A method for simulating pollution diffusion and hydrodynamic response of the Yellow River based on extreme wind field

Abstract

This invention discloses a method for simulating the diffusion and hydrodynamic response of pollution in the Yellow River based on extreme wind fields. The method includes: acquiring topographic data of the target sea area and constructing a three-dimensional high-resolution ocean numerical model based on the topographic data; setting boundary conditions, initial conditions, and external forcing conditions for the three-dimensional high-resolution ocean numerical model; setting up several parallel simulation experimental groups; based on the three-dimensional high-resolution ocean numerical model, setting passive tracer release sources at the model grid positions corresponding to land-based pollution input points, simulating the transport and diffusion of passive tracers to obtain the three-dimensional concentration field of pollutants, and simultaneously running the models of each experimental group to obtain the three-dimensional hydrodynamic field and pollutant concentration field under different wind field scenarios; and evaluating the recovery process by comparing and analyzing the simulation results of each experimental group. This invention improves the hydrodynamic simulation capability under extreme wind field conditions.

Description

Technical Field

[0001] This invention relates to the field of marine environmental numerical simulation technology, and in particular to a simulation method for the diffusion of pollution and hydrodynamic response in the Yellow River based on extreme wind fields. Background Technology

[0002] The Yellow River estuary and the Bohai Sea are important ecologically sensitive areas in my country. The Bohai Sea is a typical semi-enclosed shallow sea system. The Yellow River runoff carries a large amount of freshwater, nutrients, suspended sediment, and land-based pollutants into the Bohai Sea. Its diffusion range and path are easily and significantly affected by external forcing, including tides, runoff, background circulation, and wind stress.

[0003] In recent years, with the intensification of urbanization and agricultural activities in the Yellow River Basin, the input of land-based pollutants has increased significantly, leading to environmental problems such as eutrophication, algal blooms, and hypoxia in the Bohai Sea ecosystem. Existing studies have shown that monsoons and moderate-intensity wind processes can affect the transport pathways of pollutants, but effective simulation methods are lacking for key processes such as changes in hydrodynamic structure, enhanced vertical mixing of pollutants, and changes in diffusion range under extreme wind events.

[0004] In April 2025, an extremely strong wind event occurred in North China, caused by a rapidly southward-moving upper-level cold vortex and a surface cyclone. This wind field was characterized by its wide range, long duration, and extreme wind speeds and intensity. It is expected to significantly disturb the hydrodynamic structure of the Bohai Sea, alter the transport path of the Yellow River freshwater plume, and consequently affect pollutant distribution. Existing marine numerical simulation studies mostly focus on pollutant transport analysis under conventional wind field conditions, lacking systematic quantitative analysis methods for extreme wind events. In particular, it is difficult to distinguish the independent role of extreme wind fields relative to factors such as tides, runoff, and background circulation in the pollutant dispersion process.

[0005] Therefore, it is necessary to propose a simulation method to quantify the modulating effect of extreme wind events on hydrodynamic structures and pollutant diffusion, so as to provide a scientific basis for marine environmental prediction. Summary of the Invention

[0006] To address the shortcomings of existing technologies in characterizing hydrodynamic structures and simulating pollutant diffusion processes under extreme wind events, this paper proposes a simulation method for Yellow River pollution diffusion and hydrodynamic response based on extreme wind fields. This method aims to more accurately characterize the impact of extreme wind fields on hydrodynamics and pollutant transport in semi-enclosed sea areas, achieve quantitative analysis of pollutant diffusion differences under different wind field scenarios, and provide a reliable technical means for marine environmental prediction and ecological risk assessment under short-term forcing conditions.

[0007] To achieve the above objectives, this invention provides a method for simulating the Yellow River pollution diffusion and hydrodynamic response based on extreme wind fields, comprising:

[0008] Acquire topographic data of the target sea area, and construct a three-dimensional high-resolution marine numerical model for the target sea area based on the topographic data;

[0009] Boundary conditions, initial conditions, and external forcing conditions are set for the three-dimensional high-resolution ocean numerical model, wherein the external forcing conditions include tidal forcing, runoff forcing, and wind field forcing;

[0010] Several parallel simulation experimental groups were set up. The experimental groups, while keeping all conditions except the wind field consistent, respectively used the actual wind field containing extreme wind events, the climatological average wind field and the windless condition as wind stress input.

[0011] Based on the aforementioned three-dimensional high-resolution ocean numerical model, passive tracer release sources were set at the model grid positions corresponding to the land-based pollution input points. The transport and diffusion of passive tracers were simulated to obtain the three-dimensional concentration field of pollutants. Simultaneously, the models of each experimental group were run to obtain the three-dimensional hydrodynamic field and pollutant concentration field under different wind field scenarios.

[0012] By comparing and analyzing the simulation results of each experimental group, the independent effects of extreme wind fields on hydrodynamic structures and pollutant diffusion processes are separated and quantified. Based on the comparison between the post-wind state and the background state, the recovery process is evaluated.

[0013] Preferably, acquiring the topographic data of the target sea area includes:

[0014] This was achieved by fusing GEBCO global seabed topography data with refined nearshore topography data based on Google Earth Engine remote sensing imagery.

[0015] Preferably, the three-dimensional high-resolution ocean numerical model is constructed based on the ROMS mode. The three-dimensional high-resolution ocean numerical model is layered using terrain-following coordinates in the vertical direction and uses a curve orthogonal grid fitted by the boundary in the horizontal direction.

[0016] Preferably, the boundary conditions include open boundary conditions obtained by interpolation of large-scale ocean model results; the initial conditions are determined by the target sea area climatological dynamic field obtained after long-term simulation.

[0017] Preferably, the tidal forcing is provided by introducing several tidal constituents provided by the TPXO8 tidal model; the runoff forcing is set based on measured data from the estuary hydrological station; and the wind field forcing is provided based on sea surface wind stress calculated from ERA5 reanalysis data.

[0018] Preferably, the parallel simulation experimental group includes:

[0019] The control experimental group uses hourly real-time wind fields that include the extreme wind events mentioned above;

[0020] The climatological experimental group uses multi-year average wind fields;

[0021] In the windless experimental group, the sea surface wind stress of the model was set to zero.

[0022] Preferably, the independent effects of extreme wind fields on hydrodynamic structures and pollutant diffusion processes are separated and quantified, including:

[0023] The simulation results of the control experimental group and the climate experimental group are compared to obtain the change data caused by extreme wind events relative to normal wind fields.

[0024] The simulation results of the controlled experimental group or the climatic experimental group are compared with those of the windless experimental group to obtain data on changes caused by wind forcing.

[0025] Preferably, the assessment of the recovery process includes:

[0026] Based on simulation results prior to extreme wind events, the background benchmarks for the three-dimensional hydrodynamic field and pollutant concentration field were determined.

[0027] Calculate the deviation of sea surface velocity, three-dimensional flow field structure, and pollutant concentration distribution from the background baseline at each time point during the post-wind recovery phase;

[0028] Based on the change of the degree of deviation over time, the recovery time scale and the maximum deviation are calculated as recovery assessment indicators.

[0029] The recovery assessment indicators describe the recovery process of the hydrodynamic field and pollutant concentration field after extreme wind events.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] (1) By introducing hourly real-time wind fields, this invention enables the model to accurately reproduce the significant enhancement of surface currents, reconstruction of circulation structure, and gradual recovery process during extreme wind events. This method can capture phenomena such as increased surface velocity, change of sea surface circulation direction, and enhanced nearshore forced transport that occur in the target sea area under extreme wind forcing, significantly improving the simulation capability for sudden strong wind events.

[0032] (2) The simulation method of this invention reveals the characteristics of pollutant transport caused by extreme wind events, including significantly enhanced vertical mixing and strong turbulence and mixing caused by extreme winds, which causes pollutants to diffuse from the surface to the subsurface. Unlike the expected "enhanced diffusion" under conventional wind fields, the simulation of this invention shows that extreme winds cause pollutants to accumulate in the nearshore area. This kind of complex nonlinear response phenomenon is difficult to accurately capture by existing methods, and this invention provides a new numerical simulation capability.

[0033] (3) By designing three types of wind scenario experiments, this invention can eliminate the effect of wind, identify the difference between climatological wind and extreme wind, and quantitatively analyze the independent contribution of wind forcing to the diffusion of pollutants, thereby separating the impact of extreme wind events from other forcing factors and achieving more accurate dynamic diagnosis.

[0034] (4) By optimizing the satellite-refined topography of the target sea area and inputting hourly wind stress forcing, this invention has higher spatial accuracy than the traditional coarse resolution model. Especially in the nearshore shallow water area, it can more realistically reflect the structure of the Yellow River freshwater plume, the nearshore circulation path and the range and path of pollution diffusion, and can effectively improve the model's ability to depict the complex dynamic structure of shallow sea.

[0035] (5) Based on the simulation method of the present invention, quantitative simulation basis can be provided for coastal emergency pollution management, ecological risk assessment after extreme weather, marine environmental quality forecasting and coastal zone planning, which has significant application value. Attached Figure Description

[0036] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0037] Figure 1 This is a flowchart of a simulation method for Yellow River pollution diffusion and hydrodynamic response based on extreme wind fields, according to an embodiment of the present invention.

[0038] Figure 2 The diagram shows the distribution of pollutant concentrations under different wind field scenarios according to an embodiment of the present invention. The left column shows the three-day average distribution before the occurrence of an extreme wind event, the middle column shows the daily average distribution during the extreme wind event, and the right column shows the three-day average distribution after the end of the extreme wind event. Detailed Implementation

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0041] This embodiment takes extreme wind events as the core research object and constructs a high-resolution ocean model based on ROMS. It combines a numerical simulation method system of multi-wind field scenario comparison and three-dimensional pollutant tracing. Through this method, the hydrodynamic response and pollutant diffusion process of the Yellow River under the action of extreme wind events can be quantitatively identified. This solves the problem in the prior art that it is difficult to quantitatively identify the independent contribution of extreme wind field factors in the hydrodynamic response and pollutant diffusion process due to the combined effect of multiple external forcings.

[0042] This embodiment proposes a simulation method for pollution diffusion and hydrodynamic response in the Yellow River based on extreme wind fields, such as... Figures 1-2 ,include:

[0043] Acquire topographic data of the target sea area, and construct a three-dimensional high-resolution marine numerical model for the target sea area based on the topographic data;

[0044] Boundary conditions, initial conditions, and external forcing conditions are set for the three-dimensional high-resolution ocean numerical model, wherein the external forcing conditions include tidal forcing, runoff forcing, and wind field forcing;

[0045] Several parallel simulation experimental groups were set up. The experimental groups, while keeping all conditions except the wind field consistent, respectively used the actual wind field containing extreme wind events, the climatological average wind field and the windless condition as wind stress input.

[0046] Based on the aforementioned three-dimensional high-resolution ocean numerical model, passive tracer release sources were set at the model grid positions corresponding to the land-based pollution input points. The transport and diffusion of passive tracers were simulated to obtain the three-dimensional concentration field of pollutants. Simultaneously, the models of each experimental group were run to obtain the three-dimensional hydrodynamic field and pollutant concentration field under different wind field scenarios.

[0047] By comparing and analyzing the simulation results of each experimental group, the independent effects of extreme wind fields on hydrodynamic structures and pollutant diffusion processes are separated and quantified. Based on the comparison between the post-wind state and the background state, the recovery process is evaluated.

[0048] Furthermore, obtaining topographic data for the target sea area includes:

[0049] This was achieved by fusing GEBCO global seabed topography data with refined nearshore topography data based on Google Earth Engine remote sensing imagery.

[0050] Specifically, GEBCO (General Bathymetric Chart of the Oceans) global seafloor topography data is used as the basic seafloor topography input to provide the initial water depth information required for model calculation;

[0051] Based on the aforementioned seabed topography, the Yellow River Estuary and nearshore shoal areas are refined using the latest remote sensing imagery from Google Earth Engine to improve the accuracy of nearshore simulation. Specifically, this includes: extracting the real-time coastline locations of the Yellow River Estuary and nearshore areas from the remote sensing imagery to correct the deviation between the original topographic data and the actual coastline; reassigning the parameters of the corresponding grid nodes; embedding the corrected coastline data into the original model calculation grid to form refined nearshore seabed topographic data; and generating a high-precision topographic grid for final numerical calculations.

[0052] Furthermore, the three-dimensional high-resolution ocean numerical model is constructed based on the ROMS model. The three-dimensional high-resolution ocean numerical model uses terrain-following coordinates for layering in the vertical direction and uses a boundary-fitted curve orthogonal grid in the horizontal direction.

[0053] Specifically, the Regional Ocean Modeling System (ROMS) is adopted as the basic model framework;

[0054] The model uses stretched terrain-following coordinates (S-coordinates) to create 26 layers in the vertical dimension, enabling the vertical grid to adapt to complex shallow sea topography. In the horizontal dimension, a boundary-fitted curve orthogonal grid is used, with a horizontal resolution of 1 / 30°×1 / 30°cosφ, to finely characterize the complex shallow shoal topography of the Yellow River Estuary and the dynamic structure of the shallow sea area of ​​the Bohai Sea.

[0055] Furthermore, the boundary conditions include open boundary conditions obtained by interpolation of large-scale ocean model results; the initial conditions are determined by the target sea area climatological dynamic field obtained after long-term simulation.

[0056] Specifically, after determining the computational domain and constructing the seabed topography, boundary conditions, initial conditions, and external forcing conditions are set for the three-dimensional ocean numerical model. These conditions are used to define the constraints on physical quantities within and outside the computational domain and to drive the model's evolution over time. The specific content includes:

[0057] Boundary conditions: Temperature, salinity, current velocity and sea level height results from existing large-area ocean models of the Pacific Ocean are interpolated to the open boundary of this model as its boundary field. The east, west and north boundaries of the model are closed, and the three-dimensional flow field and temperature and salinity field at the south boundary both adopt mixed boundary conditions.

[0058] Initial conditions: In order to provide the model with a reasonable initial ocean state, avoid introducing unrealistic disturbances by starting the model from a static or idealized state, and shorten the adjustment time required for the model to reach a stable state, the model is first run under the same topography, boundary conditions and external forcing conditions for no less than 5 years to obtain a stable climatological dynamic field. Then, the three-dimensional velocity field, temperature field and salinity field are selected as the initial conditions of the model.

[0059] Furthermore, the tidal forcing is provided by introducing several tidal constituents provided by the TPXO8 tidal model; the runoff forcing is set based on measured data from the estuary hydrological station; and the wind field forcing is provided based on sea surface wind stress calculated from ERA5 reanalysis data.

[0060] Specifically, the external forcing conditions include introducing eight major tidal constituents (M2, S2, N2, K2, K1, O1, P1, Q1) provided by the TPXO8 tidal model at the open boundary of the model to drive periodic tidal movements within the computational region; setting a freshwater runoff input at the Yellow River estuary, with the runoff volume using monthly average data from the Lijin hydrological station, to describe the impact of the Yellow River freshwater input on regional hydrodynamics; and introducing sea surface wind stress calculated based on ERA5 10m / hour wind field data to describe the driving effect of atmospheric forcing on the sea surface flow field and vertical mixing; enabling the model to simultaneously consider the comprehensive impact of multiple external driving forces such as tides, runoff, and wind fields on the hydrodynamic structure and pollutant transport processes of semi-enclosed sea areas.

[0061] Furthermore, the parallel simulation experimental group includes:

[0062] The control experimental group uses hourly real-time wind fields that include the extreme wind events mentioned above;

[0063] The climatological experimental group uses multi-year average wind fields;

[0064] In the windless experimental group, the sea surface wind stress of the model was set to zero.

[0065] Specifically, because the hydrodynamic processes in semi-enclosed sea areas are simultaneously influenced by multiple factors such as tides, runoff, and background circulation, it is difficult to distinguish the relative contributions of wind field factors to pollutant dispersion without comparative experiments. Therefore, by constructing parallel simulations of different wind field scenarios, the influence of wind forcing can be separated from other external conditions, allowing for the separate identification and quantification of the impact of wind forcing on hydrodynamic structures and pollutant transport processes. This provides a comparable numerical basis for analyzing the mechanisms of extreme wind events. The following comparative experiments of wind field scenarios are set up in the model:

[0066] (1) Control experiment (CTRL): The hourly real-time wind field provided by ERA5 was used, including the process of extreme wind events, to fully reflect the characteristics of extreme wind events in April 2025;

[0067] (2) Climate-state experiment (CLIM): The multi-year average wind field is used as the wind stress input to simulate the hydrodynamic and pollutant diffusion state under background climate conditions;

[0068] (3) No-Wind Experiment: The wind stress on the sea surface of the model is set to zero, while the other conditions remain unchanged. This experiment is used to characterize the basic features of hydrodynamic structure and pollutant transport under no-wind-forced conditions.

[0069] All three wind field scenarios described above operate under the same model grid, seabed topography, boundary conditions, initial conditions, tidal forcing, and runoff forcing. The only difference is the wind stress input condition.

[0070] Building upon the steps outlined above, by simulating the release and transport of pollutant tracers, the transport, diffusion, and spatial distribution of pollutants in a semi-enclosed sea area can be analyzed under given hydrodynamic field conditions. By simultaneously releasing tracers in various wind field scenarios while maintaining consistency in tidal, runoff, and other external forcing conditions, the differences in pollutant transport characteristics under different wind field conditions can be compared and analyzed. Specific details are as follows:

[0071] (1) Based on the horizontal calculation grid used in the model, the geographical coordinates (119.30°E, 37.79°N) of the Yellow River estuary are mapped to the corresponding model grid cell, and a tracer release area is set in the grid cell and its adjacent grids to represent the actual emission location of pollutants near the Yellow River estuary. Within the release area, the initial concentration of the tracer is assigned a preset value.

[0072] (2) The tracer is only transported with the current and does not undergo biochemical reactions, and is used to represent pollutants entering the Yellow River;

[0073] (3) After the tracer is released, the three-dimensional concentration field of the tracer changing over time during the model calculation is output and analyzed to track the spatial diffusion changes of pollutants in the three-dimensional sea area. The concentration field is obtained by solving the tracer transport control equation, which is:

[0074] ;

[0075] In the formula, Represents the fluctuating component of the vertical flow velocity. and These represent the forcing term and the dissipation term, respectively. Where is the diffusion coefficient. This is a scalar field variable, used here to represent the concentration distribution of pollutant tracers. Let be the fluctuating component of the scalar field, t be the time variable, and z be the vertical coordinate. A three-dimensional velocity vector, typically represented as =(u, v, w), where 𝑢, 𝑣, and 𝑤 are the east-west, north-south, and vertical velocity components, respectively.

[0076] During model integration, the concentration values ​​of tracers in each grid cell are output according to a preset time step, forming a three-dimensional concentration data sequence that changes over time to indicate the spatial distribution of pollutants at different times. By comparing and analyzing the concentration field results simulated and output by the model, three-dimensional concentration field data from different times are selected to compare their distribution differences in the horizontal and vertical directions. This analysis examines the changes in the horizontal diffusion range and vertical mixing structure of pollutants, determines the evolution process of the pollutant diffusion range and the migration characteristics of high-concentration areas, thereby enabling the tracking of spatial diffusion changes of pollutants in the three-dimensional sea area.

[0077] Furthermore, the independent effects of extreme wind fields on hydrodynamic structures and pollutant diffusion processes were isolated and quantified, including:

[0078] The simulation results of the control experimental group and the climate experimental group are compared to obtain the change data caused by extreme wind events relative to normal wind fields.

[0079] The simulation results of the controlled experimental group or the climatic experimental group are compared with those of the windless experimental group to obtain data on changes caused by wind forcing.

[0080] Specifically, after completing hydrodynamic simulation, comparison of different wind field scenarios, and calculation of pollutant tracer transport, the simulation results of pollutant tracer transport are uniformly output and analyzed. The hydrodynamic structure and pollutant diffusion characteristics are quantitatively compared under different wind field scenarios to obtain the analysis results of the impact of extreme wind events on pollutant transport.

[0081] This embodiment outputs and analyzes the following simulation results:

[0082] (1) Three-dimensional flow field structure;

[0083] The three-dimensional flow field structure is composed of the three-dimensional velocity results calculated by the model at each time step. During the model operation, the three-dimensional velocity components corresponding to each grid cell are output. At the same time as outputting the instantaneous three-dimensional velocity components, the model performs time averaging to obtain daily average three-dimensional velocity data. The velocity data includes horizontal and vertical velocity components. By integrating the velocity distribution of different depth layers, a three-dimensional flow field structure reflecting the spatial flow characteristics of the water body is constructed.

[0084] (2) Time series of sea surface current velocity;

[0085] The sea surface velocity time series is extracted from the three-dimensional velocity field obtained by model simulation. Data on the change of velocity components over time are extracted at selected spatial locations or regions to form the sea surface velocity time series.

[0086] (3) Wind field anomalies and wind stress timing;

[0087] Wind field anomalies and wind stress time series were calculated from external wind field forcing data. The corresponding sea surface wind stress was calculated based on hourly 10m wind field data provided by ERA5, and the results of wind stress changes over time were recorded as wind stress time series. At the same time, the wind field or wind stress during extreme wind events was compared with the multi-year average wind field to calculate the wind field anomaly value.

[0088] (4) Pollutant concentration field;

[0089] The pollutant concentration field is calculated by the pollutant tracer transport equation. After the tracer is released, the model updates the concentration value of the tracer in each grid cell at each time step and records the concentration value according to the set output interval, thereby forming a three-dimensional pollutant concentration field that changes over time.

[0090] (5) The extent and location of the pollution zone's spread;

[0091] The diffusion range and center location of the pollution zone are calculated based on the pollutant concentration field. At each output time, the spatial region of the pollution zone can be directly determined based on the pollutant concentration distribution, and the outer boundary of this region is taken as the diffusion range of the pollution zone. Simultaneously, by weighting the tracer concentrations of each grid cell within the region, the centroid of the pollutant concentration distribution can be determined and taken as the center location of the pollution zone, thus describing the overall migration characteristics of the pollution zone. The diffusion range and center location of the pollution zone can be visually observed through the pollutant concentration distribution map.

[0092] Further, assess the recovery process, including:

[0093] Based on simulation results prior to extreme wind events, the background benchmarks for the three-dimensional hydrodynamic field and pollutant concentration field were determined.

[0094] Calculate the deviation of sea surface velocity, three-dimensional flow field structure, and pollutant concentration distribution from the background baseline at each time point during the post-wind recovery phase;

[0095] Based on the change in the degree of deviation over time, the recovery time scale and the maximum deviation magnitude are calculated as recovery assessment indicators.

[0096] Specifically, the recovery assessment indicators are calculated based on the time evolution characteristics of the hydrodynamic field and the pollutant concentration field. After the extreme wind event ends, the changes in sea surface velocity, three-dimensional flow field structure and pollutant concentration distribution over time are analyzed and compared with the background state before the extreme wind event. When the relevant variables recover to a state close to the background state, the corresponding time scale and change magnitude are recorded, which are used as assessment indicators for the system recovery process.

[0097] The analysis and comparison process specifically includes: Before the extreme wind event, a time interval unaffected by strong wind disturbances is selected. Based on the sea surface velocity, three-dimensional flow field structure, and pollutant concentration distribution data output by the model within this time interval, time averaging of each variable is performed to obtain the corresponding background state, which serves as a reference benchmark for subsequent comparative analysis. During the post-wind recovery phase, data at each time point are compared hourly with the corresponding background state to analyze the deviation of sea surface velocity intensity, three-dimensional flow field structure characteristics, and pollutant concentration distribution from the background state and their trends over time. For at least one variable among sea surface velocity, three-dimensional flow field structure, and pollutant concentration distribution, when its value change amplitude gradually decreases over time during the post-wind recovery phase and the difference from the background state remains within a stable range, the corresponding time point is recorded. The time interval between the time point and the end of the extreme wind event is used to characterize the recovery timescale of that variable. During the post-wind recovery process, the changes in the deviation of each variable from the background state over time are calculated, and the maximum or average deviation value during the recovery phase is extracted to characterize the intensity of the disturbance caused by the extreme wind event to the system. The recovery timescale and magnitude of change are used as evaluation indicators of the recovery process to quantitatively describe the recovery process of the hydrodynamic field and pollutant concentration field after extreme wind events.

[0098] This simulation method can be used to evaluate the impact of extreme wind events on marine ecosystems and pollutant transport.

[0099] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for simulating the Yellow River pollution diffusion and hydrodynamic response based on extreme wind field, characterized in that, The method comprises the following steps: acquiring topographic data of a target sea area and constructing a three-dimensional high-resolution ocean numerical model for the target sea area based on the topographic data; setting boundary conditions, initial conditions and external forcing conditions for the three-dimensional high-resolution ocean numerical model, wherein the external forcing conditions include tidal forcing, runoff forcing and wind field forcing; setting a plurality of parallel simulation experiment groups, wherein the experiment groups respectively use live wind field containing extreme wind events, climatic average wind field and no wind condition as wind stress input while keeping all conditions consistent except wind field; based on the three-dimensional high-resolution ocean numerical model, setting a passive tracer release source at the model grid position corresponding to the land source pollution input port, simulating the transport and diffusion of the passive tracer, obtaining the three-dimensional concentration field of the pollutant, and simultaneously running the model of each experiment group to obtain the three-dimensional hydrodynamic field and pollutant concentration field under different wind field scenarios; by comparing and analyzing the simulation results of each experiment group, separating and quantifying the independent influence of extreme wind field on hydrodynamic structure and pollutant diffusion process, and based on the comparison between post-wind state and background state, evaluating the recovery process.

2. The method of claim 1, wherein the method is characterized by, The method for acquiring the topographic data of the target sea area comprises: fusing GEBCO global seafloor topographic data and nearshore topographic refined data based on Google Earth Engine remote sensing images.

3. The method of claim 2, wherein the method is characterized by, The three-dimensional high-resolution ocean numerical model is constructed based on the ROMS mode, and the three-dimensional high-resolution ocean numerical model adopts terrain-following coordinates for layering in the vertical direction and boundary-fitted curvilinear orthogonal grids in the horizontal direction.

4. The method of claim 1, wherein the method is characterized by, The boundary conditions include open boundary conditions obtained by interpolating the results of a large-scale ocean model; the initial conditions are determined by the climatic dynamic field of the target sea area obtained after long-term simulation.

5. The method of claim 1, wherein the method is characterized by, The tidal forcing is provided by introducing several tides provided by the TPXO8 tidal model; the runoff forcing is set based on the measured data of the hydrological station at the river mouth; and the wind field forcing is provided by the sea surface wind stress calculated based on the ERA5 reanalysis data.

6. The method of claim 1, wherein the method is characterized by, The parallel simulation experiment groups comprise: a control experiment group using hourly live wind field containing the extreme wind event; a climatic experiment group using multi-year average wind field; a no-wind experiment group setting the sea surface wind stress of the model to zero.

7. The method according to claim 6, wherein, Separating and quantifying the independent influence of extreme wind field on hydrodynamic structure and pollutant diffusion process comprises: comparing the simulation results of the control experiment group and the climatic experiment group to obtain the change data caused by the extreme wind event relative to the normal wind field; comparing the simulation results of the control experiment group or the climatic experiment group with the no-wind experiment group to obtain the change data caused by wind forcing.

8. The method of claim 1, wherein the method is characterized by, Evaluating the recovery process comprises: determining the background benchmark of three-dimensional hydrodynamic field and pollutant concentration field based on the simulation results before the occurrence of the extreme wind event; calculating the deviation degree of the sea surface flow velocity, three-dimensional flow field structure and pollutant concentration distribution at each time node in the post-wind recovery stage relative to the background benchmark; calculating the recovery time scale and maximum deviation amplitude based on the change of the deviation degree with time as the recovery evaluation index; The recovery assessment indicators describe the recovery process of the hydrodynamic field and pollutant concentration field after extreme wind events.

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