Closed watershed hydrological data networked organization method based on ridge line

Abstract

The invention discloses a closed watershed hydrological data networked organization method based on a ridge line, and relates to the technical field of hydrology and geography, and the method comprises the steps: obtaining and collecting DEM data of a region where a target watershed is located, extracting the ridge line through the region DEM data in combination with a GIS, determining a research region boundary according to the ridge line, and delimiting the closed watershed; dEM raster data in the delimited research area is converted into a DEM vector point set in combination with a GIS, and a network topology structure is constructed according to the vector point set; and obtaining and collecting composite hydrological data of the target drainage basin, associating the collected hydrological data with the DEM vector point set according to the space coordinates, and storing the hydrological data into corresponding nodes of the network topology structure. According to the method, the accurate definition of the basin boundary, the networked expression of the topographic structure and the networked organization of the multi-source hydrological data are realized, and an efficient and uniform data basis is provided for the networked simulation of the hydrological process.

Description

Technical Field

[0001] This invention relates to the field of hydrogeography, specifically to a method for organizing hydrological data in a closed watershed based on ridgelines. Background Technology

[0002] Hydrological processes are complex dynamic processes influenced by various factors such as rainfall, topography, soil, and vegetation. With the development of technologies such as Geographic Information Systems (GIS), Remote Sensing (RS), and Digital Elevation Models (DEMs), watershed-scale hydrological simulation is gradually shifting from empirical formulas and analytical models to spatialized and digitalized, refined simulations. Accurate characterization of watershed hydrological features is a crucial prerequisite for simulating watershed hydrological processes.

[0003] Hydrological characteristics reflect the distribution, transformation, and movement patterns of water bodies between the surface and underground within a watershed. These characteristics often require quantification and representation based on multi-source hydrological data. Hydrological data encompasses information on topography, soil, vegetation, and other aspects of the watershed, comprehensively characterizing its hydrological features. Therefore, efficient, unified, and structured organization and management of hydrological data is a key issue supporting the simulation and refined analysis of watershed hydrological processes.

[0004] Furthermore, the delineation of watershed boundaries is crucial in hydrological analysis. Existing studies often rely on administrative boundaries or river flow direction analysis to determine watershed boundaries. However, due to complex terrain or insufficient data resolution, this can easily lead to inaccurate watershed delineation, resulting in interference from external runoff within the study area and affecting the reliability of simulation results. A ridgeline, representing the highest point of a mountain, slopes gradually downwards along its sides, serving as the boundary for drainage directions within the mountain. Using ridgelines to delineate the study area boundary can effectively ensure the hydrological independence of the watershed and improve watershed management efficiency.

[0005] Therefore, defining the research area boundary and delineating the target watershed based on the ridgeline, and combining it with ArcGIS software to organize multi-source hydrological data in a unified manner, can further improve the efficiency of data organization and management, and also provide basic support for numerical simulation of watershed hydrological processes. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a networked organization method for hydrological data in closed watersheds based on ridgelines, so as to achieve efficient, unified, and structured organization and management of hydrological data and improve watershed management efficiency.

[0007] The objective of this invention is achieved through the following technical solution: A method for networked organization of hydrological data in closed watersheds based on ridgelines includes: Obtain and collect DEM data of the target watershed area, extract ridge lines using regional DEM data in conjunction with GIS, determine the boundary of the study area and delineate the closed watershed based on the ridge lines; The DEM raster data within the defined study area is combined with GIS to transform it into DEM vector point sets, and a network topology is constructed based on the vector point sets. Acquire and collect composite hydrological data of the target watershed, associate the collected hydrological data with the DEM vector point set according to spatial coordinates, and store the hydrological data in the corresponding nodes of the network topology.

[0008] Furthermore, the acquisition and collection of DEM data for the target watershed area specifically includes: DEM data was collected from open-source datasets both domestically and internationally, and the original DEM data was obtained in raster format. ArcGIS software was used to preprocess the original DEM data, including projection transformation and geometric clipping: DEM data of flat terrain areas were selected and subjected to data spatial mapping transformation to obtain preprocessed DEM data. The process of performing data spatial mapping transformation on the DEM data of the selected flat terrain areas specifically includes: Query the statistical data values ​​in the data attributes of the DEM data after projection transformation and geometric clipping to obtain the minimum value corresponding to the data band. E Min Maximum value corresponding to the band E Max Band corresponding average value E Mean And record the original value of any pixel in the data as E 1 ; Using a raster calculator, the spatial mapping transformation of the data is calculated to obtain the raster cell values ​​of the DEM data. E 2 The calculation formula is: In the formula: F F is a scaling factor used to assess the magnitude of terrain elevation changes; a larger F value indicates that a gentle area will become steeper.

[0009] Furthermore, the extraction of ridgelines using regional DEM data in conjunction with GIS includes: Based on preprocessed DEM data, positive and negative terrain determination is made by judging the elevation relationship between each raster cell in the DEM data and its neighboring raster cells, thus identifying local highlands and local lowlands in the study area. Based on the preprocessed DEM data, the cumulative flow of each raster cell is calculated; Based on the aforementioned local highlands and areas with zero cumulative runoff, continuous ridgelines are extracted; The extracted ridgeline was pruned.

[0010] Furthermore, the extraction of ridgelines using regional DEM data in conjunction with GIS specifically includes: The preprocessed DEM data is statistically analyzed using the focus statistics tool in the ArcGIS platform. The average elevation of each pixel and its neighborhood is calculated using the average value statistics tool to obtain the DEM data after the first focus statistics. The preprocessed DEM data is then subtracted from the first focus statistics DEM data using the raster calculator to identify raster pixels with local elevations higher than their neighbors, and the difference calculation result is output. The difference calculation result is then classified using a reclassification tool. Areas with a value greater than 0 are assigned a value of 1, representing local highlands, and are output as positive terrain DEM data. Areas with a value less than or equal to 0 are assigned a value of 0, representing local lowlands, and are output as negative terrain DEM data. The depression tool in the ArcGIS platform is used to fill local depressions in the preprocessed DEM data, and the depression-filled DEM data is output. Based on the depression-filled DEM data, the flow direction tool is used to calculate the path of water flow on the terrain surface, and the flow direction data is output. Based on the output flow direction data, the flow rate tool is used to calculate the confluence accumulation of each cell. Areas with high confluence accumulation are confluence areas, while areas with zero confluence accumulation are water source areas or boundary highlands, and the confluence accumulation raster is output. Based on the cumulative flow raster, the raster calculator is used to filter areas with a cumulative flow of 0, and the raster with a cumulative flow of 0 is output. Based on the output raster with a cumulative flow of 0, the focal point statistics tool is used to calculate the average value of the area surrounding each cell, and the cells are reclassified. At the same time, a preset value V is set. Cell values ​​less than the preset value V are assigned 0, indicating insignificant highlands, and cell values ​​greater than or equal to the preset value V are assigned 1, indicating relatively significant highlands. Finally, the DEM data after the second focal point statistics is output. Based on the DEM data after the second focal point statistics, the raster calculator is used to multiply the DEM data after the second focal point statistics with the positive terrain DEM data to eliminate erroneous ridgelines existing in negative terrain. Cell values ​​of areas with a calculated value of 0 are assigned NoData. Finally, only the raster data area with a cell value of 1 remains, which is the area where the ridgeline is located, and it is output as a ridgeline raster. Based on the ridgeline raster, the raster is converted into a polyline feature layer using the raster turning line tool. In the polyline feature layer, non-main branches of continuously extending ridgeline polyline features are visually selected. The non-main branch polyline segments are truncated at the intersection with the main branch and the branches are deleted to obtain the main branch polyline element layer. The main branch polyline element layer contains multiple polyline elements. The merge tool is used to merge multiple polyline elements into a single polyline element. The entire polyline element obtained is recorded as the general boundary.

[0011] Furthermore, after determining the boundary of the study area and delineating the closed watershed based on the ridgeline, the process also includes: Based on the direction of water flow, the boundary of the study area is divided into input boundary, output boundary, and general boundary; The input boundary corresponds to the upstream of the target river boundary in the closed watershed and is used to characterize the inflow of runoff from the external region. The output boundary corresponds to the downstream of the target river boundary in the closed watershed and is used to characterize the outflow of runoff in the study area. The general boundary corresponds to other boundaries in the closed watershed and is determined by the ridgeline; the general boundary does not accept input from external runoff.

[0012] Furthermore, the step of converting DEM raster data within the defined study area into DEM vector point sets using GIS, and constructing a network topology based on the vector point sets, includes: By combining GIS, the raster-format DEM data is converted into a vector point dataset, and projected coordinates are added to the DEM vector point dataset. Traverse the vector point set, and use the resolution of the DEM data and the projection coordinates of the point set as spatial criteria to determine the spatial proximity relationship between each vector point, and generate a source and sink node list; Based on the aforementioned source and sink node list and by calling the graph learning library, a network topology is constructed, and the generated network topology is stored in the database in the form of a sparse matrix.

[0013] Furthermore, the step of converting DEM raster data within the defined study area into DEM vector point sets using GIS, and constructing a network topology based on the vector point sets, specifically includes: The preprocessed DEM data is converted into DEM vector point data using a raster-to-point tool, resulting in a DEM vector point dataset in .shp format. The coordinate system of the DEM vector point data is then converted to CGCS2000 3 Degree GKCM 105E using a projection tool, resulting in a DEM vector point dataset after projection transformation. Projected coordinates are added to the projected DEM vector point dataset, resulting in a DEM vector point dataset with added projected coordinates. Finally, the DEM vector point dataset with added projected coordinates is exported as a table file containing projected coordinates and elevation information, resulting in a DEM vector point coordinate dataset in .xlsx format. A Python script is used to traverse the DEM vector point set. Using the pixel size scale of the preprocessed DEM raster data and the projected coordinates of the DEM vector point coordinate dataset in .xlsx format as criteria, the spatial proximity of each vector point is determined, and source and sink nodes are identified, resulting in a list of source and sink nodes in .xlsx format. The calculation formula for the criteria is: or Where GSD is the resolution of the DEM data. , These are the projected coordinates of any two points in the DEM vector point coordinate dataset; Based on the source and sink node list, the dgl.Graph method in the DGL library is called to generate a graph network, and the network topology is obtained. The network topology is then stored in the database in the form of a sparse matrix.

[0014] Furthermore, the composite hydrological data includes topographic data, soil data, and vegetation data; the topographic data includes digital elevation model data and topographic slope data, the data format of which is raster data; the soil data includes soil sand percentage data, soil silt percentage data, soil clay percentage data, soil median particle size data, and soil type data, the data format of soil sand percentage data, soil silt percentage data, soil clay percentage data, and soil median particle size data is raster data, and the data format of soil type data is vector data; the vegetation data includes vegetation normalized index data, leaf area index data, and vegetation type data, the data format of vegetation normalized index data and leaf area index data is raster data, and the data format of vegetation type data is vector data.

[0015] Furthermore, the step of associating the collected hydrological data with the DEM vector point set based on spatial coordinates and storing the hydrological data in the corresponding nodes of the network topology includes: By combining GIS, composite hydrological data are uniformly converted into hydrological vector surface data, and the projection coordinate system of the hydrological data is unified. By combining GIS, the multi-source hydrological data and the DEM vector point set are associated according to the projected coordinates; The multi-source hydrological data associated with the DEM vector point set is stored in the node corresponding to the network topology.

[0016] Furthermore, the step of associating the collected hydrological data with the DEM vector point set based on spatial coordinates and storing the hydrological data in the corresponding nodes of the network topology specifically includes: The floating-point raster data in the composite hydrological data is converted to integer to obtain integer hydrological raster data. The raster to polygon tool is then used to convert the integer hydrological raster data into vector polygon data to obtain hydrological vector polygon data in .shp format. The coordinate system of the hydrological vector polygon data is then converted to CGCS2000 3 Degree GK CM105E using the projection tool to obtain the hydrological vector polygon data after projection transformation. Using the Add Spatial Connection tool, the hydrological vector surface data after projection transformation and the DEM vector point dataset after projection transformation are spatially connected one by one. The attribute table of the DEM vector point dataset after projection transformation contains the various types of hydrological data information. The attribute table of the DEM vector point dataset after spatial connection and projection transformation was exported as a table file using a table-to-Excel tool, resulting in a hydrological data table file. A Python script was then used to assign the hydrological data table file to the corresponding network nodes, and the integrated networked hydrological data was stored in the database.

[0017] The beneficial effects of this invention are: 1) This invention uses ridgelines to delineate boundaries, ensuring the hydrological enclosure of the research basin, fundamentally avoiding external runoff interference, and improving the accuracy and reliability of hydrological process simulation.

[0018] 2) This invention constructs a network topology based on DEM data, so that the constructed network structure can reflect the terrain features.

[0019] 3) This invention combines GIS spatial connectivity to associate multi-source, heterogeneous hydrological data with corresponding network nodes, achieving a high degree of unity between data and network structure, and providing a solid foundation for efficient and accurate hydrological model calculation. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for organizing hydrological data in a closed watershed based on a ridgeline, according to the present invention. Figure 2 This is a schematic diagram of the ridgeline extraction process described in this invention; Figure 3This is a schematic diagram illustrating the network topology construction described in this invention; Figure 4 This is a schematic diagram of the hydrological data organization process described in this invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0022] See Figures 1-4 The present invention provides a technical solution: like Figure 1 As shown, a method for organizing closed watershed hydrological data based on ridgelines includes: Step 1, Regional Boundary Extraction: Obtain and collect DEM data of the target watershed area, use GIS to extract ridge lines from the regional DEM data, determine the boundary of the study area based on the ridge lines, and delineate the closed watershed; The acquisition and collection of DEM data for the target watershed area specifically includes: DEM data was collected from open-source datasets both domestically and internationally, and the original DEM data was obtained in raster format. ArcGIS software was used to preprocess the original DEM data, including projection transformation and geometric clipping. DEM data of flat terrain areas were selected and subjected to data spatial mapping transformation to obtain preprocessed DEM data. Implementers can collect DEM datasets by purchasing commercial data or downloading domestic and international open-source datasets such as ALOS and Sentinel 1.

[0023] In one specific embodiment, the DEM dataset used is a ".tif" raster dataset. The 12.5m resolution DEM data from the open-source ALOS dataset is used as the raw DEM data. The raw DEM data is then processed in professional GIS software such as GeoScene or ArcGIS Pro.

[0024] The steps for acquiring and importing raw DEM data are as follows: On the EarthData data website, search for the high-resolution terrain layer data of the "ALOS PALSAR High Resolution Radiometric Terrain CorrectedProduct" data product using the polygon drawing project area search method, and click to download all to obtain the raw remote sensing image or image set in ".tif" format; Based on the acquired raw remote sensing image or image set, in the ArcGIS Pro software interface, select Add Data, select the dataset within the required project area under the file path of the storage medium, and import it into the ArcGIS-GDB database to obtain the data.

[0025] In one specific embodiment, the data preprocessing of the original DEM data using ArcGIS software includes projection transformation and geometric clipping, specifically comprising: In ArcGIS Pro, create a new map window and add the DEM layer to the new map window. Using the "Define Projection" tool, transform the "Spatial Reference" of the remote sensing image layer in the original DEM data to obtain the projected DEM data.

[0026] In some embodiments, since high-latitude regions are affected by projection methods such as UTM or Spherical Mercator, the spatial coordinate errors caused by projection distortion are large. Therefore, when selecting a projection method, it is advisable to choose a projection coordinate system based on Gauss-Kruger projection, such as “CGCS2000 3 Degree GKCM 105E” in this embodiment.

[0027] The obtained projected DEM data is geometrically clipped to output preprocessed DEM data with a valid project area. Specifically, the project area geometric boundary vector layer is created and drawn. The projected DEM data is then selected for the "Export Raster" operation. The export target path is the same ArcGIS-GDB database as the original DEM data. In the export interface, the "Clipping Geometry" drop-down box selects the aforementioned project area geometric boundary vector layer. At the same time, "Maintain Clipping Range" and "Clip Geometry Using Input Features" are checked, and the "Clipping Type" is set to "External". This step is used to avoid the algorithm defects of other similar output tools in ArcGIS Pro software and enhance robustness.

[0028] In some embodiments, since the original DEM data may contain multiple raster data layers, i.e., raster datasets, these raster data layers need to be merged before preprocessing. The method is as follows: using the "Mottling to New Raster" tool, select all raster data layers in the input raster options, set the output path to the original DEM data ArcGIS-GDB database, set the number of bands to 1, select Mean as the mosaic operator, and keep all other settings at their defaults. This outputs a merged raster data layer, which is then used to replace the geometrically clipped input DEM data after projection transformation.

[0029] In one specific embodiment, the step of performing data spatial mapping transformation on the DEM data of the selected flat terrain areas specifically includes: Query the statistical data values ​​in the data attributes of the DEM data after projection transformation and geometric clipping to obtain the minimum value corresponding to the data band. E Min Maximum value corresponding to the band E Max Band corresponding average value E Mean And record the original value of any pixel in the data as E 1 ; Using a raster calculator, the spatial mapping transformation of the data is calculated to obtain the raster cell values ​​of the DEM data. E 2 The calculation formula is: In the formula: F F is a scaling factor used to assess the magnitude of terrain elevation changes; a larger F value indicates that a gentle area will become steeper.

[0030] Plains, riverbeds, hills, and other flat areas have relatively small elevation differences, which can make subsequent steps difficult and result in poor data quality. Performing data space mapping transformation on these types of data is more conducive to preprocessing DEM data for subsequent data operations. Furthermore, a larger F value makes it easier to extract features such as ridgelines; generally, a value is a positive integer between 500 and 1000, and the selection needs to be based on the specific project requirements.

[0031] In this embodiment, the extraction of ridgelines using regional DEM data in conjunction with GIS includes: Based on preprocessed DEM data, positive and negative terrain determination is made by judging the elevation relationship between each raster cell in the DEM data and its neighboring raster cells, thus identifying local highlands and local lowlands in the study area. Based on the preprocessed DEM data, the cumulative flow of each raster cell is calculated; Based on the aforementioned local highlands and areas with zero cumulative runoff, continuous ridgelines are extracted; The extracted ridgeline was pruned.

[0032] Ridge lines are used to define the boundaries of the study area. Using ridge lines to delineate the closed watershed ensures that the watershed receives only rainfall input and is not disturbed by surface runoff from other areas.

[0033] In one specific embodiment, the extraction of ridgelines using regional DEM data in conjunction with GIS specifically includes: The preprocessed DEM data is statistically analyzed using the focus statistics tool in the ArcGIS platform. The average elevation of each pixel and its neighborhood is calculated using the average value statistics tool to obtain the DEM data after the first focus statistics. The preprocessed DEM data is then subtracted from the first focus statistics DEM data using the raster calculator to identify raster pixels with local elevations higher than their neighbors, and the difference calculation result is output. The difference calculation result is then classified using a reclassification tool. Areas with a value greater than 0 are assigned a value of 1, representing local highlands, and are output as positive terrain DEM data. Areas with a value less than or equal to 0 are assigned a value of 0, representing local lowlands, and are output as negative terrain DEM data. The depression tool in the ArcGIS platform is used to fill local depressions in the preprocessed DEM data, and the depression-filled DEM data is output. The depression-filling operation facilitates the subsequent calculation of water flow direction and prevents water flow from stagnating due to depressions. Based on the depression-filled DEM data, the flow direction tool is used to calculate the path of water flow on the terrain surface and output the water flow direction data. Based on the output water flow direction data, the flow rate tool is used to calculate the confluence accumulation of each cell. Areas with high confluence accumulation are confluence areas, while areas with zero confluence accumulation are water flow origins or boundary highlands, and the confluence accumulation raster is output. Based on the cumulative flow raster, the raster calculator is used to filter areas with a cumulative flow of 0, and the raster with a cumulative flow of 0 is output. Based on the output raster with a cumulative flow of 0, the focal point statistics tool is used to calculate the average value of the area surrounding each cell, and the cells are reclassified. At the same time, a preset value V is set. Cell values ​​less than the preset value V are assigned 0, indicating insignificant highlands, and cell values ​​greater than or equal to the preset value V are assigned 1, indicating relatively significant highlands. Finally, the DEM data after the second focal point statistics is output. Based on the DEM data after the second focal point statistics, the raster calculator is used to multiply the DEM data after the second focal point statistics with the positive terrain DEM data to eliminate erroneous ridgelines existing in negative terrain. Cell values ​​of areas with a calculated value of 0 are assigned NoData. Finally, only the raster data area with a cell value of 1 remains, which is the area where the ridgeline is located, and it is output as a ridgeline raster. Based on the ridgeline raster, the raster is converted into a polyline feature layer using the raster turning line tool. In the polyline feature layer, non-main branches of continuously extending ridgeline polyline features are visually selected. The non-main branch polyline segments are truncated at the intersection with the main branch and the branches are deleted to obtain the main branch polyline element layer. The main branch polyline element layer contains multiple polyline elements. The merge tool is used to merge multiple polyline elements into a single polyline element. The entire polyline element obtained is recorded as the general boundary.

[0034] Furthermore, after determining the boundary of the study area and delineating the closed watershed based on the ridgeline, the process also includes: Based on the direction of water flow, the boundary of the study area is divided into input boundary, output boundary, and general boundary; The input boundary corresponds to the upstream of the target river boundary in the closed watershed and is used to characterize the inflow of runoff from the external region. The output boundary corresponds to the downstream of the target river boundary in the closed watershed and is used to characterize the outflow of runoff in the study area. The general boundary corresponds to other boundaries in the closed watershed and is determined by the ridgeline extraction process; the general boundary does not accept input from external runoff.

[0035] For the input boundary, at each time step, its runoff is multiplied by a coefficient greater than 1 to characterize the inflow of runoff into the external region; for the output boundary, its runoff is multiplied by a coefficient less than 1 to characterize the outflow of runoff into the study area.

[0036] Step 2, Network Topology Construction: The DEM raster data within the defined study area is converted into DEM vector point sets using GIS. The network topology is then constructed based on these vector point sets, specifically including: By combining GIS, the raster-format DEM data is converted into a vector point dataset, and projected coordinates are added to the DEM vector point dataset. Traverse the vector point set, and use the resolution of the DEM data and the projection coordinates of the point set as spatial criteria to determine the spatial proximity relationship between each vector point, and generate a source and sink node list; Based on the aforementioned source and sink node list and by calling the graph learning library, a network topology is constructed, and the generated network topology is stored in the database in the form of a sparse matrix.

[0037] In one specific embodiment, the step of converting DEM raster data within the defined study area into a DEM vector point set using GIS, and constructing a network topology based on the vector point set, includes: The preprocessed DEM data is converted into DEM vector point data using a raster-to-point tool, resulting in a DEM vector point dataset in .shp format. The coordinate system of the DEM vector point data is then converted to CGCS2000 3 Degree GKCM 105E using a projection tool, resulting in a DEM vector point dataset after projection transformation. Projected coordinates are added to the projected DEM vector point dataset using the "Add X, Y Coordinates" tool, resulting in a DEM vector point dataset with added projected coordinates. Finally, the DEM vector point dataset with added projected coordinates is exported as a table file containing projected coordinates and elevation information using the "Table to Excel" tool, resulting in a DEM vector point coordinate dataset in .xlsx format. A Python script is used to traverse the DEM vector point set. Using the pixel size scale of the preprocessed DEM raster data and the projected coordinates of the DEM vector point coordinate dataset in .xlsx format as criteria, the spatial proximity of each vector point is determined, and source and sink nodes are identified, resulting in a list of source and sink nodes in .xlsx format. The calculation formula for the criteria is: or Where GSD is the resolution of the DEM data. , These are the projected coordinates of any two points in the DEM vector point coordinate dataset; Based on the source and sink node list, the dgl.Graph method in the DGL library is called to generate a graph network, and the network topology is obtained. The network topology is stored in the database in the form of a sparse matrix for easy use in subsequent hydrological simulation calculations.

[0038] Step 3, Hydrological Data Organization: Acquire and collect composite hydrological data of the target watershed, associate the collected hydrological data with the DEM vector point set according to spatial coordinates, and store the hydrological data in the corresponding nodes of the network topology; The composite hydrological data includes topographic data, soil data, and vegetation data. The topographic data includes digital elevation model data and topographic slope data, both in raster format. The soil data includes soil sand percentage data, soil silt percentage data, soil clay percentage data, median soil particle size data, and soil type data. The soil sand percentage data, soil silt percentage data, soil clay percentage data, and median soil particle size data are in raster format, while the soil type data is in vector format. The vegetation data includes vegetation normalized index data, leaf area index data, and vegetation type data. The vegetation normalized index data and leaf area index data are in raster format, while the vegetation type data is in vector format.

[0039] The step of associating the collected hydrological data with the DEM vector point set based on spatial coordinates and storing the hydrological data in the corresponding nodes of the network topology specifically includes: By combining GIS, composite hydrological data are uniformly converted into hydrological vector surface data, and the projection coordinate system of the hydrological data is unified. By combining GIS, the multi-source hydrological data and the DEM vector point set are associated according to the projected coordinates; The multi-source hydrological data associated with the DEM vector point set is stored in the node corresponding to the network topology.

[0040] In one specific embodiment, the step of associating the collected hydrological data with the DEM vector point set according to spatial coordinates and storing the hydrological data in the corresponding node of the network topology specifically includes: Floating-point raster data in multi-source, heterogeneous composite hydrological data is converted to integers to obtain integer hydrological raster data in .shp format. The integer hydrological raster data is then converted to vector surface data using a raster-to-surface tool to obtain hydrological vector surface data in .shp format. Finally, the coordinate system of the hydrological vector surface data is converted to CGCS2000 3 Degree GK CM 105E using a projection tool to obtain the projected hydrological vector surface data in ".shp" format. This unified projection coordinate system ensures accurate alignment between data and eliminates deviations caused by coordinate system inconsistencies. Using the Add Spatial Connection tool, the hydrological vector surface data after projection transformation and the DEM vector point dataset after projection transformation are spatially connected one by one. The attribute table of the DEM vector point dataset after projection transformation contains the various types of hydrological data information. The attribute table of the DEM vector point dataset after spatial connection and projection transformation was exported as a table file using a table-to-Excel tool, resulting in a hydrological data table file. A Python script was then used to assign the hydrological data table file to the corresponding network nodes, and the integrated networked hydrological data was stored in a database for easy access to various hydrological data during subsequent hydrological model calculations.

[0041] This invention utilizes ridgelines to delineate boundaries, ensuring the hydrological isolation of the study watershed and fundamentally avoiding external runoff interference, thereby improving the accuracy and reliability of hydrological process simulation. A network topology is constructed based on DEM data, ensuring that the network structure reflects topographic features. By combining GIS spatial connectivity, multi-source, heterogeneous hydrological data are linked to corresponding network nodes, achieving a high degree of data and network structure unity, providing a solid foundation for efficient and accurate hydrological model calculations.

[0042] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for organizing hydrological data in a closed watershed based on a ridgeline, characterized in that, include: Obtain and collect DEM data of the target watershed area, extract ridge lines using GIS and determine the boundary of the study area and delineate the closed watershed based on the ridge lines; The DEM raster data within the defined study area is combined with GIS to transform it into DEM vector point sets, and a network topology is constructed based on the vector point sets. Acquire and collect composite hydrological data of the target watershed, associate the collected hydrological data with the DEM vector point set according to spatial coordinates, and store the hydrological data in the corresponding nodes of the network topology.

2. The method for organizing closed watershed hydrological data based on ridgelines according to claim 1, characterized in that: The acquisition and collection of DEM data for the target watershed area specifically includes: DEM data was collected from open-source datasets both domestically and internationally, and the original DEM data was obtained in raster format. ArcGIS software was used to preprocess the original DEM data, including projection transformation and geometric clipping: DEM data of flat terrain areas were selected and subjected to data spatial mapping transformation to obtain preprocessed DEM data. The process of performing data spatial mapping transformation on the DEM data of the selected flat terrain areas specifically includes: Query the statistical data values ​​in the data attributes of the DEM data after projection transformation and geometric clipping to obtain the minimum value corresponding to the data band. E Min Maximum value corresponding to the band E Max Band corresponding average value E Mean And record the original value of any pixel in the data as E 1 ; Using a raster calculator, the spatial mapping transformation of the data is calculated to obtain the raster cell values ​​of the DEM data. E 2 The calculation formula is: In the formula: F F is a scaling factor used to assess the magnitude of terrain elevation changes; a larger F value indicates that a gentle area will become steeper.

3. The method for organizing closed watershed hydrological data based on ridgelines according to claim 2, characterized in that: The extraction of ridgelines using regional DEM data in conjunction with GIS includes: Based on preprocessed DEM data, positive and negative terrain determination is made by judging the elevation relationship between each raster cell in the DEM data and its neighboring raster cells, thus identifying local highlands and local lowlands in the study area. Based on the preprocessed DEM data, the cumulative flow of each raster cell is calculated; Based on the aforementioned local highlands and areas with zero cumulative runoff, continuous ridgelines are extracted; The extracted ridgeline was pruned.

4. The method for organizing closed watershed hydrological data based on ridgelines according to claim 2, characterized in that: The extraction of ridgelines using regional DEM data in conjunction with GIS specifically includes: The preprocessed DEM data is statistically analyzed using the focus statistics tool in the ArcGIS platform. The average elevation of each pixel and its neighborhood is calculated using the average value statistics tool to obtain the DEM data after the first focus statistics. The preprocessed DEM data is then subtracted from the first focus statistics DEM data using the raster calculator to identify raster pixels with local elevations higher than their neighbors, and the difference calculation result is output. The difference calculation result is then classified using a reclassification tool. Areas with a value greater than 0 are assigned a value of 1, representing local highlands, and are output as positive terrain DEM data. Areas with a value less than or equal to 0 are assigned a value of 0, representing local lowlands, and are output as negative terrain DEM data. The depression tool in the ArcGIS platform is used to fill local depressions in the preprocessed DEM data, and the depression-filled DEM data is output. Based on the depression-filled DEM data, the flow direction tool is used to calculate the path of water flow on the terrain surface, and the flow direction data is output. Based on the output flow direction data, the flow rate tool is used to calculate the confluence accumulation of each cell. Areas with high confluence accumulation are confluence areas, while areas with zero confluence accumulation are water source areas or boundary highlands, and the confluence accumulation raster is output. Based on the cumulative flow raster, the raster calculator is used to filter areas with a cumulative flow of 0, and the raster with a cumulative flow of 0 is output. Based on the output raster with a cumulative flow of 0, the focal point statistics tool is used to calculate the average value of the area surrounding each cell, and the cells are reclassified. At the same time, a preset value V is set. Cell values ​​less than the preset value V are assigned 0, indicating insignificant highlands, and cell values ​​greater than or equal to the preset value V are assigned 1, indicating relatively significant highlands. Finally, the DEM data after the second focal point statistics is output. Based on the DEM data after the second focal point statistics, the raster calculator is used to multiply the DEM data after the second focal point statistics with the positive terrain DEM data to eliminate erroneous ridgelines existing in negative terrain. Cell values ​​of areas with a calculated value of 0 are assigned NoData. Finally, only the raster data area with a cell value of 1 remains, which is the area where the ridgeline is located, and it is output as a ridgeline raster. Based on the ridgeline raster, the raster is converted into a polyline feature layer using the raster turning line tool. In the polyline feature layer, non-main branches of continuously extending ridgeline polyline features are visually selected. The non-main branch polyline segments are truncated at the intersection with the main branch and the branches are deleted to obtain the main branch polyline element layer. The main branch polyline element layer contains multiple polyline elements. The merge tool is used to merge multiple polyline elements into a single polyline element. The entire polyline element obtained is recorded as the general boundary.

5. The method for organizing closed watershed hydrological data based on ridgelines according to claim 1, characterized in that: After determining the boundary of the study area and delineating the closed watershed based on the ridgeline, the following is also included: Based on the direction of water flow, the boundary of the study area is divided into input boundary, output boundary, and general boundary; The input boundary corresponds to the upstream of the target river boundary in the closed watershed and is used to characterize the inflow of runoff from the external region. The output boundary corresponds to the downstream of the target river boundary in the closed watershed and is used to characterize the outflow of runoff in the study area. The general boundary corresponds to other boundaries in the closed watershed and is determined by the ridgeline; the general boundary does not accept input from external runoff.

6. The method for organizing closed watershed hydrological data based on ridgelines according to claim 3, characterized in that: The process of converting DEM raster data within the defined study area into DEM vector point sets using GIS, and constructing a network topology based on the vector point sets, includes: By combining GIS, the raster-format DEM data is converted into a vector point dataset, and projected coordinates are added to the DEM vector point dataset. Traverse the vector point set, and use the resolution of the DEM data and the projection coordinates of the point set as spatial criteria to determine the spatial proximity relationship between each vector point, and generate a source and sink node list; Based on the aforementioned source and sink node list and by calling the graph learning library, a network topology is constructed, and the generated network topology is stored in the database in the form of a sparse matrix.

7. The method for organizing closed watershed hydrological data based on ridgelines according to claim 4, characterized in that: The process of converting DEM raster data within the defined study area into DEM vector point sets using GIS, and constructing a network topology based on these vector point sets, specifically includes: The preprocessed DEM data is converted into DEM vector point data using a raster-to-point tool, resulting in a DEM vector point dataset in .shp format. The coordinate system of the DEM vector point data is then converted to CGCS2000 3 Degree GKCM 105E using a projection tool, resulting in a DEM vector point dataset after projection transformation. Projected coordinates are added to the projected DEM vector point dataset, resulting in a DEM vector point dataset with added projected coordinates. Finally, the DEM vector point dataset with added projected coordinates is exported as a table file containing projected coordinates and elevation information, resulting in a DEM vector point coordinate dataset in .xlsx format. A Python script is used to traverse the DEM vector point set. Using the pixel size scale of the preprocessed DEM raster data and the projected coordinates of the DEM vector point coordinate dataset in .xlsx format as criteria, the spatial proximity of each vector point is determined, and source and sink nodes are identified, resulting in a list of source and sink nodes in .xlsx format. The calculation formula for the criteria is: or Where GSD is the resolution of the DEM data. , These are the projected coordinates of any two points in the DEM vector point coordinate dataset; Based on the source and sink node list, the dgl.Graph method in the DGL library is called to generate a graph network, and the network topology is obtained. The network topology is then stored in the database in the form of a sparse matrix.

8. The method for organizing closed watershed hydrological data based on ridgelines according to claim 1, characterized in that: The composite hydrological data includes topographic data, soil data, and vegetation data. The topographic data includes digital elevation model data and topographic slope data, both in raster format. The soil data includes soil sand percentage data, soil silt percentage data, soil clay percentage data, median soil particle size data, and soil type data. The soil sand percentage data, soil silt percentage data, soil clay percentage data, and median soil particle size data are in raster format, while the soil type data is in vector format. The vegetation data includes vegetation normalized index data, leaf area index data, and vegetation type data. The vegetation normalized index data and leaf area index data are in raster format, while the vegetation type data is in vector format.

9. A method for organizing closed watershed hydrological data based on ridgelines according to claim 6, characterized in that: The step of associating the collected hydrological data with the DEM vector point set according to spatial coordinates and storing the hydrological data in the corresponding nodes of the network topology includes: By combining GIS, composite hydrological data are uniformly converted into hydrological vector surface data, and the projection coordinate system of the hydrological data is unified. By combining GIS, the multi-source hydrological data and the DEM vector point set are associated according to the projected coordinates; The multi-source hydrological data associated with the DEM vector point set is stored in the node corresponding to the network topology.

10. A method for organizing closed watershed hydrological data based on ridgelines according to claim 7, characterized in that: The step of associating the collected hydrological data with the DEM vector point set based on spatial coordinates and storing the hydrological data in the corresponding nodes of the network topology specifically includes: The floating-point raster data in the composite hydrological data is converted to integer to obtain integer hydrological raster data. The raster to polygon tool is then used to convert the integer hydrological raster data into vector polygon data to obtain hydrological vector polygon data in .shp format. The coordinate system of the hydrological vector polygon data is then converted to CGCS2000 3 Degree GK CM105E using the projection tool to obtain the hydrological vector polygon data after projection transformation. Using the Add Spatial Connection tool, the hydrological vector surface data after projection transformation and the DEM vector point dataset after projection transformation are spatially connected one by one. The attribute table of the DEM vector point dataset after projection transformation contains the various types of hydrological data information. The attribute table of the DEM vector point dataset after spatial connection and projection transformation was exported as a table file using a table-to-Excel tool, resulting in a hydrological data table file. A Python script was then used to assign the hydrological data table file to the corresponding network nodes, and the integrated networked hydrological data was stored in the database.

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