High-resolution meteorology-driven planning carbon sink numerical evaluation method, system and terminal

Abstract

The application discloses a high-resolution meteorological driving planning carbon sink numerical evaluation method and system and a terminal, and belongs to the technical field of numerical simulation. The method comprises the following steps: generating high-resolution meteorological driving data with a spatial resolution of 1km-5km; and simulating the first net productivity of vegetation in the research area range by using a land surface model based on meteorological driving data and underlying surface data. The application fully considers the influence of meteorological conditions and underlying surface data on the carbon sink effect of vegetation, and carries out 30m-100m resolution high-precision vegetation NPP simulation suitable for the planning field by combining the generated high-resolution meteorological driving data with modifiable high-resolution land use type data to drive the land surface model, quantitatively evaluates the carbon sink effect difference generated by different planning land scenarios, and further develops an optimized spatial planning scheme under the carbon neutralization target, reasonably arranges construction indexes and an implementation time sequence plan to provide decision reference, and has high practicability.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, and in particular to a high-resolution meteorological-driven numerical assessment method, system, and terminal for planned carbon sinks. Background Technology

[0002] "Carbon peaking" and "carbon neutrality" will be important themes of socio-economic development for a considerable period of time in the future. Using net primary productivity (NPP) to characterize the carbon sink role of vegetation is one of the mainstream methods for carbon sequestration of green spaces such as forests and grasslands, both domestically and internationally.

[0003] Currently, carbon sink capacity is estimated primarily using remote sensing process modeling and GIS spatial analysis modules. Remote sensing process modeling involves three main approaches: first, establishing a remote sensing model of soil carbon flux ecological incentives using light energy utilization and soil basic respiration models to estimate regional carbon storage; second, constructing spectral indices to establish a remote sensing inversion model of soil carbon flux to estimate regional carbon storage; and third, estimating vegetation net productivity using remote sensing imagery and establishing a remote sensing model of soil carbon flux ecological mechanisms using soil basic respiration models to estimate soil carbon sink capacity. GIS methods, on the other hand, establish quantitative relationships between land use characteristics, carbon emission coefficients, and energy carbon sink capacity to obtain changes in ecosystem carbon input and output under different land use patterns.

[0004] Regarding the aforementioned mainstream methods for estimating the first net productivity of vegetation, both methods have certain limitations. On the one hand, both remote sensing model estimation methods and GIS spatial analysis module estimation methods are highly dependent on remote sensing image data. Due to the influence of data accuracy, it is difficult to achieve high-resolution estimation at the planning land scale. On the other hand, vegetation growth is closely related to land use type, meteorological conditions, and soil type. The net productivity of vegetation is affected by the geographical location and meteorological conditions of the region. Existing technical methods cannot comprehensively consider the interaction and relationship between meteorological conditions, underlying surface type, and carbon sink. In addition, existing technical methods are mostly for single scenarios and are difficult to assess the changes in carbon sink under different land spatial planning scenarios. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of the prior art and provide a high-resolution weather-driven numerical assessment method, system and terminal for planning carbon sinks.

[0006] The objective of this invention is achieved through the following technical solution: a meteorological-driven method for assessing changes in carbon sequestration in planned land use, the method comprising the following steps:

[0007] Generate high-resolution meteorological driving data with a spatial resolution of 1km-5km;

[0008] Based on meteorological and underlying surface data, the primary net productivity of vegetation in the study area was simulated using a land surface model.

[0009] In one example, high-resolution meteorological driving data of 1km-5km is obtained by using analytical field data, reanalysis field data, or forecast field data through meteorological model simulation.

[0010] In one example, the simulation of the first net productivity of vegetation within the study area also includes:

[0011] The meteorological driving data is format-converted and interpolated to generate the initial field and boundary field;

[0012] The land surface model-driven data processing program is rewritten and compiled into a meteorological model-driven data processing program that outputs meteorological driving data, thereby generating the initial field file and boundary field file required for the land surface model to simulate the first net productivity of vegetation.

[0013] In one example, the method further includes an underlying surface data update step:

[0014] Map the land use types in the existing land use type data with the land use types in the meteorological model and the land surface model;

[0015] The existing underlying surface data is obtained by using the grid latitude and longitude range, and the area proportion of different land use types in each grid is calculated to obtain the land use type matrix.

[0016] The static data files of the meteorological model and the land surface model are updated based on the land use type matrix, thereby completing the update of the underlying surface data.

[0017] In one example, obtaining the land use type matrix includes:

[0018] The land use type data is cropped based on the latitude and longitude of the grid points in the meteorological model simulation. The proportion of land use type within the current grid point is calculated based on the amount of data of a specific land use type. This process completes the proportion of different land use types in all grid points within the meteorological model simulation grid, resulting in a land use type matrix.

[0019] In one example, the method further includes a surface data update step under the planning scenario:

[0020] The updated underlying surface data is used to replace the increments of the corresponding land use types in the simulation grids of the planning scenario area, while other land use types are reduced. The proportion of different land use types in each grid is recalculated, and then the static data files of the meteorological model and the land surface model are updated to realize the conversion of the planning scenario to the model simulation scenario.

[0021] In one example, the method further includes a simulation result analysis and processing step:

[0022] Based on the simulation results of the first net productivity of vegetation, the first net productivity of vegetation and vegetation type information are extracted and stored as a simplified result file.

[0023] Projection information is generated based on the simulated grid latitude and longitude information, actual latitude and longitude information, and standard longitude STAND_LON information. A spatial distribution map is then generated by combining the simulation results of the first net productivity of vegetation.

[0024] It should be further noted that the technical features corresponding to the above examples can be combined or replaced to form new technical solutions.

[0025] This invention also includes a weather-driven planning land carbon sequestration change assessment system, which shares the same technical concept as the weather-driven planning land carbon sequestration change assessment method formed by any or more of the above examples, and the system includes:

[0026] Meteorological models are used to generate high-resolution meteorological driving data with a spatial resolution of 1km-5km;

[0027] Land surface models are used to simulate the primary net productivity of vegetation within the study area based on meteorological driving data and underlying surface data.

[0028] The system also includes a meteorological element extraction unit, used to perform the following steps:

[0029] The meteorological driving data is format-converted and interpolated to generate the initial field and boundary field;

[0030] The land surface model-driven data processing program is rewritten and compiled into a meteorological model-driven data processing program that outputs meteorological driving data, thereby generating the initial field file and boundary field file required for the land surface model to simulate the first net productivity of vegetation.

[0031] It should be further noted that the technical features corresponding to the above system examples can be combined or replaced to form new technical solutions.

[0032] The present invention also includes a storage medium storing computer instructions that, when executed, perform the steps of the weather-driven carbon sink change assessment method for planned land use formed by any or more of the above examples.

[0033] The present invention also includes a terminal comprising a memory and a processor, the memory storing computer instructions executable on the processor, the processor executing the steps of the weather-driven planned land carbon sink change assessment method formed by any or more of the above examples when executing the computer instructions.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] 1. In one example, the present invention fully considers the impact of meteorological conditions and underlying surface data on the carbon sequestration effect of vegetation. It drives the land surface model to carry out high-precision vegetation NPP simulation with a resolution of 30m-100m applicable to the planning field by generating high-resolution meteorological driving data. It quantitatively evaluates the differences in carbon sequestration effects generated by different planning land use scenarios, and provides decision-making reference for optimizing spatial planning schemes, rationally arranging construction indicators and implementation timelines under the carbon neutrality target. It has high practicality.

[0036] 2. In one example, the initial field file and boundary field file required for simulating the first net productivity of vegetation (NPP) of a land surface model are generated, so that high-resolution meteorological driving data can drive the land surface model to carry out high-resolution NPP simulation at the 100-meter level. This fills the technical gap in using high-resolution meteorological data to drive the land surface model to carry out carbon sink simulation research and application, and provides a new approach for high-precision NPP numerical simulation.

[0037] 3. In one example, by updating the underlying surface data, the impact of different planning scenarios on meteorological conditions can be reflected, and the generation of meteorological driving data can be corrected in reverse, thereby improving the accuracy of meteorological driving data. Then, by using the changed meteorological conditions to further drive the land surface model, combined with the updated underlying surface data, the changes in vegetation carbon sinks can be more accurately quantified and assessed.

[0038] 4. In one example, by planning the underlying surface data update steps, different underlying surface scenarios can be designed to simulate the first net productivity of vegetation under multiple scenarios, taking into account the current situation of the study area. This provides a decision-making reference for optimizing spatial planning schemes, rationally arranging construction indicators, and implementing time-series plans under the carbon neutrality target. Attached Figure Description

[0039] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, which are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to denote the same or similar parts. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application.

[0040] Figure 1 This is a flowchart of a method in an example of the present invention;

[0041] Figure 2 This is a schematic diagram illustrating the generation of high-resolution meteorological driving data in an example of the present invention;

[0042] Figure 3 This is a schematic diagram of the first net productivity assessment of vegetation in a planning scenario, as shown in an example of the present invention.

[0043] Figure 4 This is a spatial distribution map of the annual average NPP value in the Chengdu area in an example of the present invention;

[0044] Figure 5 A 50m resolution map showing the current land use carbon sink distribution in a typical area;

[0045] Figure 6 for Figure 5 The corresponding example shows a 50m resolution assessment map of planned land carbon sink changes in the same typical area. Detailed Implementation

[0046] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] In the description of this invention, it should be noted that the directions or positional relationships indicated by terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" are based on the directions or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the use of ordinal numbers (e.g., "first and second," "first to fourth," etc.) is for distinguishing objects and is not limited to this order, and should not be construed as indicating or implying relative importance.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] In one example, such as Figure 1 As shown, a meteorological-driven method for assessing changes in carbon sequestration in planned land use is presented. This method specifically includes the following steps:

[0051] S1: Generate spatial high-resolution meteorological driving data, where high-resolution meteorological driving data refers to high-resolution meteorological driving data with a resolution of 1km-5km, including vegetation growth-related elements such as air temperature, precipitation, soil temperature, and soil moisture.

[0052] S2: Based on meteorological driving data and underlying surface data, the primary net productivity of vegetation within the study area is simulated using a land surface model.

[0053] Specifically, the land surface model HRLDAS (High-Resolution Land Data Assimilation System) is currently widely used in land surface process simulation, focusing on soil-related information and soil issues. Currently, the publicly available HRLDAS driving data in my country is GLDAS, with a spatial resolution of 0.25°, approximately 25 km. The resolution of related meteorological elements is relatively low, making it difficult to meet the requirements for high-resolution simulation of vegetation first net productivity within 1 km. To address this issue, this invention first generates high-resolution meteorological driving data for the study area using a meteorological model such as the Weather Research and Forecasting Model (WRF), obtaining hourly resolution simulation results for meteorological elements.

[0054] Furthermore, the underlying surface data includes land use type, soil type, leaf area index, etc. When the land surface model of this invention simulates the first net productivity of vegetation within the study area, it fully considers the impact of meteorological conditions and underlying surface data on the vegetation carbon sink effect. By generating high-resolution meteorological driving data, the land surface model is driven to carry out high-precision vegetation NPP (net first productivity of vegetation) simulation, quantitatively assess the differences in carbon sink effects generated by different planning land use scenarios, and provide decision-making reference for optimizing spatial planning schemes, rationally arranging construction indicators and implementing time-series plans under the carbon neutrality target. It has high practicality.

[0055] As an option, the present invention can simulate the first net productivity of vegetation based on historical meteorological conditions, and can also conduct carbon sink change analysis for the next six months or so based on meteorological driving data provided by forecast fields. The simulation analysis is flexible and can take into account both historical retrospective simulation and forward-looking prediction simulation.

[0056] In one example, high-resolution meteorological driving data of 1km-5km are obtained through simulation using analytical field data (including but not limited to the Final Operational Global Analysis (FNL) released by the U.S. National Center for Environmental Prediction (NECP), reanalysis field data (including but not limited to the fifth-generation meteorological reanalysis dataset ERA5 of global climate released by the European Centre for Medium-Range Weather Forecasts (ECMWF), or forecast field data (including but not limited to the Global Forecast System (GFS) dataset released by the U.S. National Weather Service). Specifically, high-resolution meteorological driving data simulation within the study area is carried out using the WRF model. Depending on the application, different meteorological driving data are selected for simulation. Taking historical review simulation as an example, this example uses 1° resolution FNL analytical field data for meteorological driving, and downscaling is completed through hierarchical nesting to obtain meteorological driving data simulation results with a spatial resolution of 1.5km. Compared to traditional WRF meteorological simulations, which use the Lambert conformal projection, this invention requires setting the projection type to lat-lon (latitude and longitude projection) and the grid resolution to degrees instead of meters, which is used in conventional simulations. In addition, the meteorological simulation output (wrfout) file time is set to hours, meaning one wrfout file is output every hour.

[0057] In one example, specifically, since the HRLDAS model does not provide an interface for processing WRF data and does not support using the WRF model as driving data, this invention developed a meteorological element extraction program, such as... Figure 2 As shown, the following steps were performed before simulating the first net productivity of vegetation within the study area:

[0058] S00: Perform format conversion and interpolation on meteorological driving data to generate initial and boundary fields;

[0059] S01: Rewrite the land surface model driving data processing program and compile it into a meteorological model driving data processing program that outputs meteorological driving data, thereby generating the initial field file and boundary field file required for the land surface model to simulate the first net productivity of vegetation.

[0060] The initial field includes 2m air temperature (T2), canopy water content (CANWAT), surface air temperature (TSK), soil temperature (TSLB), soil moisture (SMOIS), snowfall (SNOW), and elevation (HGT) output by the WRF model; the boundary field includes 2m air temperature (T2), 10m wind field (U10, V10), surface air pressure (PSFC), 2m specific humidity (Q2), subsurface shortwave radiation (SWDOWN), subsurface longwave radiation (LWDNB), and precipitation (RC, RN) output by the WRF model. The meteorological element extraction program described above extracts the meteorological data output by the WRF model, reads the required meteorological elements hourly according to time intervals, converts the data according to the data units required by the HRLDAS model, creates folders according to variables, and stores them in NetCDF format. Then, the HRLDAS driving data processing program create_forcing.exe is rewritten and compiled into wrf_forcing.exe to make it compatible with the initial field and boundary field files created by the extraction program, generating the HRLDAS_setup initial field file and LDASIN boundary field file required for HRLDAS simulation, thereby driving the HRLDAS model, reducing the model simulation result errors caused by the driving data, and meeting the requirements of HRLDAS model to carry out high-resolution simulations at the hundred-meter level.

[0061] This invention generates high-resolution initial field and boundary field files required for simulating the first net productivity (NPP) of vegetation using land surface models. This enables high-resolution meteorological driving data to drive land surface models to conduct high-resolution NPP simulations at the 100-meter level, filling the technological gap in using high-resolution meteorological data to drive land surface models for carbon sink simulation research and application, and providing a new approach for high-precision NPP numerical simulation.

[0062] In one example, the HRLDAS model requires the geogrid module of the WRF model to generate static data, and underlying surface data is an important component of WRF static data. Although the WRF model provides basic data, in terms of land use type, my country's urbanization speed is rapid, and underlying surface changes are quite significant. The underlying surface dataset included with the WRF model is from 1992 (USGS) and 2002 (MODIS), which are not very timely. To better reflect the urbanization process in recent years, fully consider the impact of underlying surface data on vegetation growth, and improve the accuracy of NPP simulation, it is necessary to update the benchmark high-resolution underlying surface data, including the following sub-steps:

[0063] S11: Map the land use types in the existing land use type data with the land use types in the meteorological model and the land surface model;

[0064] S12: Obtain existing underlying surface data using grid latitude and longitude range, calculate the proportion of different land use types in each grid, and then obtain the land use type matrix;

[0065] S13: Update the static data files of the meteorological model and the land surface model based on the land use type matrix, thereby completing the update of the underlying surface data.

[0066] Specifically, existing land use type data can be obtained through officially published information disclosed on the public internet. Taking the FROM-GLC global 30m land cover dataset released by Tsinghua University as an example, a mapping table is established between land use types in this dataset and land use types in the WRF meteorological model, and the mapping is performed as follows:

[0067] Table 1 Land Use Type Mapping Table

[0068]

[0069]

[0070] For land use in the planning scenario, it is necessary to refer to the table above to establish a land use mapping. After completing the classification mapping, as an option, the land use type matrix C is obtained. Xi,Yi,k Specifically, it includes:

[0071] Land use type data is cropped based on the latitude and longitude of the four boundaries of the grid points simulated by the WRF model, according to a specific land use type data Lu. i The proportion of land use types within the current grid point is calculated, and then the proportion of different land use types at all grid points within the meteorological model simulation grid is obtained, resulting in a land use type matrix. More specifically, the formula for calculating the proportion of the k-th land use type in the WRF model simulation grid with coordinates (Xi, Yi) is as follows:

[0072]

[0073] Where n represents the number of land use types; Lu represents all land use types.

[0074] Furthermore, updating the static data files of the meteorological model and the land surface model specifically includes:

[0075] Formatting the land use type matrix C Xi,Yi,kThis can be used to update the LANDUSEF (land use type) variable in the geo_em static data file of the WRF model. Calculate the dominant land use type as the one with the largest proportion among different land use types, which can then be used to update the LU_INDEX (dominant land use type index) variable. Then, select the grid cells in the LU_INDEX variable whose dominant land use type is water, and set the corresponding grid cells of the land mask LANDMASK variable to 0 to complete the update of land use type data.

[0076] In this example, the underlying surface data update will be applied to both the geo_em static data file required by the WRF model and the geo file required by HRLDAS. Typically, the geo static file required by HRLDAS has a higher spatial resolution. Compared to the WRF model's maximum resolution of 3km, the spatial resolution of the geo file required by HRLDAS will reach the hundred-meter level or even higher.

[0077] In this example, by updating the underlying surface data, we can reflect its impact on meteorological conditions under different planning scenarios, and reversely correct the generation of meteorological driving data, thereby improving the accuracy of meteorological driving data. That is, updating land use type data has a certain improvement effect on the numerical simulation of meteorological elements. Then, by using the changed meteorological conditions to further drive the land surface model, combined with the updated underlying surface data, we can more accurately quantify and assess the changes in vegetation carbon sink.

[0078] In one example, the planning scenario refers to a land use planning scenario for a specific area. Different planning scenarios can affect meteorological conditions. To ensure simulation accuracy, the method of this invention also includes updating the underlying surface data of the planning scenario in the geo file of the HRLDAS model. Specifically, the planning scenario is first stored in GeoTIFF file format, and new land use types are recorded in raster form. The scenario land use type file is read, and calculations are performed using the same method as the baseline high-resolution underlying surface data update steps. The increments of the corresponding land use types within the simulation grid are replaced, other land use types are reduced, the proportion of different land use types is recalculated, and the dominant land use type and land-water mask are calculated. The results are then written into the geo file of the HRLDAS model, realizing the conversion of the planning scenario into the model simulation scenario.

[0079] As an option, when conducting detailed studies, the surface data update method under this planning scenario is also applied to the geo_em file required by the WRF model to re-simulate the changes in meteorological field conditions caused by the planning scenario, such as quantifying changes in soil temperature and humidity, precipitation, and other elements, thereby improving the accuracy of the assessment.

[0080] In this example, based on the model's basic data update procedure, multiple underlying surface types are established according to spatial planning. This allows for the replacement of specified areas, ranges, and underlying surface types, enabling a quantitative assessment of the carbon sequestration impact of different underlying surface types at different spatial locations. Simultaneously, through the underlying surface data update steps for the planning scenarios, different underlying surface scenarios can be designed to simulate the first net productivity of vegetation under various scenarios, taking into account the current status of the study area. This provides decision-making references for optimizing spatial planning schemes, rationally allocating construction indicators, and implementing timelines under the carbon neutrality target.

[0081] Combining the above steps yields a preferred example for conducting NPP evaluations in multi-planning scenarios, such as... Figure 3 As shown, the high-resolution vegetation first net productivity simulation at this time includes the following steps:

[0082] S1': Prepare high-resolution land use types to cover the simulated area and its surroundings, and complete the update of WRF benchmark high-resolution underlying surface data;

[0083] S2': High-resolution meteorological field simulation was carried out using the WRF model to obtain a high-resolution meteorological field covering the study area at 1km-5km.

[0084] S3': Read high-resolution meteorological field data, complete HRLDAS driven data conversion, and generate HRLDAS high-resolution meteorological driven data;

[0085] S4': NPP simulation using the HRLDAS model;

[0086] S5': Complete data extraction and processing to generate GeoTIFF format NPP raster data.

[0087] Furthermore, the differences in NPP data obtained from simulations of different planning scenarios represent the differences in first net productivity of vegetation caused by those planning scenarios. The actual simulation process requires at least one month of initial simulation to obtain high-precision soil temperature and humidity data.

[0088] In summary, this invention couples a meteorological model with a land surface model and uses numerical simulation technology to fully reflect the changes in the first net productivity of vegetation under the influence of meteorological conditions. It utilizes vegetation growth-related factors such as air temperature, precipitation, soil temperature, and soil moisture provided by high-resolution meteorological data, combined with vegetation and soil data from high-resolution land use type data, to simulate the first net productivity of plants. By adjusting the vegetation cover in the land use type data, it quantitatively evaluates the first net productivity of plants under different national land space planning scenarios.

[0089] In one example, to facilitate the analysis and utilization of simulation results in the planning field, the process also includes analyzing and processing the simulation results. First, based on the simulation results of the first net productivity of vegetation (NPP), information on NPP and vegetation type is extracted and stored as a simplified result file. Then, projection information is generated based on the simulation grid latitude and longitude information, actual latitude and longitude information, and standard longitude (STAND_LON) information. Finally, a spatial distribution map is generated by combining this with the simulation results of NPP. Taking the NPP simulation results of Chengdu as an example, the specific implementation process for generating the spatial distribution map is as follows:

[0090] S61: Read the NPP simulation results file of HRLDAS, use the ncks program of the nco package to extract the NPP and IVGTYP variables in the simulation results, which correspond to the first net productivity of vegetation and vegetation type, respectively, and store them as a simplified HRLDAS results file to reduce the file size and improve the speed of subsequent processing.

[0091] S62: Read the HRLDAS simplified result file and generate projection information using file information such as LAT1 (corresponding to the latitude of the simulated grid center), LON1 (corresponding to the longitude of the simulated grid center), TRUELAT1 (corresponding to the standard latitude 1 of the Lambert projection), TRULAT2 (corresponding to the standard latitude 2 of the Lambert projection), and STAND_LON (consistent with the longitude of the grid center);

[0092] S63: Read the NPP simulation results, perform statistical analysis by time period, and generate a GeoTIFF file by combining the projection information. Figure 4 As shown, this is used for subsequent analysis of changes in carbon sequestration.

[0093] To further illustrate the technical effects of the present invention, in the following ways... Figure 5 Based on the current land use carbon sequestration situation of a typical area at 50m resolution, the preferred example method of this invention is used to simulate the planned land use carbon sequestration of the typical area at 50m resolution, yielding the following results: Figure 6 The carbon sink change assessment map of the planned land shown in the figure demonstrates that the land surface model driven by the high-resolution meteorological data generated by this invention can carry out high-resolution (50m in this example) high-precision vegetation NPP simulation, which is suitable for carbon sink change assessment under planning scenarios. It provides decision-making reference for optimizing spatial planning schemes, rationally arranging construction indicators and implementing time-series plans under the carbon neutrality target, and has high practicality.

[0094] In one example, the present invention also provides a storage medium having the same inventive concept as the weather-driven planned land carbon sink change assessment method formed by any or more of the above examples, wherein computer instructions are stored thereon, which, when executed, perform the steps of the weather-driven planned land carbon sink change assessment method formed by any or more of the above examples.

[0095] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] In one example, the present invention also provides a terminal having the same inventive concept as any or a combination of examples corresponding to the above-described meteorology-driven planned land carbon sequestration change assessment method, including a memory and a processor. The memory stores computer instructions executable on the processor, which, when executing the computer instructions, performs the steps of the above-described meteorology-driven planned land carbon sequestration change assessment method. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0097] In one example, the terminal, i.e., the electronic device, is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0098] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section above, based on various exemplary embodiments of the present invention. For example, the processing unit can perform the above-described weather-driven method for assessing changes in planned land carbon sequestration.

[0099] The storage unit may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 3201 and / or a cache storage unit, and may further include a read-only memory (ROM).

[0100] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0101] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0102] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0103] Through the above description, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method of the exemplary embodiment of this application.

[0104] This invention also includes a weather-driven planning land carbon sequestration change assessment system, which shares the same inventive concept as the weather-driven planning land carbon sequestration change assessment method formed by any or more of the above examples. The system includes:

[0105] A meteorological model is used to generate high-resolution meteorological driving data with a spatial resolution of 1km-5km; the preferred meteorological model is the WRF model, which is used to drive the land surface model based on the generated meteorological driving data.

[0106] The land surface model, preferably the HRLDAS model, is used to simulate the first net productivity of vegetation within the study area based on meteorological driving data and underlying surface data.

[0107] In one example, the system also includes a meteorological element extraction unit for performing the following steps:

[0108] The meteorological driving data is format-converted and interpolated to generate the initial field and boundary field;

[0109] The land surface model-driven data processing program is rewritten and compiled into a meteorological model-driven data processing program that outputs meteorological driving data, thereby generating the initial field file and boundary field file required for the land surface model to simulate the first net productivity of vegetation.

[0110] In one example, the system also includes an underlying surface data update unit for performing the following steps:

[0111] Map the land use types in the existing land use type data with the land use types in the meteorological model and the land surface model;

[0112] The existing underlying surface data is obtained by using the grid latitude and longitude range, and the proportion of different land use types in each grid is calculated to obtain the land use type matrix.

[0113] The static data files of the meteorological model and the land surface model are updated based on the land use type matrix, thereby completing the update of the underlying surface data.

[0114] In one example, the system also includes a planning scenario underlying surface data update unit, which performs the following steps: replacing the increment of the corresponding land use type in the simulation grid corresponding to the planning scenario area with the updated underlying surface data, reducing other land use types, recalculating the proportion of different land use types in each grid, and then updating the static data files of the meteorological model and the land surface model to realize the conversion of the planning scenario to the model simulation scenario.

[0115] In one example, the system also includes a simulation result analysis and processing unit for performing the following steps:

[0116] Based on the simulation results of the first net productivity of vegetation, the first net productivity of vegetation and vegetation type information are extracted and stored as a simplified result file.

[0117] Projection information is generated based on the simulated grid latitude and longitude information, actual latitude and longitude information, and STAND_LON information. A spatial distribution map is then generated by combining the simulation results of the first net productivity of vegetation.

[0118] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A meteorological-driven method for assessing changes in carbon sequestration in planned land use, characterized by: It includes the following steps: Generate high-resolution meteorological driving data with a spatial resolution of 1km-5km; Based on meteorological driving data and underlying surface data, the primary net productivity of vegetation within the study area was simulated using a land surface model. The simulation of the first net productivity of vegetation within the study area also includes: The meteorological element extraction program extracts the meteorological data output by the WRF model, reads the required meteorological elements hourly according to time intervals, converts them according to the data units required by the HRLDAS model, creates folders according to variables, and stores them in NetCDF format; then, the HRLDAS driver data processing program create_forcing.exe is rewritten and compiled into wrf_forcing.exe, making wrf_forcing.exe compatible with the initial field and boundary field files created by the extraction program, generating the HRLDAS_setup initial field file and LDASIN boundary field file required for HRLDAS simulation; The method also includes an underlying surface data update step: Map the land use types in the existing land use type data with the land use types in the meteorological model and the land surface model; Existing underlying surface data is obtained using the latitude and longitude range of the grid. Land use type data is cropped according to the latitude and longitude of the grid point boundary in the meteorological model simulation. The proportion of land use type within the current grid point is calculated based on the area of ​​the raster data of any land use type. Then, the proportion of different land use types in all grid points within the meteorological model simulation is completed, resulting in a land use type matrix. Land use types include evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, closed shrubland, open shrubland, tree-covered grassland, savanna, grassland, permanent wetland, crops, urban and built-up areas, mosaic of crops and natural vegetation, snow or ice, bare land or low vegetation cover, water bodies, savanna, mixed tundra, and bare tundra. Update the static data files of the meteorological model and land surface model based on the land use type matrix: The land use type matrix is ​​formatted to update the LANDUSEF variable of land use type in the geo_em static data file of the WRF model. The dominant land use type is calculated as the one with the largest proportion among different land use types and used to update the dominant land use type index LU_INDEX variable. Then, the grid cells in the LU_INDEX variable with water as the dominant land use type are selected, and the corresponding grid cells of the land mask LANDMASK variable are set to 0 to complete the update of land use type data.

2. The method for assessing changes in carbon sequestration in planned land use based on meteorological driving as described in claim 1, characterized in that: High-resolution meteorological driving data of 1km-5km are obtained by using analytical field data, reanalysis field data, or forecast field data through meteorological model simulation.

3. The method for assessing changes in carbon sequestration in planned land use based on meteorological driving as described in claim 1, characterized in that: The method also includes a planning scenario underlying surface data update step: The updated underlying surface data is used to replace the increments of the corresponding land use types in the simulation grids of the planning scenario area, while other land use types are reduced. The proportion of different land use types in each grid is recalculated, and then the static data files of the meteorological model and the land surface model are updated to realize the conversion of the planning scenario to the model simulation scenario.

4. The method for assessing changes in carbon sequestration in planned land use based on meteorological driving as described in claim 1, characterized in that: The method also includes a simulation result analysis and processing step: Based on the simulation results of the first net productivity of vegetation, the first net productivity of vegetation and vegetation type information are extracted and stored as a simplified result file. Projection information is generated based on the simulated grid latitude and longitude information, actual latitude and longitude information, and standard longitude information. A spatial distribution map is then generated by combining the simulation results of the first net productivity of vegetation.

5. A weather-driven assessment system for changes in carbon sequestration in planned land use, characterized in that: It includes: Meteorological models are used to generate high-resolution meteorological driving data with a spatial resolution of 1km-5km; Land surface model, used to simulate the primary net productivity of vegetation within the study area based on meteorological driving data and updated underlying surface data; The simulation of the first net productivity of vegetation within the study area also includes: The meteorological element extraction program extracts the meteorological data output by the WRF model, reads the required meteorological elements hourly according to time intervals, converts them according to the data units required by the HRLDAS model, creates folders according to variables, and stores them in NetCDF format; then, the HRLDAS driver data processing program create_forcing.exe is rewritten and compiled into wrf_forcing.exe, making wrf_forcing.exe compatible with the initial field and boundary field files created by the extraction program, generating the HRLDAS_setup initial field file and LDASIN boundary field file required for HRLDAS simulation; Underlying surface data updates include: Map the land use types in the existing land use type data with the land use types in the meteorological model and the land surface model; Existing underlying surface data is obtained using the latitude and longitude range of the grid. Land use type data is cropped according to the latitude and longitude of the grid point boundary in the meteorological model simulation. The proportion of land use type within the current grid point is calculated based on the area of ​​the raster data of any land use type. Then, the proportion of different land use types in all grid points within the meteorological model simulation is completed, resulting in a land use type matrix. Land use types include evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, closed shrubland, open shrubland, tree-covered grassland, savanna, grassland, permanent wetland, crops, urban and built-up areas, mosaic of crops and natural vegetation, snow or ice, bare land or low vegetation cover, water bodies, savanna, mixed tundra, and bare tundra. Update the static data files of the meteorological model and land surface model based on the land use type matrix: The land use type matrix is ​​formatted to update the LANDUSEF variable of land use type in the geo_em static data file of the WRF model. The dominant land use type is calculated as the one with the largest proportion among different land use types and used to update the dominant land use type index LU_INDEX variable. Then, the grid cells in the LU_INDEX variable with water as the dominant land use type are selected, and the corresponding grid cells of the land mask LANDMASK variable are set to 0 to complete the update of land use type data.

6. The meteorological-driven carbon sequestration change assessment system for planned land use according to claim 5, characterized in that: The system also includes a meteorological element extraction unit, used to perform the following steps: The meteorological driving data is format-converted and interpolated to generate the initial field and boundary field; The land surface model-driven data processing program is rewritten and compiled into a meteorological model-driven data processing program that outputs meteorological driving data, thereby generating the initial field file and boundary field file required for the land surface model to simulate the first net productivity of vegetation.

7. A terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that: When the processor executes the computer instructions, it performs the steps of the weather-driven carbon sink change assessment method for planned land use as described in any one of claims 1-4.

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