A method for extracting spatiotemporal continuity features of wind and water erosion in the black soil region of Northeast China

Abstract

This invention discloses a method for extracting the spatiotemporal continuity features of wind and water erosion in the Northeast Black Soil Region. Through analysis of historical meteorological data, it summarizes patterns and divides the annual soil erosion period in the Northeast Black Soil Region into four time nodes: the start and end times of the wind-dominated period I, and the start and end times of the water-dominated period. Using meteorological data and cluster analysis techniques, it systematically reveals the spatiotemporal continuity features of wind and water erosion in the Black Soil Region. This invention provides important theoretical support and practical tools for soil protection and sustainable development in the Northeast Black Soil Region. Simultaneously, this method also provides a referable approach and framework for similar soil erosion studies in other regions. By integrating meteorological data and cluster analysis techniques, this invention strives to comprehensively and systematically reveal the spatiotemporal characteristics of soil erosion in the Black Soil Region, providing strong support for ecological environmental protection and sustainable agricultural development.

Description

Technical Field

[0001] This invention relates to the technical field of soil erosion, and more specifically, to a method for extracting the spatiotemporal continuity features of wind and water erosion in the Northeast black soil region. Background Technology

[0002] The black soil region, due to its fertile soil characteristics, has become an important agricultural production base in my country. However, with the aggravation of climate change, improper land use, and natural disasters, soil erosion in this region has become increasingly serious, severely impacting agricultural production and the sustainability of the ecological environment. Therefore, systematically analyzing the spatiotemporal characteristics of soil erosion has become an important topic in scientific research and management practice.

[0003] Soil erosion in the Northeast Black Soil Region is primarily influenced by both wind and water forces. Wind erosion is particularly pronounced under drought and strong wind conditions, while water erosion often occurs after heavy rainfall events. To effectively address these erosion problems, a deep understanding of the spatiotemporal dynamics of these two erosion mechanisms is essential. Currently, there is no existing technology for extracting the spatiotemporal continuity features of wind and water erosion in the Northeast Black Soil Region. Therefore, this invention proposes a systematic feature extraction method, aiming to identify and analyze soil erosion phenomena in this region through scientific data analysis. Summary of the Invention

[0004] To address the aforementioned technical problems in related technologies, this invention provides a method for extracting the spatiotemporal continuity features of wind and water erosion in the Northeast black soil region, which can solve the above problems.

[0005] To achieve the above-mentioned technical objectives, the technical solution of the present invention is implemented as follows:

[0006] A method for extracting spatiotemporal continuity features of wind and water erosion in the black soil region of Northeast China includes the following steps:

[0007] S1. Divide a year into four time points:

[0008] Date1: Start time of the wind-dominant period I

[0009] Date2: End time of wind-dominant period I.

[0010] Date3: Start time of Wind Dominance Period II.

[0011] Date4: End time of Wind Dominance Period II;

[0012] S2. Determine the scope of the black soil region study area and prepare meteorological station data within the study area, and obtain multi-year daily precipitation data and multi-year daily maximum wind speed data for each meteorological observation station.

[0013] S3. Iterate through the daily precipitation and maximum wind speed data of each station over many years, and find the start and end times of the wind-dominant period I and the start and end times of the wind-dominant period II for the corresponding meteorological station according to specific conditions.

[0014] S4. Obtain four sets of different meteorological stations and the average date data of the meteorological stations from step S3. Cluster each set of data and divide it into five categories.

[0015] S5. Re-cluster the four clustering results obtained in step S4 and the latitude and longitude coordinates of each meteorological station, totaling six dimensions of data, and divide them into five categories.

[0016] S6. Perform spatial interpolation on the six-dimensional data clustering results obtained in step S5, reclassify them into five categories, and recalculate the average dates of the start and end times of the wind-dominant period I and the start and end times of the wind-dominant period II according to the five reclassified categories.

[0017] Furthermore, the end time of wind-dominant period I in step S1 is also the start time of water-dominant period, and the start time of wind-dominant period II is also the end time of water-dominant period.

[0018] Furthermore, "many years" in step S2 refers to twenty years or more.

[0019] Furthermore, in step S3, the daily precipitation and maximum wind speed data for each station over many years are traversed, and according to a specific condition (a period of 6 days), the following are searched:

[0020] Find the last day in February or March when precipitation is greater than 0 mm, and calculate the multi-year average date for each station, in the format: station id, xx month xx day;

[0021] Find the last day with rainfall greater than 12mm from June to October, and calculate the multi-year average date for each station, in the format: station id, xx month xx day;

[0022] Find the last day from March to July when the maximum wind speed is greater than 8 m / s, and calculate the multi-year average date for each station, in the format: station id, xx month xx day;

[0023] Find the last day in September or October when the maximum wind speed is greater than 8 m / s, and calculate the multi-year average date for each station, in the format: station id, xx month xx day.

[0024] Further, step S4 specifically involves obtaining four groups of different meteorological stations and their average date data from step S3. The average date of each group of meteorological stations is then converted to the xth day of the year. The converted date data for each group is then clustered using the k-means clustering method, with each group divided into 5 classes.

[0025] Furthermore, step S5 employs the k-means clustering method.

[0026] Further, step S6 specifically involves: importing the six-dimensional data clustering results obtained in step S5 into ArcGIS software in the form of an attribute table, linking the meteorological station attribute table, performing spatial interpolation, and reclassifying the data into five categories to obtain a polygon shapefile with five categories. The attributes of the polygons are the categories. Based on the range of each category of the polygons containing all meteorological stations, the categories of the meteorological stations are redefined. Finally, based on the categories of the meteorological stations, the average dates of Date1, Date2, Date3, and Date4 are calculated respectively, ultimately obtaining the spatiotemporal continuity characteristics of wind and water erosion in the Northeast Black Soil Region.

[0027] The beneficial effects of this invention are as follows: This invention provides important theoretical support and practical tools for soil protection and sustainable development in the Northeast Black Soil Region. At the same time, this method also provides a reference for similar soil erosion studies in other regions. By integrating meteorological data and cluster analysis technology, this invention strives to comprehensively and systematically reveal the spatiotemporal characteristics of soil erosion in the Black Soil Region, providing strong support for ecological environmental protection and sustainable agricultural development. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in 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.

[0029] The present invention will now be described in further detail with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a method for extracting spatiotemporal continuity features of wind and water erosion in the black soil region of Northeast China, as described in an embodiment of the present invention.

[0031] Figure 2 This is a map showing the extent of the Northeast Black Soil Region in this embodiment of the invention;

[0032] Figure 3 This is a spatial distribution map of meteorological stations in an embodiment of the present invention;

[0033] Figure 4 This is a clustering diagram of the day of year for four meteorological stations in this embodiment of the invention;

[0034] Figure 5 It is a polygon shape diagram obtained by clustering six-dimensional data in the embodiments of the present invention. Detailed Implementation

[0035] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0036] like Figures 1-5 As shown, this invention discloses a method for extracting spatiotemporal continuity features of wind and water erosion in the Northeast black soil region, comprising the following steps:

[0037] S1. Divide a year into four time points:

[0038] Date1: Start time of the wind-dominant period I

[0039] Date2: End time of wind-dominated period I (start time of water-dominated period).

[0040] Date3: Start time of wind-dominant period II (end time of water-dominant period).

[0041] Date4: End time of Wind Dominance Period II;

[0042] S2. Determine the scope of the black soil region study area and prepare meteorological station data within the study area. Obtain multi-year daily precipitation data and multi-year daily maximum wind speed data for each meteorological observation station, where "multi-year" refers to twenty years or more.

[0043] S3. Iterate through the multi-year daily precipitation and maximum wind speed data for each station, and according to specific conditions, find the start and end times of the dominant wind period I and the start and end times of the dominant wind period II for the corresponding meteorological station. The specific conditions refer to searching within a 6-day period.

[0044] Find the last day in February or March when precipitation is greater than 0 mm, and calculate the multi-year average date (Date1) for each station, in the format: station id, xx month xx day.

[0045] Find the last day with rainfall greater than 12mm from June to October, and calculate the multi-year average date (Date2) for each station, in the format: station id, month xx day.

[0046] Find the last day from March to July when the maximum wind speed exceeds 8 m / s, and calculate the multi-year average date (Date3) for each station, in the format: station id, month xx day.

[0047] Find the last day in September and October when the maximum wind speed exceeds 8 m / s, and calculate the multi-year average date (Date4) for each station, in the format: station id, month xx day.

[0048] A 6-day cycle refers to the number of days used to calculate the average value of meteorological elements. For example, the last day when the precipitation from June to October is greater than 12 mm is the last day of the last cycle within the range of months where the average precipitation over a 6-day cycle is greater than 12 mm.

[0049] S4. Obtain four groups of different meteorological stations and their average date data from step S3. Convert the average date ("month and day") of each group of meteorological stations into the xth day of the year (day of year, range 1-366). Then, cluster the day of year for each group using the k-means clustering method, dividing each group into 5 classes.

[0050] S5. The four clustering results obtained in step S4, along with the latitude and longitude coordinates of each meteorological station, totaling six dimensions of data, are re-clustered using the k-means clustering method to divide them into five categories.

[0051] S6. Import the six-dimensional data clustering results obtained in step S5 into ArcGIS software as an attribute table, link the meteorological station attribute table, perform spatial interpolation, and reclassify into 5 categories after interpolation to obtain a polygon shapefile of 5 categories. The attributes of the polygons are the categories. Based on the range of each category of the polygons in which all meteorological stations are located, redefine the category of the meteorological station (which of the 5 categories it belongs to). Finally, based on the category of the meteorological station, calculate the average date of Date1, Date2, Date3, and Date4 respectively, and finally obtain the spatiotemporal continuity characteristics of wind and water erosion in the Northeast Black Soil Region.

[0052] Example:

[0053] The spatial range of the Northeast Black Soil Region provided by the Ministry of Water Resources is used as the study area for this invention. Figure 2 As shown.

[0054] Using this as the spatial scope, precipitation and wind speed data were obtained from 210 national-level ground meteorological observation stations, such as... Figure 3 As shown, multi-year daily precipitation data and multi-year daily maximum wind speed data were obtained for each of the 210 meteorological stations.

[0055] Iterate through the multi-year daily precipitation and maximum wind speed data for each station. Using a 6-day period, find the last day of precipitation greater than 0 mm from February to March and calculate the multi-year average date for each station (Date1), in the format: station id, month xx day (same below); find the last day of precipitation greater than 12 mm from June to October and calculate the multi-year average date for each station (Date2); find the last day of maximum wind speed greater than 8 m / s from March to July and calculate the multi-year average date for each station (Date2); find the last day of maximum wind speed greater than 8 m / s from September to October and calculate the multi-year average date for each station (Date4).

[0056] The above yielded four groups of different meteorological stations and their dates. Each group (Date1, Date2, Date3, Date4) was then clustered into five categories, as follows: Figure 4 As shown.

[0057] The four clustering results and the latitude and longitude coordinates of each meteorological station, totaling six dimensions of data, were re-clustered and divided into five categories.

[0058] The obtained six-dimensional data clustering results were spatially interpolated and reclassified into five categories. The start and end times of the wind-dominant period I and the average start and end dates of the water-dominant period were recalculated according to the five reclassified categories. The final results are as follows:

[0059]

[0060] Among them, the category is Figure 5 The categories of polygon shape maps obtained by clustering six-dimensional data.

[0061] In summary, this invention analyzes historical meteorological data, summarizes patterns, and divides the annual soil erosion period in the Northeast Black Soil Region into four time nodes: the start and end times of the wind-dominated period I, and the start and end times of the water-dominated period. Furthermore, it systematically reveals the spatiotemporal continuity characteristics of wind and water erosion in the Black Soil Region through meteorological data and cluster analysis techniques.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for extracting spatiotemporal continuity features of wind and water erosion in the black soil region of Northeast China, characterized in that, Includes the following steps: S1. Divide a year into four time points: Date1: Start time of the wind-dominant period I Date2: End time of wind-dominant period I. Date3: Start time of Wind Dominance Period II. Date4: End time of Wind Dominance Period II; S2. Determine the scope of the black soil region study area and prepare meteorological station data within the study area, and obtain multi-year daily precipitation data and multi-year daily maximum wind speed data for each meteorological observation station. S3. Iterate through the daily precipitation and maximum wind speed data of each station over many years, and find the start and end times of the wind-dominant period I and the start and end times of the wind-dominant period II for the corresponding meteorological station according to specific conditions. In step S3, the daily precipitation and maximum wind speed data for each station over many years are iterated, and according to a specific condition (a period of 6 days), the following are searched: Find the last day in February or March when precipitation is greater than 0 mm, and calculate the multi-year average date for each station, in the format: station id, xx month xx day; Find the last day with rainfall greater than 12mm from June to October, and calculate the multi-year average date for each station, in the format: station id, xx month xx day; Find the last day from March to July when the maximum wind speed is greater than 8 m / s, and calculate the multi-year average date for each station, in the format: station id, xx month xx day; Find the last day in September and October when the maximum wind speed is greater than 8 m / s, and calculate the multi-year average date for each station, in the format: station id, xx month xx day; S4. Obtain four sets of different meteorological stations and the average date data of the meteorological stations from step S3. Cluster each set of data and divide it into five categories. Step S4 specifically involves obtaining four groups of different meteorological stations and their average date data from step S3. The average date of each group of meteorological stations is then converted to the xth day of the year. The converted date data for each group is then clustered using the k-means clustering method, with each group divided into 5 classes. S5. Re-cluster the four clustering results obtained in step S4 and the latitude and longitude coordinates of each meteorological station, totaling six dimensions of data, and divide them into five categories. In step S5, the k-means clustering method is used; S6. Perform spatial interpolation on the six-dimensional data clustering results obtained in step S5, reclassify them into five categories, and recalculate the average dates of the start and end times of the wind-dominant period I and the start and end times of the wind-dominant period II according to the five reclassified categories. Step S6 specifically involves: importing the six-dimensional data clustering results obtained in step S5 into ArcGIS software as an attribute table, linking the meteorological station attribute table, performing spatial interpolation, and reclassifying the data into five categories to obtain a polygon shapefile with five categories. The attributes of the polygons are the categories. Based on the range of each category of the polygons containing all meteorological stations, the categories of meteorological stations are redefined. Finally, based on the categories of meteorological stations, the average dates of Date1, Date2, Date3, and Date4 are calculated respectively, ultimately obtaining the spatiotemporal continuity characteristics of wind and water erosion in the Northeast Black Soil Region.

2. The method for extracting spatiotemporal continuity features of wind and water erosion in the Northeast black soil region according to claim 1, characterized in that, The end time of wind-dominant period I in step S1 is also the start time of water-dominant period, and the start time of wind-dominant period II is also the end time of water-dominant period.

3. The method for extracting spatiotemporal continuity features of wind and water erosion in the black soil region of Northeast China according to claim 1, characterized in that, In step S2, "many years" refers to twenty years or more.

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