A method and related device for coupling and parallel computing of slope and river confluence

Abstract

The application discloses a kind of slope and river channel confluence coupling parallel computing method and related device, method includes: the cumulative confluence area of each cell is calculated based on the flow direction relationship between each cell to identify slope cell and river channel cell;Based on the flow direction relationship between each cell, simulate the slope confluence movement of study area, and calculate the hydrological flow data of each slope cell and river channel cell in parallel;Simulate the river channel confluence movement of study area, according to the hydrological flow data, calculate the inflow data of each river channel cell at the current time step in parallel;According to inflow data, update the water storage of each river channel cell at the next time step in parallel based on linear storage confluence method;Based on water storage, update the outflow data of each river channel cell at the next time step in parallel.The application realizes the coupling simulation of slope and river channel confluence;Through linear storage, the water quantity of each cell is updated, and the river channel confluence does not need sequential iteration, so as to realize the synchronous update of whole watershed cell.

Description

Technical Field

[0001] This invention relates to the field of hydrological simulation technology, and in particular to a parallel calculation method and related apparatus for coupled slope and river confluence. Background Technology

[0002] Flash floods are a serious natural disaster threatening life and property in mountainous and riverine areas. They are characterized by their suddenness, destructive power, and short duration, making rapid and accurate flash flood simulation and prediction crucial for large-scale operations. Simulations of flash flood confluence processes are characterized by high spatial and temporal scales and large computational demands. The flash flood confluence process includes two main stages: slope confluence and river channel confluence. Slope confluence is the initial stage of flash flood formation, where water flows from the slope into the river channel; river channel confluence is the process of water transmission and collection within the river system. Traditional methods for calculating river channel confluence (such as the Muskingan method and numerical solutions to the Saint-Venant equation) often employ sequential iterative models, relying on the outflow results from upstream units, making rapid updates difficult in large-scale, high-resolution watersheds. With increasing simulation spatial resolution and shorter warning time requirements, the shortcomings of traditional methods in terms of computational efficiency, memory usage, and parallel scalability have become increasingly prominent, making it difficult to support large-scale real-time warning needs. Summary of the Invention

[0003] This invention provides a parallel calculation method and related apparatus for coupled slope and river confluence, which solves the technical problem that existing confluence calculation methods adopt a sequential iterative mode, whose calculations depend on the outflow results of upstream units, making it difficult to achieve rapid updates in a large-scale, high-resolution watershed, thus making it difficult to support the technical requirements for large-scale real-time early warning of flash floods.

[0004] This invention provides a parallel computational method for coupled slope and river confluence, the method comprising:

[0005] The study area is discretized into multiple cells and the hydrogeographic attributes of each cell are initialized.

[0006] Based on the D8 algorithm, the flow direction relationship between each cell is determined according to the hydrogeographic attributes of each cell.

[0007] Based on the flow direction relationship between each cell, the cumulative runoff area of ​​each cell is calculated, and the slope cells and river cells are identified according to the cumulative runoff area of ​​the cells.

[0008] Based on the flow direction relationship between each cell, the slope confluence motion of the study area is simulated, and the hydrological flow data of each slope cell and the river cell are calculated in parallel according to the hydrogeographic attributes.

[0009] The river confluence motion in the study area is simulated. Based on the hydrological flow data of the slope cells and the river cells, the inflow data of each river cell at the current time step is calculated in parallel. Based on the linear reservoir confluence method, the water storage of each river cell at the next time step is updated in parallel based on the inflow data. Based on the water storage, the outflow data of each river cell at the next time step is updated in parallel.

[0010] Optionally, the step of discretizing the study area into multiple cells and initializing the hydrogeographic attributes of each cell includes:

[0011] Acquire basic data of the study area, and discretize the study area into a two-dimensional regular cellular grid based on the basic data of the study area; the two-dimensional regular cellular grid includes multiple cells.

[0012] The hydrogeographic attributes of all cells in the two-dimensional regular cell grid are initialized based on the aforementioned basic data.

[0013] Optionally, the step of determining the flow direction relationship between each cell based on the hydrogeographic attributes of each cell using the D8 algorithm includes:

[0014] Based on the D8 algorithm, the distance weight difference between each cell and its neighboring cells is determined according to the hydrogeographic attributes.

[0015] The adjacent cell with the largest distance weight difference is selected as the downstream cell, thereby determining the flow direction relationship between each cell.

[0016] Optionally, the step of calculating the cumulative runoff area of ​​each cell based on the flow direction relationship between each cell, and identifying slope cells and channel cells based on the cumulative runoff area of ​​the cells, includes:

[0017] Based on the flow direction relationship between each cell, calculate the cumulative catchment area of ​​each cell;

[0018] The cells are initially divided based on the cumulative confluence area; if the cumulative confluence area is greater than or equal to a preset area threshold, the cell is determined to be a river cell; otherwise, the cell is determined to be a slope cell.

[0019] Optionally, the step of simulating slope confluence motion in the study area based on the flow direction relationship between each of the cells, and calculating hydrological flow data for each of the slope cells and the channel cells in parallel, includes:

[0020] Based on the flow direction relationship between each cell, the slope confluence motion of the study area is simulated;

[0021] Calculate the hydraulic gradient of each slope cell and channel cell at the current time step based on the hydrogeographic attributes; calculate the flow velocity and flow rate of each slope cell and channel cell at the current time step based on the Manning formula and the hydraulic gradient.

[0022] Based on the flow rate, the transferable water volume of each slope cell and the channel cell within the time step interval is determined under physical constraints.

[0023] Based on the transferable water volume, the water depth and water storage volume of each slope cell and the channel cell at the next time step are predicted.

[0024] Optionally, the steps of simulating the river confluence movement in the study area, which involves parallel calculation of the inflow data for each river cell at the current time step based on the hydrological flow data of the slope cells and the river cells; updating the water storage of each river cell at the next time step based on the inflow data using the linear reservoir confluence method; and updating the outflow data of each river cell at the next time step based on the water storage, include:

[0025] Simulate the confluence movement of the river channels in the study area, and determine the amount of water transferred by the upstream cells of each river channel cell at the current time step based on the hydrological flow data of the slope cells and the river channel cells, thereby determining the total inflow of each river channel cell at the current time step.

[0026] The inflow rate of each channel cell is calculated based on the total inflow rate of each channel cell within the time step interval.

[0027] Based on the linear reservoir capacity confluence method, the predicted water storage volume obtained in the slope confluence motion is updated according to the total inflow and inflow rate of each channel cell within the time step interval, so as to obtain the target water storage volume of each channel cell in the next time step.

[0028] Based on the target water storage volume of each channel cell in the next time step, calculate the outflow of each channel cell in the next time step;

[0029] Calculate the outflow velocity of each channel cell in the next time step based on the outflow rate of each channel cell in the next time step.

[0030] Optionally, the total inflow rate of the channel cell is calculated as follows:

[0031]

[0032] In the formula: For the river cells at the current time step Total inflow; The total amount of water transferred from the upstream slope cell to the channel cell i at time step t; The total amount of water transferred from upstream channel cell to channel cell i at time step t; The rainfall intensity falling into the slope water storage unit at time step t; This represents the actual area of ​​the cell;

[0033] The inflow rate of the river cell is calculated as follows:

[0034]

[0035] In the formula: The inflow rate of the river cell; The time step interval;

[0036] The method for calculating the target water storage capacity of the river cell in the next time step is as follows:

[0037]

[0038] In the formula: The target water storage volume for the river cell in the next time step t+1; The water storage of a river cell at time step t; This is the storage capacity constant; For stock coefficient;

[0039] The outflow rate of the channel cell in the next time step is calculated as follows:

[0040]

[0041] In the formula: The outflow rate of the river cell at the next time step t+1;

[0042] The outflow velocity of the channel cell in the next time step is calculated as follows:

[0043]

[0044] In the formula: Let be the outflow velocity of the river cell at the next time step t+1.

[0045] This invention also provides a parallel computing system coupling slope and river confluence, the system comprising:

[0046] A cell initialization unit is used to discretize the study area into multiple cells and initialize the hydrogeographic attributes of each cell.

[0047] A cell flow direction relationship determination unit is used to determine the flow direction relationship between each cell based on the D8 algorithm and the hydrogeographic attributes of each cell.

[0048] The slope and river cell identification unit is used to calculate the cumulative runoff area of ​​each cell based on the flow direction relationship between each cell, and to identify slope cells and river cells based on the cumulative runoff area of ​​the cells.

[0049] The slope confluence simulation unit is used to simulate the slope confluence movement in the study area based on the flow direction relationship between each cell, and to calculate the hydrological flow data of each slope cell and the river cell in parallel according to the hydrogeographic attributes.

[0050] The river confluence simulation unit is used to simulate the river confluence movement in the study area. It calculates the inflow data of each river cell in parallel at the current time step based on the hydrological flow data of the slope cells and the river cells. Based on the linear reservoir confluence method, it updates the water storage of each river cell in parallel at the next time step based on the inflow data. Based on the water storage, it updates the outflow data of each river cell in parallel at the next time step.

[0051] The present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the parallel computation method for coupled slope and river confluence as described above.

[0052] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the parallel computation method for coupled slope and river confluence as described above.

[0053] As can be seen from the above technical solutions, the present invention has the following advantages:

[0054] This invention provides a parallel computation method and related apparatus for coupled slope and river confluence. The method includes: discretizing the study area into multiple cells and initializing the hydrogeographic attributes of each cell; determining the flow direction relationship between each cell based on the D8 algorithm according to the hydrogeographic attributes of each cell; calculating the cumulative confluence area of ​​each cell based on the flow direction relationship between each cell, and identifying slope cells and river cells based on the cumulative confluence area; simulating slope confluence movement in the study area based on the flow direction relationship between each cell, and calculating the hydrological flow data of each slope cell and river cell in parallel according to the hydrogeographic attributes; simulating river confluence movement in the study area, and calculating the inflow data of each river cell in parallel at the current time step based on the hydrological flow data of the slope cells and river cells; updating the water storage of each river cell in parallel at the next time step based on the linear reservoir confluence method according to the inflow data; and updating the outflow data of each river cell in parallel at the next time step based on the water storage.

[0055] This invention decomposes the physical process of slope and river confluence into two highly parallelizable hydrodynamic computational stages, achieving coupled simulation of slope and river confluence. While ensuring water conservation and numerical stability, it completely resolves the sequential dependency problem in river confluence through a linear reservoir analytical solution. The water volume update of each cell depends only on the previous time-stamped water storage and inflow, eliminating the need for sequential iteration of river confluence. This allows for parallel updates of each grid within the same time step, thus achieving synchronous updates of cells across the entire watershed. While ensuring the rationality of the hydrophysical mechanism, it significantly improves the computational speed and scalability of large-scale, high-resolution flash flood simulation, providing efficient and reliable technical support for real-time early warning and refined watershed management. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating the steps of a parallel computation method for coupled slope and river confluence provided in an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of the D8 algorithm provided in an embodiment of the present invention;

[0059] Figure 3 This is a generalized diagram of cell recognition provided in an embodiment of the present invention;

[0060] Figure 4 A schematic diagram of slope confluence provided in an embodiment of the present invention;

[0061] Figure 5 A schematic diagram of river confluence provided for an embodiment of the present invention;

[0062] Figure 6 A schematic diagram of flood calculation using the Muskingan method, which can be implemented using existing technologies, provided as an embodiment of the present invention;

[0063] Figure 7 This is a structural block diagram of a parallel computing system coupled with the confluence of slope and river channel provided in an embodiment of the present invention. Detailed Implementation

[0064] This invention provides a parallel calculation method and related apparatus for coupled slope and river confluence, which solves the technical problem that existing confluence calculation methods adopt a sequential iterative mode, whose calculations depend on the outflow results of upstream units, making it difficult to achieve rapid updates in a large-scale, high-resolution watershed, thus making it difficult to support the technical requirements for large-scale real-time early warning of flash floods.

[0065] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0066] It should be noted that, in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of the present invention involve data related to an object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region.

[0067] This application involves concepts related to hydrological simulation. For ease of understanding, the relevant concepts of hydrological simulation involved in this application are introduced below.

[0068] Runoff: The process by which precipitation, after falling to the ground, undergoes processes such as interception and infiltration to form surface runoff.

[0069] Convergence: The process by which runoff from different locations converges along the terrain to a river channel or outlet.

[0070] Slope runoff refers to the process by which surface water flows down a terrain slope from higher to lower elevations after rainfall or runoff and flows into rivers or depressions. The main driving forces of slope runoff are gravity and topographic slope. The process is influenced by factors such as surface roughness, underlying surface type, soil permeability, and rainfall intensity, and it is the initial stage of flash flood formation.

[0071] River confluence refers to the process by which water flows into a river system and is transported, collected, and discharged along the main channel and its tributaries. River confluence is characterized by effects such as peak propagation, flow lag, and peak reduction, and serves as the main channel for water flow movement in a basin and a key element in downstream flood response.

[0072] Underlying surface type: This refers to the specific type of land cover, such as vegetation, bare land, buildings, roads, etc. It determines the behavior of water infiltration, runoff, and evaporation after rainfall. Different underlying surface types have very different runoff characteristics; for example, grasslands are easily infiltrated, while urban paved surfaces generate runoff rapidly.

[0073] Water storage capacity: The amount of water temporarily stored in the model unit / cell.

[0074] DEM: A grid of data that digitally represents ground elevation. Each cell represents the elevation value of a location and is used to determine water flow direction, slope, confluence path, etc.

[0075] Elevation: refers to the absolute vertical distance from a point on the ground along the plumb line to a recognized reference surface (usually mean sea level).

[0076] Grid cell: The study area is divided into several small grids (cells). Each grid represents a small area of ​​the earth's surface and is the smallest computational unit of the model.

[0077] Cellular Automata (CA), as a discrete mathematical model, is characterized by its simplicity, parallel computation capability, and ease of integration into Geographic Information Systems (GIS). In recent years, it has been widely researched and applied in the dynamic simulation of GIS. A CA model typically consists of five basic features: cells representing discrete space, each cell having a state, the distribution of neighboring cells, a discrete time step, and a set of transition rules. Cellular automata can divide a watershed into pixels or cells, each with its own state and rules. By simulating and updating the states of cells, the runoff generation and merging processes of the watershed can be simulated. The simplifying nature of cellular automata allows models to run in parallel environments, significantly improving modeling efficiency. CA models are a simple and general method for modeling complex physical systems. Compared to physical models, this simplification greatly reduces the computational load of CA models.

[0078] The Muskingan method is a classic method for calculating river confluence, describing the relationship between inflow, outflow, and storage in a river segment through a continuity equation and a storage equation. Its core parameters are the lag coefficient K (representing the time it takes for water to pass through the segment) and the weighting coefficient X (reflecting the degree of influence of inflow on storage). While the Muskingan method is simple in form and computationally stable, it relies on upstream results when calculating downstream flow, thus limiting its parallel computation capabilities and making it unsuitable for large-scale, high-resolution flood simulations.

[0079] Linear reservoir capacity runoff method: It is assumed that there is a linear relationship between the water storage S and the outflow Q in the reservoir (or runoff unit). By combining the water volume continuity equation, a first-order differential equation can be obtained. The linear reservoir method has fewer parameters, better stability, and can be updated in parallel under the framework of cellular automata, making it suitable for large-scale hydrological and hydrodynamic simulation.

[0080] Manning's formula refers to an empirical formula for the flow or velocity of open channels, which is often used in physical calculations, water conservancy construction, and other activities.

[0081] Channel storage equation: used to describe the relationship between water accumulation and outflow in confluence channels (such as slopes or ditches).

[0082] Water balance equation: Based on the principle of mass conservation, the amount of water entering the system, the amount of water leaving the system, and the change in storage volume must be equal.

[0083] Courant number condition: The Courant number (denoted as C) is an important dimensionless parameter in numerical computation that measures the relationship between the "time step" and the "spatial step," and is used to ensure computational stability. To ensure computational stability, C ≤ 1 is usually required.

[0084] Please see Figure 6 The following section provides further explanation of flood calculations using the Muskingan method, which can be implemented with existing technology:

[0085] The Muskingan method is a river flow calculation algorithm based on the channel storage equation and the water balance equation. As floodwaters enter the river channel from upstream and flow out downstream, the flow rate does not immediately follow the upstream changes, but rather exhibits effects such as lag and peak shaving.

[0086] Therefore, the Muskingan method is a simplified model of water balance and storage relationship, used to characterize the relationship between inflow, outflow, and river storage. The parameters involved include the time lag parameter K and the weighting coefficient X.

[0087] (1) Water continuity equation:

[0088] Within a certain section of a river, during a certain period of time :

[0089]

[0090] In the formula: S is the river storage capacity; I is the inflow rate; O is the outflow rate.

[0091] (2) Water storage equation:

[0092] The water storage capacity S in a cell depends on the inflow I and the outflow O. However, in reality, the water storage capacity in a river channel is not entirely determined by either the inflow or the outflow, but rather by a coupling of both. Therefore, Muskingan's method assumes a linear weighted relationship between the water storage capacity in the river channel and the inflow and outflow:

[0093]

[0094] Where: K is the time delay parameter of the river section (time, representing the approximate time required for water to flow through the river section); X is the weighting coefficient (dimensionless, between 0 and 0.5, reflecting the degree of influence of the inflow on the water storage).

[0095] (3) Calculation formula:

[0096] After discretization, the commonly used flood calculation formula is:

[0097]

[0098] in: This represents the total outflow for the next time period; This refers to the inflow into the river segment at time t+Δt (usually the outflow from the upstream section at time t+Δt). This represents the inflow into the river segment at time t (usually the outflow from the upstream section at time t). This represents the outflow from the downstream section at time t; , and These are the outflow coefficients for the previous time period;

[0099] The coefficients are calculated as follows:

[0100]

[0101]

[0102]

[0103] The length L of the river segment must satisfy the Courant number condition with respect to the time interval step Δt and the flood wave velocity c:

[0104]

[0105] The Muskingan method, a classic hydrological runoff method, relates parameters K and X to factors such as river length, slope, and flood wave propagation velocity. Its simplicity and intuitive parameters have led to its widespread application in practice. However, this method relies on flow inputs from upstream and downstream at different times, making parallel computation impossible. Consequently, it cannot fully leverage the advantages of large-scale parallel computing on CPUs and GPUs during flood simulations.

[0106] According to the flood calculation formula of the Muskingan method, to calculate the outflow of a certain river segment at time t+1... It requires the use of upstream inflow. But upstream The data itself must be calculated using the Muskingan method. This creates an upstream-downstream sequential dependency at the current time level. In other words, the Muskingan method's flood calculation must calculate upstream data first before calculating downstream data; it cannot be performed in parallel across the entire watershed at once.

[0107] To address the technical challenge of existing runoff calculation methods employing a sequential iterative approach, which relies on the outflow results of upstream units and struggles to achieve rapid updates across large-scale, high-resolution watersheds, this invention proposes a parallel computation method coupling slope and channel runoff. This method uses a linear reservoir capacity analytical solution as its core, discretizing the watershed into a regular cellular grid, and achieving unified calculation of slope runoff generation, slope runoff, and channel runoff through local evolution rules. Within each time step, the state update of each cell depends only on the water storage volume of the previous time step and external inputs, without relying on sequential iteration of river segments, thus fully leveraging the large-scale parallel computing advantages of CPUs / GPUs. Compared to traditional methods, this invention not only ensures the lag and peak-shaving characteristics of flood propagation but also overcomes numerical stability constraints and sequential iteration bottlenecks, making it possible to simulate flash floods in complex river networks and large-scale, high-resolution scenarios.

[0108] Please see Figure 1 This invention provides a parallel computational method for coupled slope and river confluence, the method comprising:

[0109] Step 101: Discretize the study area into multiple cells and initialize the hydrogeographic attributes of each cell;

[0110] Understandably, to achieve parallelizable slope and river network coupled runoff calculations, the study area needs to be discretized into two-dimensional cells, each representing a slope or river channel unit, to ensure a balance between simulation accuracy and computational efficiency. The hydrogeographic attributes of each cell can include elevation, underlying surface type, permeability coefficient, and water depth. Each cell possesses complete geographic and hydrological characteristic information, providing a data foundation for subsequent calculations of slope runoff generation, runoff processes, and river flow transport, reflecting both topographic features and the influence of surface type on flow processes.

[0111] In one specific implementation, step 101 may include the following sub-steps:

[0112] S11. Obtain the basic data of the study area, and discretize the study area into a two-dimensional regular cellular grid based on the basic data of the study area; the two-dimensional regular cellular grid includes multiple cells.

[0113] S12. Initialize the hydrogeographic attributes of all cells in the two-dimensional regular cell grid based on the basic data.

[0114] It should be noted that the basic data for the study area includes topography, land use, soil, and meteorology. First, based on the topographic data, the study area is discretized into a two-dimensional regular cellular grid. Each grid cell is defined as a unit of computation, representing the basic unit of slope or river channel, and a corresponding identifier is assigned to each cell. Next, the hydrogeographic attributes of the cells are initialized based on the topographic, land use, soil, and meteorological data, including key hydrogeographic information such as elevation, underlying surface type, permeability coefficient, water depth, and water storage capacity. This provides an accurate spatial framework and physical basis for simulating slope runoff and river runoff processes.

[0115] Step 102: Based on the D8 algorithm, determine the flow direction relationship between each cell according to the hydrogeographic attributes of each cell.

[0116] It should be noted that the D8 algorithm (Deterministic 8 algorithm) is a commonly used method for calculating water flow direction, mainly used in hydrological analysis in digital elevation models (DEMs). This embodiment is based on the D8 algorithm. By using the elevation data in the hydrogeographic attributes of each cell, the flow direction relationships between each cell can be determined. This constructs a deterministic, terrain-driven flow direction topology network for all cells, which forms the basis for subsequent calculations of cumulative catchment area, slope and channel cell identification, and parallel water transport calculations among all cells. This ensures the terrain-driven nature of the simulation process and the stability of the computational framework.

[0117] In one specific embodiment, step 102 may include the following steps:

[0118] S21. Based on the D8 algorithm, determine the distance weight difference between each cell and its neighboring cells according to hydrological and geographical attributes.

[0119] S22. Select the adjacent cell with the largest distance weight difference as the downstream cell to determine the flow relationship between each cell.

[0120] It should be noted that the D8 algorithm assumes that water flow in a single grid can only flow into the eight adjacent grids, and the direction of water flow is determined by calculating the distance weight difference between the central grid and each adjacent grid.

[0121] Specifically, please refer to Figure 2 Based on the D8 algorithm, it is assumed that a single cell (with the central cell in the diagram) For example, water can only flow into the 8 adjacent cells. ~ In the process, the steepest slope method is used to determine the direction of water flow. That is, on a 3×3 DEM cell, the distance weight difference between the central cell and each adjacent cell is calculated, and the cell with the largest distance weight difference is taken as the outflow cell (downstream cell) of the central cell.

[0122] The D8 algorithm is used to determine the flow direction of each cell. Each cell i has a definite downstream cell j in each time step (j Given the flow direction, a terrain-driven cellular topology network can be dynamically constructed, laying the structural foundation for subsequent parallel computing.

[0123] In this specific embodiment, the D8 algorithm is used to determine the water level elevation difference between each cell and its neighboring cells based on the elevation and water depth information in the hydrogeographic attributes, thereby determining the distance weight difference between each cell and its neighboring cells; the neighboring cell with the largest distance weight difference is selected as the downstream cell.

[0124] The specific formula for the D8 algorithm is as follows:

[0125]

[0126]

[0127] In the formula: The difference in water level between cell i and cell j; and These are the center elevations of cells i and j, respectively; and The water depths of cells i and j are respectively; and The ground elevations of cells i and j are respectively. The distance weight difference between cell i and cell j The center-to-center distance between cells i and j; This is a mathematical operator that returns the neighboring cell that maximizes the difference in distance weights. The index position.

[0128] Step 103: Based on the flow direction relationship between each cell, calculate the cumulative runoff area of ​​each cell, and identify the slope cells and river cells based on the cumulative runoff area of ​​the cells.

[0129] It should be noted that the cumulative catchment area reflects the cell's position in the catchment network and its contribution to the catchment area. It is a fundamental indicator for determining the river's origin, flow direction topology, and the division between slopes and channels. A larger cumulative catchment area indicates that the cell collects more runoff and is more likely to be located in the direction of the river channel or main flow. This embodiment identifies slope cells and channel cells based on the calculated cumulative catchment area.

[0130] In one specific embodiment, step 103 may include the following steps:

[0131] S31. Based on the flow direction relationship between each cell, calculate the cumulative catchment area of ​​each cell;

[0132] S32. The cells are initially divided according to the cumulative runoff area; if the cumulative runoff area is greater than or equal to the preset area threshold, the cell is determined to be a river cell; otherwise, the cell is determined to be a slope cell.

[0133] Based on the flow relationships between each cell, the cumulative catchment area of ​​each cell can be calculated recursively, i.e.:

[0134]

[0135]

[0136] In the formula: Let i be the cumulative flow area of ​​cell i; Let i be the cumulative number of confluence cells in cell i; The actual area of ​​each cell; Let i be the set of all upstream units that flow to cell i.

[0137] During the confluence of flash floods, the characteristics of water flow vary significantly at different locations. Water flow on slopes is usually mainly surface runoff and infiltration, with a slower flow velocity and dispersed volume; however, after entering the river channel, the water flow rapidly concentrates, forming a significant confluence and backwater process, with a faster flow velocity and more dramatic changes in flow rate.

[0138] If the same confluence parameters and calculation methods are used without distinguishing between slopes and channels during calculation, the simulation results will be biased, thus affecting the accuracy of flash flood confluence prediction. Therefore, this specific implementation identifies slope cells and channel cells to use different confluence calculation methods respectively, thereby achieving accurate characterization of the physical process and improving the overall calculation accuracy and stability of the model.

[0139] By calculating the cumulative catchment area based on the D8 flow direction algorithm, the catchment contribution range of each cell can be quantitatively identified. When the cumulative catchment area exceeds a preset area threshold... When the value is determined, it indicates that the cell has significant water catchment characteristics and can be classified as a river channel cell; the rest are slope cells. This method enables the model to automatically divide slopes and river networks under terrain-driven conditions, providing a spatial structural foundation for subsequent hydrodynamic coupling and parallel computation. A preset area threshold is used. The calibration can be performed based on the DEM resolution of the study area, existing river system vectors, or measured river network density in the watershed, thereby ensuring that the identified river network is consistent with the actual hydrological characteristics.

[0140] If the cumulative catchment area of ​​a cell meets a preset area threshold, the cell is assigned to the river cell set. Conversely, it is classified into the slope cell set. ,Right now:

[0141]

[0142] Understandably, to further improve the accuracy of the division, the preliminary division results of Equation (12) can be corrected based on measured river system vector data, remote sensing interpretation of river network numbers, and topographic indices. Through comparison and iterative correction, the spatial distribution of river channels identified by the model can be made consistent with the actual hydrological and geomorphological characteristics, thereby improving the accuracy and stability of subsequent slope-channel coupled confluence calculations. The simplified cellular division diagram of the study area is shown below. Figure 3 As shown.

[0143] Step 104: Based on the flow direction relationship between each cell, simulate the slope confluence movement in the study area, and calculate the hydrological flow data of each slope cell and river cell in parallel according to the hydrogeographic attributes.

[0144] In this embodiment, potential water transfer paths are determined based on the flow direction relationships between each cell. Cellular automata (CA) are used to simulate terrain-driven slope confluence motion, performing hydrodynamic calculations on all cells within the watershed (including channel and slope cells). This updates the hydrological flow data for each slope and channel cell, achieving efficient parallel computation across the entire watershed. This significantly improves simulation speed and provides the necessary input water volume data for the next stage of regulation calculations for channel cells. The hydrological flow data for each cell includes hydraulic gradient, velocity, flow rate, and water depth.

[0145] Understandably, each cell can independently store and update its hydrological state, and simulate the evolution of water flow through local rules. Water flow spontaneously converges (merging processes) through interactions between cells. Through integrated generation and confluence computational analysis, the direction, velocity, and volume of water flow are dynamically calculated to update the water depth in each cell.

[0146] In one specific implementation, step 104 may include the following steps:

[0147] S41. Based on the flow direction relationship between each cell, simulate the slope confluence motion of the study area;

[0148] S42. Calculate the hydraulic gradient of each slope cell and channel cell at the current time step based on hydrogeographic attributes; calculate the flow velocity and flow rate of each slope cell and channel cell at the current time step based on the Manning formula and the hydraulic gradient.

[0149] S43. Based on flow rate, determine the transferable water volume of each slope cell and channel cell under physical constraints.

[0150] S44. Based on the transferable water volume, predict the water depth and storage volume of each slope cell and channel cell at the next time step.

[0151] In this specific embodiment, based on the established flow direction relationship, hydrodynamic calculations are performed simultaneously on all slope cells and channel cells within the watershed. Each cell determines its hydraulic gradient based on the difference in water level elevation between itself and its downstream neighboring cells, independently calculates the flow velocity and flow rate using the Manning formula, and determines the actual transferable water volume through physical constraints. Finally, the water depth and water storage of each slope cell and channel cell are updated and predicted in parallel.

[0152] The flow direction relationship is based on the principle of water flowing naturally from high ground to low ground. The hydraulic gradient is determined by the elevation difference between adjacent cells, i.e.:

[0153]

[0154] In the formula: Let i be the hydraulic gradient between cell i and cell j at time step t. and Let be the water depths of cells i and j at time step t, respectively.

[0155] If the elevation of the central cell i is The elevation of the neighboring cell j of the lowest point is ,like This indicates that the central cell i can flow to its neighboring cell j. The Manning formula is a classic empirical formula in hydraulics and hydrology used to calculate the velocity or flow rate of open channels (such as rivers and ditches). In this specific embodiment, the Manning formula is used to calculate the velocity of the flow from cell i to cell j, that is:

[0156] (14)

[0157] In the formula: for is the roughness coefficient; b is the width of the cell.

[0158] Flow rate is the volume of water flowing through a cross-section per unit time. Based on Manning's formula, the flow rate from cell i to cell j is calculated according to the flow velocity from cell i to cell j, that is:

[0159]

[0160] In the formula: The flow rate (m) from cell i to cell j 3 / s).

[0161] Therefore, the time step interval of cell i can be calculated. The amount of water flowing into the downstream cell j ,Right now:

[0162]

[0163] It is worth noting that, for a given flow direction, when calculating the flow volume, the water flow stops if the potential energy difference between the central cell and the neighboring cells is zero or if the central cell has insufficient water supply. Therefore, the maximum transferable water volume is determined by the following formula:

[0164]

[0165] In the formula: Let be the maximum amount of water that can be transferred from cell i to cell j.

[0166] According to equation (17), when hour, The water stops flowing. This illustrates that the amount of water flowing to neighboring cells is determined by the smaller of the potential flow capacity (elevation difference) between the central cell and neighboring cells and the actual flowable water volume (water depth) of the central cell. Therefore, the actual transferable water volume of slope runoff needs to satisfy physical constraints, including gravity constraints and water conservation constraints. Under gravity constraints, water in a cell can only flow to neighboring cells with a lower total water level (ground elevation + water depth). If the water level of a neighboring cell is higher, water cannot be allocated to it. Under water conservation constraints, the amount of water a cell can allocate cannot exceed the amount of water corresponding to its total water depth at the current moment.

[0167] To ensure that excessive water volume is allocated beyond physical constraints, the actual transferable water volume of slope runoff is:

[0168]

[0169] In the formula: This represents the actual amount of water that can be transferred in cell i.

[0170] Based on equation (18), the propagated water volume is moved from the upstream cell to the downstream cell. For each time step, the water depth and volume of each cell need to be updated.

[0171] For cell i, within the time step interval Δt of the next time step t+1 at time t, the amount of water exchanged is the sum of the amount of water transferred by i as the central cell and the amount of water acquired by i as a downstream cell of other cells. Therefore, the water depth of cell i at time t+1 is:

[0172] (19)

[0173] In the formula: Let i be the water depth at the next time step t+1; Let i be the water depth at the current time step t; Time step interval The total amount of water flowing into cell i, including the amount of water transferred from upstream cells and the precipitation at the current time step; Time step interval The total amount of water flowing out of cell i. Then, in the next time step t+1, if... Figure 4 As shown, downstream cell j will receive the actual transferable water volume from cell i. ,as well as The precipitation within the cell is taken as the total inflow of water onto the slope of cell j.

[0174] After calculating the water depth of each cell, the water depth can be multiplied by the actual area of ​​the cell to calculate the water storage of each cell in the next time step.

[0175] Step 105: Simulate the confluence movement of the river channels in the study area. Calculate the inflow data of each channel cell in parallel at the current time step based on the hydrological flow data of the slope cells and channel cells. Based on the linear reservoir confluence method, update the water storage of each channel cell in parallel at the next time step based on the inflow data. Update the outflow data of each channel cell in parallel at the next time step based on the water storage.

[0176] In this embodiment, the cellular automata uses hydrological flow data from each cell provided during the slope confluence stage as input to perform parallel calculations for the channel cells. The inflow data includes inflow volume and inflow rate; the outflow data includes outflow volume and outflow velocity. First, based on the hydrological flow data from both the slope and channel cells, the inflow data for each channel cell is obtained by parallel aggregating the water volume from all upstream cells (slope and channel cells). Then, using this inflow data as input, a new storage capacity is calculated in parallel using a linear reservoir capacity analytical solution. Finally, based on the principle of water conservation, the outflow data that each channel cell should pass to the next time step is calculated synchronously.

[0177] This embodiment leverages the analytical properties of the linear reservoir capacity model to achieve dependency-free parallel updates of the water storage and outflow states of all river cells within the same time step, overcoming the sequential computation bottleneck of traditional river confluence methods. This not only accurately simulates the flood regulation effect of rivers but also enables highly efficient and refined dynamic simulation of large-scale river networks.

[0178] In one specific implementation, step 105 may include the following steps:

[0179] S51. Simulate the confluence movement of the river in the study area. Based on the hydrological flow data of the slope cells and the river cells, determine the amount of water transferred by the upstream cells of each river cell within the time step interval, thereby determining the total inflow of each river cell within the time step interval.

[0180] S52. Calculate the inflow rate of each channel cell based on the total inflow rate of each channel cell within the time step interval.

[0181] S53. Based on the linear reservoir capacity confluence method, the predicted water storage volume obtained in the slope confluence motion is updated according to the total inflow and inflow rate of each channel cell in the time step interval, so as to obtain the target water storage volume of each channel cell in the next time step.

[0182] S54. Calculate the outflow of each channel cell in the next time step based on the target water storage volume of each channel cell in the next time step.

[0183] S55. Calculate the outflow velocity of each channel cell in the next time step based on the outflow rate of each channel cell in the next time step.

[0184] Please see Figure 5 For any channel cell i, within each time step from t to t+1, the total inflow into channel cell i includes the slope storage transferred from upstream cells, the channel storage, and the rainfall at time t. Therefore, the total inflow is:

[0185]

[0186] In the formula: For the river cells at the current time step Total inflow; The total amount of water transferred from the upstream slope cell to the channel cell i at time step t; The total amount of water transferred from upstream channel cell to channel cell i at time step t; The rainfall intensity falling into the slope water storage unit at time step t; This represents the actual area of ​​the cell.

[0187] Inflow rate of channel cells (m)3 The calculation method for / s is as follows:

[0188]

[0189] In the formula: The inflow rate of the river cell.

[0190] Based on the analytical solution of linear storage capacity The method for calculating the target water storage capacity of the river cell in the next time step is derived as follows:

[0191]

[0192] In the formula: The target water storage volume for the river cell in the next time step t+1; The water storage of a river cell at time step t; This is the storage capacity constant; For stock coefficient;

[0193] The outflow rate of river cell i at time t+1 is calculated as follows:

[0194]

[0195] In the formula: The outflow rate of the river cell at the next time step t+1;

[0196] Therefore, the outflow velocity of the channel cell in the next time step is calculated as follows:

[0197]

[0198] In the formula: Let be the outflow velocity of the river cell at the next time step t+1.

[0199] This invention achieves synchronous confluence evolution of slopes and river networks through parallel computation using cellular automata. Each cell independently updates its water state, and the update depends only on the water inventory and external inflows (including upstream slope and river inflows and precipitation) from the previous time step. This avoids the dependence on the real-time calculation results of neighboring cells in traditional methods, thus achieving global parallel iteration of the entire watershed within the same time step. This method not only ensures numerical stability and water conservation but also significantly improves computational efficiency, enabling rapid simulation of large-scale watersheds even under high spatial resolution conditions.

[0200] Furthermore, the linear reservoir capacity merging method employed in this invention effectively avoids the numerical instability and accuracy loss problems that may occur in traditional methods. The computational framework fully leverages the advantages of parallel hardware such as GPUs to achieve synchronous iterative computation over large-scale, complex regions. This method breaks through the limitations of traditional sequential iteration modes, significantly improving the adaptability of computational speed and scale, and providing a new technical path for achieving fast, precise, and reliable flash flood simulation and forecasting.

[0201] For ease of understanding, the following is a derivation and description of the linear reservoir capacity merging method and analytical solution involved in this embodiment:

[0202] Linear reservoir capacity refers to a reservoir capacity model in which the stored water volume and outflow volume in a runoff system satisfy a linear relationship. Its basic assumptions are:

[0203] Continuity equation:

[0204]

[0205] Linear outflow relationship:

[0206]

[0207] In the formula: S is the water storage capacity of the cell at time t (m³); I is the constant inflow at time t (m³ / s), which is assumed to be constant in this step; Q is the outflow rate at time t (m³ / s); K is the reservoir capacity constant (s), which represents the reservoir's ability to retain water.

[0208] The derivative of the linear storage capacity is derived as follows:

[0209] Combining the continuity equation (25) and the linear outflow relation (26), we obtain the first-order linear ordinary differential equation:

[0210]

[0211] Equation (27) is a linear first-order ordinary differential equation, which can be solved using the integral factor method. Both sides are multiplied by... have to:

[0212]

[0213] Based on the fundamental formula of calculus, this can be transformed into the form of a left-hand derivative:

[0214]

[0215] Integrate both sides over the interval [0, Δt]:

[0216]

[0217] In engineering implementation, the inflow within the interval is often approximated as a constant, therefore, when substituted... In the interval [0, Δt], the integral on the right side of equation (30) can be directly calculated:

[0218]

[0219] Multiply both sides simultaneously have to:

[0220]

[0221] Therefore, the water storage volume at the next moment is obtained, and the definition is... (0< <1):

[0222]

[0223] Therefore, the analytical solution for linear storage capacity can be written as:

[0224]

[0225] because The outflow rate at time t+1 is:

[0226]

[0227] The core of the derivation of the basic equation for linear reservoir capacity confluence lies in the fact that the synchronous updating of the structure within a time step can be achieved through the analytical solution of linear reservoir capacity. Specifically, the outflow of the next step can be calculated from the previous step's water storage plus inflow. Observing the analytical solution, it only contains: 1. The previous step's water storage at this grid point. 1. (Given); 2. Inflow I at this time step (Given); 3. Parameters K and Therefore, as long as the inflow I of each cell is known before the update, parallel computation can be performed directly. and All river cells are computed within the same time step, with no upstream or downstream dependencies or temporal order between them, forming a global parallel update structure at the time step level.

[0228] Specifically, this is achieved by using the outflow V from the upstream time t. t Construct I for the current time step. All V t For data with known time steps, we can first perform parallel computation across the entire domain, then aggregate the I values ​​for each cell, and finally apply this parallel computation to the analytical solution to update the state of each cell. During this time step, all cells in the entire watershed can be updated simultaneously, without any temporal order between them.

[0229] Therefore, by using this linear reservoir capacity analytical solution method, this invention only requires one parameter K for control in the runoff calculation, which greatly simplifies the model setting and parameter calibration process compared to traditional methods that require multiple empirical parameters or calibration steps. Its advantages are: firstly, it reduces dependence on observational data, making the model more portable and adaptable; secondly, the physical meaning of the parameters is clear (representing the water body's storage capacity), facilitating its application across different watersheds. Simultaneously, this method preserves the lag and peak-shaving characteristics of flood evolution, balancing computational simplicity and physical rationality. In terms of parallel implementation, since the update of each cell depends only on the local water storage and inflow, all cells can be independently updated using existing inputs within the same time step, thus enabling efficient operation on parallel architectures such as GPUs, achieving rapid and refined flash flood simulation in large-scale watersheds.

[0230] Due to its high efficiency and scalability, this invention is applicable not only to the simulation and early warning of flash floods in small mountain watersheds but also to the study of rainstorm and flood evolution in large river basins. It can be used for rapid risk assessment of sudden disasters such as urban flooding and mountain debris flows, and can also be extended to fields such as watershed water resource management, reservoir scheduling, and eco-hydrological process simulation. Against the backdrop of global climate change and increasingly frequent extreme weather events, this method provides a novel technical approach for achieving cross-basin, multi-scale, real-time hydrological and hydrodynamic simulation, offering strong support for disaster prevention and mitigation, water conservancy project planning, ecological environmental protection, and smart water management.

[0231] The parallel computing system for coupled slope and river confluence provided in the embodiments of this application is described below. The parallel computing system for coupled slope and river confluence described below can be referred to in correspondence with the parallel computing method for coupled slope and river confluence described above.

[0232] Please see Figure 7 The present invention also provides a parallel computing system for coupled slope and river confluence, the system comprising:

[0233] Cell initialization unit 201 is used to discretize the study area into multiple cells and initialize the hydrogeographic attributes of each cell.

[0234] Cell flow direction relationship determination unit 202 is used to determine the flow direction relationship between each cell based on the hydrogeographic attributes of each cell using the D8 algorithm.

[0235] The slope and channel cell identification unit 203 is used to calculate the cumulative runoff area of ​​each cell based on the flow direction relationship between each cell, and to identify slope cells and channel cells based on the cumulative runoff area of ​​the cells.

[0236] The slope confluence simulation unit 204 is used to simulate the slope confluence movement in the study area based on the flow direction relationship between each cell, and to calculate the hydrological flow data of each slope cell and river cell in parallel according to the hydrogeographic attributes.

[0237] The river confluence simulation unit 205 is used to simulate the river confluence movement in the study area. It calculates the inflow data of each river cell in the current time step in parallel based on the hydrological flow data of the slope cells and the river cells. Based on the linear reservoir confluence method, it updates the water storage of each river cell in the next time step in parallel based on the inflow data. Based on the water storage, it updates the outflow data of each river cell in the next time step in parallel.

[0238] The present invention also provides a computer-readable storage medium storing a computer program or instructions thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of any of the above-mentioned parallel computation methods for slope and river confluence coupling.

[0239] The present invention also provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of any of the above-mentioned parallel computation methods for slope and river confluence coupling are implemented.

[0240] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0241] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0242] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0243] Furthermore, in each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0244] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., each of these media capable of storing program code.

[0245] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 each of the foregoing embodiments, or equivalent substitutions can be made to some of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A parallel computational method for coupled slope and river confluence, characterized in that, The method includes: The study area is discretized into multiple cells and the hydrogeographic attributes of each cell are initialized. Based on the D8 algorithm, the flow direction relationship between each cell is determined according to the hydrogeographic attributes of each cell. Based on the flow direction relationship between each cell, the cumulative runoff area of ​​each cell is calculated, and the slope cells and river cells are identified according to the cumulative runoff area of ​​the cells. Based on the flow direction relationship between each cell, the slope confluence motion of the study area is simulated; Calculate the hydraulic gradient of each slope cell and channel cell at the current time step based on the hydrogeographic attributes; calculate the flow velocity and flow rate of each slope cell and channel cell at the current time step based on the Manning formula and the hydraulic gradient. Based on the flow rate, the transferable water volume of each slope cell and the channel cell within the time step interval is determined under physical constraints. Based on the transferable water volume, predict the water depth and water storage volume of each slope cell and the channel cell at the next time step; Simulate the confluence movement of the river channels in the study area, and determine the amount of water transferred by the upstream cells of each river channel cell at the current time step based on the hydrological flow data of the slope cells and the river channel cells, thereby determining the total inflow of each river channel cell at the current time step. The inflow rate of each channel cell is calculated based on the total inflow rate of each channel cell within the time step interval. Based on the linear reservoir capacity confluence method, the predicted water storage volume obtained in the slope confluence motion is updated according to the total inflow and inflow rate of each channel cell within the time step interval, so as to obtain the target water storage volume of each channel cell in the next time step. Based on the target water storage volume of each channel cell in the next time step, calculate the outflow of each channel cell in the next time step; Calculate the outflow velocity of each channel cell in the next time step based on the outflow rate of each channel cell in the next time step.

2. The parallel calculation method for coupled slope and river confluence as described in claim 1, characterized in that, The step of discretizing the study area into multiple cells and initializing the hydrogeographic attributes of each cell includes: Acquire basic data of the study area, and discretize the study area into a two-dimensional regular cellular grid based on the basic data of the study area; the two-dimensional regular cellular grid includes multiple cells. The hydrogeographic attributes of all cells in the two-dimensional regular cell grid are initialized based on the aforementioned basic data.

3. The parallel calculation method for coupled slope and river confluence according to claim 1, characterized in that, The step of determining the flow direction relationship between each cell based on the D8 algorithm and the hydrogeographic attributes of each cell includes: Based on the D8 algorithm, the distance weight difference between each cell and its neighboring cells is determined according to the hydrogeographic attributes. The adjacent cell with the largest distance weight difference is selected as the downstream cell, thereby determining the flow direction relationship between each cell.

4. The parallel calculation method for coupled slope and river confluence according to claim 1, characterized in that, The step of calculating the cumulative runoff area of ​​each cell based on the flow direction relationship between each cell, and identifying slope cells and channel cells based on the cumulative runoff area of ​​the cells, includes: Based on the flow direction relationship between each cell, calculate the cumulative catchment area of ​​each cell; The cells are initially divided based on the cumulative confluence area; if the cumulative confluence area is greater than or equal to a preset area threshold, the cell is determined to be a river cell; otherwise, the cell is determined to be a slope cell.

5. The parallel calculation method for coupled slope and river confluence according to claim 1, characterized in that, The total inflow rate of the river cell is calculated as follows: In the formula: For the river cells at the current time step Total inflow; In time step t Upstream slope cells transfer to channel cells i Total water volume; In time step t Upstream channel cells transfer to channel cells i Total water volume; In time step t The intensity of rainfall falling into the slope water storage unit; This represents the actual area of ​​the cell; The inflow rate of the river cell is calculated as follows: In the formula: The inflow rate of the river cell; The time step interval; The method for calculating the target water storage capacity of the river cell in the next time step is as follows: In the formula: For river cells in the next time step t+ 1. Target water storage capacity; For river cells in time step t The water storage capacity; This is the storage capacity constant; For stock coefficient; The outflow rate of the channel cell in the next time step is calculated as follows: In the formula: For river cells in the next time step t+ 1 outflow rate; The outflow velocity of the channel cell in the next time step is calculated as follows: In the formula: For river cells in the next time step t+ 1 outflow velocity.

6. A parallel computing system coupling slope and river confluence, characterized in that, The system includes: A cell initialization unit is used to discretize the study area into multiple cells and initialize the hydrogeographic attributes of each cell. A cell flow direction relationship determination unit is used to determine the flow direction relationship between each cell based on the D8 algorithm and the hydrogeographic attributes of each cell. The slope and river cell identification unit is used to calculate the cumulative runoff area of ​​each cell based on the flow direction relationship between each cell, and to identify slope cells and river cells based on the cumulative runoff area of ​​the cells. A slope confluence simulation unit is used to simulate the slope confluence motion of the study area based on the flow direction relationship between each cell; calculate the hydraulic gradient of each slope cell and the channel cell at the current time step according to the hydrogeographic attributes; calculate the flow velocity and flow rate of each slope cell and the channel cell at the current time step based on the Manning formula and the hydraulic gradient; determine the transferable water volume of each slope cell and the channel cell within the time step interval based on the flow rate under physical constraints; and predict the water depth and water storage of each slope cell and the channel cell at the next time step based on the transferable water volume. The river confluence simulation unit is used to simulate the river confluence movement in the study area. Based on the hydrological flow data of the slope cells and the river cells, it determines the amount of water transferred by the upstream cells of each river cell at the current time step, thereby determining the total inflow of each river cell at the current time step. It calculates the inflow rate of each river cell based on the total inflow of each river cell within the time step interval. Based on the linear reservoir confluence method, it updates the predicted and updated water storage volume obtained in the slope confluence movement according to the total inflow and inflow rate of each river cell within the time step interval, obtaining the target water storage volume of each river cell in the next time step. Based on the target water storage volume of each river cell in the next time step, it calculates the outflow of each river cell in the next time step. Based on the outflow of each river cell in the next time step, it calculates the outflow velocity of each river cell in the next time step.

7. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the parallel computation method for coupled slope and river confluence as described in any one of claims 1-5.

8. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the parallel computation method for coupled slope and river confluence as described in any one of claims 1-5.

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