Southwest wine grape climate zoning method based on multi-source data

Abstract

The invention discloses a southwest wine grape climate zoning method based on multi-source data, and relates to the technical field of agrometeorological information, and the method comprises the following steps: collecting and preprocessing multi-source basic data; key climate and growth indexes are calculated; spatial interpolation analysis; generating a zoning map; analyzing and verifying the trend; according to the method, by integrating multi-source meteorological data and high-precision topographic data, a more comprehensive southwest wine grape climate zoning index system is established; according to the method, multiple factors such as heat, moisture and terrain are comprehensively considered, the limitation of single climate index analysis is made up, the zoning result can more accurately reflect the difference of microclimate under the complex terrain, a scientific basis with more regional pertinence is provided for planting layout, and the zoning practicability and guidance value are improved.

Description

Technical Field

[0001] This invention belongs to the field of agricultural meteorological information technology, and in particular relates to a climate zoning method for wine grapes in Southwest China based on multi-source data. Background Technology

[0002] The growth, development, and quality formation of wine grapes are strictly dependent on the climate conditions of their growing region. Therefore, scientific climate zoning is fundamental to guiding grape variety selection and industry layout. However, existing climate zoning studies and methods often face challenges in the emerging wine-producing region of Southwest my country, which has extremely complex topography and climate. On the one hand, traditional methods rely heavily on observational data from limited meteorological stations, making it difficult to accurately capture and represent the dramatic microclimate spatial differentiation caused by complex terrains such as mountains and valleys. On the other hand, most zoning focuses on static climate condition analysis, failing to effectively integrate long-term climate change trend information. This results in zoning outcomes that may not reflect the dynamic migration of suitable planting areas under global warming, making it difficult to support the industry's long-term adaptive planning in response to climate change. Therefore, we propose a climate zoning method for wine grapes in Southwest China based on multi-source data. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a climate zoning method for wine grapes in Southwest China based on multi-source data, comprising the following steps: Step S1: Multi-source basic data collection and preprocessing: Collect meteorological data and digital elevation model data from the southwest production area; clean the meteorological data and remove data that is missing for 3 consecutive days or more; perform spatial resampling, stitching, and cropping on the digital elevation model data, and extract terrain parameters. Step S2: Calculation of key climate and growth indicators: Based on the preprocessed meteorological data, calculate the three core indicators: frost-free period, growing season aridity, and growing season accumulated temperature. Among them, the growing season aridity is calculated using a specific formula, and the growing season accumulated temperature is accumulated based on a daily average temperature greater than or equal to 10 degrees Celsius. Step S3: Spatial interpolation analysis: Convert the preprocessed meteorological data and topographic parameters into a specified format and import them into ANUSPLIN software; use the local thin plate spline smoothing method for spatial interpolation to output continuous spatial distribution data of climate elements; the interpolation model used in this method is a specific expression. Step S4: Zoning map generation: Import the spatial interpolation results into ArcGIS software, and after symbolization, overlay analysis, reclassification and map algebra operations, generate the climate suitability zoning map and variety zoning map of Southwest wine grapes in sequence; Step S5: Trend Analysis Verification: Univariate linear regression trend analysis and Mann-Kendall trend test are used to analyze the time change trend of core indicators through corresponding formulas to verify the timeliness of the zoning results.

[0004] Furthermore, step S1 includes the following steps: Step S11: Collect meteorological data and digital elevation model (DEM) data for the Southwest production area. The meteorological data comes from the Southwest Regional Meteorological Center of the China Meteorological Administration and includes daily data from over 470 meteorological stations over the past fifty years. The data includes station latitude and longitude, altitude, daily average temperature, daily maximum temperature, daily minimum temperature, daily precipitation, daily average relative humidity, and sunshine duration. The DEM data comes from data from the U.S. Geological Survey's Space Shuttle Radar Topographic Mapping Mission, with an original resolution of 90 meters, converted to a 1000-meter resolution through spatial resampling.

[0005] Step S12: Data preprocessing. Clean the meteorological data, removing data with three or more consecutive missing days; perform spatial resampling, stitching, and cropping on the DEM data. Use ArcGIS software's spatial analysis tools to extract terrain parameters, including slope, aspect, and topographic relief. Convert the data format using SPSS software to meet the input requirements of ANUSPLIN software.

[0006] Furthermore, step S2 includes the following steps: Step S21: Define and calculate the frost-free period and the accumulated temperature during the growing season; the frost-free period is calculated as the number of days between the last frost in spring when the daily minimum temperature is less than or equal to 0 degrees Celsius and the earliest frost in autumn when the daily minimum temperature is less than or equal to 0 degrees Celsius; the accumulated temperature during the growing season is calculated as the cumulative sum of the daily average temperatures during the growing season that are greater than or equal to 10 degrees Celsius. Step S22: Calculate the growing season aridity; the growing season aridity equals the grape growing season evapotranspiration divided by the precipitation during the same period; wherein, the grape growing season evapotranspiration equals the crop coefficient multiplied by the reference crop evapotranspiration; the crop coefficient is taken as 0.85, and the reference crop evapotranspiration is calculated using the Penman-Monteith formula; Step S23: Divide the data into time periods for analysis; divide the core indicator data of nearly fifty years into five consecutive 10-year periods, calculate the average value of the indicators for each period, and use it to assist in the analysis of climate change trends.

[0007] Furthermore, step S3 includes the following steps: Step S31: Interpolation is performed using the local thin-plate spline smoothing method. This interpolation model expression describes the meteorological element value of the i-th station as equal to the main effects of the meteorological elements at that station plus the effects of covariates, plus the error term; where the covariates are typically topographic parameters. Step S32: Process the data using specific modules of the ANUSPLIN software; use the SPLINA module to interpolate the preprocessed index data and convert it into a suitable format; use the LAPGRD module to output the processed data as continuous spatial distribution data in grd format.

[0008] Furthermore, step S4 includes the following steps: Step S41: Symbolize the frost-free period, growing season aridity, and growing season accumulated temperature data obtained by spatial interpolation; Step S42: After reclassifying the frost-free period layer and the growing season aridity layer respectively, generate a climate suitability zoning map through map algebra operations; Step S43: Overlay the generated climate suitability zoning map with the growing season activity accumulated temperature layer to generate a wine grape variety zoning map.

[0009] Furthermore, step S5 includes the following steps: Step S51: Perform univariate linear regression trend analysis; the analysis formula indicates that the climate index term equals the linear trend term multiplied by the time term plus the constant term; where, ten times the value of the linear trend term is called the 10-year climate trend rate, which is used to quantify the rate of change. Step S52: Perform the Mann-Kendall trend test; calculate the specific statistic S and its variance, and obtain the test value Z according to the formula; determine the trend direction by judging the sign of the Z value: Z greater than 0 indicates that the indicator is on an upward trend, and Z less than 0 indicates that the indicator is on a downward trend.

[0010] The present invention has the following beneficial effects: 1. This invention establishes a more comprehensive climate zoning index system for wine grapes in Southwest China by integrating multi-source meteorological data and high-precision topographic data. It comprehensively considers multiple factors such as heat, moisture and topography, making up for the limitations of single climate index analysis. This allows the zoning results to more accurately reflect the differences in microclimates under complex terrain, providing a more regionally targeted scientific basis for planting layout and enhancing the practicality and guiding value of the zoning.

[0011] 2. This invention combines spatial interpolation technology with trend analysis methods. It utilizes interpolation algorithms to process massive amounts of discrete station data, generating continuous and smooth spatial distribution maps of climate elements, intuitively showcasing the spatial pattern of climate resources. Simultaneously, it introduces time-series trend verification, effectively identifying the long-term direction and stability of key climate indicators. This ensures that the zoning results not only reflect historical and current conditions but also possess the potential for dynamic assessment and prediction of future suitability changes, enhancing the zoning's foresight and adaptability.

[0012] 3. This invention constructs a process from data preprocessing, index calculation, spatial analysis to map generation; the method is clear and highly repeatable, reducing the technical threshold and subjective arbitrariness of climate zoning work; the generated series of zoning maps are intuitive and systematic, making them easy for agricultural decision-makers, growers and researchers to understand and apply, and helping to promote the scientific planning and refined management of the wine grape industry in Southwest China.

[0013] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a climate zoning method for wine grapes in Southwest China based on multi-source data, according to the present invention. Figure 2 This is a structural block diagram of a climate zoning method for wine grapes in Southwest China based on multi-source data, according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments 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.

[0017] Please see Figure 1-2 As shown, this invention is a method for climate zoning of wine grapes in Southwest China based on multi-source data, comprising the following steps: Step S1: Multi-source basic data collection and preprocessing: Collect meteorological data and digital elevation model data from the southwest production area; clean the meteorological data and remove data that is missing for 3 consecutive days or more; perform spatial resampling, stitching, and cropping on the digital elevation model data, and extract terrain parameters. Step S2: Calculation of key climate and growth indicators: Based on the preprocessed meteorological data, calculate the three core indicators: frost-free period, growing season aridity, and growing season accumulated temperature. Among them, the growing season aridity is calculated using a specific formula, and the growing season accumulated temperature is accumulated based on a daily average temperature greater than or equal to 10 degrees Celsius. Step S3: Spatial interpolation analysis: Convert the preprocessed meteorological data and topographic parameters into a specified format and import them into ANUSPLIN software; use the local thin plate spline smoothing method for spatial interpolation to output continuous spatial distribution data of climate elements; the interpolation model used in this method is a specific expression. Step S4: Zoning map generation: Import the spatial interpolation results into ArcGIS software, and after symbolization, overlay analysis, reclassification and map algebra operations, generate the climate suitability zoning map and variety zoning map of Southwest wine grapes in sequence; Step S5: Trend Analysis Verification: Univariate linear regression trend analysis and Mann-Kendall trend test are used to analyze the time change trend of core indicators through corresponding formulas to verify the timeliness of the zoning results.

[0018] Step S1 includes the following steps: Step S11: Collect meteorological data and digital elevation model (DEM) data for the Southwest production area. The meteorological data comes from the Southwest Regional Meteorological Center of the China Meteorological Administration, containing daily data from over 470 meteorological stations over the past fifty years. This data includes station latitude and longitude, altitude, daily average temperature, daily maximum temperature, daily minimum temperature, daily precipitation, daily average relative humidity, and sunshine duration. The DEM data comes from data from the U.S. Geological Survey's Space Shuttle Radar Topographic Mapping mission, with an original resolution of 90 meters, converted to a 1000-meter resolution through spatial resampling.

[0019] Step S12: Data preprocessing. Clean the meteorological data, removing data with three or more consecutive missing days; perform spatial resampling, stitching, and cropping on the DEM data. Use ArcGIS software's spatial analysis tools to extract terrain parameters, including slope, aspect, and topographic relief. Convert the data format using SPSS software to meet the input requirements of ANUSPLIN software.

[0020] Step S2 includes the following steps: Step S21: Define and calculate the frost-free period and the accumulated temperature during the growing season; the frost-free period is calculated as the number of days between the last frost in spring when the daily minimum temperature is less than or equal to 0 degrees Celsius and the earliest frost in autumn when the daily minimum temperature is less than or equal to 0 degrees Celsius; the accumulated temperature during the growing season is calculated as the cumulative sum of the daily average temperatures during the growing season that are greater than or equal to 10 degrees Celsius. Step S22: Calculate the growing season aridity; the growing season aridity equals the grape growing season evapotranspiration divided by the precipitation during the same period; wherein, the grape growing season evapotranspiration equals the crop coefficient multiplied by the reference crop evapotranspiration; the crop coefficient is taken as 0.85, and the reference crop evapotranspiration is calculated using the Penman-Monteith formula; Step S23: Divide the data into time periods for analysis; divide the core indicator data of nearly fifty years into five consecutive 10-year periods, calculate the average value of the indicators for each period, and use it to assist in the analysis of climate change trends.

[0021] Step S3 includes the following steps: Step S31: Interpolation is performed using the local thin-plate spline smoothing method. This interpolation model expression describes the meteorological element value of the i-th station as equal to the main effects of the meteorological elements at that station plus the effects of covariates, plus the error term; where the covariates are typically topographic parameters. Step S32: Process the data using specific modules of the ANUSPLIN software; use the SPLINA module to interpolate the preprocessed index data and convert it into a suitable format; use the LAPGRD module to output the processed data as continuous spatial distribution data in grd format.

[0022] Step S4 includes the following steps: Step S41: Symbolize the frost-free period, growing season aridity, and growing season accumulated temperature data obtained by spatial interpolation; Step S42: After reclassifying the frost-free period layer and the growing season aridity layer respectively, generate a climate suitability zoning map through map algebra operations; Step S43: Overlay the generated climate suitability zoning map with the growing season activity accumulated temperature layer to generate a wine grape variety zoning map.

[0023] Step S5 includes the following steps: Step S51: Perform univariate linear regression trend analysis; the analysis formula indicates that the climate index term equals the linear trend term multiplied by the time term plus the constant term; where, ten times the value of the linear trend term is called the 10-year climate trend rate, which is used to quantify the rate of change. Step S52: Perform the Mann-Kendall trend test; calculate the specific statistic S and its variance, and obtain the test value Z according to the formula; determine the trend direction by judging the sign of the Z value: Z greater than 0 indicates that the indicator is on an upward trend, and Z less than 0 indicates that the indicator is on a downward trend.

[0024] One specific application of this embodiment is: Step S1: Collection and preprocessing of multi-source basic data: Collect meteorological data and digital elevation model (DEM) data from the southwest production area; clean the meteorological data and remove data that is missing for 3 consecutive days or more; perform spatial resampling, stitching and cropping on the DEM data to extract topographic parameters. Step S2, Calculation of key climate and growth indicators: Based on the preprocessed meteorological data, calculate the three core indicators: frost-free period, growing season aridity, and growing season accumulated temperature. Among them, the growing season aridity is calculated, and the growing season accumulated temperature is based on a daily average temperature ≥10℃. Step S3, Spatial Interpolation Analysis: Convert the preprocessed meteorological data and topographic parameters into a specified format, import them into ANUSPLIN software, and perform spatial interpolation using the local thin plate spline smoothing method to output continuous spatial distribution data of climate elements; the interpolation model of ANUSPLIN software uses a specific expression. Step S4, Zoning Map Generation: Import the spatial interpolation results into ArcGIS software, and after symbolization, overlay analysis, reclassification and map algebra operations, generate the Southwest Wine Grape Climate Suitability Zoning Map and Variety Zoning Map in sequence; Step S5, Trend Analysis and Verification: Univariate linear regression trend analysis and Mann-Kendall (MK) trend test are used to analyze the time change trend of core indicators through corresponding formulas to verify the timeliness of the zoning results.

[0025] In step S1, the meteorological data comes from the Southwest Regional Meteorological Center of the China Meteorological Administration, which contains daily data from more than 470 meteorological stations from 1972 to 2021. The data includes the station's latitude and longitude, altitude, daily average temperature, daily maximum temperature, daily minimum temperature, daily precipitation, daily average relative humidity, and sunshine duration. The DEM data comes from the U.S. Geological Survey's (USGS) Space Shuttle Radar Topography Mission (SRTM), with an original resolution of 90m, which was converted to a 1000m resolution through spatial resampling.

[0026] In step S1, terrain parameters are extracted using the spatial analysis tools of ArcGIS software, including slope, aspect, and topographic relief; data format conversion is achieved using SPSS software to make the data conform to the input requirements of ANUSPLIN software.

[0027] In step S2, the frost-free period is calculated as the number of days between the last frost in spring (temperature ≤ 0℃) and the first frost in autumn (temperature ≤ 0℃); the accumulated temperature during the growing season is calculated as the sum of the daily average temperatures ≥ 10℃ during the growing season.

[0028] In step S2, the growing season dryness index (DI) is calculated using the following formula:

[0029] in, ; In the formula, Dryness during the growing season This refers to transpiration during the grape growing season. This is the precipitation for the same period. This is the crop coefficient (value 0.85). The Penman-Monteith formula was used to calculate the crop evapotranspiration as a reference. In step S3, the local thin-plate spline smoothing method model expression used by the ANUSPLIN software is as follows: (in ); In the formula, For the first Meteorological element values ​​for each station, For the first The main effects of meteorological elements at each station, covariates The impact, This is the error term; The ANUSPLIN software uses the SPLINA and LAPGRD modules to process data; the SPLINA module interpolates the preprocessed indicator data into an adapted format, and the LAPGRD module outputs the data in grd format.

[0030] In step S4, the zoning map generation specifically includes: symbolizing the frost-free period, growing season aridity, and growing season active accumulated temperature data; reclassifying the frost-free period and aridity layers and then multiplying and superimposing them using map algebra to generate a climate suitability zoning map; and superimposing the climate suitability zoning map with the active accumulated temperature layer to generate a variety zoning map.

[0031] In step S5, the formula for the univariate linear regression trend analysis is:

[0032] In the formula, For climate indicators, For linear trend terms, For time terms, For constant terms, Ten times the value is the 10-year climate tendency rate; The Mann-Kendall (MK) trend test is calculated using the following formula. Values ​​used to determine trend direction:

[0033] In the formula, It is on an upward trend. It is a downward trend. To test the statistic, for The variance.

[0034] In step S2, the core indicator data is divided into five 10-year periods, and the average value of each period is calculated to analyze climate change trends.

[0035] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0036] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for climate zoning of wine grapes in Southwest China based on multi-source data, characterized in that: Includes the following steps: Step S1: Multi-source basic data collection and preprocessing: Collect meteorological data and digital elevation model data from the southwest production area; clean the meteorological data and remove data that is missing for 3 consecutive days or more. The digital elevation model data is spatially resampled, stitched, and cropped, and terrain parameters are extracted. Step S2: Calculation of key climate and growth indicators: Based on the preprocessed meteorological data, calculate the three core indicators: frost-free period, growing season aridity, and growing season accumulated temperature. Among them, the growing season aridity is calculated using a specific formula, and the growing season accumulated temperature is accumulated based on a daily average temperature greater than or equal to 10 degrees Celsius. Step S3: Spatial interpolation analysis: Convert the preprocessed meteorological data and topographic parameters into a specified format and import them into ANUSPLIN software; use the local thin plate spline smoothing method for spatial interpolation to output continuous spatial distribution data of climate elements; the interpolation model used in this method is a specific expression. Step S4: Zoning map generation: Import the spatial interpolation results into ArcGIS software, and after symbolization, overlay analysis, reclassification and map algebra operations, generate the climate suitability zoning map and variety zoning map of Southwest wine grapes in sequence; Step S5: Trend Analysis Verification: Univariate linear regression trend analysis and Mann-Kendall trend test are used to analyze the time change trend of core indicators through corresponding formulas to verify the timeliness of the zoning results.

2. The method for climate zoning of wine grapes in Southwest China based on multi-source data according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect meteorological data and digital elevation model (DEM) data for the Southwest production area. The meteorological data comes from the Southwest Regional Meteorological Center of the China Meteorological Administration and includes daily data from over 470 meteorological stations over the past fifty years. The data includes station latitude and longitude, altitude, daily average temperature, daily maximum temperature, daily minimum temperature, daily precipitation, daily average relative humidity, and sunshine duration. The DEM data comes from data from the U.S. Geological Survey's Space Shuttle Radar Topographic Mapping Mission, with an original resolution of 90 meters, converted to a 1000-meter resolution through spatial resampling. Step S12: Data preprocessing. Clean the meteorological data, removing data with three or more consecutive missing days; perform spatial resampling, stitching, and cropping on the DEM data. Use ArcGIS software's spatial analysis tools to extract terrain parameters, including slope, aspect, and topographic relief. Convert the data format using SPSS software to meet the input requirements of ANUSPLIN software.

3. The method for climate zoning of wine grapes in Southwest China based on multi-source data according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Define and calculate the frost-free period and the accumulated temperature during the growing season; the frost-free period is calculated as the number of days between the last frost in spring when the daily minimum temperature is less than or equal to 0 degrees Celsius and the earliest frost in autumn when the daily minimum temperature is less than or equal to 0 degrees Celsius; the accumulated temperature during the growing season is calculated as the cumulative sum of the daily average temperatures during the growing season that are greater than or equal to 10 degrees Celsius. Step S22: Calculate the growing season aridity; the growing season aridity equals the grape growing season evapotranspiration divided by the precipitation during the same period; wherein, the grape growing season evapotranspiration equals the crop coefficient multiplied by the reference crop evapotranspiration; the crop coefficient is taken as 0.85, and the reference crop evapotranspiration is calculated using the Penman-Monteith formula; Step S23: Divide the data into time periods for analysis; divide the core indicator data of nearly fifty years into five consecutive 10-year periods, calculate the average value of the indicators for each period, and use it to assist in the analysis of climate change trends.

4. The method for climate zoning of wine grapes in Southwest China based on multi-source data according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Interpolation is performed using the local thin-plate spline smoothing method. This interpolation model expression describes the meteorological element value of the i-th station as equal to the main effects of the meteorological elements at that station plus the effects of covariates, plus the error term; where the covariates are typically topographic parameters. Step S32: Process the data using specific modules of the ANUSPLIN software; use the SPLINA module to interpolate the preprocessed index data and convert it into a suitable format; use the LAPGRD module to output the processed data as continuous spatial distribution data in grd format.

5. The method for climate zoning of wine grapes in Southwest China based on multi-source data according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Symbolize the frost-free period, growing season aridity, and growing season accumulated temperature data obtained by spatial interpolation; Step S42: After reclassifying the frost-free period layer and the growing season aridity layer respectively, generate a climate suitability zoning map through map algebra operations; Step S43: Overlay the generated climate suitability zoning map with the growing season activity accumulated temperature layer to generate a wine grape variety zoning map.

6. The method for climate zoning of wine grapes in Southwest China based on multi-source data according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform univariate linear regression trend analysis; the analysis formula indicates that the climate index term equals the linear trend term multiplied by the time term plus the constant term; where, ten times the value of the linear trend term is called the 10-year climate trend rate, which is used to quantify the rate of change. Step S52: Perform the Mann-Kendall trend test; calculate the specific statistic S and its variance, and obtain the test value Z according to the formula; determine the trend direction by judging the sign of the Z value: Z greater than 0 indicates that the indicator is on an upward trend, and Z less than 0 indicates that the indicator is on a downward trend.

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