Methods, apparatus, equipment and storage media for recursive calculation of sediment yield in multi-watershed basins

Abstract

This application provides a method, apparatus, equipment, and storage medium for recursive calculation of sediment yield in multi-reservoir watersheds. It relates to the field of watershed sediment simulation technology. The method includes: extracting flow rate, flow direction grids, and reservoir vector points based on watershed DEM data and reservoir vector area data; obtaining grid paths using breadth-first search and converting them to vector lines to determine connectivity, constructing a directed graph including construction year and reservoir capacity attributes; obtaining effective sub-watersheds after topological sorting; traversing the sorted dictionary; calculating the soil erosion modulus according to the modified general soil loss equation; and combining sediment transport ratio and capture efficiency to recursively simulate watershed sediment yield and the transport process after multi-stage interception, achieving refined simulation. This application, based on DEM and remote sensing data and combined with flow direction modeling methods, achieves realistic topological network reconstruction between ponds, solving the problems of traditional models merging ponds and distorted representation.

Description

Technical Field

[0001] This application relates to the field of watershed sediment simulation technology, and in particular to a method, apparatus, equipment and storage medium for recursive calculation of sediment yield in multi-watershed watersheds. Background Technology

[0002] Water storage bodies (such as ponds and small reservoirs) are typical small-scale agricultural water conservancy projects, numerous and widely distributed. They not only play a vital ecological service role in providing irrigation water and reducing pollutants in watersheds, but also significantly intercept sediment, thereby altering downstream sediment transport processes. Furthermore, the amount of sediment deposited in water storage bodies reflects the sediment yield upstream. However, in existing watershed sediment yield simulation methods, the contribution of water storage bodies to sediment transport interception is often overlooked or poorly considered. This results in poor sediment prediction performance in watersheds with multiple water storage bodies, hindering the scientific allocation of management measures.

[0003] Existing technologies have the following shortcomings when simulating sediment transport in watersheds with multiple water bodies:

[0004] 1. Insufficient characterization of the spatial distribution of water storage bodies.

[0005] Limited by the resolution of remote sensing data (such as a 30m×30m DEM), existing models (such as SWAT and AnnAGNPS) usually merge water bodies into a single water body or classify them into farmland types because the area of ​​a single water body is small, thus losing the fine distribution information of water bodies.

[0006] 2. The water storage body's interception mechanism is too simplistic.

[0007] Due to differences in reservoir capacity and catchment area, the sediment interception capacity of different water bodies varies significantly. Existing models often use a uniform interception coefficient or a fixed interception amount, failing to reflect the individual differences of water bodies and resulting in decreased model accuracy.

[0008] 3. Lack of characterization of the cascade interception effect of multiple water bodies.

[0009] Multiple reservoirs have different spatial connectivity and topological structures within the watershed. The interception efficiency of downstream reservoirs is affected by the residual sediment interception of upstream reservoirs. However, existing models generally do not consider the connectivity of reservoirs and their dynamic feedback effect on sediment transport in the watershed. Summary of the Invention

[0010] This application provides a method, apparatus, equipment, and storage medium for recursive calculation of sediment yield in multi-reservoir watersheds. Specifically, for watersheds with distributed multi-reservoir areas, it offers a sediment transport simulation scheme that considers the topological connectivity of the reservoirs and the cascading interception effect of the reservoir network. This scheme can significantly improve the accuracy of watershed sediment yield simulation, providing technical support for the optimized allocation of soil and water conservation measures and comprehensive watershed management.

[0011] Firstly, this application provides a method for recursively calculating sediment yield in multi-watershed basins, including:

[0012] Based on watershed DEM data and reservoir vector surface data, extract flow raster data, flow direction raster data, and reservoir vector point data;

[0013] Based on the flow grid data and water storage vector point data, a breadth-first search is used to search according to the maximum flow direction in eight directions in the grid, extract the grid path from the water storage point to the outlet, and convert it into vector line data.

[0014] Based on the vector line data, the connectivity of the water storage bodies is determined;

[0015] Using the vector point data of the water storage body as nodes, and connecting them into directed edges according to the flow direction based on the connectivity of the water storage body, a directed graph structure is constructed, and the construction year and original storage capacity attributes of the water storage body are added to the nodes.

[0016] The directed graph structure is topologically sorted, and the effective sub-basin range of each water storage body is obtained based on the flow direction grid. The effective sub-basin range of each water storage body is written into a dictionary with topological sorting to obtain a dictionary structure.

[0017] The dictionary structure is traversed, and the soil erosion modulus of each effective sub-basin is calculated according to the modified general soil loss equation. Based on the sediment transport ratio and the soil erosion of the effective sub-basin, the amount of sediment entering the water storage body is calculated. Combined with the sediment capture efficiency of the water storage body, the amount of sediment intercepted and input downstream is calculated.

[0018] In one possible design, based on watershed DEM data, flow raster data, flow direction raster data, and reservoir vector point data are extracted, including:

[0019] Based on watershed DEM data, the D8 flow direction algorithm was used to extract flow raster data and flow direction raster.

[0020] The flow rate raster data is overlaid with the reservoir surface vector data, and the largest raster cell in each reservoir surface is extracted.

[0021] The largest grid cell in each water storage area is converted into water storage vector point data, and the grid cell with the largest flow rate in the watershed is extracted as the outlet.

[0022] In one possible design, determining the connectivity of the water storage body based on the vector line data includes:

[0023] Traverse the distances from the starting point in the vector line data to the water body vector point data s in the vector line data, sort the water body points in ascending order of distance, and obtain the connection relationship between the water bodies as the water body connectivity relationship.

[0024] In one possible design, the modified universal soil loss equation is expressed as:

[0025] ;

[0026] In the formula, The soil erosion modulus due to hydrodynamics within each unit; It is the erosivity factor of rainfall; It is a soil erodibility factor; This refers to the slope length and gradient factor. C Vegetation cover management factors; P Factors related to soil and water conservation measures;

[0027] The formula for calculating soil erosion within the effective sub-basin area is as follows:

[0028] ;

[0029] In the formula, For the effective sub-basin soil erosion, Let i be the soil erosion modulus of grid cell i; Let i be the area of ​​grid cell i; This represents the amount of sediment transported to the upstream pond. If there is no upstream pond, this value is 0.

[0030] In one possible design, the formula for calculating the sediment transport ratio is:

[0031] ;

[0032] In the formula, The sediment transport ratio of the reservoir's sub-basin; , These are undetermined coefficients, depending on the characteristics of the watershed; The area of ​​the sub-basin.

[0033] In one possible design, the formula for calculating the sediment capture efficiency is:

[0034] ;

[0035] In the formula, The sediment capture efficiency of the water storage body w; , Let w be the hydraulic residence time of the water reservoir. This refers to the current reservoir capacity. The average annual cumulative flow at the inlet of the reservoir is w. It is a constant, usually taking the value 1.

[0036] In one possible design, the amount of sediment input downstream after sediment interception is calculated using the following formula:

[0037] ;

[0038] In the formula, The amount of sediment input downstream after sediment interception; The sediment capture efficiency of the water storage body w; The sediment transport ratio of the reservoir's sub-basin; denoted as the effective watershed soil erosion modulus for water storage body w.

[0039] Secondly, this application provides a recursive calculation device for sediment yield in a multi-watershed basin, comprising:

[0040] The target selection module is configured to select interaction targets based on neighborhood configuration; wherein, the neighborhood configuration includes two interaction methods, based on buffer and based on graph topology, to find the neighborhood attributes of the target plot, and the neighborhood attributes include the size of the neighborhood radiation range, the number of plots, the radiation intensity, and the strength of the interaction influence of the plots.

[0041] The feature extraction module is configured to extract features from the interactive target to obtain features of the interactive target; wherein, the features of the interactive target include land use category, spatial variable features and non-spatial variable features;

[0042] The graph structure construction module is configured to construct a graph structure based on the features of the interaction target; wherein the graph structure includes edges and nodes, the nodes include features of the interaction target, and the edges contain the adjacency relationships of adjacent interaction targets;

[0043] The network construction module is configured to construct a high-order graph attention network based on the graph structure. During the training phase of constructing the high-order graph attention network, updated node feature representations are generated by weighted aggregation of neighbor nodes, the model is optimized using the cross-entropy loss function, and the suitability score of land use change of the plot is output. After the high-order graph attention network has been constructed, a self-attention mechanism is introduced through the graph attention network to calculate the interaction weights between neighbor nodes in order to capture spatial heterogeneity and long-distance dependencies.

[0044] An extended simulation module is configured to integrate a vector cellular automaton into the higher-order graph attention network to obtain an HGAT-VCA model, and to perform recursive calculation of sediment yield in multi-watershed basins based on the HGAT-VCA model; wherein, the vector cellular automaton is used to calculate the overall migration probability and determine the land use type.

[0045] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the recursive calculation method for sediment yield in a multi-watershed basin as described in the first aspect and various possible designs of the first aspect.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the method for recursive calculation of sediment yield in a multi-watershed basin as described in the first aspect and various possible designs of the first aspect.

[0047] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method for recursive calculation of sediment yield in a multi-watershed basin as described in the first aspect and various possible designs of the first aspect.

[0048] Compared with the prior art, this application has the following significant advantages:

[0049] (1) Accurately depict the spatial structure and connectivity of the water storage body.

[0050] Based on DEM and reservoir vector surface data, combined with flow direction modeling methods, we can reconstruct the real topological network between reservoirs, solving the problems of merging reservoirs and distorted representation in traditional models.

[0051] (2) Dynamically calculate the individual interception effect of the water storage body.

[0052] By considering the different reservoir capacities and catchment areas of water bodies, errors caused by uniform interception coefficients are avoided, thus improving the physical realism of the model.

[0053] (3) Realize the recursive simulation of sediment transport.

[0054] Based on the network topology, the stepwise interception and release of sediment in the water storage system are recursively simulated, comprehensively reflecting the regulatory role of the cascade interception effect of the water storage network on the sediment production process in the basin.

[0055] (4) Data input is simple and has a wide range of applications.

[0056] This application boasts excellent applicability and simplicity, requiring only the input of a digital elevation model (DEM) and surface vector data of the reservoir bodies to automatically construct a topological network of relationships between them. Compared to traditional methods, this method is less dependent on data accuracy and watershed scale, and can operate stably in areas with limited data availability or low resolution, making it suitable for large-scale, multi-scale watershed sediment yield simulation and management practices. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] Figure 1 A flowchart illustrating a method for recursively calculating sediment yield in a multi-watershed basin, as provided in this application embodiment;

[0059] Figure 2 A flowchart illustrating a method for recursively calculating sediment yield in a multi-watershed basin, as provided in this application embodiment;

[0060] Figure 3 A schematic diagram of directed graph construction provided for an embodiment of this application; wherein, (a) relation 1; (b) relation 2; (c) directed graph G;

[0061] Figure 4 The sub-basin pond topology and directed graph construction diagram provided in the embodiments of this application; wherein, (a), the overall layout of the basin; (b), a local enlarged view; (c), a topology diagram composed of nodes and directed edges (lines);

[0062] Figure 5 A schematic diagram of the pond topology and sub-basin division provided for embodiments of this application; wherein (a) shows how the pond is divided into sub-basins; and (b) shows the sediment transport process affected by the year the pond was constructed.

[0063] Figure 6 A simulation result diagram of multi-year sediment yield of the watershed provided for an embodiment of this application;

[0064] Figure 7 A schematic diagram of the structure of the sediment yield recursive calculation device for multi-water-reservoir basins provided in this application embodiment;

[0065] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0066] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0067] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0068] The collection, storage, use, processing, transmission, provision, and disclosure of information such as financial data, user data, or medical image data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0069] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0070] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0071] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0072] Example 1:

[0073] This application provides a method for recursively calculating sediment yield in a multi-water-body basin. It should be noted that the water bodies mentioned herein include, but are not limited to, distributed water storage facilities with sediment interception functions such as artificial ponds, irrigation ponds, small silt-retaining dams, and small reservoirs.

[0074] Figure 1 This is a flowchart illustrating a method for recursively calculating sediment yield in a multi-watershed basin, as provided in an embodiment of this application. Figure 1 As shown, the method for recursively calculating the sediment yield of a multi-water-reservoir basin can be implemented using an electronic terminal. The method includes the following steps S10 to S60.

[0075] S10: Based on the watershed DEM data and reservoir vector surface data, extract flow raster data, flow direction raster data, and reservoir vector point data.

[0076] In this embodiment, based on the watershed DEM data, the flow rate raster (flow_acc) and flow direction raster (flow_dir) are extracted using the D8 flow direction algorithm based on the WhiteboxTools library. The flow rate raster data (flow_acc) is overlaid with the reservoir vector surface data (pond) (where the vector surface data pond has two attributes: its original reservoir capacity (basic_volume) and construction year (year)). The largest raster in each reservoir surface is extracted and converted into reservoir vector point data (pond_points). At the same time, the raster with the largest flow rate in the watershed, i.e., the outlet, is extracted and converted into outlet vector point data (max_points).

[0077] S20: Based on flow raster data and water storage body vector point data, a breadth-first search is adopted to search according to the maximum flow direction in eight directions in the raster, extract the raster path from the water storage body point to the outlet, and convert it into vector line data.

[0078] In this embodiment, based on the flow grid data flow_acc generated in S10 and the extracted reservoir vector point data pond_points, the Breadth-First Search (BFS) algorithm is used to search according to the maximum flow direction in eight directions in the grid, extract the grid path from the reservoir point to the outlet, and convert it into vector line data stream.

[0079] S30: Determine the connectivity of water storage bodies based on vector line data.

[0080] In this embodiment, the distance from the start_point of the vector line data stream obtained in S20 to the pont_points of the water storage body vector points in the vector line data is traversed, and the water storage body points are sorted in ascending order of distance to obtain the connection relationships between the water storage bodies.

[0081] S40: Using the vector point data of the water storage body as nodes, connect them into directed edges according to the flow direction based on the connectivity of the water storage bodies, construct a directed graph structure, and add the construction year and original reservoir capacity attributes of the water storage body to the nodes.

[0082] In this embodiment, the reservoir vector point data pond_points is used as nodes, and the connections obtained in S30 are connected in the order of flow direction to form directed edges, thereby constructing a directed graph structure. At the same time, the reservoir construction year and original reservoir capacity attributes are added to the nodes.

[0083] S50: Perform topological sorting on the directed graph structure, and based on the flow direction grid, obtain the effective sub-basin range of each water storage body, and write the effective sub-basin range of each water storage body into a dictionary with topological sorting to obtain the dictionary structure.

[0084] In this embodiment, the directed graph is topologically sorted. Based on the flow direction grid flow_dir obtained in S10, the inverse D8 algorithm is used to obtain the effective sub-basin range of each water body (the upstream watershed part that provides flow or sediment to the current pond), and write it into a dictionary with topological sorting.

[0085] S60: Traverse the dictionary structure, calculate the soil erosion modulus for each effective sub-basin area according to the modified general soil loss equation, calculate the amount of sediment entering the water storage body according to the sediment transport ratio and the amount of soil erosion in the effective sub-basin area, and calculate the amount of sediment input downstream after sediment interception by combining the sediment capture efficiency of the water storage body.

[0086] In this embodiment, the dictionary structure described above is traversed, and the soil erosion modulus for each effective sub-basin is calculated using the Revised Universal Soil Loss Equation (RUSLE). The soil erosion amount is then calculated using the formula for calculating the soil erosion amount of the effective sub-basin. Finally, based on the Sediment Delivery Ratio (SDR), the amount of sediment entering the reservoir is calculated by multiplying the soil erosion amount by the SDR. Finally, the amount of sediment intercepted and input downstream is calculated using the Sediment Trapping Efficiency (STE) of the reservoir. Specifically, the amount of sediment entering the reservoir multiplied by the STE is the amount of sediment captured by the reservoir. Subtracting the amount of sediment captured by the reservoir from the amount entering the reservoir gives the amount of sediment intercepted and input downstream. In actual calculations, each reservoir is calculated using the above method, recursively realizing the sediment yield of the basin after multi-stage interception by reservoirs within the basin. For example, by calculating the amount of sediment input downstream after the current water body intercepts sediment, when calculating the next water body, the amount of soil erosion in the effective sub-basin can be calculated based on the amount of sediment input downstream after the current water body intercepts sediment. In this way, the sediment yield of the basin after interception by each level of water body can be obtained through recursive calculation.

[0087] In some embodiments, the formula for calculating the soil erosion modulus is:

[0088] ;

[0089] In the formula:

[0090] The soil erosion modulus due to hydrodynamics within each unit, i.e., the average soil erosion rate (t·ha). -1 ·a -1 );

[0091] Rainfall erosivity factor (MJ·mm / (hm)) 2 ·h·a));

[0092] Soil erodibility factor (t·hm) 2 ·h·hm -2 MJ -1 ·mm -1 );

[0093] , where is the slope length and slope factor (dimensionless);

[0094] C Vegetation cover management factors (dimensionless);

[0095] P The water and soil conservation measures factor (dimensionless).

[0096] In some embodiments, the soil erosion amount of the effective sub-watershed is calculated using the following formula:

[0097] ;

[0098] In the formula, For the effective sub-basin soil erosion, Let i be the soil erosion modulus of grid cell i; Let i be the area of ​​grid cell i; This represents the amount of sediment transported to the upstream pond. If there is no upstream pond, this value is 0.

[0099] In some embodiments, the SDR calculation formula is as follows:

[0100] ;

[0101] In the formula: The sediment transport ratio of the reservoir's sub-basin; , These are undetermined coefficients, depending on the characteristics of the watershed; The drainage area of ​​the sub-basin (km²) 2 ).

[0102] In some embodiments, the STE calculation formula is as follows:

[0103] ;

[0104] In the formula: The sediment capture efficiency of the water storage body w; , Let w be the hydraulic residence time of the water reservoir. The current reservoir capacity (m) of the water storage body. 3 ), The average annual cumulative flow at the inlet of the reservoir (m³) 3 ), The value is 1.

[0105] In some embodiments, the formula for calculating the amount of sediment input downstream after sediment interception is as follows:

[0106] ;

[0107] In the formula, The amount of sediment input downstream after sediment interception; The sediment capture efficiency of the water storage body w; The sediment transport ratio of the reservoir's sub-basin; denoted as the effective watershed soil erosion modulus for water storage body w.

[0108] Example 2:

[0109] This application provides a method for recursively calculating sediment yield in a multi-watershed basin. This embodiment uses a pond as an example of a watershed. Figure 2 The diagram shows a flowchart of a method for recursively calculating sediment yield in a multi-watershed basin according to an embodiment of this application. This method includes the following steps S1 to S4.

[0110] S1: Construction of the basic dataset.

[0111] In this embodiment, the basic dataset includes watershed attribute data and pond attribute data. The watershed data mainly consists of a Digital Elevation Model (DEM) (5m×5m), land use data for two periods (5m×5m), specifically data before and after the 2005 boundary, including seven categories: paddy fields, dry land, forest land, grassland, water areas, urban construction land, and unused land; surface soil texture data at a depth of 0-20cm (90m×90m), including the percentages of silt, sand, clay, and soil organic carbon; and daily rainfall data from 1965 to 2024. The pond attribute data mainly includes the areal vector data of the ponds, the year the ponds were constructed, and the original storage capacity data of the ponds.

[0112] S2: Construction of topological relationships, including the following steps S201-S204.

[0113] S201: Pond surface point conversion and watershed outlet point identification.

[0114] Based on DEM data, the D8 flow direction algorithm from the WhiteboxTools library was used to extract the corresponding cumulative flow raster (flow_acc) and flow direction raster (flow_dir). Information on the original pond surface data, including the original capacity and construction year, was added. By overlaying the flow raster data (flow_acc) with the pond surface vector data, the largest raster in each pond surface was extracted and converted into pond vector point data (pond_points). Simultaneously, the largest flow raster point in the watershed was identified and converted into vector point data (max_points).

[0115] S202: Extraction of the path from the pond to the watershed outlet.

[0116] Combining the flow rate raster data (flow_acc) from S201 with the pond vector point data (pond_points) and the outlet vector point data (max_points), a breadth-first search (BFS) algorithm is used. A directional lookup table is set up according to the eight directions in the raster, and the search is continuously performed in the direction of maximum flow, recording the position (row and column number) until the watershed outlet is found. The raster path from each pond point to the watershed outlet is extracted and converted into vector line data (stream).

[0117] S203: Construction of directed graph structures.

[0118] First, iterate through the runoff data stream extracted from S202, calculate the distance from each pond point on the runoff to the runoff starting point, and sort the data in ascending order based on the distance. This creates a relationship stored according to the pond number and distance. For example... Figure 3 The diagram shown is a schematic diagram of directed graph construction provided in an embodiment of this application. Figure 3 In the graph, relation 1 shows a simple directed chain, where node A points to B via a directed edge, and B then points to C, forming a unidirectional connection A→B→C. Similarly, relation 2 is also a directed chain, where node E points to B via a directed edge, and B then points to C, presenting a unidirectional connection E→B→C. Directed graph G is a comprehensive directed graph integrating the previous two relations, where nodes A and E both point to B via directed edges, and B then points to C, constructing a directed connection structure where A and E both point to B and then flow to C, clearly demonstrating the convergence of multiple nodes to the same node and its subsequent transitive relationships.

[0119] Subsequently, a directed graph structure is constructed based on the relationships, thereby eliminating duplicate nodes and storing these scattered pond relationships into a complete directed graph. Key information, such as the year the ponds were constructed and their original capacity, is stored in the nodes of the graph. To clearly present the connectivity characteristics of the pond system, this embodiment selects a typical sub-basin containing 12 ponds for visualization. For example... Figure 4The diagram shown illustrates the sub-basin pond topology and directed graph construction provided in this embodiment of the application. Figure 4 In the diagram, (a) shows the overall layout of the watershed, including key elements such as ponds, watershed outlets, sub-watershed areas, and the watershed extent, accompanied by a compass rose and scale, clearly presenting the geographical distribution of the watershed. Sub-watershed and watershed line map: (b) is a magnified view, showing the watershed line, ponds, and sub-watershed areas in detail, also with a compass rose and scale, focusing on the distribution of water systems and ponds within the sub-watersheds. (c) is a topological graph composed of nodes and directed edges (lines), revealing the connections between ponds and reflecting the topological structure of the pond network within the watershed. It should be noted that... Figure 4 The numbers 0-12 in the diagram refer to the pond numbers.

[0120] S204: Topological sorting and sub-basin partitioning.

[0121] Based on the directed graph structure obtained in S203, the pond nodes are topologically sorted. This involves using the in-degree property of the nodes in the directed graph (the number of edges with a given node as the endpoint), selecting nodes with an in-degree of 0, and controlling the order of ponds at the same level using their own node numbers. Then, the currently selected node is removed, and the process of iteratively selecting nodes with an in-degree of 0 continues, thus completing the topological sorting of the ponds. Based on the sorting results, the reverse D8 algorithm is applied to divide the ponds into sub-basins. Specifically, as follows... Figure 5 As shown, dividing the sub-basin area requires determining whether there are other ponds upstream of the pond. If so, the watershed area containing the upstream pond is removed.

[0122] S3: Calculate the sediment yield of the basin in the current year.

[0123] As shown in Figure 5, when calculating the sediment yield of the watershed, the sediment yield of the effective sub-watershed where the upstream pond is located is first calculated. Then, the portion that was not successfully intercepted by the pond is transferred to the effective sub-watershed of the downstream pond. Based on the topological sorting result of S2, the process is iterated continuously until the watershed outlet is reached, thereby obtaining the overall watershed sediment yield. Specifically, step S3 can be implemented through the following steps S301-S305.

[0124] S301: Applying the above topological sorting and effective sub-basin division results, the soil erosion is calculated by applying the modified general soil loss equation RUSLE to the watersheds where the ponds are located in sequence, as shown in the following formula:

[0125] ;

[0126] In the formula, the rainfall erosivity factor is assumed to be consistent across the entire watershed. The soil erosibility factor, slope length and gradient factor, vegetation cover management factor, and soil and water conservation measures factor are respectively the masking results for the sub-watershed where pond w is located. The calculation methods for each factor can refer to the standard methods.

[0127] S302: Calculate the soil erosion of the effective sub-watershed using the following formula:

[0128] ;

[0129] In the formula, For the effective sub-basin soil erosion, Let i be the soil erosion modulus of grid cell i; Let i be the area of ​​grid cell i; This represents the amount of sediment transported to the upstream pond. If there is no upstream pond, this value is 0.

[0130] S303: Calculate the sediment transport ratio (SDR) of the sub-basin, using the following formula: The sediment transport ratio (SDR) used in this invention is obtained by calibrating the parameters in 52 watersheds with observation data ponds based on the method of this invention. The resulting calculation formula is expressed as follows:

[0131] ;

[0132] In the formula Let w be the effective catchment area of ​​the pond's sub-basin.

[0133] S304: Calculate the sediment capture efficiency of the pond. This is based on the hydraulic retention time. Based on the current storage capacity and the average annual cumulative flow at the pond inlet The calculation is based on the average water yield coefficient of Kaizhou District in Chongqing Municipality, which is located within the Kaizhou District. The annual rainfall depth is 0.57. The calculation is based on (m). Simultaneously, the current reservoir capacity due to siltation in the pond for the current year is recorded. and the amount of input to downstream The specific calculation formula is as follows:

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] In the formula, The drainage area of ​​the pond sub-basin (m²) 2 ).

[0139] For the average soil bulk density The density of the soil was calculated by collecting sediment samples from 70 ponds, drying and weighing them, and then calculating the average soil bulk density of these ponds to be 1.15, which facilitates the transfer to other ponds in the watershed.

[0140] S305: After continuous iteration, the sediment yield of the outlet part of the basin will be calculated, that is, the sediment yield of the entire basin. The calculation method is the same as that of S301~S304.

[0141] S4: Calculate the sediment yield of the watershed over many years.

[0142] Starting from the earliest year the pond was built, i.e., 1965, a 60-year watershed sediment yield simulation was conducted. This application takes into account the dynamic changes in pond sedimentation, including not only annual changes in rainfall and land use, but also, more importantly, the introduction of a dynamic iterative cycle in the sediment yield calculation process to update the pond capacity and corresponding capture efficiency year by year. This allows for the calibration of the attenuating effect of pond siltation on watershed sediment yield in subsequent years. Specifically, after completing the calculation for one year, this method updates the pond's post-siltation capacity based on the amount of sediment intercepted in that year, and recalibrates the capture efficiency for the next year. This breaks the inherent assumption of a constant STE in traditional models, greatly improving the accuracy and reliability of multi-year, long-sequence watershed sediment yield simulation and prediction.

[0143] like Figure 6 The figure shown is a simulation result of the multi-year sediment yield of the watershed provided in the embodiment of this application. Figure 6 In the diagram, the left vertical axis represents annual sediment yield (t), the right vertical axis represents cumulative sediment yield (t), and the horizontal axis represents the year (y), covering 1965-2024. The scatter plot represents annual sediment yield, showing fluctuations with a peak around 1980; the solid line represents cumulative sediment yield, showing a continuous upward trend. This illustrates the sediment yield and cumulative sediment load in different years of the watershed, reflecting the changing pattern of sediment yield over time. This embodiment is based on the modified general soil loss equation and other methods, recursively calculating sediment yield by combining pond attributes. From the fluctuations in annual sediment yield (scatter plot) and the continuous increase in cumulative sediment yield (scatter plot), it can be seen that it can accurately simulate sediment yield and transport after interception in different years, reflecting the dynamic changes of sediment under multi-stage pond interception within the watershed, and achieving effective simulation of the long-term sediment yield process in the watershed.

[0144] As shown in Tables 1 and 2, the system displays the total erosion of the effective sub-basin, sediment transport ratio (SDR), sediment input into the ponds, sediment capture efficiency (STE), sediment captured by the ponds, and the change in the final pond capacity during the sediment transport process of individual ponds among the 462 ponds in the watershed from 1965 to 2024, over a period of 60 years. It can be seen that the sediment transport ratio (SDR) remains constant over the years because, in this invention, SDR is only related to the watershed area; the change in STE is not very significant because, as can be seen from the STE formula, it is related not only to the current pond capacity but also to the average annual cumulative flow at its inlet. Although the changes in individual ponds may have a small impact on the entire watershed, the cumulative effect of these 462 ponds on the watershed sediment yield is significant.

[0145] Table 1. Information on sediment transport processes in individual ponds within the watershed from 1965 to 2000.

[0146]

[0147] Table 2. Information on sediment transport processes in individual ponds within the watershed from 2001 to 2024.

[0148]

[0149] Example 3:

[0150] Figure 7 This is a schematic diagram of the structure of a recursive sediment yield calculation device for a multi-watershed basin provided in an embodiment of this application. This application also provides a recursive sediment yield calculation device for a multi-watershed basin. Figure 7 As shown, the sediment yield recursive calculation device for this multi-reservoir basin includes:

[0151] Data extraction module 701 is configured to extract flow raster data, flow direction raster data, and reservoir vector point data based on watershed DEM data;

[0152] The path extraction module 702 is configured to use breadth-first search based on the flow grid data and the water storage body vector point data, and search according to the maximum flow direction in eight directions in the grid to extract the grid path from the water storage body point to the outlet, and convert it into vector line data.

[0153] The connectivity construction module 703 is configured to determine the connectivity of the water storage body based on the vector line data;

[0154] The directed graph construction module 704 is configured to use the vector point data of the water storage body as nodes, connect them into directed edges according to the flow direction based on the connectivity of the water storage body, construct a directed graph structure, and add the construction year and original reservoir capacity attributes of the water storage body to the nodes.

[0155] The topology sorting module 705 is configured to perform topology sorting on the directed graph structure, and based on the flow direction grid, obtain the effective sub-basin range of each water storage body, and write the effective sub-basin range of each water storage body into a dictionary with topology sorting to obtain a dictionary structure.

[0156] The recursive calculation module 706 is configured to traverse the dictionary structure, calculate the soil erosion modulus for each effective sub-basin range according to the modified general soil loss equation, calculate the amount of sediment entering the water storage body according to the sediment transport ratio, and calculate the amount of sediment input downstream after sediment interception by combining the sediment capture efficiency of the water storage body.

[0157] The sediment yield recursive calculation device for multi-water-body basins provided in this application embodiment can be used to execute the technical solution of the sediment yield recursive calculation method for multi-water-body basins in the above embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0158] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8 As shown, the electronic device may include a processor 801 and a memory 802, wherein the processor 801 and the memory 802 can communicate; for example, the processor 801 and the memory 802 communicate via a communication bus 803.

[0159] The processor 801 executes computer execution instructions stored in the memory 802, causing the processor 801 to perform the scheme in the above embodiments. The processor 801 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0160] The communication bus 803 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. System buses can be divided into address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0161] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0162] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the above-described method for recursive calculation of sediment yield in multi-watershed basins.

[0163] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the recursive calculation method for sediment yield in multi-watershed basins described in the above embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0165] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0166] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0167] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0168] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0169] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0170] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0171] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0172] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0173] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for recursively calculating sediment yield in a multi-watershed basin, characterized in that, include: Based on watershed DEM data and reservoir vector surface data, extract flow raster data, flow direction raster data, and reservoir vector point data; Based on the flow grid data and water storage vector point data, a breadth-first search is used to search according to the maximum flow direction in eight directions in the grid, extract the grid path from the water storage point to the outlet, and convert it into vector line data. Based on the vector line data, the connectivity of the water storage bodies is determined; Using the vector point data of the water storage body as nodes, and connecting them into directed edges according to the flow direction based on the connectivity of the water storage body, a directed graph structure is constructed, and the construction year and original storage capacity attributes of the water storage body are added to the nodes. The directed graph structure is topologically sorted, and the effective sub-basin range of each water storage body is obtained based on the flow direction grid. The effective sub-basin range of each water storage body is written into a dictionary with topological sorting to obtain a dictionary structure. The dictionary structure is traversed, and the soil erosion modulus of each effective sub-basin is calculated according to the modified general soil loss equation. Based on the sediment transport ratio and the soil erosion of the effective sub-basin, the amount of sediment entering the water storage body is calculated. Combined with the sediment capture efficiency of the water storage body, the amount of sediment intercepted and input downstream is calculated.

2. The method according to claim 1, characterized in that, Based on the watershed DEM data, flow raster data, flow direction raster data, and reservoir vector point data are extracted, including: Based on watershed DEM data, the D8 flow direction algorithm was used to extract flow raster data and flow direction raster. The flow rate raster data is overlaid with the reservoir surface vector data, and the largest raster cell in each reservoir surface is extracted. The largest grid cell in each water storage area is converted into water storage vector point data, and the grid cell with the largest flow rate in the watershed is extracted as the outlet.

3. The method according to claim 1, characterized in that, Based on the vector line data, the connectivity of the water storage bodies is determined, including: Traverse the distances from the starting point in the vector line data to the water body vector point data s in the vector line data, sort the water body points in ascending order of distance, and obtain the connection relationship between the water bodies as the water body connectivity relationship.

4. The method according to claim 1, characterized in that, The modified general soil loss equation is expressed as follows: ； In the formula, E The soil erosion modulus due to hydrodynamics within each unit; It is the erosivity factor of rainfall; It is a soil erodibility factor; This refers to the slope length and gradient factor. C Vegetation cover management factors; P Factors related to soil and water conservation measures; The formula for calculating soil erosion within the effective sub-basin area is as follows: ； In the formula, For the effective sub-basin soil erosion, Let i be the soil erosion modulus of grid cell i; Let i be the area of ​​grid cell i; This represents the amount of sediment transported by the upstream pond. If there is no upstream pond, the amount of sediment transported by the upstream pond is 0.

5. The method according to claim 1, characterized in that, The formula for calculating the sediment transport ratio is as follows: ； In the formula, The sediment transport ratio of the reservoir's sub-basin; , These are undetermined coefficients, depending on the characteristics of the watershed; The area of ​​the sub-basin.

6. The method according to claim 1, characterized in that, The formula for calculating the sediment capture efficiency is as follows: ； In the formula, The sediment capture efficiency of the water storage body w; , Let w be the hydraulic residence time of the water reservoir. This refers to the current reservoir capacity. The average annual cumulative flow at the inlet of the reservoir is w. It is a constant.

7. The method according to claim 4, characterized in that, The amount of sediment input downstream after sediment interception is calculated using the following formula: ； In the formula, The amount of sediment input downstream after sediment interception; The sediment capture efficiency of the water storage body w; The sediment transport ratio of the water storage body w in the sub-basin.

8. A recursive calculation device for sediment yield based on a multi-watershed basin, characterized in that, include: The data extraction module is configured to extract flow raster data, flow direction raster data, and reservoir vector point data based on watershed DEM data and reservoir vector surface data. The path extraction module is configured to use breadth-first search based on the flow grid data and the reservoir vector point data, and search according to the maximum flow direction in eight directions in the grid to extract the grid path from the reservoir point to the outlet, and convert it into vector line data. The connectivity construction module is configured to determine the connectivity of the water storage body based on the vector line data; The directed graph construction module is configured to use the vector point data of the water storage body as nodes, connect them into directed edges according to the flow direction based on the connectivity of the water storage body, construct a directed graph structure, and add the construction year and original reservoir capacity attributes of the water storage body to the nodes. The topology sorting module is configured to perform topology sorting on the directed graph structure, and based on the flow direction grid, obtain the effective sub-basin range of each water storage body, and write the effective sub-basin range of each water storage body into a dictionary with topology sorting to obtain a dictionary structure. The recursive calculation module is configured to traverse the dictionary structure, calculate the soil erosion modulus for each effective sub-basin area according to the modified general soil loss equation, calculate the amount of sediment entering the water storage body according to the sediment transport ratio, and calculate the amount of sediment input downstream after sediment interception by combining the sediment capture efficiency of the water storage body.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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