Compare it with the actual calculated carbon density of 8 T C / ha. The former is greater than the latter, that is, the size relationship between the two has changed. Stop the loop and record {A: 12, B: 18, C: 33} as the upper limits of parameters A, B, and C. Obtain the value range of parameter A as [9, 12], the value range of parameter B as [15, 18], and the value range of parameter C as [30, 33]. At the same time, through the above steps, it can be ensured that the parameters for which the simulated value of forest carbon density is closest to the true carbon density are included in the above intervals.

[0158] 3. Determine multiple possible values of each physiological phenological parameter in an equal-step manner starting from the lower limit. For example, set the parameter to be divided into 4 equal parts, and divide the parameter into four parts to obtain the following inversion parameter dataset: Parameter A is [9, 10, 11, 12], parameter B is [15, 16, 17, 18], and parameter C is [30, 31, 32, 33];

[0159] 4. Traverse this inversion parameter dataset, and use each set of parameters to simulate a forest carbon density simulation value. Suppose the results are as follows:

[0160] {A[9], B

[15] , C

[30] }, the forest carbon density simulation value is 5 T C / ha;

[0161] {A

[10] , B

[16] , C

[31] }, the forest carbon density simulation value is 6.5 T C / ha;

[0162] {A

[11] , B

[175] , C

[32] }, the forest carbon density simulation value is 9 T C / ha;

[0163] {A

[12] , B

[18] , C

[33] }, the forest carbon density simulation value is 10 T C / ha;

[0164] Obviously, the simulated forest carbon density value of 9 T C / ha obtained from the parameter set {A

[11] , B

[175] , C

[32] } is the set of parameters closest to the actual calculated carbon density of 8 T C / ha. Therefore, this set of parameters is selected as the physiological phenological parameters of this plot in 2019.

[0165] This process fills the solution that it is impossible to conduct actual measurements on the physiological phenological parameters of all forest plots by consuming computer energy efficiency. Through this inversion process, it can be ensured that each forest plot obtains a set of physiological phenological parameters that can better reflect the actual carbon density of the plot, further improving the simulation accuracy of the Forest-DNDC model.

[0166] Step S5: Input the forest plot soil and the simulated results of forest carbon density into a pre-trained random forest model, and combine the forest plot environmental information dataset to predict the carbon density of other areas outside the forest plot, obtaining the spatial distribution raster data of soil carbon density and forest carbon density covering the entire study area under future scenarios.

[0167] Furthermore, in order to improve the efficiency of the random forest model and simplify the calculation results, before inputting the simulation results into the pre-trained random forest model, it also includes: selecting the simulated results of forest plot soil and forest carbon density year by year for all forest plots under different climate changes and different scenarios at fixed year intervals (for example, every 5 years from 2020 to 2060), and performing data formatting processing to obtain a forest plot simulation dataset; inputting the formatted forest plot simulation dataset into the pre-trained random forest model.

[0168] Among them, data formatting processing refers to organizing the simulated results of forest plot soil and forest carbon density year by year under different climate changes and different scenarios output by the Forest-DNDC model into the input data format required by the random forest model.

[0169] Assuming that the DEM, slope, and aspect of this area do not change in the future, input the forest plot soil and the simulated results of forest carbon density into a pre-trained random forest model. The input independent variables are: total annual precipitation, elevation, slope, and aspect in the future, and the dependent variables are soil carbon density and forest carbon density respectively. Predict other areas outside the forest plot through the random forest model to obtain the spatial distribution raster data of soil carbon density and forest carbon density covering the entire study area under future scenarios.

[0170] Step S6: Use this spatial distribution raster data as the initial field and the simulated results of forest plot soil and forest carbon density output by the Forest-DNDC model as control conditions to perform HASM simulation on the forest ecosystem carbon sink under future scenarios, obtaining high-precision forest and soil future scenario raster data at the specified resolution.

[0171] Specifically, the spatially distributed raster data of soil carbon density and forest carbon density covering the entire study area in the future scenario predicted by random forest is used as the trend surface data in HASM. Combining with the predicted values of soil and forest carbon density output by the Forest-DNDC model, the accuracy is refined. Finally, high-precision carbon density surfaces of forests and soils every five years starting from 2020 are obtained, that is, high-precision raster data of future scenarios of forests and soils at the specified resolution.

[0172] Furthermore, in order to make the results more in line with the actual situation, the results in farmland areas, waters or other places without forests in the final forest carbon density results are masked, while the soil carbon density does not require masking.

[0173] Finally, by plotting and analyzing the spatio-temporal distribution maps of forest and soil carbon density under different climate conditions and different scenarios to analyze the spatio-temporal changes. Taking the natural development scenario under the SSP2-4.5 climate condition as an example, the high-precision carbon density surfaces of forests and soils are graphically displayed to form a spatio-temporal distribution map of forest carbon density and a spatio-temporal distribution map of soil carbon density, as Figure 5 、 Figure 6 shown.

[0174] By separately plotting the change trend maps of the impacts of different scenarios and different climate conditions on forest and soil carbon density, the changes in forest and soil carbon density under different scenarios and different climate conditions are analyzed. Still taking the natural development scenario under the SSP2-4.5 climate condition as an example, the change trends of forest and soil carbon density under different scenarios and different climate conditions are as Figure 7 、 Figure 8 shown.

[0175] Based on the high-precision simulation data of the future scenario of the forest ecosystem carbon sink covering the entire county scale area, through the above analysis, it can provide data support for the study of the evolution of forest carbon density at the subtropical county scale under different climate change scenarios, especially for the systematic analysis of the long-term impacts of mild and extreme climate scenarios on forest carbon density. At the same time, it provides a technical basis for the complexity analysis of the effects of different management policies in different climate scenarios at the subtropical county scale in terms of space and time dimensions.

[0176] In summary, the method provided in this embodiment can reveal the complex impacts of climate change and management policies on the forest and soil carbon density and carbon sink in the study area (county scale range), provide effective technical support for designing forest management policies adapted to different climate scenarios. At the same time, the method provided in this embodiment provides reference opinions for understanding the combined impacts of climate change and management policies on the forest ecosystem carbon density in the study area, and can provide a certain scientific basis for global carbon management and climate change response strategies.

[0177] Example 2:

[0178] This example provides a future scenario simulation system for the carbon sink of forest ecosystems. This system uses the Forest-DNDC model as the scenario simulation model, including:

[0179] A data acquisition module, used to acquire climate data, soil data, forest plot data, and management measures of the study area;

[0180] A data processing module, used to process climate data and soil data respectively based on the HASM method to obtain climate grid data and soil grid data with a specified resolution, and extract the pixel values at the positions of forest plots in the climate grid data and soil grid data into the forest plot data to obtain a forest plot environmental information dataset;

[0181] A scenario setting module, used to design multiple future scenarios of forest ecosystems under different management measures according to the management measures actually recorded in the historical forest management of the study area;

[0182] A plot carbon density simulation module, used to simulate the carbon density of each future scenario using the Forest-DNDC model according to the forest plot environmental information dataset, considering various future climate change situations, to obtain the annual simulation results of forest plot soil and forest carbon density of all forest plots under different climate changes and different scenarios;

[0183] A carbon density initial field prediction module, used to input the simulation results of forest plot soil and forest carbon density into a pre-trained random forest model, and combine with the forest plot environmental information dataset to predict the carbon density of other areas outside the forest plots, to obtain the spatial distribution grid data of soil carbon density and forest carbon density covering the entire study area under future scenarios;

[0184] A carbon density downscaling module, used to use this spatial distribution grid data as the initial field and the simulation results of forest plot soil and forest carbon density output by the Forest-DNDC model as control conditions to perform HASM simulation on the carbon sink of forest ecosystems under future scenarios to obtain high-precision forest and soil future scenario grid data with a specified resolution.

[0185] The future scenario simulation system for the carbon sink of forest ecosystems provided in this example can implement the steps and processes of the future scenario simulation method for the carbon sink of forest ecosystems provided in any of the above examples and achieve the same technical effects, which will not be elaborated here one by one.

[0186] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for simulating future scenarios of carbon sequestration in forest ecosystems. The method uses the Forest-DNDC model as the scenario simulation model, and includes the following steps: Step S1: Obtain climate data, soil data, forest plot data, and management measures of the study area; Step S2: Based on the High-accuracy Surface Modeling (HASM) method, process the climate data and soil data respectively to obtain climate grid data and soil grid data at a specified resolution, and extract the pixel values at the locations of the forest plots in the climate grid data and soil grid data into the forest plot data to obtain a forest plot environmental information dataset; Step S3: Design multiple future scenarios of the forest ecosystem under different management measures according to the management measures actually recorded in the historical forest management of the study area; Step S4: According to the forest plot environmental information dataset, considering various future climate change situations, use the Forest-DNDC model to simulate the carbon density of each future scenario, and obtain the simulation results of the forest plot soil and forest carbon density year by year for all forest plots under different climate changes and different scenarios; Step S5: Input the simulation results of the forest plot soil and forest carbon density into a pre-trained random forest model, and combine with the forest plot environmental information dataset to predict the carbon density of other areas outside the forest plots, and obtain the spatial distribution grid data of the soil carbon density and forest carbon density covering the entire study area under future scenarios; Step S6: Use this spatial distribution grid data as the initial field, and use the simulation results of the forest plot soil and forest carbon density output by the Forest-DNDC model as the control conditions to perform HASM simulation on the carbon sequestration of the forest ecosystem under future scenarios, and obtain high-precision forest and soil future scenario grid data at a specified resolution; In Step S4, before using the Forest-DNDC model to simulate the carbon density of each future scenario, it also includes: a step of performing high-accuracy inversion on the forest physiological phenology parameters of the base year, specifically as follows: For each forest plot in the forest plot environmental information dataset, calculate the calculated value of forest carbon density according to the measured data of the forest plot using an empirical formula; Determine the inversion parameter dataset of the current forest plot; Traverse the inversion parameter dataset in order, and perform the following steps for each set of parameters: Set the current set of parameters as the physiological phenology parameters of the Forest-DNDC model, and perform simulation based on the Forest-DNDC model to obtain the simulated value of forest carbon density; Calculate the error between the simulated value of forest carbon density and the calculated value of forest carbon density. If the error does not meet the preset threshold requirement, set the next set of parameters as the physiological phenology parameters of the Forest-DNDC model, re-perform simulation using the Forest-DNDC model to obtain a new simulated value of forest carbon density, and calculate the error between the new simulated value of forest carbon density and the calculated value of forest carbon density until the error meets the preset threshold requirement, and end the loop; Take the physiological phenology parameters corresponding to the end of the loop as the final physiological phenology parameters of the current forest plot; The inversion parameter dataset for determining the current forest plot includes: Input the dominant forest species and forest age of the current forest plot into the Forest-DNDC model to obtain the default physiological and phenological parameters generated by the Forest-DNDC model based on the empirical model, and then generate a simulated value of forest carbon density based on the default physiological and phenological parameters; Compare the magnitude relationship between the simulated value of forest carbon density and the calculated value of forest carbon density. According to this magnitude relationship, record the value of the current default physiological and phenological parameters as the upper or lower limit of each parameter value. At the same time, change the value of the forest age according to this magnitude relationship, and re-enter the changed forest age into the Forest-DNDC model to obtain new default physiological and phenological parameters. Use these new default physiological and phenological parameters for simulation to obtain a new simulated value of forest carbon density; Compare the magnitude of the new simulated value of forest carbon density and the calculated value of forest carbon density again. If the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, record the corresponding new default physiological and phenological parameters as the lower or upper limit of each parameter, and then obtain the value range of each physiological and phenological parameter according to the upper and lower limits of each parameter; otherwise, continue to change the forest age and re-simulate until the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, and stop the loop; According to the value range of each physiological and phenological parameter, determine multiple possible values of each physiological and phenological parameter in an equal step size manner starting from the lower limit, and form the inversion parameter dataset of the current forest plot with all possible values of all physiological and phenological parameters.

2. The method according to claim 1, characterized in that, In step S2, based on the HASM method, the climate data is processed, including the following steps: Perform regression simulation on the historical data of all meteorological stations in the climate data, and then use the inverse distance weighting method for interpolation to obtain a daily-scale climate trend surface with a specified resolution; Subtract the actual observed value of the station from the pixel prediction value at the location of each meteorological station in the daily-scale climate trend surface to obtain the residual of each meteorological station; Select multiple meteorological stations in the study area and its surrounding areas, and crop them in the daily-scale climate trend surface according to the spatial range of the selected meteorological stations; Use the cropped result as the climate initial field of the HASM method, and use the residuals of the selected multiple meteorological stations as the climate control conditions of the HASM method to carry out HASM simulation to obtain a daily-scale residual surface with a specified resolution; Sum the daily-scale climate trend surface and the residual surface to obtain climate grid data with a specified resolution.

3. The method according to claim 1, wherein In step S2, based on the HASM method, the soil data is processed to obtain soil grid data with a specified resolution, including: Calculate the organic carbon density at different soil depths for each soil sampling point; Use the calculated soil organic carbon density as the dependent variable, and use multiple soil properties and environmental factors of the soil sampling point data as independent variables, and use the random forest model to predict the spatial distribution of soil organic carbon at different soil depths. Taking the spatial distribution of soil organic carbon at different soil depths as the initial field and the soil sampling point data as the control conditions, perform HASM simulation on soil organic carbon to obtain a soil organic carbon raster surface with a specified resolution; Use the ordinary linear regression method to obtain the trend surface data of other soil parameters, and perform HASM simulation on other soil parameters with the second national soil survey data and soil sampling point data as the control conditions to obtain raster surfaces of other soil parameters with a specified resolution.

4. The method according to claim 1, characterized in that In step S4, before using the Forest-DNDC model to simulate the carbon density of each future scenario, it further includes: the step of improving the management measure module of the Forest-DNDC model, specifically as follows: Add judgment conditions to each management measure in the management measure module of the Forest-DNDC model, and set the management measure time as a cycle, and the judgment conditions are used to judge whether the management measure should be executed.

5. The method according to claim 1, characterized in that, In step S5, before inputting the forest plot soil and the simulation results of forest carbon density into the pre-trained random forest model, it further includes: Select the simulation results of forest plot soil and forest carbon density year by year for all forest plots under different climate changes and different scenarios at fixed year intervals, and perform data formatting processing to obtain a forest plot simulation data set; Input the formatted forest plot simulation data set into the pre-trained random forest model.

6. A simulation system for future scenarios of forest ecosystem carbon sinks. The system uses the Forest-DNDC model as the scenario simulation model, including: A data acquisition module for acquiring climate data, soil data, forest plot data, and management measures of the study area; A data processing module for processing climate data and soil data respectively based on the high-precision surface simulation HASM method to obtain climate raster data and soil raster data with a specified resolution, and extracting the pixel values at the positions of forest plots in the climate raster data and soil raster data into the forest plot data to obtain a forest plot environmental information data set; A scenario setting module for designing multiple future scenarios of the forest ecosystem under different management measures according to the management measures actually recorded in the historical forest management of the study area; A plot carbon density simulation module for simulating the carbon density of each future scenario using the Forest-DNDC model considering various future climate change situations based on the forest plot environmental information data set to obtain the simulation results of forest plot soil and forest carbon density year by year for all forest plots under different climate changes and different scenarios; A carbon density initial field prediction module for inputting the simulation results of forest plot soil and forest carbon density into the pre-trained random forest model, and combining with the forest plot environmental information data set to predict the carbon density of other areas outside the forest plots to obtain the spatial distribution raster data of soil carbon density and forest carbon density covering the entire study area under future scenarios; The carbon density downscaling module is used to take the spatial distribution raster data as the initial field and the simulated results of forest plot soil and forest carbon density output by the Forest-DNDC model as the control conditions, and perform HASM simulation on the forest ecosystem carbon sink under future scenarios to obtain high-precision forest and soil future scenario raster data with a specified resolution; In step S4, before using the Forest-DNDC model to simulate the carbon density of each future scenario, it also includes: the step of performing high-precision inversion on the forest physiological phenology parameters of the base year, specifically as follows: For each forest plot in the forest plot environmental information dataset, according to the measured data of the forest plot, the calculated value of forest carbon density is obtained by using an empirical formula; Determine the inversion parameter dataset of the current forest plot; Traverse the inversion parameter dataset in order, and perform the following steps for each set of parameters: Set the current set of parameters as the physiological phenology parameters of the Forest-DNDC model, and perform simulation based on the Forest-DNDC model to obtain the simulated value of forest carbon density; Calculate the error between the simulated value of forest carbon density and the calculated value of forest carbon density. If the error does not meet the preset threshold requirement, set the next set of parameters as the physiological phenology parameters of the Forest-DNDC model, re-perform simulation with the Forest-DNDC model to obtain a new simulated value of forest carbon density, and calculate the error between the new simulated value of forest carbon density and the calculated value of forest carbon density until the error meets the preset threshold requirement, and end the loop; Take the physiological phenology parameters corresponding to the end of the loop as the final physiological phenology parameters of the current forest plot; The determination of the inversion parameter dataset of the current forest plot includes: Input the dominant tree species and forest age of the current forest plot into the Forest-DNDC model, obtain the default physiological phenology parameters generated by the Forest-DNDC model based on the empirical model, and then generate the simulated value of forest carbon density based on the default physiological phenology parameters; Compare the magnitude relationship between the simulated value of forest carbon density and the calculated value of forest carbon density, record the value of the current default physiological phenology parameters as the upper or lower limit of each parameter value according to this magnitude relationship, and at the same time change the value of the forest age according to this magnitude relationship, and re-enter the changed forest age into the Forest-DNDC model to obtain new default physiological phenology parameters, and use the new default physiological phenology parameters to perform simulation to obtain a new simulated value of forest carbon density; Compare the magnitude of the new simulated value of forest carbon density and the calculated value of forest carbon density again. If the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, record the new default physiological phenology parameters as the lower or upper limit of each parameter value, and then obtain the value range of each physiological phenology parameter according to the upper and lower limits of each parameter; otherwise, continue to change the forest age and re-perform simulation until the magnitude relationship between the new simulated value of forest carbon density and the calculated value of forest carbon density changes, and stop the loop; According to the value range of each physiological phenological parameter, starting from the lower limit, multiple possible values of each physiological phenological parameter are determined by using an equal step size method, and all possible values of all physiological phenological parameters are combined to form the inversion parameter dataset of the current forest plot.

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