Estuary bay pollution self-cleaning capability assessment method based on multi-model coupling

Abstract

The invention relates to an estuary and gulf pollution self-purification capability assessment method based on multi-model coupling, and is suitable for the technical field of estuary and gulf environment hydrodynamic prediction. The method comprises the following steps: constructing a grid model of a to-be-evaluated estuary bay area; constructing a hydrodynamic model of the region to be evaluated, wherein model input comprises hydrological and meteorological boundary conditions, hydrodynamic initial conditions and hydrodynamic parameters of the region; on the basis of calibration operation of the hydrodynamic model, a pollutant transport model of a to-be-evaluated area is constructed, model input comprises pollutant boundary concentration, pollutant initial concentration and convection and diffusion coefficients of the area, and model output comprises pollutant concentration spatial and temporal distribution of the area; on the basis of calibration operation of the hydrodynamic model and the pollutant transport model, a water body updating model of the to-be-evaluated region is constructed, and model output comprises water body updating time of the region; and based on output results of the hydrodynamic model, the pollutant transport model and the water body updating model, evaluating the estuary and gulf pollution self-purification capability.

Description

Technical Field

[0001] This invention relates to a method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling. It is applicable to the field of environmental hydrodynamic prediction technology for estuaries and bays. Background Technology

[0002] Estuaries and bays serve multiple functions, including port shipping, tidal power generation, aquaculture, biological habitats, and tourism. Globally, these regions are home to large populations and metropolitan areas, serving as centers of population residence and socio-economic development. Many of the world's most famous cities, such as New York City, Amsterdam, London, Shanghai, Guangzhou, Shenzhen, and Hong Kong, are located in estuaries and bays. However, with the large-scale influx of people into these areas and the acceleration of urbanization and industrialization, saltwater intrusion, water pollution, eutrophication, and hypoxia have seriously threatened the local aquatic ecosystem. Furthermore, the semi-enclosed nature of estuaries and bays means they have limited water exchange capacity and poor self-purification ability, making them highly susceptible to catastrophic consequences should a major, sudden water pollution incident occur. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, in order to address the problems mentioned above.

[0004] The technical solution adopted in this invention is: a method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, comprising: Construct a grid model of the estuary and bay area to be evaluated; Construct a hydrodynamic model for the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions, and hydrodynamic parameters of the area. The model output includes the flow velocity, water level, and water depth of the area. Based on the calibration and operation of the hydrodynamic model, a pollutant transport model for the area to be evaluated is constructed. The model inputs include the boundary concentration of pollutants in the area, the initial concentration of pollutants, and the convection and diffusion coefficients. The model outputs include the spatiotemporal distribution of pollutant concentrations in the area. Based on the calibration and operation of the hydrodynamic model and the pollutant transport model, a water body renewal model for the area to be evaluated is constructed. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated. The self-purification capacity of estuaries and bays is assessed based on the output results of hydrodynamic models, pollutant transport models, and water body renewal models.

[0005] The construction of the grid model for the estuary and bay area to be evaluated includes: The shoreline boundaries of the estuary and bay areas were obtained through remote sensing imagery and marine maps. Topographic data of the estuary and bay area were obtained through on-site measurements. The study area is divided into a certain number of grids by a water surface modeling grid system, and key study areas and complex areas are densified. The terrain data is automatically interpolated to each grid node.

[0006] The momentum and continuity governing equations of the hydrodynamic model are as follows: ; .

[0007] The hydrological boundary conditions include river runoff and offshore tide data; the meteorological boundary conditions include air pressure, wind stress, evaporation precipitation, air temperature, shortwave radiation, and longwave radiation. The initial hydrodynamic conditions include water depth and flow velocity; The hydrodynamic parameters include simulation time, time step, bottom friction coefficient, Coriolis force coefficient, and wind stress drag coefficient.

[0008] The discrete calculation of the hydrodynamic model is performed using the finite volume method.

[0009] The spectral equation of the water renewal model is: ; ; Where p and d are the generation and dissipation rates, respectively, u is the water flow velocity, and K is the eddy diffusion coefficient vector; The concentration of tracer substances in water is described as the integral of the time spectrum over time, and the average update time is described as the first-order matrix of the spectrum. The governing equations for tracer concentration and update time are respectively ; .

[0010] The governing equations of the water body renewal model are solved using the split operator method, dividing a certain time step into two time processes. The first half of the time step is solved explicitly by local changes and convection and horizontal diffusion terms. The second half step size is implicitly solved in space by the local variation term and the vertical diffusion term. Solve iteratively.

[0011] Solving the governing equations of the water body renewal model includes: ; ; Among them, M i Let Q be the mass of the material in the i-th grid. in and Q outC represents the flow rates into and out of the i-th grid, respectively. i C up C dwg and C j They are the i-th grid and the upstream Q, respectively. up Downstream Q dwg The concentration of the substance in the j-th grid, where α is the weighting factor, and R... ij W is the volume exchange coefficient between the i-th and j-th grids. i For the external input of the i-th grid, k i is the reduction coefficient of the material in the i-th grid.

[0012] The model outputs based on hydrodynamic models, pollutant transport models, and water regeneration models are used to assess the self-purification capacity of estuaries and bays, including: Based on the model outputs of hydrodynamic models, pollutant transport models, and water renewal models, we conducted an analysis of the spatiotemporal variations of pollutant concentration and water renewal time in estuaries and bays under different operating conditions, clarifying the intrinsic relationship and driving mechanism between pollutant transport processes, pollutant concentrations, and water renewal time.

[0013] A multi-model coupling-based system for assessing the self-purification capacity of estuaries and bays, comprising: The grid model building module is used to build a grid model of the estuary and bay area to be evaluated. The first model building module is used to build a hydrodynamic model of the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions and hydrodynamic parameters of the area. The model output includes the flow velocity, water level and water depth of the area. The second model building module is used to build a pollutant transport model for the area to be evaluated based on the calibration and operation of the hydrodynamic model. The model input includes the boundary concentration of pollutants in the area, the initial concentration of pollutants, and the convection and diffusion coefficients. The model output includes the spatiotemporal distribution of pollutant concentration in the area. The third model construction module, based on the calibration and operation of the hydrodynamic model and the pollutant transport model, constructs a water body renewal model for the area to be evaluated. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated. The assessment module is used to evaluate the self-purification capacity of estuaries and bays based on the output results of hydrodynamic models, pollutant transport models, and water body renewal models.

[0014] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the method for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling.

[0015] An estuarine bay pollution self-purification capacity assessment device based on multi-model coupling has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the estuarine bay pollution self-purification capacity assessment method based on multi-model coupling.

[0016] The beneficial effects of this invention are: this invention couples hydrodynamic models, pollutant transport models and water body renewal models to achieve quantitative research on the water body renewal time of regional estuaries and bays and the spatiotemporal distribution characteristics of pollutants during the water body renewal process.

[0017] This invention can identify the correlation between water renewal time and pollutant concentration under the influence of various factors. It takes human activities, natural environment, hydrodynamics and other factors as the core driving forces for the spatiotemporal evolution of water renewal capacity in estuaries and bays. It can predict and analyze the water renewal capacity of estuaries and bays under complex environments and the process of pollutant transport and diffusion during water renewal. On this basis, it reveals the dynamic mechanism and causes of water environment deterioration problems such as eutrophication and saltwater intrusion.

[0018] This invention uses the finite volume method to discretize the control equations, which can effectively ensure mass conservation; at the same time, it uses unstructured triangular meshes to improve the fitting degree to key and complex regions, which can effectively improve model accuracy and computational efficiency. Attached Figure Description

[0019] Figure 1 The flowchart is provided by the present invention for the method of assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling.

[0020] Figure 2 This is the distribution of measuring points in the Huangmao Bay area in this embodiment of the invention.

[0021] Figure 3 This serves as a verification of the coupling model for the Huangmao Bay area in this embodiment of the invention.

[0022] Figure 4 The surface residual flow field structure of the Huangmaohai area in this embodiment of the invention is shown in (a) summer and (b) winter.

[0023] Figure 5 The surface water quality parameters of the Huangmaohai area in this embodiment of the invention are as follows: (a) summer salinity; (b) winter salinity; (c) summer chlorophyll concentration; (d) winter chlorophyll concentration; (e) summer water temperature; (b) winter water temperature.

[0024] Figure 6 This illustrates the seasonal variation of solar radiation in the Huangmaohai area in this embodiment of the invention.

[0025] Figure 7 The time distribution of surface water renewal in the Huangmaohai area in this embodiment of the invention is as follows: (a) summer, (b) winter. Detailed Implementation

[0026] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0027] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0028] Example 1: As Figure 1 As shown in the figure, this embodiment is a method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, including: S100. Collect and obtain topographic and shoreline data within the estuary and bay area to be studied, and construct a grid model of the estuary and bay area to be evaluated.

[0029] S110. The shoreline boundaries of the estuary and bay areas under study are obtained through remote sensing images and marine maps.

[0030] S120. Topographic data of the estuary and bay area were obtained through on-site measurements.

[0031] S130. The study area is divided into grids using the Water Surface Modeling (SMS) grid system, with key and complex areas receiving enhanced density. The terrain data is automatically interpolated to each grid node. In this embodiment, the Huangmao Bay study area is divided into 29,317 computational grids.

[0032] S200. Construct a hydrodynamic model for the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions, and hydrodynamic parameters of the area. The model output includes data such as flow velocity, water level, and water depth of the area.

[0033] In this embodiment, the hydrological boundary conditions include river runoff and offshore tide data. River runoff is obtained from on-site monitoring, and offshore tide is obtained through investigation and analysis of four major tidal constituents (M2, S2, K1, and O1) and four minor tidal constituents (N2, K2, Q2, and P1). Meteorological boundary conditions include air pressure, wind stress, evaporation precipitation, air temperature, shortwave radiation, and longwave radiation.

[0034] In this example, the initial hydrodynamic conditions include water depth and flow velocity. Reasonable initial conditions can accelerate the model stabilization time and reduce the model simulation time.

[0035] In this embodiment, the hydrodynamic parameters include simulation time, time step, bottom friction coefficient, Coriolis force coefficient, and wind stress drag coefficient.

[0036] In this example, the momentum and continuity governing equations of the hydrodynamic model are: (1) (2) in, : Water flow velocity vector (including x, y, and z direction components); :time; Hydrodynamic parameters; Water pressure; Water density; : Coefficient of water viscosity; Hamiltonian operator; External forces:

[0037] In this embodiment, the hydrodynamic model employs a method of alternating calculations using separate inner and outer modules to save computation time. The free surface elevation and depth-average velocity are obtained in the outer module by integrating and solving the vertical average momentum equation, while the velocity at each layer is obtained in the inner module by integrating and solving the three-dimensional momentum equation. In this example, the time steps for the outer and inner modules are set to 5 s and 40 s, respectively, and the bottom friction coefficient is set to 0.02.

[0038] In this example, the hydrodynamic model uses the finite volume method to discretize the control equations, which has the following advantages: ① High computational efficiency: The finite volume method directly processes partial differential equations during the discretization process, thus having high computational efficiency; ② Conservation property: The finite volume method uses the conservation of physical quantities in the control unit to discretize and solve partial differential equations, thus theoretically possessing the strongest conservation property; ③ Adaptability to complex estuarine and bay areas: The finite volume method can adapt to the complex geometry and boundary conditions, complex boundary conditions and flow fields of estuarine and bay areas, and has high accuracy and stability, thus having a great advantage in solving practical problems.

[0039] S300. Based on the calibration and operation of the hydrodynamic model, a pollutant transport model for the area to be evaluated is constructed. The model inputs include the boundary concentration of pollutants in the area (concentration at the river inlet and the open sea), the initial concentration of pollutants, and the convection and diffusion coefficients. The model outputs include the spatiotemporal distribution of pollutant concentrations in the area.

[0040] The calibration of the pollutant transport model in this embodiment includes: S310. First, based on existing research literature and field investigations, the parameters of the pollutant model were initially determined; S320. Conduct sensitivity analysis. Based on the comparison between the simulation results of the model area and the measured pollutant concentrations, adjust the model parameter settings and calibrate the model.

[0041] S400. Based on the calibration and operation of the hydrodynamic model and the pollutant transport model, a water body renewal model for the area to be evaluated is constructed. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated.

[0042] The spectral equation of the water renewal model is: (3) in, : The time-spectral concentration of the tracer substance; : Time spectrum variables; : The rate of tracer substance production; : The rate of decay of tracer substances; : Water flow velocity vector; : Eddy diffusion coefficient vector; : Overall concentration of tracer material. Although the eddy diffusion coefficient vector can be used to calculate the spectral distribution of water renewal time in a water system, this calculation method often requires a very large amount of computation.

[0043] In this embodiment, the concentration of tracer substances in the water can be described as the integral of the time spectrum over time. The average water renewal time A first-order matrix described as a spectrum When defined Then there is (4)

[0044] The concentration equations for tracer substances and the water renewal time concentration equation are obtained by integrating equation (3) and equation (3)×τ over τ. When only one type of tracer substance is discharged into the study area, and the source and sink terms in the control equations are ignored, the control equations for tracer substance concentration and water renewal time can be expressed as follows: (5) (6) in : The concentration of tracer substance at time t and position x; : Average water renewal time at time t and location x. Eddy diffusion coefficient K (including horizontal convection diffusion coefficient A) h and vertical convection diffusion coefficient K h The calculation of A is crucial. h The results were obtained using the Smagorinsky eddy parameter method: (7) : Empirical constant; : Strain rate tensor related parameters (describe the intensity of spatial variation in flow velocity); Prandtl number (the ratio of momentum diffusion to heat diffusion); , : The water flow velocity in the x and y directions; , : Horizontal spatial coordinates.

[0045] K h The Mellor-Yamada 2.5 turbulence closure model (the commonly used q-ql model, where q is the turbulent kinetic energy and l is the turbulent scale) is used. In the boundary layer approximation, the turbulent kinetic energy is generated by the vertical shear of the horizontal flow near the boundary layer. (8) (9) Where K q F is the vertical eddy diffusion coefficient of turbulent kinetic energy. q and F l P represents the horizontal diffusion coefficient at the turbulent kinetic energy and turbulent scale. s and P b These are the shear and buoyancy terms of turbulent kinetic energy, where ε is the turbulent kinetic energy dissipation rate. It is the wall proximity function, where κ=0.4 is the von Kármán constant, and E1 and E2 are model closure constants.

[0046] Turbulent kinetic energy and turbulent scale are closed by the following equation: (10) in In the unstable layer (ρ) z The upper limit of >0) is 0.023, and the stable layer (ρ) z The lower limit for <0) is -0.28.

[0047] In this embodiment, the governing equations of the water body renewal model are solved using the split operator method, which divides a certain time step into two time processes. The first half of the time step is solved explicitly by local changes and convection and horizontal diffusion terms. The second half step size is implicitly solved in space by the local variation term and the vertical diffusion term. The solution is obtained through iterative iteration; the source and sink terms of biological processes are calculated using an explicit format.

[0048] (11) (12) Where M i Let Q be the mass of the material in the i-th grid. in and Q out C represents the flow rates into and out of the i-th grid, respectively. i C up C dwg and C j They are the i-th grid and the upstream Q, respectively. in Downstream Q out The concentration of matter in the j-th grid, α is the weighting factor (α=0 for upwind scheme; α=1 for central difference scheme), R ij W is the volume exchange coefficient between the i-th and j-th grids. i For the external input of the i-th grid, k i is the reduction coefficient of the material in the i-th grid.

[0049] The S500 model, based on the outputs of hydrodynamic models, pollutant transport models, and water regeneration models, is used to assess the self-purification capacity of estuaries and bays.

[0050] In this embodiment, the model calculation can obtain elements such as water level field, flow velocity field, water renewal time, and pollutant concentration of the entire area under different natural environments, physical processes, and human activities. Matlab is used to process and plot the calculation results, analyze the water renewal capacity of estuaries and bays and the spatiotemporal distribution patterns of pollutants during the renewal process, and explore the driving mechanisms of water renewal time and pollutant self-purification capacity, providing support for pollutant forecasting and treatment, and estuary and bay development.

[0051] The following is a detailed description of this embodiment, using examples and accompanying figures to illustrate the forecasting of pollutant transport and diffusion processes during the water renewal process in the Huangmaohai area: The coastline of Huangmao Bay was drawn based on remote sensing imagery and watershed maps. The Huangmao Bay area was divided into 29,317 computational grids, and measured topographic data of Huangmao Bay were interpolated onto each grid. A model input file was created, and model computational parameters were set to run the model. The model was calibrated using measured data such as tide level and salinity of Huangmao Bay. Figure 2 For the location of the measuring station, Figure 3 (As model validation results), the absolute errors of flow velocity and salinity at station U1 were 0.07, 1.68, and 1.75 psu, respectively. This indicates that the model reasonably and accurately reflects the transport and diffusion process of substances. Therefore, this model can be used to further explore the spatiotemporal variation characteristics of water renewal time in Huangmao Bay and its feedback mechanism on pollutant transport and diffusion.

[0052] Influenced by the monsoon climate, the runoff in the Huangmaohai area exhibits significant seasonal variation characteristics. Figure 4 (a) and (b). 80% of the total annual runoff of Huangmaohai Lake is concentrated in summer, while the runoff in winter is very small. The residual flow field structure is basically the same in winter and summer, the difference being that due to the larger runoff in summer, a larger residual flow to the sea is generated than in winter. From 1990 to 2024, after the implementation of the Huangmaohai Lake channel narrowing project and the land-to-island project connecting Gaolan Island to the mainland, the upstream flow velocity of Huangmaohai Lake increased, and water exchange between the downstream of Huangmaohai Lake and the open sea area was obstructed.

[0053] Water quality parameters such as salinity, chlorophyll concentration, and water temperature in Huangmaohai Lake exhibit distinct seasonal characteristics. Due to runoff dilution, salinity is lowest at the upstream river boundaries and gradually increases towards the open sea. Compared to summer, reduced runoff in winter exacerbates saltwater intrusion into Huangmaohai, leading to a significant increase in salinity; the 30 psu salinity isoplethora reaches the central part of Huangmaohai. Figure 5 a and b). Chlorophyll a is a key parameter reflecting algal biomass. In summer, chlorophyll concentration is mainly located on the western coast of Huangmaohai, with the highest concentration reaching over 8 μg / L. Figure 5 c), mainly due to upstream runoff scouring towards the west coast and high temperatures on the west coast ( Figure 5 This is due to (e). In winter, chlorophyll a appears in irregular bands in the Huangmaohai area, with a concentration of only 2-5 μg / L (e). Figure 5 d), and affected by low winter temperatures ( Figure 5 f) Low solar radiation ( Figure 6 Factors such as these lead to reduced phytoplankton photosynthesis, resulting in lower chlorophyll concentrations compared to summer. Spatially, summer water temperature distribution shows lower temperatures in the open sea and higher temperatures in the upper reaches of estuaries, reaching above 32℃. Figure 5 e). The spatial distribution of water temperature in winter shows the opposite trend to that in winter, that is, it gradually increases from the upper reaches of the estuary to the open sea, and the highest temperature in the open sea can reach above 21℃. Figure 5 f).

[0054] In winter, the water renewal time in almost the entire Huangmaohai area is significantly shorter than in summer. The time required for the tracer to travel from its upstream release source to the Huangmaohai estuary is 19.2 days; in summer, it takes 13.1 days. This indicates that the water renewal time distribution in Huangmaohai is significantly affected by runoff, with stronger water renewal capacity in summer than in winter. Figure 7 (a and b), thus revealing the driving mechanism of winter saltwater intrusion.

[0055] Example 2: This example is a system for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, including: The grid model building module is used to build a grid model of the estuary and bay area to be evaluated. The first model building module is used to build a hydrodynamic model of the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions and hydrodynamic parameters of the area. The model output includes the flow velocity, water level and water depth of the area. The second model building module is used to build a pollutant transport model for the area to be evaluated based on the calibration and operation of the hydrodynamic model. The model input includes the boundary concentration of pollutants in the area, the initial concentration of pollutants, and the convection and diffusion coefficients. The model output includes the spatiotemporal distribution of pollutant concentration in the area. The third model construction module, based on the calibration and operation of the hydrodynamic model and the pollutant transport model, constructs a water body renewal model for the area to be evaluated. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated. The assessment module is used to evaluate the self-purification capacity of estuaries and bays based on model outputs from hydrodynamic models, pollutant transport models, and water regeneration models.

[0056] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the method for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling described in Example 1.

[0057] Example 4: This example is a device for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling. It has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling described in Example 1.

[0058] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0061] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0062] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0064] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0065] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, characterized in that, include: Construct a grid model of the estuary and bay area to be evaluated; Construct a hydrodynamic model for the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions, and hydrodynamic parameters of the area. The model output includes the flow velocity, water level, and water depth of the area. Based on the calibration and operation of the hydrodynamic model, a pollutant transport model for the area to be evaluated is constructed. The model inputs include the boundary concentration of pollutants in the area, the initial concentration of pollutants, and the convection and diffusion coefficients. The model outputs include the spatiotemporal distribution of pollutant concentrations in the area. Based on the calibration and operation of the hydrodynamic model and the pollutant transport model, a water body renewal model for the area to be evaluated is constructed. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated. The self-purification capacity of estuaries and bays is assessed based on the output results of hydrodynamic models, pollutant transport models, and water body renewal models.

2. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The construction of the grid model for the estuary and bay area to be evaluated includes: The shoreline boundaries of the estuary and bay areas were obtained through remote sensing imagery and marine maps. Topographic data of the estuary and bay area were obtained through on-site measurements. The study area is divided into a certain number of grids by a water surface modeling grid system, and key study areas and complex areas are densified. The terrain data is automatically interpolated to each grid node.

3. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The momentum and continuity governing equations of the hydrodynamic model are as follows: ； 。 4. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The hydrological boundary conditions include river runoff and offshore tide data; the meteorological boundary conditions include air pressure, wind stress, evaporation precipitation, air temperature, shortwave radiation, and longwave radiation. The initial hydrodynamic conditions include water depth and flow velocity; The hydrodynamic parameters include simulation time, time step, bottom friction coefficient, Coriolis force coefficient, and wind stress drag coefficient.

5. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The discrete calculation of the hydrodynamic model adopts the finite volume method.

6. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The spectral equation of the water renewal model is: ； Where p and d are the generation and dissipation rates, respectively, u is the water flow velocity, and K is the eddy diffusion coefficient vector; The concentration of tracer substances in the water body is described as the integral of the water age spectrum over the water age, and the average water age is described as the first-order matrix of the spectrum. The governing equations for tracer concentration and water age are respectively ； 。 7. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 6, characterized in that, The governing equations of the water body renewal model are solved using the split operator method, which divides a certain time step into two time processes. The first half of the time step is solved explicitly by local changes and convection and horizontal diffusion terms. The second half step length is implicitly solved in space by the local variation term and the vertical diffusion term. Solve iteratively.

8. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 7, characterized in that, Solving the governing equations of the water body renewal model includes: ； ； Among them, M i Let Q be the mass of the material in the i-th grid. in and Q out C represents the flow rates into and out of the i-th grid, respectively. i C up C dwg and C j They are the i-th grid and the upstream Q, respectively. in Downstream Q out The concentration of the substance in the j-th grid, where α is the weighting factor, and R... ij W is the volume exchange coefficient between the i-th and j-th grids. i For the external input of the i-th grid, k i is the reduction coefficient of the material in the i-th grid.

9. The method for assessing the self-purification capacity of estuaries and bays based on multi-model coupling according to claim 1, characterized in that, The model outputs based on hydrodynamic models, pollutant transport models, and water regeneration models are used to assess the self-purification capacity of estuaries and bays, including: Based on the model outputs of hydrodynamic models, pollutant transport models, and water regeneration models, we conducted an analysis of the spatiotemporal variations of pollutant concentration and water regeneration time in estuaries and bays under different operating conditions, clarifying the intrinsic relationship between water regeneration time and pollutant concentration and its driving mechanism.

10. A system for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, characterized in that, include: The grid model building module is used to build a grid model of the estuary and bay area to be evaluated. The first model building module is used to build a hydrodynamic model of the area to be evaluated. The model input includes the hydrological and meteorological boundary conditions, initial hydrodynamic conditions and hydrodynamic parameters of the area. The model output includes the flow velocity, water level and water depth of the area. The second model building module is used to build a pollutant transport model for the area to be evaluated based on the calibration and operation of the hydrodynamic model. The model input includes the boundary concentration of pollutants in the area, the initial concentration of pollutants, and the convection and diffusion coefficients. The model output includes the spatiotemporal distribution of pollutant concentration in the area. The third model construction module, based on the calibration and operation of the hydrodynamic model and the pollutant transport model, constructs a water body renewal model for the area to be evaluated. The model output includes the water body renewal time in the area, that is, the time when pollutants leave the area to be evaluated. The assessment module is used to evaluate the self-purification capacity of estuaries and bays based on the output results of hydrodynamic models, pollutant transport models, and water body renewal models.

11. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the method for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling as described in any one of claims 1 to 9.

12. A device for assessing the self-purification capacity of estuaries and bays based on multi-model coupling, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the method for assessing the self-purification capacity of estuary and bay pollution based on multi-model coupling as described in any one of claims 1 to 9.

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