Method for evaluating windbreak effect of farmland shelterbelt based on land-atmosphere coupling model

Abstract

The application discloses a farmland shelterbelt regional windproof effect evaluation method based on a land-air coupling model, and relates to the field of agricultural science and technology. High-precision terrain data of a target farmland shelterbelt region is collected and preprocessed, and spatial distribution information of the farmland shelterbelt is obtained by combining remote sensing image interpretation. Based on the terrain data and the spatial distribution information, terrain undulation and surface coverage characteristics are analyzed, and a refined underlying surface classification system is constructed. High-resolution grid parameters of the WRF model are configured, and the Noah-MP land surface process model is coupled. The farmland shelterbelt regional windproof effect evaluation method based on the land-air coupling model can accurately depict the terrain undulation and surface heterogeneity of the farmland shelterbelt region by combining high-resolution terrain data and multi-source remote sensing images. The WRF model is coupled with the Noah-MP land surface process model to realize fine simulation of complex terrain and multiple underlying surfaces, overcome the limitations of the traditional uniform underlying surface assumption, and significantly improve the accuracy of windproof effect evaluation.

Description

TECHNICAL FIELD

[0001] The application relates to the field of agricultural science and technology, in particular to a farmland shelterbelt regional windproof effect evaluation method based on a land-atmosphere coupling model. BACKGROUND

[0002] With the intensification of global climate change, the frequency of extreme weather events increases, and farmland wind damage, as an important problem in agricultural production, especially in some windy and dry areas, strong winds not only directly damage crops, but also may cause soil erosion, affecting the sustainable use of land, therefore, evaluating the windproof effect of the shelterbelt can help determine the optimal layout and design of the windbreak forest belt, improve agricultural production and protect farmland resources.

[0003] In the prior art, the windproof effect evaluation mostly adopts the assumption of uniform underlying surface, which easily ignores the terrain undulation and surface heterogeneity of the farmland shelterbelt region, resulting in distorted simulation of the windproof effect and systematic deviation in the windproof effect evaluation, therefore, how to use the WRF model to depict the spatial distribution of the underlying surface types in the farmland shelterbelt region through high-resolution terrain data, and couple the Noah-MP land surface process model to simulate the disturbance effect of the terrain on airflow movement, so as to realize parallel simulation of multiple underlying surfaces and improve the spatial heterogeneity simulation capability and evaluation precision of the windproof effect, is a problem to be solved by the application, and for this purpose, the application provides a farmland shelterbelt regional windproof effect evaluation method based on a land-atmosphere coupling model SUMMARY

[0004] To solve the above technical problems, the application is implemented by the following technical scheme: a farmland shelterbelt regional windproof effect evaluation method based on a land-atmosphere coupling model, comprising the following steps:

[0005] S1, collecting and preprocessing high-precision terrain data of a target farmland shelterbelt region, and obtaining spatial distribution information of the farmland shelterbelt in combination with remote sensing image interpretation;

[0006] S2, analyzing terrain undulation and surface coverage characteristics based on the terrain data and the spatial distribution information, constructing a refined underlying surface classification system, and clearly defining the spatial distribution of various underlying surfaces;

[0007] S3, configuring high-resolution grid parameters of the WRF model, coupling the Noah-MP land surface process model, and obtaining a WRF-Noah-MP coupling model to simulate the thermal and dynamic characteristics of different underlying surfaces;

[0008] S4, inputting classification data of the refined underlying surface classification system into the WRF model, so that each type of underlying surface is corresponded to a WRF grid cell in space;

[0009] S5. Simulate the movement of air flow under high-resolution terrain using the WRF model, and analyze the disturbance effect of terrain undulation on air flow lifting, compression and turning;

[0010] S6. Run the WRF-Noah-MP coupled model, and simulate the influence of different underlying surface types on air flow movement in parallel, to obtain wind speed and wind direction wind protection effect parameters;

[0011] S7. Based on the output results of the WRF-Noah-MP coupled model, evaluate the wind protection effect of the target farmland shelterbelt area, and make improvement suggestions.

[0012] Preferably, S1 specifically comprises:

[0013] For the target farmland shelterbelt area, high-precision terrain data is collected, and at the same time, multi-source remote sensing images of the target farmland shelterbelt area are obtained by means of remote sensing image interpretation technology;

[0014] The collected terrain data is geographically registered, cropped and spliced to accurately cover the target farmland shelterbelt area, and the multi-source remote sensing images are subjected to atmospheric correction and geometric correction to eliminate errors and improve data quality;

[0015] Using the preprocessed multi-source remote sensing images, combined with terrain data, the spatial distribution of farmland shelterbelt is interpreted, the position, range and structure information of the shelterbelt are extracted, and a shelterbelt distribution map is generated.

[0016] Preferably, S2 specifically comprises:

[0017] Using GIS tools, the terrain data is analyzed for slope, aspect and relief, and the terrain undulation characteristics of the target farmland shelterbelt area are quantified;

[0018] Combined with multi-source remote sensing images and terrain data, image classification technology is used to extract land cover information and identify underlying surface types including farmland, forest land and bare land;

[0019] Integrate terrain undulation characteristics and land cover information, divide multi-class underlying surface, clarify its spatial distribution, construct a refined underlying surface classification system, and eliminate classification noise through morphological filtering, correct misclassified areas combined with field sample data, output refined underlying surface distribution map, and clarify the spatial proportion and boundary accuracy of each underlying surface.

[0020] Preferably, S3 specifically comprises:

[0021] Configure the high-resolution grid parameters of the WRF model, including horizontal resolution, number of nested layers, projection method and vertical layers, define the simulation area boundary, time step and physical process options, generate terrain and land use data, ensure matching with the input requirements of the Noah-MP model, and complete the model initialization;

[0022] In the WRF model, the Noah-MP land surface process model is activated, the soil layering, vegetation parameters and surface heat flux scheme are configured, the coupling time step and data exchange frequency are adjusted to ensure the bidirectional feedback of thermal and dynamic processes, and the model interface compilation and linking are completed;

[0023] The WRF-Noah-MP coupled simulation is performed, and the variables of surface temperature, sensible / latent heat flux and soil moisture are output, and the thermal and dynamic characteristics of different underlying surfaces are extracted through post-processing tools to analyze the spatial and temporal differences and driving mechanisms.

[0024] Preferably, the S4 specifically comprises:

[0025] The classification data of the refined underlying surface classification system is converted into a format recognizable by the WRF model, ensuring that the geographic coordinates and projection method of the data are consistent with the WRF model, so as to facilitate subsequent processing, and the converted underlying surface classification data is spatially matched with the grid cells of the WRF model, each type of underlying surface is accurately matched to the grid cells of the WRF model through GIS tools, and the spatial consistency of the data is ensured.

[0026] The matched underlying surface classification data is imported into the WRF model, and the path of the underlying surface classification data is specified in the configuration file of the WRF model to complete the loading of the data.

[0027] Preferably, the S5 specifically comprises:

[0028] High-precision topographic data of the target farmland shelterbelt area is collected and preprocessed to ensure that its resolution and coverage meet the simulation requirements, and the topographic data is configured in the WRF model to set the corresponding horizontal resolution and vertical layer number to accurately capture the topographic details.

[0029] The WRF model is run to simulate the movement of air flow under high-resolution topography, and the wind field data output by the model is used to analyze the disturbance effect of topographic relief on air flow lifting, compression and turning, and to reveal the influence mechanism of topography on air flow movement.

[0030] Preferably, the process of revealing the influence mechanism of topography on air flow movement is:

[0031] The preprocessed high-resolution topographic data is embedded in the WRF model, the terrain elevation and land use data are mapped to the model grid through the geogrid program, the horizontal resolution and vertical layer number are configured, and the physical parameters are adjusted to adapt to the terrain to generate initial field and boundary condition files.

[0032] The WRF model is started for numerical integration to simulate the three-dimensional movement process of air flow under high-resolution topography, to calculate the variables of wind field and potential height by solving the Navier-Stokes equation in combination with a topographic forcing term, and to output three-dimensional wind field data at a fixed time step, covering the target area and the surrounding buffer zone, to ensure the spatial and temporal continuity of the data;

[0033] Based on the wind field data output by WRF, the terrain slope and curvature are calculated, and the correlation between them and the vertical velocity (lifting), horizontal wind speed (compression) and vorticity (turning) is analyzed. Through the visualization of vertical profile and horizontal wind field, the disturbance intensity and spatial distribution of terrain undulation on air flow are quantified, and the physical mechanism of terrain affecting the air flow path and speed by changing the pressure gradient force and turbulent mixing is revealed based on the dynamics theory.

[0034] Preferably, the S6 specifically comprises:

[0035] The WRF namelist.input is modified to enable the Noah-MP land surface scheme, set up multiple underlying surface types (farmland / forest / bare land), configure MPI parallel parameters (number of nodes, number of threads), compile the coupled model and verify the parallel efficiency;

[0036] Control experiments are designed, different underlying surface scenarios are run, meteorological driving data is fixed, and high spatial and temporal resolution three-dimensional wind field (U / V / W) and underlying surface characteristic variables are output to the specified directory through dynamic solution of air flow equation by WRF;

[0037] The average wind speed attenuation rate, wind direction deflection angle and turbulence intensity of each underlying surface are calculated, the spatial distribution of wind protection effect parameters is counted, and the inhibition mechanism of underlying surface roughness on air flow is analyzed in combination with dynamic diagnosis.

[0038] Preferably, the process of analyzing the inhibition mechanism of underlying surface roughness on air flow is:

[0039] A three-dimensional ultrasonic anemometer and a wind tower gradient observation system are used to synchronously collect wind speed, wind direction, temperature and humidity data at a height of 10m above different underlying surfaces at a sampling frequency of ≥10Hz and a continuous observation time of ≥72 hours. The original data are subjected to quality control, abnormal values are removed and instrument errors are compensated, 10-minute average wind speed, wind direction and turbulence fluctuation velocity of each measuring point are calculated, the surface roughness is inverted based on the logarithmic wind profile model, the underlying surface type distribution is extracted in combination with satellite remote sensing images, and a spatial database is constructed;

[0040] According to the block boundary wind speed calculation method, the average value of wind speed at the height of 1.5m of the inlet and outlet boundary is counted, the wind speed attenuation rate and the wind direction deflection angle are calculated, wherein the turbulence intensity is determined by the ratio of the standard deviation of the fluctuation velocity to the average wind speed, the Kriging interpolation method is used to generate the spatial distribution map of each parameter, and the GIS technology is combined to divide the high / medium / low attenuation area, the deflection angle gradient zone and the turbulence intensity partition, and the spatial coupling relationship between the parameters and the underlying surface type is analyzed;

[0041] Based on the theory of fluid mechanics, a numerical model of the relationship between the roughness of the underlying surface and the air resistance is established, the inhibitory effect of the height and density of the roughness element on the momentum transfer is quantified, the accuracy of the model is verified through wind tunnel test, and the physical process of turbulence generation mechanism and wind speed attenuation under different roughness is diagnosed.

[0042] Preferably, the S7 specifically comprises:

[0043] The 10m height wind speed, wind direction, turbulence kinetic energy and surface roughness data of the target shelterbelt area are extracted from the output of the WRF-Noah-MP coupling model, the spatial and temporal matching and abnormal value elimination are carried out according to the observation period, and the coordinate system is unified to the shelterbelt layout projection;

[0044] The wind speed attenuation rate and turbulence intensity of the upwind and downwind of the shelterbelt are calculated, the spatial coupling relationship between the shelterbelt density, direction and wind protection efficiency is analyzed combined with the roughness inversion result;

[0045] Based on the wind protection efficiency threshold and turbulence inhibition requirement, the area with weak wind protection effect is identified, and the targeted improvement suggestions are put forward, including optimizing the layout of the shelterbelt and increasing the vegetation coverage, so as to improve the wind protection capacity of the target area and improve the ecological environment.

[0046] The application provides a farmland shelterbelt area wind protection effect evaluation method based on a land-atmosphere coupling model.

[0047] (1) The farmland shelterbelt area wind protection effect evaluation method based on the land-atmosphere coupling model can accurately depict the terrain undulation and surface heterogeneity of the farmland shelterbelt area by combining high-resolution terrain data and multi-source remote sensing images, realize fine simulation of complex terrain and multiple underlying surfaces by using the WRF model coupled with the Noah-MP land surface process model, overcome the limitations of the traditional uniform underlying surface assumption, and significantly improve the accuracy of the wind protection effect evaluation.

[0048] (II) The farmland shelterbelt regional wind protection effect evaluation method based on the land-atmosphere coupling model can more accurately capture the disturbance effect of terrain on airflow movement, that is, airflow lifting, compression and turning, so as to better simulate the three-dimensional movement process of airflow under complex terrain and enhance the model simulation capability for complex terrain.

[0049] (III) The farmland shelterbelt regional wind protection effect evaluation method based on the land-atmosphere coupling model can divide the target region into farmland, forest land and bare land multi-class underlying surface by constructing a refined underlying surface classification system, input the underlying surface into the WRF model, simulate the influence of different underlying surface types on airflow movement in parallel by using the WRF-Noah-MP coupling model, and obtain wind protection effect parameters including wind speed and wind direction, so as to comprehensively evaluate the contribution of different underlying surface types to the wind protection effect and provide scientific guidance for optimizing shelterbelt layout and design. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 FIG. 1 is a workflow schematic diagram of the farmland shelterbelt regional wind protection effect evaluation method based on the land-atmosphere coupling model of the present application;

[0051] Figure 2 FIG. 1 is a workflow schematic diagram of the farmland shelterbelt regional wind protection effect evaluation method based on the land-atmosphere coupling model of the present application; DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0053] Embodiment 1, please refer to Figure 1 , Figure 2 The present application provides a technical solution: a farmland shelterbelt regional wind protection effect evaluation method based on a land-atmosphere coupling model, including the following steps:

[0054] S1, collect and pretreat high-precision topographic data of the target farmland shelterbelt area, obtain the spatial distribution information of the farmland shelterbelt by combining remote sensing image interpretation, collect high-precision topographic data (DEM, SRTM, etc.) for the target farmland shelterbelt area, ensure that the data is complete in coverage and meets the research requirements in resolution, at the same time, obtain multi-source remote sensing images of the target farmland shelterbelt area by means of remote sensing image interpretation technology, georeference, crop and splice the collected topographic data to make it accurately cover the target farmland shelterbelt area, and correct the atmospheric and geometric errors of the multi-source remote sensing images to improve the data quality, use the pretreated multi-source remote sensing images combined with the topographic data to interpret the spatial distribution of the farmland shelterbelt, extract the position, range and structure information of the shelterbelt, and generate a shelterbelt distribution map;

[0055] The specific work content is: for the target farmland shelterbelt area, collect high-precision topographic data, including digital elevation model (DEM), space shuttle radar terrain mapping mission (SRTM) and other data types, during the collection process, ensure that the data completely covers the target farmland shelterbelt area, avoid data missing, at the same time, control the data resolution to meet the accuracy requirements of subsequent research, in addition, obtain multi-source remote sensing images of the target farmland shelterbelt area by means of remote sensing image interpretation technology, which reflect the characteristics of farmland shelterbelt from different angles and different bands; after collecting the topographic data and remote sensing images, pretreat them, for the topographic data, georeference to determine its accurate position in geographic space; crop the data outside the target farmland shelterbelt area to focus the data accurately on the target farmland shelterbelt range; if the data is composed of multiple parts, it needs to be spliced to ensure the continuity of the data, for multi-source remote sensing images, eliminate the influence of atmosphere on the images by atmospheric correction, and correct the geometric deformation of the images by geometric correction, thereby eliminating errors and significantly improving data quality; use the pretreated multi-source remote sensing images combined with the topographic data to interpret the spatial distribution of the farmland shelterbelt, extract the position, range and structure information of the shelterbelt from the multi-source remote sensing images and the topographic data, clearly present the distribution characteristics of the farmland shelterbelt in space and its own composition, based on the extracted information, generate a farmland shelterbelt distribution map to directly show the distribution of the farmland shelterbelt;

[0056] S2, based on the terrain data and spatial distribution information, analyzing the terrain undulation and surface cover characteristics, dividing the target farmland shelterbelt area into multiple categories of underlying surface, including farmland, forest land and bare land, constructing a refined underlying surface classification system, clarifying the spatial distribution of various underlying surfaces, using GIS tools to analyze the slope, slope direction and undulation of the terrain data, quantifying the terrain undulation characteristics of the target farmland shelterbelt area, combining multi-source remote sensing images and terrain data, using image classification technology to extract surface cover information, identifying the underlying surface types including farmland, forest land and bare land, integrating terrain undulation characteristics and surface cover information, dividing multiple underlying surfaces, clarifying their spatial distribution, constructing a refined underlying surface classification system, and eliminating classification noise through morphological filtering, combining with field sample data to correct misclassified areas, outputting a refined underlying surface distribution map, clarifying the spatial proportion and boundary accuracy of various underlying surfaces;

[0057] The specific work content is: using GIS tools to analyze the collected high-precision terrain data in detail, quantifying the terrain undulation characteristics of the target farmland shelterbelt area, including calculating the slope, slope direction and undulation, among which, the slope analysis is used to reveal the steepness of the terrain, the slope direction analysis determines the orientation of the ground surface, and the undulation analysis evaluates the overall change degree of the terrain to identify complex terrain areas; combining multi-source remote sensing images and terrain data, using image classification technology to extract the surface cover information of the target farmland shelterbelt area, identifying the main underlying surface types including farmland, forest land and bare land, farmland areas have higher vegetation index and lower texture characteristics; forest land shows higher vegetation coverage and complex texture structure; bare land is mainly characterized by lower vegetation index and higher reflectivity, through the fusion of multi-source data, the classification accuracy is improved to ensure the accuracy and integrity of the surface cover information; integrating terrain undulation characteristics and surface cover information, the target farmland shelterbelt area is classified into refined underlying surface, clarifying the spatial distribution of various underlying surfaces, constructing a refined underlying surface classification system, and using morphological filtering technology to eliminate noise in the classification process, improving the continuity and accuracy of the classification results, combining with field sample data to correct misclassified areas, ensuring that the classification results are consistent with the actual situation, and finally outputting the refined underlying surface distribution map which clarifies the spatial proportion and boundary accuracy of various underlying surfaces;

[0058] S3, configure the high-resolution grid parameters of the WRF model, couple the Noah-MP land surface process model, obtain the WRF-Noah-MP coupled model to simulate the thermal and dynamic characteristics of different underlying surfaces, configure the high-resolution grid parameters of the WRF model, including horizontal resolution, number of nested layers, projection method and vertical layer number, and define the simulation region boundary, time step and physical process options, generate terrain and land use data to ensure matching with the input requirements of the Noah-MP model, complete model initialization, activate the Noah-MP land surface process model in the WRF model, configure soil layering (4 layers), vegetation parameters (leaf area index, root depth) and surface heat flux scheme, adjust the coupling time step and data exchange frequency to ensure two-way feedback of thermal and dynamic processes, complete model interface compilation and linking, execute WRF-Noah-MP coupled simulation, output variables of surface temperature, sensible / latent heat flux and soil moisture, and extract thermal and dynamic characteristics of different underlying surfaces through post-processing tools, analyze the spatial and temporal differences and driving mechanisms, and verify the model's simulation capability for complex underlying surfaces;

[0059] The specific work content is: configuring WRF model including high-resolution grid parameters of horizontal resolution, number of nested layers, projection method and vertical layers, wherein the horizontal resolution is set to 1km or less to capture the details of the terrain and surface features; the number of nested layers is flexibly set according to the size and requirements of the research area, generally 2-3 layers, the outer layer covers a large range of background area, and the inner layer focuses on the target farmland shelterbelt area; the projection method selects the geographic coordinate system suitable for the target farmland shelterbelt area to ensure the accuracy of geographic information; the vertical layer configuration considers the atmospheric boundary layer and the structure of the troposphere, generally set to 30-50 layers, at the same time, define the simulation area boundary to ensure covering the target area and its surrounding influence range, the time step is adjusted according to the Courant-Friedrichs-Lewy condition, which is tens of seconds to minutes, the physical process options include boundary layer scheme, microphysical scheme or cumulus parameterization, select the appropriate scheme according to the target farmland shelterbelt area and regional climate characteristics, ensure that the data format and resolution are consistent with the input requirements of Noah-MP model when generating terrain and land use data, complete the model initialization; activate the Noah-MP land surface process model in the WRF model, set 4 layers when configuring soil layering to describe the thermal and moisture properties of soil; vegetation parameter configuration includes leaf area index and root depth, which directly affects the evapotranspiration and soil moisture absorption process of vegetation; the selection of surface heat flux scheme considers the complexity of surface energy balance to ensure that the model can accurately simulate the energy exchange between the surface and the atmosphere, adjust the coupling time step and data exchange frequency to ensure the bidirectional feedback between thermal and dynamic processes, after the model interface compilation and linking, the model can be coupled and simulated; execute WRF-Noah-MP coupled simulation, output variables including surface temperature, sensible and latent heat fluxes and soil moisture, reflecting the energy and water exchange processes between the surface and the atmosphere, and extract the thermal and dynamic characteristics of different underlying surfaces through post-processing tools to analyze their spatial and temporal differences and driving mechanisms, verify the model's simulation capability for complex underlying surfaces, at the same time, compare with observation data to evaluate the accuracy of the model;

[0060] S4, input the classification data of the refined underlying surface classification system into the WRF model, so that each type of underlying surface type corresponds to the WRF grid cell in space, convert the classification data of the refined underlying surface classification system into a format recognizable by the WRF model, ensure that the geographic coordinates and projection method of the data are consistent with the WRF model, for subsequent processing, and spatially match the converted underlying surface classification data with the grid cells of the WRF model, accurately correspond each type of underlying surface type to the grid cells of the WRF model through GIS tools, ensure the spatial consistency of the data, and import the matched underlying surface classification data into the WRF model, specify the path of the underlying surface classification data in the configuration file of the WRF model, complete the loading of the data;

[0061] The specific work content is: converting the classification data of the refined underlying surface classification system into a format recognizable by the WRF model, formatting the original classification data, ensuring that the geographic coordinates and projection method are completely consistent with the geographic information framework adopted by the WRF model, through GIS tools, coordinate conversion and projection correction of the classification data, make it meet the geographic information requirements of the WRF model, including adjustment of data accuracy, to meet the demand of WRF model for high resolution data; after completing data format conversion and geographic information consistency check, spatial matching of the converted underlying surface classification data and the grid unit of the WRF model, using the spatial analysis function of GIS tools, accurately corresponding each type of underlying surface to the grid unit of the WRF model, ensuring that each underlying surface type can be accurately mapped to the corresponding grid unit, through spatial matching, realizing one-to-one correspondence between underlying surface classification data and WRF model grid unit, so as to ensure the accuracy and consistency of data in spatial distribution; import the matched underlying surface classification data into the WRF model, specify the path of the underlying surface classification data in the configuration file of the WRF model, accurately edit the configuration file of the WRF model, ensure that all related parameters are set correctly, through correct configuration file setting, the WRF model reads the underlying surface classification data and applies it to the simulation process;

[0062] S5, using WRF model to simulate the movement of air flow under high resolution terrain, analyze the disturbance effect of terrain undulation on air flow lifting, compression and turning, collect and preprocess high precision terrain data of target farmland shelterbelt area, ensure that its resolution and coverage meet the simulation requirements, configure terrain data in WRF model, set corresponding horizontal resolution and vertical layer number to accurately capture terrain details, run WRF model to simulate the movement process of air flow under high resolution terrain, through wind field data output by the model, analyze the disturbance effect of terrain undulation on air flow lifting, compression and turning, and reveal the influence mechanism of terrain on air flow movement;

[0063] In addition, the process of revealing the mechanism of the influence of terrain on airflow movement is: embedding the preprocessed high-resolution terrain data in the WRF model, mapping the terrain elevation and land use data to the model grid through the geogrid program, configuring the horizontal resolution (≤1km) and the number of vertical layers (30-50 layers), ensuring that the vertical layers are encrypted near the ground to capture the boundary layer dynamics, and adjusting the physical parameters (such as the boundary layer scheme, the microphysical scheme) to adapt to the terrain, generating the initial field and boundary condition file; start the WRF model for numerical integration, simulate the three-dimensional movement process of airflow under high-resolution terrain, calculate the variables of wind field and geopotential height by solving Navier-Stokes equation combined with terrain forcing term, output three-dimensional wind field data at fixed time step, cover the target area and surrounding buffer zone, ensure the spatial and temporal continuity of the data; based on the wind field data output by WRF, calculate the terrain slope and curvature, analyze its correlation with vertical velocity (lifting), horizontal wind speed (compression) and vorticity (turning), quantify the disturbance intensity and spatial distribution of terrain undulation to airflow through visualization means of vertical profile and horizontal wind field, combined with dynamics theory, reveal the physical mechanism of terrain affecting airflow movement path and speed by changing pressure gradient force and turbulent mixing;

[0064] The specific work content is: collect high-precision terrain data of the target farmland shelterbelt area, including digital elevation model (DEM) and space shuttle radar topography mission (SRTM) data, ensure that the data resolution and coverage meet the simulation requirements, preprocess the collected terrain data, including geographic registration, cropping and splicing, to ensure the accuracy and integrity of the data, geographic registration is used to determine the accurate position of terrain data in geographic space, cropping operation focuses on the target area, removes the redundant part, splicing ensures the continuity of the data; configure the preprocessed terrain data in the WRF model, set the corresponding horizontal resolution and vertical layer number to accurately capture the terrain details, set the horizontal resolution to 1km or less to ensure that the details of the terrain and surface features can be captured, set the vertical layer number to 30-50 layers considering the atmospheric boundary layer and troposphere structure, through fine grid configuration, the WRF model simulates the movement process of airflow under complex terrain; run the WRF model to simulate the movement process of airflow under high-resolution terrain, analyze the disturbance effect of terrain undulation on airflow lifting, compression and turning through the wind field data output by the model, the undulation of terrain will change the movement path and speed of airflow, produce lifting, compression and turning phenomenon, reveal the influence mechanism of terrain on airflow movement;

[0065] S6, run the WRF-Noah-MP coupled model, and simulate the influence of different underlying surface types on airflow movement in parallel, obtain wind speed and wind direction wind protection effect parameters;

[0066] S7, based on the output results of the WRF-Noah-MP coupling model, evaluating the windproof effect of the target farmland shelterbelt area to propose improvement suggestions.

[0067] Embodiment 2, as shown in Figure 1 、 Figure 2 on the basis of embodiment 1, the application provides a technical solution: S6 specifically includes: modifying the WRF namelist.input to enable the Noah-MP land surface scheme, setting multiple underlying surface types (farmland / forest land / bare land), configuring MPI parallel parameters (number of nodes, number of threads), compiling the coupling model and verifying the parallel efficiency, designing a control experiment, running different underlying surface scenarios respectively, fixing meteorological driving data, solving the airflow equation dynamically by WRF, outputting high spatiotemporal resolution three-dimensional wind field (U / V / W) and underlying surface characteristic variables to the specified directory, calculating the average wind speed attenuation rate, wind direction deflection angle and turbulence intensity of each underlying surface, and statistically analyzing the spatial distribution of windproof effect parameters, combining with the dynamic diagnosis to analyze the inhibition mechanism of underlying surface roughness on airflow;

[0068] In addition, the process of analyzing the inhibition mechanism of underlying surface roughness on airflow is: using a three-dimensional ultrasonic anemometer and a wind tower gradient observation system to synchronously collect wind speed, wind direction, temperature and humidity data at a height of 10m above different underlying surfaces, with a sampling frequency of ≥10Hz and a continuous observation time of ≥72 hours, performing quality control on the original data, removing outliers and compensating for instrument errors, calculating 10-minute average wind speed, wind direction and turbulence fluctuation velocity at each measurement point, based on the logarithmic wind profile model to retrieve the surface roughness, combining with satellite remote sensing images to extract the underlying surface type distribution, constructing a spatial database, according to the wind speed calculation method of land boundary, calculating the average wind speed at the height of 1.5m of the wind inlet and outlet boundary, calculating the wind speed attenuation rate and wind direction deflection angle, wherein the turbulence intensity is determined by the ratio of the standard deviation of the fluctuation velocity to the average wind speed, using the Kriging interpolation method to generate spatial distribution maps of each parameter, combining with GIS technology to divide high / medium / low attenuation areas, deflection angle gradient zones and turbulence intensity zones, analyzing the spatial coupling relationship between parameters and underlying surface types, based on fluid mechanics theory, establishing a numerical model of the relationship between underlying surface roughness and airflow resistance, quantifying the inhibition effect of roughness element height and density on momentum transfer, verifying the accuracy of the model through wind tunnel test, and diagnosing the physical process of turbulence generation mechanism and wind speed attenuation under different roughness.

[0069] The specific work content is: in the WRF model, modify the namelist.input file to enable the Noah-MP land surface scheme, activate the multi-underlying surface type function by setting the sf_surface_physics parameter to 4, and define the spatial distribution proportion of farmland, forest land and bare land in the landusef field, configure MPI parallel parameters, set nodes and tasks_per_node according to the cluster resources, combine OpenMP thread number to optimize hybrid parallel efficiency, link NETCDF and MPI library during compilation, and enable-O3 optimization option to improve computing performance, after completing the compilation, verify the parallel efficiency through strong scaling test, ensure that the speedup ratio is close to linear growth when increasing the number of nodes, monitor memory occupation and communication overhead, and adjust task allocation strategy to reduce load imbalance; Design three sets of control experiments to simulate pure farmland, pure forest land and pure bare land underlying surface scenes, analyze meteorological driving data (temperature, humidity and wind pressure), enable dynamic core solution Navier-Stokes equation when running WRF, output high spatio-temporal resolution (1km / 10min) three-dimensional wind field (U / V / W component) and underlying surface characteristic variables (surface temperature, vegetation height and roughness length), specify the output path through the history_out parameter, store in NETCDF4 format, and set interval_seconds to control the output frequency, ensure that the initial field and boundary conditions are consistent in the experiment, only modify the LANDUSEF array to isolate the influence of underlying surface, and record the model running time and iteration steps; Based on the output data, calculate the average wind speed attenuation rate of each underlying surface (through linear fitting of the vertical height wind speed profile), wind direction deflection angle (the angle between the horizontal wind vector and the initial direction) and turbulence intensity (the ratio of standard deviation to average wind speed), use GIS tools to calculate the spatial distribution of wind protection effect parameters, identify high attenuation rate and strong turbulence area, and analyze the inhibition mechanism of roughness to air flow: by calculating the momentum flux and surface resistance coefficient, quantifying the loss of wind speed caused by underlying surface friction; Analyze the vertical gradient of vertical turbulent mixing coefficient to reveal the balance between turbulence generation and dissipation caused by roughness elements, and form the quantitative relationship between underlying surface type and wind protection effect;

[0070] S7 specifically includes: extracting the 10m height wind speed, wind direction, turbulent kinetic energy and surface roughness data of the target shelterbelt area from the output of the WRF-Noah-MP coupling model, performing spatio-temporal matching and outlier rejection according to the observation period (≥72h), unifying the coordinate system to the shelterbelt layout projection, calculating the wind speed attenuation rate and turbulent intensity of the upwind direction (500m outside) and the downwind direction (within the forest network) of the shelterbelt, combining the roughness retrieval results, analyzing the spatial coupling relationship between the forest belt density, orientation and wind protection efficiency, based on the wind protection efficiency threshold (≥0.3) and the requirement of turbulent suppression, identifying the areas with weak wind protection effect, and putting forward targeted improvement suggestions, including optimizing the layout of forest belts and increasing vegetation coverage, to improve the wind protection capacity of the target area and improve the ecological environment;

[0071] The specific work content is: from the output results of the WRF-Noah-MP coupling model, accurately extract the 10m height wind speed, wind direction, turbulent kinetic energy and surface roughness data of the target shelterbelt area within the observation period (≥72h), use the post-processing tool provided by the model, and perform spatial clipping according to the shelterbelt boundary vector file, only keep the data of the target farmland shelterbelt area, perform spatio-temporal matching on the extracted data to ensure that different variables correspond to the same time step and spatial grid, use the statistical distribution-based method to remove outliers, calculate the mean and standard deviation of the wind speed data, and remove data points outside the range of mean ±3 times standard deviation, and at the same time, unify the data coordinate system to the shelterbelt layout projection to ensure the spatial consistency of subsequent analysis; based on the preprocessed data, determine the representative areas of the upwind direction (500m outside) and the downwind direction (within the forest network) of the shelterbelt respectively, calculate the wind speed attenuation rate of the upwind direction and the downwind direction, and obtain the turbulent intensity by comparing the difference between the 10m height average wind speed of the two areas and the ratio of the upwind wind speed, and further analyze the spatial coupling relationship between the forest belt density, orientation and wind protection efficiency combined with the surface roughness retrieval results; according to the wind protection efficiency threshold (≥0.3) and the requirement of turbulent suppression, comprehensively evaluate the wind protection effect of the target shelterbelt area, identify the areas with weak wind protection effect through spatial analysis method, which are characterized by low wind speed attenuation rate or ineffective suppression of turbulent intensity, and put forward targeted improvement suggestions for the identified weak areas, including adjusting the orientation and spacing of the forest belt according to the wind direction frequency and terrain conditions to make the forest belt more effectively block and weaken the wind, and increasing the appropriate vegetation types and density to improve the surface roughness and further enhance the wind protection capacity, through the implementation of these improvement measures, improve the wind protection capacity of the target area, improve the ecological environment, and realize the sustainable development of the shelterbelt.

[0072] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it is intended to be limited only by the words recited in the appended claims. It is to be understood that the terms "including", "comprising", "consisting" and variations thereof do not preclude the addition of further integers to the claimed combination of integers. It is to be understood that the terms "including", "comprising", "consisting" and variations thereof encompass the various features of the embodiments described herein, and are not restricted to the use of only the most preferred embodiments. It is further to be understood that the use of relational terms such as first and second, and the like, do not denote a physical or logical order or relationship among the various elements, but are used simply for distinguishing between various elements for clarity. It is to be understood that the terms "comprising", "including", and "having" and variations thereof are intended to be equivalent and open-ended, and include the various embodiments of the present application as recited in the claims. It is to be understood that the terms "including", "comprising", "consisting" and variations thereof encompass the various features of the embodiments described herein, and are not restricted to the use of only the most preferred embodiments. It is further to be understood that the use of relational terms such as first and second, and the like, do not denote a physical or logical order or relationship among the various elements, but are used simply for distinguishing between various elements for clarity. It is to be understood that the terms "comprising", "including", and "having" and variations thereof are intended to be equivalent and open-ended, and include the various embodiments of the present application as recited in the claims.

[0073] While the embodiments of the application have been shown and described herein, it is understood that modifications, substitutions, changes, and alterations can be made by those skilled in the art without departing from the spirit of the present application, which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model, characterized in that, Includes the following steps: S1. Collect and preprocess high-precision topographic data of the target farmland shelterbelt area, and combine remote sensing image interpretation to obtain spatial distribution information of farmland shelterbelts; S2. Based on topographic data and spatial distribution information, analyze topographic relief and surface cover characteristics, and construct a refined underlying surface classification system; S3. Configure the high-resolution mesh parameters for the WRF mode, couple the Noah-MP land surface process model, and obtain the WRF-Noah-MP coupled model, specifically including: Configure the high-resolution mesh parameters for WRF mode, including horizontal resolution, number of nested layers, projection method and number of vertical layers, and define simulation area boundaries, time step and physical process options to generate terrain and land use data; Activate the Noah-MP land surface process model in WRF mode, configure soil stratification, vegetation parameters and surface heat flux scheme, adjust coupling time step and data exchange frequency, and complete model interface compilation and linking. We performed a WRF-Noah-MP coupled simulation, outputting variables such as surface temperature, sensible / latent heat flux, and soil moisture. We then used post-processing tools to extract the thermal and dynamic characteristics of different underlying surfaces and analyzed their spatiotemporal differences and driving mechanisms. S4. Input the classification data of the refined underlying surface classification system into the WRF mode so that each type of underlying surface corresponds to a WRF grid cell in space; S5. Use the WRF model to simulate the movement of airflow under high-resolution terrain and analyze the disturbance effect of terrain undulation on airflow. S6. Run the WRF-Noah-MP coupled model to simulate the effects of different underlying surface types on airflow motion in parallel, and obtain windbreak effect parameters including wind speed and wind direction; S7. Based on the output results of the WRF-Noah-MP coupled model, evaluate the windbreak effect of the target farmland shelterbelt area and propose improvement suggestions.

2. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 1, characterized in that: S1 specifically includes: For the target farmland shelterbelt area, high-precision topographic data was collected. At the same time, multi-source remote sensing images of the target farmland shelterbelt area were obtained by using remote sensing image interpretation technology. The collected topographic data is georeferenced, cropped, and stitched together, and atmospheric and geometric corrections are performed on the multi-source remote sensing images. Using preprocessed multi-source remote sensing images and combined with topographic data, the spatial distribution of farmland shelterbelts is interpreted, and the location, extent, and structural information of the shelterbelts are extracted to generate a shelterbelt distribution map.

3. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 1, characterized in that: S2 specifically includes: Using GIS tools, slope, aspect, and undulation analysis were performed on the terrain data to quantify the terrain undulation characteristics of the target farmland shelterbelt area; By combining multi-source remote sensing images and topographic data, image classification technology is used to extract land cover information and identify underlying surface types including farmland, woodland and bare land. By integrating topographic relief features and land cover information, multiple types of underlying surfaces are classified, their spatial distribution is clarified, a refined underlying surface classification system is constructed, and classification noise is eliminated through morphological filtering. Misclassified areas are corrected by combining field sample data, and a refined underlying surface distribution map is output, clarifying the spatial proportion and boundary accuracy of each type of underlying surface.

4. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 1, characterized in that: S4 specifically includes: The classification data of the refined underlying surface classification system is converted into a format recognizable by the WRF model, and the converted underlying surface classification data is spatially matched with the grid cells of the WRF model. Using GIS tools, each type of underlying surface is accurately mapped to the grid cells of the WRF model. Import the matched underlying surface classification data into the WRF mode. Specify the path to the underlying surface classification data in the WRF mode configuration file to complete the data loading.

5. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 1, characterized in that: S5 specifically includes: Collect and preprocess high-precision terrain data of the target farmland shelterbelt area, configure the terrain data in WRF mode, and set the corresponding horizontal resolution and vertical layer number; By running the WRF model to simulate the movement of airflow under high-resolution terrain, and analyzing the disturbance effects of terrain undulation on the lifting, compression and turning of airflow through the wind field data output by the model, the mechanism of terrain influence on airflow movement is revealed.

6. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 5, characterized in that: The process of revealing the mechanism by which topography affects airflow is as follows: Preprocessed high-resolution terrain data is embedded in the WRF model. The terrain elevation and land use data are mapped to the model grid using the geogrid program. The horizontal resolution and vertical number of layers are configured, and the physical parameters are adjusted to adapt to the terrain to generate the initial field and boundary condition files. The WRF mode is started to perform numerical integration to simulate the three-dimensional motion of airflow under high-resolution terrain. By solving the Navier-Stokes equations and combining the terrain forcing term, the variables of wind field and geopotential height are calculated, and three-dimensional wind field data with timed step sizes are output, covering the target area and the surrounding buffer zone. Based on wind field data output by WRF, we calculate terrain slope and curvature, analyze their correlation with vertical velocity, horizontal wind speed and vorticity, and quantify the disturbance intensity and spatial distribution of airflow caused by terrain undulation through visualization methods such as vertical profile map and horizontal wind field map. Combined with dynamic theory, we reveal the physical mechanism by which terrain affects the airflow path and velocity by changing pressure gradient force and turbulent mixing.

7. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 1, characterized in that: S6 specifically includes: Modify WRF namelist.input to enable the Noah-MP land surface scheme, set multiple underlying surface types, configure MPI parallel parameters, compile the coupling model and verify the parallel efficiency; Design a control experiment, run different underlying surface scenarios, fix meteorological driving data, dynamically solve the airflow equation through WRF, and output the high spatiotemporal resolution three-dimensional wind field and underlying surface characteristic variables to a specified directory. Calculate the average wind speed attenuation rate, wind direction deflection angle, and turbulence intensity of each underlying surface, statistically analyze the spatial distribution of windbreak effect parameters, and combine dynamic diagnostic analysis to understand the suppression mechanism of airflow by underlying surface roughness.

8. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 7, characterized in that: The process of analyzing the mechanism by which surface roughness inhibits airflow is as follows: A three-dimensional ultrasonic anemometer and a wind tower gradient observation system were used to simultaneously collect wind speed, wind direction, temperature and humidity data at a height of 10m on different underlying surfaces. The sampling frequency was ≥10Hz and the continuous observation time was ≥72 hours. The raw data were quality controlled, outliers were removed and instrument errors were compensated. The 10-minute average wind speed, wind direction and turbulent pulse velocity of each measuring point were calculated. The surface roughness was inverted based on the logarithmic wind profile model. The distribution of underlying surface types was extracted by combining satellite remote sensing images and a spatial database was constructed. Based on the method for calculating wind speed at the site boundary, the average wind speed at a height of 1.5m at the inlet and outlet boundaries is statistically analyzed. The wind speed attenuation rate and wind direction deflection angle are calculated. Turbulence intensity is determined by the ratio of the standard deviation of the fluctuating velocity to the average wind speed. Kriging interpolation is used to generate spatial distribution maps of each parameter. Combined with GIS technology, high / medium / low attenuation zones, deflection angle gradient zones, and turbulence intensity zones are divided. The spatial coupling relationship between parameters and underlying surface type is analyzed. Based on fluid mechanics theory, a numerical model of the relationship between surface roughness and airflow resistance is established. The inhibitory effects of roughness element height and density on momentum transport are quantified. The accuracy of the model is verified through wind tunnel tests, and the physical processes of turbulence generation and wind speed attenuation under different roughness conditions are diagnosed.

9. The method for evaluating the windbreak effect of farmland shelterbelts based on a land-atmosphere coupling model according to claim 8, characterized in that: Specifically, S7 includes: The wind speed, wind direction, turbulent kinetic energy and surface roughness data at a height of 10m in the target shelterbelt area were extracted from the output of the WRF-Noah-MP coupled model. Spatiotemporal matching and outlier removal were performed according to the observation period, and the coordinate system was unified to the shelterbelt layout projection. Calculate the wind speed attenuation rate and turbulence intensity in the upwind and downwind directions of the shelterbelt, and analyze the spatial coupling relationship between the density, orientation and windbreak effectiveness of the shelterbelt inversion results. Based on the windbreak efficiency threshold and turbulence suppression requirements, areas with weak windbreak effects were identified, and targeted improvement suggestions were proposed, including optimizing the forest belt layout and increasing vegetation cover.

Images