River flood inundation fast calculation method and system based on discrete water level constraint

Abstract

The application discloses a kind of river flood inundation fast calculation method and system based on discrete water level constraint, belong to water conservancy engineering flood control and disaster mitigation technical field.This method obtains the water level data of river center line, measured water level control point and digital elevation model;River center line mileage is generated sampling point sequence;Each sampling point along the water level is obtained by one-dimensional along the water level inversion model;After determining local mainstream direction, along the water level is extended to grid cell using asymmetric direction weight function, generating two-dimensional water level surface that increases along the flow and weakens against the flow;Then, the terrain resistance grid is constructed according to the terrain fluctuation, and the minimum cumulative resistance is calculated.When water depth and resistance conditions meet the threshold value, it is determined as inundation.The application solves the problem that fast inundation calculation under extremely simple input cannot consider physical rationality, and can generate inundation range that effectively suppresses backflow and abnormal diffusion within minutes.

Description

Technical Field

[0001] This invention relates to the field of flood control and disaster reduction technology in water conservancy projects, specifically to a method and system for rapid calculation of river flood inundation based on discrete water level constraints. Background Technology

[0002] In flood emergency response scenarios during the flood season, water conservancy departments typically only have access to real-time water level data from a few hydrological stations or temporary monitoring points, and need to assess the potential impact range of the flood within a very short time. While two-dimensional hydrodynamic models can accurately describe the flood evolution process, their modeling relies on a large amount of river cross-sectional data, boundary conditions, and parameter calibration work, and the computation time is usually on the order of hours or more, making it difficult to meet the minute-level rapid response requirements in sudden flood scenarios.

[0003] Several rapid flood inundation calculation methods have emerged in the existing technology. For example, the planar interpolation method based on geographic information systems directly generates water surface using measured water points. While the calculation speed is fast, it completely ignores the physical constraints of the main river channel direction and water surface slope, often resulting in unreasonable phenomena such as water level backflow and abnormal valley inundation extending to ridges; another example is... (The text abruptly ends here, likely due to an incomplete sentence or missing information.) CN 120688383 A The patent literature discloses a rapid simulation method for river overflow flood evolution based on river adaptive segmentation and strong water balance constraints. This type of scheme introduces physical constraints such as water balance to improve reliability, but the required input data and parameter organization are still relatively complex, making it difficult to directly adapt to emergency operational conditions with only a small amount of measured water levels and basic geographic data. Therefore, how to balance calculation speed and the physical rationality of inundation results under extremely limited input data remains an unsolved problem in this field.

[0004] Therefore, there is an urgent need to develop a method that can complete flood inundation calculations in minutes using only a small number of measured water levels, river centerlines, and digital elevation models, and can effectively suppress backflow propagation and topographic anomaly diffusion. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a rapid calculation method and system for river flood inundation based on discrete water level constraints. This solves the problem that, under extremely simplified input conditions with only the river centerline, a few measured water level control points, and a digital elevation model, traditional planar interpolation methods, lacking constraints on the main river channel direction and topographic barriers, result in the generated inundation range exhibiting reverse propagation and abnormal diffusion, failing to simultaneously meet the requirements of minute-level rapid calculation and the physical plausibility of the inundation results. To achieve the above objectives, this invention provides a rapid calculation method for river flood inundation based on discrete water level constraints, comprising: Acquire water level data of the river centerline, at least two measured water level control points, and a digital elevation model, and determine the upstream to downstream direction of the river centerline; wherein, the water level data includes the spatial location and measured water level of the measured water level control points, and the digital elevation model includes multiple grid cells covering the study area, each of the grid cells carrying ground elevation; The river centerline is mileage-based to generate a sequence of sampling points distributed along the river centerline; The measured water level control point data and the sampling point sequence are used as inputs and processed by a one-dimensional friction water level inversion model to obtain the friction water level corresponding to each sampling point. The one-dimensional friction water level inversion model includes an objective function, which includes at least the measured water level fitting error and the water surface slope constraint. Based on the river centerline, determine the local mainstream direction corresponding to each sampling point; Based on the water level along the flow path and the local mainstream direction corresponding to each sampling point, the water level along the flow path of each sampling point is extended to each grid cell in the study area using a preset asymmetric direction weighting function to obtain a two-dimensional water level surface; the asymmetric direction weighting function assigns a weight to the grid cell that is enhanced along the downstream direction and weakened along the upstream direction. Based on the digital elevation model, a terrain resistance value is assigned to each grid cell to construct a terrain resistance grid. For any target grid cell, the two-dimensional water level value of the target grid cell is determined based on the two-dimensional water level surface, and the minimum cumulative resistance from each sampling point to the target grid cell is determined based on the terrain resistance grid. When the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold, and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be submerged. Based on the target grid cells determined to be submerged, the submerged grid area is obtained.

[0006] Preferably, the river centerline is mileage-based to generate a sequence of sampling points distributed along the river centerline, specifically including: The cumulative mileage is calculated along the centerline of the river channel, and the sampling points are arranged at equal intervals according to a preset spacing, or the sampling points are arranged evenly according to a preset number of samples. Each sampling point is assigned a corresponding mileage value to form the sampling point sequence. Each measured water level control point is mapped to a mileage position on the center line of the river channel, establishing a correspondence between the measured water level control point and the mileage value.

[0007] Preferably, the one-dimensional frictional water level inversion model processing specifically includes: Construct the objective function for:

[0008] in, The number of measured water level control points. For the first The calculated water level corresponds to the mileage position of each of the measured water level control points mapped to the river centerline. For the first The measured water levels at the aforementioned measured water level control points. The total number of sampling points in the sampling point sequence. and The first The and the first The water level along the sampling points mentioned above For the first The and the first The distance along the path between the sampling points mentioned above For the first The water surface slope constraint term of the river section To add regular expression terms, These are the weighting coefficients. The number of additional regular expression terms; By minimizing the objective function The water level along the sampling point is obtained; The water surface slope constraint item Expressed using Manning's formula:

[0009] in, For roughness, For traffic, For the water flow area, The hydraulic radius; The additional regularization terms The first-order or second-order smoothing term of the water level along the path is adopted.

[0010] Preferably, the local mainstream direction corresponding to each sampling point is determined based on the river centerline, specifically including: For each sampling point, the direction of the line connecting adjacent sampling points along the upstream to downstream direction on the river centerline is taken as the local mainstream direction. When the sampling point is the upstream endpoint of the river centerline, the direction from the upstream endpoint to the adjacent sampling point is taken as the local mainstream direction; when the sampling point is the downstream endpoint of the river centerline, the direction from the adjacent sampling point to the downstream endpoint is taken as the local mainstream direction. For river sections with significant bends, the river centerline is first pre-smoothed, or the local mainstream direction is corrected by averaging the direction vectors of several sampling points before and after the current.

[0011] Preferably, based on the water level along the sampling point and the local mainstream direction, a preset asymmetric direction weighting function is used to extend the water level along the sampling point to each of the grid cells within the study area to obtain a two-dimensional water level surface, specifically including: For any target grid cell Calculate its value from each of the sampling points. Obtained normalized weights :

[0012] in, Let be the distance decay function. For the target grid unit With the sampling point distance, As a direction factor, For the sampling point Pointing to the target grid cell azimuth angle, For the sampling point The local mainstream direction angle at that location, The total number of sampling points in the sampling point sequence. l This serves as the index for summing up each sample point in the sample point sequence; pass Calculate the target grid cell The two-dimensional water level values, among which For the sampling point The water level along the course forms the two-dimensional water level surface.

[0013] Preferably, the distance decay function Using the exponential decay form, the expression is:

[0014] in, This is the distance attenuation coefficient; The direction factor The expression is:

[0015] Wherein, the azimuth angle With the aforementioned local mainstream direction angle The difference is calculated using the minimum included angle. For directional bandwidth parameters, It is an asymmetric strength parameter and , It is a symbolic function.

[0016] Preferably, based on the digital elevation model, each of the grid cells is assigned a terrain resistance value to construct a terrain resistance grid, specifically including: Calculate the slope and / or relative elevation of each grid cell based on the digital elevation model; Grid cells with a slope greater than a preset slope threshold and / or a relative elevation greater than a preset elevation threshold are identified as first-type barrier grid cells and assigned a first resistance value. Grid cells located in valleys, low-lying areas, or low-resistance propagation channel areas formed by connecting adjacent low-elevation grid cells are identified as second-type propagation grid cells and assigned a second resistance value. Wherein, the first resistance value is greater than the second resistance value.

[0017] Preferably, determining the minimum cumulative resistance from each sampling point to the target grid cell based on the terrain resistance grid specifically includes: Using the terrain resistance grid as the cost surface, for any target grid cell Calculate from each sampling point Departure to reach the target grid cell The cumulative resistance of each path is calculated, and the minimum value of the cumulative resistance corresponding to all sampling points is taken as the target grid unit. Minimum cumulative resistance :

[0018] in To sample points To target grid cell The set of feasible propagation paths For grid cells on the path The resistance value in the terrain resistance grid.

[0019] Preferably, the water depth threshold value ranges from 0.1. m Up to 0.3 m The resistance threshold is set according to the degree of topographic relief in the study area, which is characterized by slope, relative elevation and / or elevation fluctuation, and the resistance threshold is negatively correlated with the degree of topographic relief.

[0020] A rapid calculation system for river flood inundation based on discrete water level constraints, comprising: The data acquisition module is used to acquire water level data of the river centerline, at least two measured water level control points, and a digital elevation model, and to determine the upstream to downstream direction of the river centerline; wherein, the water level data includes the spatial location and measured water level of the measured water level control points, and the digital elevation model includes multiple grid cells covering the study area, each of the grid cells carrying ground elevation; The centerline processing module is used to perform mileage processing on the river centerline and generate a sequence of sampling points distributed along the river centerline; The one-dimensional water level inversion module is used to take the water level data of the measured water level control point and the sampling point sequence as input, and process them through the one-dimensional friction water level inversion model to obtain the friction water level corresponding to each sampling point; the one-dimensional friction water level inversion model includes an objective function, which includes at least the measured water level fitting error and the water surface slope constraint. The flow direction determination module is used to determine the local mainstream direction corresponding to each sampling point based on the river centerline; A two-dimensional water level extension module is used to extend the water level along the sampling point to each grid cell within the study area based on the water level along the sampling point and the local mainstream direction, using a preset asymmetric direction weighting function to obtain a two-dimensional water level surface; the asymmetric direction weighting function assigns a weight to the grid cell that is enhanced along the downstream direction and weakened along the upstream direction. The terrain resistance construction module is used to assign terrain resistance values ​​to each grid cell according to the digital elevation model, and construct a terrain resistance grid. The flooding determination module is used to determine the two-dimensional water level value of the target grid cell based on the two-dimensional water level surface, and to determine the minimum cumulative resistance from each sampling point to the target grid cell based on the terrain resistance grid; when the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold, and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be flooded; based on the target grid cells determined to be flooded, the flooded grid area is obtained.

[0021] Compared with the prior art, the beneficial effects of the present invention are: This invention requires only three types of readily available data to quickly calculate flood inundation, namely, the river centerline, a small amount of measured water level control point data, and a digital elevation model. It eliminates the need for complete measured river cross-section data and complex parameter calibration, thereby reducing the complexity of data preparation and model construction.

[0022] This invention transforms a small number of discrete water level constraints into a continuous water surface line that conforms to the water surface slope law through a one-dimensional water level inversion model. Then, it extends the water level along the flow to the grid cells to generate a two-dimensional water level surface using an asymmetric directional weight function that enhances downstream flow and weakens upstream flow, thereby suppressing upstream propagation and lateral irregular diffusion phenomena from a mechanism perspective.

[0023] This invention determines connectivity flooding by constructing a terrain resistance grid and calculating the minimum cumulative resistance path from the sampling point to the target grid cell. This ensures that floods can only inundate low-lying areas when low-resistance propagation channels exist, effectively eliminating false flooding zones and abnormal diffusion boundaries that cross highlands or ridges, and significantly improving the spatial accuracy of the flooding range.

[0024] The system of this invention enables the entire calculation process to be completed within minutes, and the output results can be directly superimposed with disaster-bearing bodies such as roads, residential areas, bridges and farmland to generate risk statistical indicators, which is very suitable for flood emergency simulation, rapid early warning and operational risk assessment during the flood season. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the rapid calculation method for river flood inundation based on discrete water level constraints according to the present invention.

[0026] Figure 2 This is a schematic diagram showing the river centerline, sampling points, and measured water level location for the rapid calculation method of river flood inundation based on discrete water level constraints according to the present invention.

[0027] Figure 3 This is a schematic diagram of a one-dimensional water level inversion model along the river based on the discrete water level constraint rapid calculation method of the present invention.

[0028] Figure 4 This is a two-dimensional asymmetric directional weight structure diagram of the river flood inundation rapid calculation method based on discrete water level constraints of the present invention.

[0029] Figure 5 This is a schematic diagram of flood propagation based on topographic resistance, which is the method for rapid calculation of river flood inundation based on discrete water level constraints according to the present invention.

[0030] Figure 6 This is a schematic diagram showing the superimposed output of the inundation results and the disaster-bearing body in the rapid calculation method for river flood inundation based on discrete water level constraints of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] See attached Figures 1-6 As shown, this invention addresses the problems of long calculation cycles and high data requirements of existing two-dimensional hydrodynamic models, as well as the lack of physical constraints in traditional planar interpolation methods leading to insufficient rationality of inundation results. It proposes a rapid calculation method for river flood inundation based on discrete water level constraints: requiring only the river centerline, water level data from at least two measured water level control points, and a digital elevation model, the method sequentially employs river centerline mileage and sampling, a one-dimensional water level inversion model along the river, determination of the local mainstream direction, expansion of the two-dimensional water level surface with asymmetric directional weights, construction of a topographic resistance grid, and determination of minimum cumulative resistance inundation. Within minutes of calculation, it obtains an inundation grid area that conforms to the river flood propagation law and effectively avoids backflow propagation and abnormal diffusion, thus balancing rapid emergency response with the physical rationality of the results.

[0033] See Figure 1 As shown in the figure, this embodiment details the specific implementation of each step of the method of the present invention. First, the river centerline of the study river section, water level data from at least two measured water level control points, and a digital elevation model are obtained. The upstream to downstream direction of the river centerline is determined. The digital elevation model includes multiple grid cells covering the study area, each grid cell carrying ground elevation. The spatial coordinates of the river centerline and each measured water level control point are unified with the digital elevation model to the same projected coordinate system, aligning all types of data under the same spatial reference and establishing a consistent coordinate reference for subsequent spatial calculations. See Appendix. Figure 2 As shown, the river centerline is then mileage-based: the cumulative mileage along the river centerline is calculated, and sampling points are evenly distributed at preset intervals or evenly distributed according to the preset number of samples based on the total length of the river centerline. Each sampling point is assigned a corresponding mileage value, forming a sampling point sequence. Simultaneously, each measured water level control point is mapped to the mileage position on the river centerline, establishing a correspondence between the measured water level control points and mileage values. This parameterizes the river spatial curve into a one-dimensional mileage coordinate, accurately binding the discrete measured water level control points to the geometric position of the river, providing accurate positional constraints for the inversion of water levels along the river. When the number of measured water level control points increases, the inversion constraint strength can be further improved.

[0034] See Figure 3As shown, the measured water level data of the control points and the sampling point sequence are used as inputs. A one-dimensional friction-line water level inversion model is used to obtain the friction-line water level at each sampling point. This one-dimensional friction-line water level inversion model employs an objective function that includes the measured water level fitting error and the water surface slope constraint. The objective function is constructed as follows: for:

[0035] in The number of measured water level control points. For the first The calculated water level corresponds to the mileage position of each measured water level control point mapped to the river centerline. For the first The measured water level at each measured water level control point. The total number of sampling points in the sampling point sequence. and The first The and the first Water levels along the sampling points For the first The and the first The distance along the path between each sampling point For the first The water surface slope constraint term of the river section To add regular expression terms, These are the weighting coefficients. The number of additional regularization terms is used to solve for the friction water level at each sampling point by minimizing this objective function.

[0036] Water surface slope constraint Expressed using Manning's formula as ,in For the first The roughness of the river section For traffic, For the water flow area, The hydraulic radius is used; when complete cross-sectional data is lacking, the flow rate, water flow area, and hydraulic radius are estimated based on typical river cross-sectional conditions, empirical gradient, or preset parameters under business scenarios.

[0037] Additional regular expressions A first-order or second-order smoothing term along the water level curve is used to constrain its smoothness and suppress non-physical fluctuations. For example, a suitable smoothing term can be adopted. The second-order smoothed form is obtained. This inversion step, by taking into account both the measured water level approximation and the physical laws of water surface slope, obtains a continuous, smooth water level curve along the path that conforms to hydraulic characteristics with fewer measured constraints, providing a reliable water level source for two-dimensional extension.

[0038] Next, the local mainstream direction at each sampling point is determined based on the river centerline: for each sampling point, the direction of the line connecting its upstream and downstream adjacent sampling points along the river centerline is taken as the local mainstream direction for that sampling point; when the sampling point is the upstream endpoint of the river centerline, the direction from the upstream endpoint to the adjacent sampling point is taken as the local mainstream direction; when the sampling point is the downstream endpoint of the river centerline, the direction from the adjacent sampling point to the downstream endpoint is taken as the local mainstream direction; for river sections with significant bends, the river centerline can be pre-smoothed, or the average value of the direction vectors of several consecutive sampling points can be used to correct the local mainstream direction to eliminate abrupt changes in flow direction caused by local oscillations of the river centerline and improve the stability of the two-dimensional water level expansion. The obtained local mainstream direction accurately depicts the basic direction of flood propagation along the river channel, providing a clear directional reference for subsequent asymmetric weight allocation.

[0039] See Figure 4 As shown, based on the water level along the sampling points and the local mainstream direction, a preset asymmetric directional weighting function is used to extend the water level along the sampling points to each grid cell within the study area, resulting in a two-dimensional water level surface. The asymmetric directional weighting function assigns a weight to each grid cell that is stronger along the downstream direction and weaker along the upstream direction. Specifically, for each target grid cell… Calculate its value from each sampling point Obtained normalized weights :

[0040] in Let be the distance decay function. For target grid cell With sampling points distance, As a direction factor, Sampling points Pointing to target grid cell azimuth angle, Sampling points The local mainstream direction angle at that location, The total number of sampling points. l This serves as an index for summing at each sampling point; subsequently, through... Calculate target raster cells The two-dimensional water level values, among which Sampling points The water level along the course forms a two-dimensional water level surface covering the study area. Distance decay function Using exponential decay , This is the distance attenuation coefficient, controlling the rate at which the influence range decreases with distance. Direction factor. The preferred form is:

[0041] in This is the directional bandwidth parameter, used to control directional sensitivity; It is an asymmetric strength parameter and ; The sign function is used; the difference between the azimuth angle and the local mainstream direction angle is calculated using the minimum included angle to eliminate the problem of angular periodicity. This asymmetric direction weighting function makes the water level information of each sampling point propagate further downstream and shorter upstream along the local mainstream direction. The influence zone around each sampling point is extended downstream to form a downstream enhancement zone and contracted upstream to form a countercurrent suppression zone. The resulting two-dimensional water level surface can effectively suppress countercurrent propagation and unreasonable lateral diffusion, making the inundation pattern more consistent with the natural laws of river flood propagation.

[0042] See Figure 5 As shown, a terrain resistance grid is constructed based on the digital elevation model, which assigns resistance values ​​to grid cells according to the degree of terrain undulation. Grid cells with a slope greater than a preset slope threshold and / or a relative elevation greater than a preset elevation threshold are assigned a first resistance value. Grid cells located in valleys, low-lying areas, or areas formed by connecting adjacent low-elevation grid cells to create low-resistance propagation channels are assigned a second resistance value, and the first resistance value is greater than the second resistance value. This forms a terrain resistance grid that reflects the degree of terrain obstruction. This grid provides a spatial cost benchmark for subsequent minimum cumulative resistance path calculation, enabling flooding determination to distinguish between valleys with real low-resistance channels and isolated low-lying areas lacking connecting paths. Then, for any target grid cell, the two-dimensional water level value of the target grid cell is determined based on the two-dimensional water level surface, and combined with the terrain resistance grid, the minimum cumulative resistance from each sampling point to the target grid cell is determined; when the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be submerged; based on the target grid cells determined to be submerged, the submerged grid area is obtained. Minimum cumulative resistance The calculation method is as follows: using the terrain resistance grid as the cost surface, for any target grid cell... Calculate from each sampling point Departure to target grid cell The cumulative resistance of each path is calculated, and the minimum cumulative resistance among all sampling points is taken, i.e.:

[0043] In the formula To sample points To target grid cell The set of feasible propagation paths For grid cells on the path The resistance value in the terrain resistance grid; the source sampling point corresponding to the minimum cumulative resistance is the sampling point determined from all sampling points using the minimum cumulative resistance as a metric, not the sampling point with the closest geometric distance. The flooding determination can be expressed as: for any target grid cell... ,when and At that time, the target grid cell If it is determined to be submerged, then it is not submerged. For target grid cell Two-dimensional water level values; For target grid cell The ground elevation carried, The water depth threshold, To minimize cumulative resistance, Resistance threshold. Water depth threshold. Based on business requirements and empirically determined terrain resolution, a value of 0.1 is preferred. m Up to 0.3 m Resistance threshold Based on the topographic relief and propagation path continuity of the study area, relatively larger values ​​can be used in plains and gentle slopes, while relatively smaller values ​​are used when the topography is more undulating or mountainous. This judgment step also checks the water depth conditions and propagation path connectivity, ensuring that flood propagation prioritizes low-resistance channels rather than directly crossing high-resistance areas. This fundamentally avoids false inundation and abnormal diffusion boundaries when there are no reasonable propagation channels, significantly improving the physical rationality and operational reliability of the inundation results. After obtaining the inundation grid area, the inundation grid can be extracted into inundation polygons and overlaid with layers of disaster-bearing bodies such as roads, bridges, residential areas, and farmland for analysis. This outputs the length of affected roads, the number of affected residential areas, the number of affected bridges, and other risk statistics, providing direct evidence for operational applications during the flood season. Figure 6 As shown.

[0044] This invention also provides a rapid calculation system for river flood inundation based on discrete water level constraints, comprising: The data acquisition module is used to acquire water level data of the river centerline, at least two measured water level control points, and a digital elevation model, and to determine the upstream to downstream direction of the river centerline; wherein, the water level data includes the spatial location and measured water level of the measured water level control points, and the digital elevation model includes multiple grid cells covering the study area, each of the grid cells carrying ground elevation; The centerline processing module is used to perform mileage processing on the river centerline and generate a sequence of sampling points distributed along the river centerline; The one-dimensional water level inversion module is used to take the water level data of the measured water level control point and the sampling point sequence as input, and process them through the one-dimensional friction water level inversion model to obtain the friction water level of each sampling point; the one-dimensional friction water level inversion model processing adopts an objective function that includes the measured water level fitting error and the water surface slope constraint. The flow direction determination module is used to determine the local mainstream direction corresponding to each sampling point based on the river centerline; A two-dimensional water level extension module is used to extend the water level along the sampling points to each grid cell within the study area based on the water level along the sampling points and the local mainstream direction, using a preset asymmetric direction weighting function, to obtain a two-dimensional water level surface; the asymmetric direction weighting function assigns a weight to the grid cell that is enhanced along the downstream direction and weakened along the upstream direction; The terrain resistance construction module is used to assign terrain resistance values ​​to each grid cell according to the digital elevation model, and construct a terrain resistance grid. The flooding determination module is used to determine the two-dimensional water level value of any target grid cell based on the two-dimensional water level surface, and to determine the minimum cumulative resistance from each sampling point to the target grid cell based on the terrain resistance grid; when the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold, and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be flooded; based on the target grid cells determined to be flooded, the flooded grid area is obtained.

[0045] This invention offers significant advantages in terms of data requirements. It requires only three readily available basic data sets: the river centerline, a small amount of measured water level control point data, and a digital elevation model. This significantly reduces reliance on complete two-dimensional hydrodynamic modeling data, compressing data preparation and model building time to the minute level, making it particularly suitable for rapid emergency response in sudden flood scenarios. Regarding physical plausibility, a one-dimensional along-flow water level inversion model transforms sparse measured water level constraints into a continuous along-flow water surface line conforming to the water surface slope law. Then, an asymmetric directional weight expansion using downstream enhancement and upstream suppression generates a two-dimensional water level surface, forcibly constraining the water flow along the mainstream direction of the river channel. This effectively suppresses the upstream propagation and lateral irregular diffusion phenomena that easily occur in traditional planar interpolation methods. Simultaneously, a topographic resistance grid is constructed, and a connectivity inundation determination based on the minimum cumulative resistance path is introduced. This ensures that floods can only inundate low-lying areas under conditions where low-resistance propagation channels exist, effectively eliminating the formation of false inundation zones and abnormal diffusion boundaries when floods cross highlands or ridges, greatly improving the spatial accuracy and interpretability of the inundation range. It achieves a good balance between computational efficiency and business applicability. The entire process can complete all calculations from basic data to flood results output in minutes. The output results can be directly superimposed with disaster-bearing bodies such as roads, residential areas, bridges and farmland to generate risk statistical indicators, which makes it easier for emergency management departments to directly carry out risk assessment and disposal decisions. It is very suitable for flood emergency simulation, rapid early warning and operational assessment of river flood risk during the flood season.

[0046] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A rapid calculation method for river flood inundation based on discrete water level constraints, characterized in that, include: Acquire water level data of the river centerline, at least two measured water level control points, and a digital elevation model, and determine the upstream to downstream direction of the river centerline; wherein, the water level data includes the spatial location and measured water level of the measured water level control points, and the digital elevation model includes multiple grid cells covering the study area, each of the grid cells carrying ground elevation; The river centerline is mileage-based to generate a sequence of sampling points distributed along the river centerline; The measured water level control point data and the sampling point sequence are used as inputs and processed by a one-dimensional friction water level inversion model to obtain the friction water level corresponding to each sampling point. The one-dimensional friction water level inversion model includes an objective function, which includes at least the measured water level fitting error and the water surface slope constraint. Based on the river centerline, determine the local mainstream direction corresponding to each sampling point; Based on the water level along the flow path and the local mainstream direction corresponding to each sampling point, the water level along the flow path of each sampling point is extended to each grid cell in the study area using a preset asymmetric direction weighting function to obtain a two-dimensional water level surface; the asymmetric direction weighting function assigns a weight to the grid cell that is enhanced along the downstream direction and weakened along the upstream direction. Based on the digital elevation model, a terrain resistance value is assigned to each grid cell to construct a terrain resistance grid. For any target grid cell, the two-dimensional water level value of the target grid cell is determined based on the two-dimensional water level surface, and the minimum cumulative resistance from each sampling point to the target grid cell is determined based on the terrain resistance grid. When the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold, and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be submerged. Based on the target grid cells determined to be submerged, the submerged grid area is obtained.

2. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, The river centerline is mileage-based to generate a sequence of sampling points distributed along the river centerline, specifically including: The cumulative mileage is calculated along the centerline of the river channel, and the sampling points are arranged at equal intervals according to a preset spacing, or the sampling points are arranged evenly according to a preset number of samples. Each sampling point is assigned a corresponding mileage value to form the sampling point sequence. Each measured water level control point is mapped to a mileage position on the center line of the river channel, establishing a correspondence between the measured water level control point and the mileage value.

3. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, The one-dimensional frictional water level inversion model specifically includes: Construct the objective function for: in, The number of measured water level control points; For the first The calculated water level corresponds to the mileage position of the measured water level control point mapped to the centerline of the river channel; For the first The measured water level at each of the aforementioned measured water level control points; The total number of sampling points in the sampling point sequence; and The first The and the first Water level along the sampling points; For the first The and the first The distance along the path between the sampling points; For the first Water surface slope constraint term for the river section; To add regular expression terms, These are the weighting coefficients; The number of additional regular expression terms; By minimizing the objective function The water level along the sampling point is obtained; The water surface slope constraint item Expressed using Manning's formula: in, For roughness; For traffic; The water flow area; The hydraulic radius; The additional regularization terms The first-order or second-order smoothing term of the water level along the path is adopted.

4. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, Based on the river centerline, the local mainstream direction corresponding to each sampling point is determined, specifically including: For each sampling point, the direction of the line connecting adjacent sampling points along the upstream to downstream direction on the river centerline is taken as the local mainstream direction. When the sampling point is the upstream endpoint of the river centerline, the direction from the upstream endpoint to the adjacent sampling point is taken as the local mainstream direction; when the sampling point is the downstream endpoint of the river centerline, the direction from the adjacent sampling point to the downstream endpoint is taken as the local mainstream direction. For river sections with significant bends, the river centerline is first pre-smoothed, or the local mainstream direction is corrected by averaging the direction vectors of several sampling points before and after the current.

5. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, Based on the friction surface level and the local mainstream direction at each sampling point, and using a preset asymmetric direction weighting function, the friction surface level at each sampling point is extended to each grid cell within the study area to obtain a two-dimensional water level surface, specifically including: For any target grid cell Calculate its value from each of the sampling points. Obtained normalized weights : in, It is the distance decay function; For the target grid unit With the sampling point The distance; Direction factor; For the sampling point Pointing to the target grid cell The azimuth angle; For the sampling point The local mainstream direction angle at that location; The total number of sampling points in the sampling point sequence; l This serves as the index for summing up each sample point in the sample point sequence; pass Calculate the target grid cell The two-dimensional water level values, among which, For the sampling point The water level along the course forms the two-dimensional water level surface.

6. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 5, characterized in that, The distance decay function Using the exponential decay form, the expression is: in, This is the distance attenuation coefficient; The direction factor The expression is: Wherein, the azimuth angle With the local mainstream direction angle The difference is calculated using the minimum included angle. For directional bandwidth parameters, It is an asymmetric strength parameter and , It is a symbolic function.

7. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, Based on the digital elevation model, terrain resistance values ​​are assigned to each grid cell to construct a terrain resistance grid, specifically including: Calculate the slope and / or relative elevation of each grid cell based on the digital elevation model; Grid cells with a slope greater than a preset slope threshold and / or a relative elevation greater than a preset elevation threshold are identified as first-type barrier grid cells and assigned a first resistance value. Grid cells located in valleys, low-lying areas, or low-resistance propagation channel areas formed by connecting adjacent low-elevation grid cells are identified as second-type propagation grid cells and assigned a second resistance value. Wherein, the first resistance value is greater than the second resistance value.

8. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, Determining the minimum cumulative resistance from each sampling point to the target grid cell based on the terrain resistance grid specifically includes: Using the terrain resistance grid as the cost surface, for any target grid cell Calculate from each sampling point Departure to reach the target grid cell The cumulative resistance of each path is calculated, and the minimum value of the cumulative resistance corresponding to all sampling points is taken as the target grid unit. Minimum cumulative resistance : in To sample points To target grid cell The set of feasible propagation paths For grid cells on the path The resistance value in the terrain resistance grid.

9. The method for rapid calculation of river flood inundation based on discrete water level constraints according to claim 1, characterized in that, The water depth threshold value ranges from 0.

1. m Up to 0.3 m The resistance threshold is set according to the degree of topographic relief in the study area, which is characterized by slope, relative elevation and / or elevation fluctuation, and the resistance threshold is negatively correlated with the degree of topographic relief.

10. A rapid calculation system for river flood inundation based on discrete water level constraints, characterized in that, The system is used to execute the rapid calculation method for river flood inundation based on discrete water level constraints as described in any one of claims 1-9, and the system comprises: The data acquisition module is used to acquire water level data of the river centerline, at least two measured water level control points, and a digital elevation model, and to determine the upstream to downstream direction of the river centerline; wherein, the water level data includes the spatial location and measured water level of the measured water level control points, and the digital elevation model includes multiple grid cells covering the study area, each of the grid cells carrying ground elevation; The centerline processing module is used to perform mileage processing on the river centerline and generate a sequence of sampling points distributed along the river centerline; The one-dimensional water level inversion module is used to take the water level data of the measured water level control point and the sampling point sequence as input, and process them through the one-dimensional friction water level inversion model to obtain the friction water level corresponding to each sampling point; the one-dimensional friction water level inversion model includes an objective function, which includes at least the measured water level fitting error and the water surface slope constraint. The flow direction determination module is used to determine the local mainstream direction corresponding to each sampling point based on the river centerline; A two-dimensional water level extension module is used to extend the water level along the sampling point to each grid cell within the study area based on the water level along the sampling point and the local mainstream direction, using a preset asymmetric direction weighting function to obtain a two-dimensional water level surface; the asymmetric direction weighting function assigns a weight to the grid cell that is enhanced along the downstream direction and weakened along the upstream direction. The terrain resistance construction module is used to assign terrain resistance values ​​to each grid cell according to the digital elevation model, and construct a terrain resistance grid. The flooding determination module is used to determine the two-dimensional water level value of the target grid cell based on the two-dimensional water level surface, and to determine the minimum cumulative resistance from each sampling point to the target grid cell based on the terrain resistance grid; when the difference between the two-dimensional water level value and the ground elevation carried by the target grid cell is greater than a preset water depth threshold, and the minimum cumulative resistance is less than a preset resistance threshold, the target grid cell is determined to be flooded; based on the target grid cells determined to be flooded, the flooded grid area is obtained.

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