Mountain torrent debris flow numerical simulation method coupled with hydrodynamic model

Abstract

The invention relates to the technical field of disaster prediction, and discloses a mountain torrent debris flow numerical simulation method coupled with a hydrodynamic model. According to the method, DEM data, rainfall data and land utilization data are used as input parameters, a HiPIMS model is used for simulating the mountain torrent condition in a drainage basin, then the HiPIMS model result is used as the flow hydrograph input of FLO-2D, a HiPIMS-FLO-2D coupling model is constructed, the debris flow condition in a region is simulated, and the debris flow condition in the region is simulated. And finally, evaluating the overall precision of the model through the mountain torrent precision index F and the debris flow precision index omega. The HiPIMS-FLO-2D coupling model provided by the invention can effectively simulate the high transient mountain torrent and debris flow disaster process, significantly improves the simulation precision, and provides a powerful tool and reference for scientific research and prediction of related disasters.

Description

Technical Field

[0001] The present application relates to the field of mountain flood and debris flow models, and in particular to a numerical simulation method for mountain flood and debris flow coupling a hydrodynamic model. Background Art

[0002] Mountain flood and debris flow is a common natural disaster. In recent years, the frequent occurrence of mountain flood and debris flow has seriously threatened people's lives and property and the safety of major projects, becoming an important factor restricting social and economic development. Therefore, it is of extremely important practical significance to conduct in-depth research on the formation mechanism, evolution process and disaster effects of mountain flood and debris flow. Currently, numerical simulation technology is an important means to study mountain flood and debris flow. Common mountain flood and hydrological models include HEC-HMS, SWAT and TOPMODEL, etc. However, these hydrological models have limitations. They ignore the mechanical factors of flow during the construction process and are difficult to accurately describe the formation and evolution process of mountain flood. The HiPIMS model is a hydrodynamic model based on the Godunov format to solve the Riemann approximate solution, which can more accurately simulate the slope flow dynamics, including complex phenomena such as shock waves, thus effectively simulating the mountain flood process. However, the HiPIMS model has deficiencies in describing the movement of debris flow and cannot comprehensively reflect the complex characteristics of debris flow. In the field of debris flow simulation, common models include Geoflow, Massflow-2D, RAMMS, FlO-2D, DAN-3D and CFX, etc. Among them, the FLO-2D model has been internationally recognized because of its convenient operation, ability to accurately simulate the spatio-temporal changes of debris flow, and the simulation results being highly consistent with the actual situation. However, the FLO-2D model also has some limitations. For example, it cannot consider shock waves, hydraulic jumps and channel erosion phenomena, and the calculated flow hydrograph will bring relatively large errors. For example, the Chinese patent with the publication number CN113553792B proposes a numerical simulation and danger prediction method for the whole process of mountain disasters, including steps: S1: high spatio-temporal rainfall prediction in mountainous areas; S2: hydrodynamic process and numerical simulation: establishing a hydrodynamic process model and solving the hydrodynamic process model; S3: mountain flood and debris flow disaster movement model and numerical simulation; S4: small watershed disaster risk analysis and danger prediction. Based on climate prediction data, this invention realizes the prediction of disaster hazards and the dynamic quantitative assessment of risk losses through the scenario simulation of the whole process of disasters, upgrades the traditional disaster level prediction to danger prediction, and provides a scientific basis for accurate disaster prevention and rescue. However, the spatial resolution of the rainfall prediction data in this application is interpolated from 9KM to 0.01°×0.01°. Although the spatial resolution is improved, the interpolation process may introduce errors, resulting in a reduction in the numerical simulation accuracy of mountain flood and debris flow.

[0003] Therefore, developing a numerical simulation method that can couple a hydrodynamic model and a debris flow model to more accurately simulate the process of high-transient mountain flood and debris flow disasters is an important research direction at present. Summary of the Invention

[0004] To solve the above technical problems, the present application provides a numerical simulation method for mountain flood and debris flow coupling a hydrodynamic model, which is used to better simulate the high-transient fluid disaster process, solve the problem that FLO-2D cannot consider shock waves, improve the simulation accuracy of debris flow, and provide an effective tool for the simulation research of mountain flood and debris flow.

[0005] To achieve the above invention purpose, the present invention proposes a numerical simulation method for mountain flood and debris flow coupling a hydrodynamic model, including:

[0006] Collect basic data within the research area. The basic data includes DEM data, land use data, and rainfall data. After preprocessing the basic data based on Python, initial data is obtained, and the DEM data in the initial data is edited to obtain high-precision DEM data;

[0007] Determine the hydrodynamic parameters within the research area, and set the initial conditions and boundary conditions;

[0008] Input the high-precision DEM data, the land use data and rainfall data in the initial data into the HiPIMS model to obtain a result file, and the result file includes the water depth of the research area;

[0009] Based on Python, batch process the result file to obtain a HYD file, determine the parameters of the FLO-2D model, and determine the location of the catchment points in the research area;

[0010] Input the HYD file and the location of the catchment points into the FLO-2D model to construct a HiPIMS-FLO-2D coupling model, and obtain a shape result file. The shape result file includes the mud depth and flow velocity;

[0011] Verify the simulation accuracy of HiPIMS-FLO-2D to determine the simulation effect.

[0012] Further, the editing of the DEM data in the initial data includes the following steps:

[0013] Identify the abnormal areas in the DEM data. The abnormal areas include low-lying areas and steep slopes, and correct the abnormal areas based on the OpenCV library.

[0014] Further, the hydrodynamic parameters include Manning coefficient, infiltration coefficient, hydraulic conductivity, and soil water content. The initial conditions include the initial water depth of the whole domain and the upstream boundary incoming water flow rate, and the boundary condition is a dynamic rainfall boundary.

[0015] Further, the batch processing of the result file based on Python includes the following steps:

[0016] In Python, after calculating the flow rate based on the water depth and flow velocity output by HiPIMS using the Math, Os, and Rasterio libraries, the format conversion of the calculated flow rate values is completed based on the Pandas library to obtain the HYD file.

[0017] Further, determining the location of the catchment point includes the following steps:

[0018] Based on the maximum flow rate raster distribution output by HiPIMS, continuous raster areas with a flow rate value ≥ 10 m 3 / s are extracted. The mountain flood confluence path is generated through the Rasterio, Numpy, and Richdem libraries of Python, and the channel confluence points with a sudden increase in flow rate exceeding 30% of the mean value are marked. The spatial overlay analysis is performed on the confluence point coordinates and the loose sediment source distribution map obtained from the field survey. The confluence areas with a sediment source thickness ≥ 1 m and a slope between 25° - 45° are screened out. In the confluence area, a candidate point is set every 50 m along the center line of the main channel, and the candidate point with a water depth ≥ 0.8 m at the peak flow rate moment is selected as the final catchment point.

[0019] Further, the parameters of the FLO-2D model include Manning coefficient, volume concentration, Bingham viscosity coefficient, Bingham yield coefficient, soil-rock specific gravity coefficient, and laminar flow retardation coefficient.

[0020] Further, using F as the mountain flood evaluation index and Ω as the debris flow evaluation index, the simulation accuracy of HiPIMS-FLO-2D is verified.

[0021] Further, the calculation formula for the mountain flood evaluation index F is:

[0022]

[0023] A r is the actually observed inundated area; A s is the inundated area simulated by the model; A0 is the area of the overlapping part of A r and A s .

[0024] Further, the calculation formula for the debris flow evaluation index Ω is:

[0025] Ω = α - β - γ + δ, where

[0026]

[0027] In the formula, A observed , Vobserved is the measured stacking area and volume, A X , A Y and A z represent the areas of the correct judgment area, the wrong judgment area and the missing judgment area, V x represents the volume of the correct judgment area, Xm represents the area of the m-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Yj represents the area of the j-th single grid cell of the wrong judgment, ny represents the total number of grid cells of the wrong judgment, Zi represents the area of the i-th single grid cell of the missing judgment, nz represents the total number of grid cells of the missing judgment, Xg represents the volume of the g-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Zw represents the volume of the w-th single grid cell of the missing judgment, nz represents the total number of grid cells of the missing judgment..

[0028] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0029] In the technical solution provided by the present application, first, DEM, rainfall sequence, and land use are used as model inputs, and the HiPIMS model is used to simulate the mountain flood situation in the basin to obtain results such as the mountain flood inundation depth in the basin. Then, based on the HiPIMS model results as the input of the flow hydrograph of FLO-2D, a HiPIMS-FLO-2D coupling model is constructed to simulate the debris flow situation in the area to obtain the mud depth and flow velocity of the debris flow. Finally, the model accuracy is evaluated through the mountain flood accuracy index F and the debris flow accuracy index Ω. The numerical simulation method of mountain flood and debris flow with a coupled hydrodynamic model proposed by the present invention can better simulate the high-transient mountain flood and debris flow disaster process, improve the simulation accuracy, and provide a scientific reference for the research of mountain flood and debris flow disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 is the data flow diagram of the present invention;

[0032] Figure 2 is a schematic diagram of the application example research area of the present invention;

[0033] Figure 3 is a comparison diagram before and after processing the abnormal area based on Python of the present invention;

[0034] Figure 4It is the time series diagram of the inundation simulation of the "8.13" rainstorm and mountain torrent in the present invention;

[0035] Figure 5 It is the distribution diagram of the movement and accumulation characteristics of debris flow in the present invention;

[0036] Figure 6 It is the statistical chart of the scouring scale of debris flow in the present invention;

[0037] Figure 7 It is the comparison diagram of the maximum simulated range and the measured range of mountain torrent in the present invention;

[0038] Figure 8 It is the method diagram of debris flow accuracy evaluation in the present invention;

[0039] Figure 9 It is the accuracy evaluation diagram of debris flow in the present invention; Specific implementation manners

[0040] The embodiment of the present application provides a numerical simulation method for mountain torrent and debris flow coupling a hydrodynamic model. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] The area selected for the present invention is the Longxi River Basin in Sichuan, which is located in Longchi Town, Dujiangyan City, Sichuan. The basin area is 77.6 km 2 , the terrain is high in the north and low in the south, with a significant height difference. The climate is a subtropical humid monsoon area, with concentrated precipitation from May to September, an average annual precipitation of about 1168.8 mm, and frequent rain. The rock strata are Proterozoic and Sinian rocks that are easy to break, with a large earthquake impact and poor slope stability. The channel has a large drop, obvious terrain undulation, and is extremely prone to mountain torrent and debris flow disasters during extreme rainfall. The rainfall data is selected as the hourly rainfall data from 12:00 to 24:00 on August 13, 2010 obtained from the Longchi meteorological station. The land use data adopts the GlobeLand30 series data, and the elevation data selects the 12.5 m DEM collected by the ALOS satellite. The data is processed in each step in combination with the Python program. Now, in combination with Figures 1 - 9 The mountain torrent and debris flow simulation method coupling a hydrodynamic model is introduced, including the following steps:

[0042] Collect the basic data within the research area. The basic data includes DEM data, land use data, and rainfall data. After preprocessing the basic data based on Python, obtain the initial data, and edit the DEM data in the initial data to obtain high-precision DEM data.

[0043] Specifically, during preprocessing, according to the basic data such as DEM data, land use data, and rainfall data collected within the research area, generate a rainfall mask:.ASCII file within the research area based on the Gdal library in Python, and represent the rainfall mask in the form of spatial rasterization. And convert the rainfall sequence data into.dat format with a time step of 3600s; resample the land use data based on the rasterio library to make the raster row and column numbers of the land use data the same as those of the DEM data.

[0044] Determine the hydrodynamic parameters within the research area, and set the initial conditions and boundary conditions.

[0045] Input the high-precision DEM data, land use data, and rainfall data in the initial data into the HiPIMS model to obtain the result file, and the result file includes the water depth of the research area.

[0046] Input the processed DEM, land use, and rainfall data into the HiPIMS model, and a flooded water depth sequence from 15:00 to 24:00 can be obtained, specifically as Figure 4 shown.

[0047] Perform batch processing on the result file based on Python to obtain the HYD file, determine the parameters of the FLO-2D model, and determine the location of the catchment points in the research area.

[0048] Input the HYD file and the location of the catchment points into the FLO-2D model to construct the HiPIMS-FLO-2D coupled model, and obtain the shape result file, and the shape result file includes the mud depth and flow velocity.

[0049] Verify the simulation accuracy of HiPIMS-FLO-2D to determine the simulation effect.

[0050] The editing of the DEM data in the initial data includes the following steps:

[0051] Identify the abnormal areas in the DEM data. The abnormal areas include low-lying areas and steep slope areas, and correct the abnormal areas based on the OpenCV library.

[0052] In Python, process the elevation abnormal areas within the research area based on the OpenCV and Scikit-image libraries. The elevation abnormal areas include but are not limited to: low-lying areas, steep slope areas. The comparison before and after processing the abnormal areas is as Figure 3 shown.

[0053] The hydrodynamic parameters include Manning coefficient, infiltration coefficient, hydraulic conductivity, soil water content. The initial conditions include the initial water depth of the whole area and the incoming water flow at the upstream boundary. The boundary condition is the dynamic rainfall boundary.

[0054] The parameter values of Manning coefficient (manning), infiltration coefficient (sewer_sink), hydraulic conductivity (hydraulic_conductivity), soil water content (water_content_diff), etc. are shown in Table 1. The initial water depth in the model area is set to 1m, and the upstream incoming water flow is set to 75m 3 / s.

[0055] Table 1 Hydrodynamic parameters of the HiPIMS model

[0056] Parameter name Manning coefficient Infiltration coefficient Hydraulic conductivity Soil water content Value range 0.12 -0.00001 0.000035 0.001

[0057] Batch processing of the result files based on Python includes the following steps:

[0058] In Python, based on the Math, Os, and Rasterio libraries, the flow rate is calculated for the water depth and flow velocity output by HiPIMS, and then based on the Pandas library, the format conversion of the calculated flow rate values is completed to obtain the HYD file.

[0059] The flow rate is calculated through the following formula,

[0060]

[0061] In the formula, Q is the calculated flow rate, a is the DEM resolution, u and v are the flow velocities in the x and y directions output by the HiPIMS model, and h is the water depth value output by HiPIMS.

[0062] Determining the location of the catchment points includes the following steps:

[0063] Based on the maximum flow rate raster distribution output by HiPIMS, extract the continuous raster area where the flow rate value ≥ 10m 3 / s. Generate the mountain flood confluence path through the Rasterio, Numpy, and Richdem libraries of Python, mark the channel confluence points where the flow rate suddenly increases by more than 30% of the mean value, conduct a spatial overlay analysis of the confluence point coordinates with the loose sediment source distribution map obtained from the field survey, screen out the confluence areas where the source thickness ≥ 1m and the slope is between 25° - 45°. In the confluence area, set a candidate point every 50m along the center line of the main channel, and select the candidate point where the water depth ≥ 0.8m at the peak flow rate moment as the final catchment point. The location of the catchment point is as Figure 2 shown.

[0064] The parameters of the FLO-2D model include Manning coefficient, volume concentration, Bingham viscosity coefficient, Bingham yield coefficient, soil-rock specific gravity coefficient, and laminar flow retardation coefficient. The specific values are shown in Table 2.

[0065] Table 2 Parameters of the FLO-2D Model

[0066]

[0067] Use F as the evaluation index for flash floods and Ω as the evaluation index for debris flows to verify the simulation accuracy of HiPIMS-FLO-2D.

[0068] The calculation formula for the flash flood evaluation index F is:

[0069]

[0070] A r is the actually observed inundated area; A s is the inundated area simulated by the model; A0 is the area of the overlapping part of A r and A s .

[0071] The calculation formula for the debris flow evaluation index Ω is:

[0072] Ω = α - β - γ + δ, where

[0073]

[0074] In the formula, A observed , V observed are the measured deposition area and volume, A X , A Y and A z represent the areas of the correct judgment area, wrong judgment area, and missing judgment area, V x represents the volume of the correct judgment area, Xm represents the area of the m-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Yj represents the area of the j-th single grid cell of the wrong judgment, ny represents the total number of grid cells of the wrong judgment, Zi represents the area of the i-th single grid cell of the missing judgment, nz represents the total number of grid cells of the missing judgment, Xg represents the volume of the g-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Zw represents the volume of the w-th single grid cell of the missing judgment, nz represents the total number of grid cells of the missing judgment.

[0075] The value range of Ω is [-2, 2]. When Ω = -2, it means that there is no overlap between the simulated area and the actual area; when Ω = 2, it means that the simulated area completely coincides with the actual area. The closer Ω is to 2, the more accurate the simulation is.Figure 8 Shows the sub-region definitions of X, Y, and Z. Figure 9 Shows the debris flow accuracy Ω value.

[0076] Figure 4 Is the water depth distribution at each time point during the rainfall on August 13. It can be seen that extensive waterlogging mainly occurred from 16:00 to 18:00 on August 13. The runoff in the basin first converged in the tributaries of each small watershed and then gradually flowed into the main river channel, and flowed from the upstream to the downstream mouth along the main river channel. Specifically, at 15:00 - 16:00, obvious runoff depths appeared in each small watershed within the basin. The average water depth of the inundation in the basin was 0.381m, and the inundated area was 3.436 km 2 . The average water depths of DF01, DF02, DF03, and DF04 in the high debris flow occurrence area were 0.246m, 0.058m, 0.086m, and 0.058m respectively. The inundated areas were 0.368 km 2 , 0.020 km 2 , 0.008 km 2 , 0.021 km 2 . From 16:00 to 17:00, due to the rapid increase in rainfall, the flood inundation range increased sharply, and the inundated area was 13.062 km 2 , and the average inundation water depth was 0.426m. Among them, the average water depths of DF01, DF02, DF03, and DF04 were 0.416m, 0.189m, 0.185m, and 0.278m respectively, and the inundated areas were 1.151 km 2 , 0.122 km 2 , 0.122 km 2 , 0.112 km 2 . After 18:00, due to the decrease in precipitation, the waterlogging converged to the low-lying areas and flowed into the main river channel along each tributary, gradually concentrating from the upstream to the downstream outlet, and the water depth in the downstream exceeded 2m. By 24:00, the average inundation water depth reached a maximum of 1.222m, among which DF01 was 1.609m, DF02 was 0.537m, DF03 was 0.528m, and DF04 was 0.743m. However, after the infiltration of rainwater, the inundation range gradually became smaller, and the inundated area was 3.002 km 2 , among which DF01 was 0.228 km 2 , DF02 was 0.005 km 2 , DF03 was 0.007 km 2 , DF04 was 0.006 km 2 .

[0077] Figure 5Shows the accumulation fan distribution of FLO-2D and HiPIMS-FLO-2D under the 20-year recurrence interval and the 8.13 event. As can be seen from the figure, under the 20-year recurrence interval scenario, the total area of the accumulation fans of the 4 gullies in FLO-2D is approximately 1,448,500 m 2 , and the total scouring volume is 401,874 m 3 . The maximum accumulation depth is 6.95 m, and the average accumulation depth is 5.13 m. The total area of the accumulation fans of the 4 gullies in HiPIMS-FLO-2D is approximately 162,160 m 2 , and the total scouring volume is 425,508 m 3 . The maximum accumulation depth is 7.45 m, and the average accumulation depth is 5.75 m; under the 8.13 event scenario, the total area of the accumulation fans of the 4 gullies in FLO-2D is approximately 152,480 m 2 , and the total scouring volume is 404,409 m 3 . The maximum accumulation depth is 7.68 m, and the average accumulation depth is 5.58 m. The total area of the accumulation fans of the 4 gullies in HiPIMS-FLO-2D is approximately 174,450 m 2 , and the total scouring volume is 436,547 m 3 . The maximum accumulation depth is 7.87 m, and the average accumulation depth is 5.98 m. Due to the loose sediment source of the tributary gully flowing into the main gully, the accumulation depth of DF01 is the largest under different scenarios. In addition, compared with the 20-year recurrence interval scenario, the accumulation area and volume of the 8.13 event are generally larger, and the accumulation depth gradually increases from the fan top to the front edge of the accumulation fan, showing the accumulation characteristics of viscous debris flow. To sum up, whether under the 20-year recurrence interval or the 8.13 event scenario, the accumulation fan area, volume, and accumulation depth of HiPIMS-FLO-2D and FLO-2D are relatively close, but the simulation values of HiPIMS-FLO-2D are generally slightly higher.

[0078] Figure 7 Shows the comparison of the maximum water depth inundation range and the observed inundation range of the 8.13 event. The average inundation water depth of the entire basin is 0.593 m, of which DF01 is 0.66 m, DF02 is 0.191 m, DF03 is 0.188 m, and DF04 is 0.282 m. The inundation area of the entire basin is 13.254 km 2 , of which DF01 is 1.152 km 2 , DF02 is 0.123 km 2 , DF03 is 0.123 km 2 , DF04 is 0.113 km 2 . The simulation verification is carried out in the DF01-DF04 area where the mountain flood and debris flow disasters are the most serious. The inundation area delimited by remote sensing images is DF01 1.155 km 2 , DF02 0.131 km 2, DF03 is 0.126 km 2 , DF04 is 0.115 km 2 . The F-statistic of DF01 is 0.85, the F-statistic of DF02 is 0.75, the F-statistic of DF03 is 0.81, and the F-statistic of DF04 is 0.84. It shows that the overall goodness of fit is good, and the maximum inundation range simulated by the model is roughly consistent with the actual inundation range. To sum up, the model performs well in the inundation simulation of mountain floods and can provide effective support for debris flow simulation.

[0079] Figure 9 Displays the accuracy parameters of the debris flow simulation of four gullies. In the scenario of once-in-20-year recurrence interval, the Ω values of FLO-2D are 1.402, 1.539, 1.427, 1.499, and the average value is 1.467, while the Ω values of HiPIMS-FLO-2D are 1.614, 1.678, 1.623, 1.624, and the average value is 1.635. It shows that in this scenario, the accuracy of HiPIMS-FLO-2D is on average increased by 11.45% compared with FLO-2D, and the spatial position of the simulation result is closer to the true value; in the 8.13 scenario, the Ω values of FLO-2D are 1.423, 1.600, 1.562, 1.517, and the average value is 1.526. The Ω values of HiPIMS-FLO-2D are 1.703, 1.736, 1.738, 1.710, and the average value is 1.722. The accuracy of HiPIMS-FLO-2D is on average increased by 12.84%. To sum up, whether in the scenario of once-in-20-year recurrence interval or the 8.13 scenario, the Ω values of HiPIMS-FLO-2D are generally greater than 1.7, showing high accuracy, and HiPIMS-FLO-2D is closer to the true value compared with FLO-2D. For different types of gullies, HiPIMS-FLO-2D can achieve satisfactory simulation results.

[0080] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A numerical simulation method for mountain flood and debris flow coupling with a hydrodynamic model, the method comprising the following steps: Collect basic data within the study area, the basic data including DEM data, land use data, and rainfall data. After preprocessing the basic data based on Python, initial data is obtained, and the DEM data in the initial data is edited to obtain high-precision DEM data; Determine the hydrodynamic parameters within the study area, and set initial conditions and boundary conditions; Input the high-precision DEM data, the land use data and rainfall data in the initial data into the HiPIMS model to obtain a result file, the result file including the water depth of the study area; Batch process the result file based on Python to obtain a HYD file, determine the parameters of the FLO-2D model, and determine the location of the catchment points in the study area; Input the HYD file and the location of the catchment points into the FLO-2D model to construct a HiPIMS-FLO-2D coupling model, and obtain a shape result file, the shape result file including the mud depth and flow velocity; Verify the simulation accuracy of HiPIMS-FLO-2D to determine the simulation effect.

2. The method according to claim 1, wherein The editing of the DEM data in the initial data includes the following steps: Identify the abnormal areas in the DEM data, the abnormal areas including low-lying areas and steep slope areas, and correct the abnormal areas based on the OpenCV library.

3. The method according to claim 1, wherein The hydrodynamic parameters include Manning coefficient, infiltration coefficient, hydraulic conductivity, soil water content, the initial conditions include the initial water depth of the whole area and the upstream boundary incoming water flow rate, and the boundary condition is a dynamic rainfall boundary.

4. The method according to claim 1, wherein Batch processing the result file based on Python includes the following steps: In Python, based on the Math, Os, and Rasterio libraries, calculate the flow rate of the water depth and flow velocity output by HiPIMS, and then complete the format conversion of the calculated flow rate values based on the Pandas library to obtain the HYD file.

5. The method according to claim 1, wherein Determining the location of the catchment points includes the following steps: Based on the maximum flow raster distribution output by HiPIMS, extract continuous raster areas with a flow value ≥ 10 m 3 / s, generate flash flood confluence paths through the Rasterio, Numpy, and Richdem libraries in Python, mark the channel confluence points where the flow suddenly increases by more than 30% of the mean, perform spatial overlay analysis on the confluence point coordinates and the loose sediment source distribution map obtained from field surveys, screen out the confluence areas with a sediment source thickness ≥ 1 m and a slope between 25° - 45°, and in the said confluence areas, set a candidate point every 50 m along the centerline of the main channel, and select the candidate points with a water depth ≥ 0.8 m at the peak flow moment as the final catchment points.

6. The method according to claim 1, wherein The parameters of the FLO-2D model include Manning coefficient, volume concentration, Bingham viscosity coefficient, Bingham yield coefficient, soil-rock specific gravity coefficient, and laminar flow retardation coefficient.

7. The method according to claim 1, characterized in that, Use F as the mountain flood evaluation index and Ω as the debris flow evaluation index to verify the simulation accuracy of HiPIMS-FLO-2D.

8. The method according to claim 7, characterized in that, The calculation formula for the mountain flood evaluation index F is: A r is the actually observed inundated area; A s is the inundated area simulated by the model; A0 is the area of the r overlap between A s and A.

9. The method according to claim 7, wherein The calculation formula for the debris flow evaluation index Ω is: Ω = α - β - γ + δ, where, Wherein, A observed ,V observed are the measured stacking area and volume. A X ,A Y and A z represent the areas of the correct judgment area, the wrong judgment area and the missing judgment area, and V x represents the volume of the correct judgment area. Xm represents the area of the m-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Yj represents the area of the j-th single grid cell of the wrong judgment, ny represents the total number of grid cells of the wrong judgment, Zi represents the area of the i-th single grid cell of the missing judgment, nz represents the total number of grid cells of the missing judgment, Xg represents the volume of the g-th single grid cell of the correct judgment, nx represents the total number of grid cells of the correct judgment, Zw represents the volume of the w-th single grid cell of the missing judgment, and nz represents the total number of grid cells of the missing judgment.

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