Shallow lake DOM migration and transformation coupling simulation method

Abstract

The invention provides a shallow lake DOM migration and conversion coupling simulation method, and relates to the technical field of water environment numerical simulation. The method comprises the following steps: inverting lake parameters by using multi-source remote sensing data; a hydrodynamic force-water temperature coupling model is constructed, and a DOM migration and transformation coupling model is constructed according to lake parameters; providing dynamic boundary conditions and initial conditions for a hydrodynamic force-water temperature coupling model according to the inverted lake parameters; performing time sequence solution on the two models to obtain a DOM concentration field changing along with time; and comparing and matching a DOM concentration field solved by the model with a remote sensing inversion DOM concentration field in a corresponding time period in a whole space range, and carrying out iterative adjustment on DOM migration and transformation coupling model parameters based on a parameter search algorithm. According to the method, fine simulation and prediction of the DOM under a longer time sequence and a higher temporal-spatial resolution can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation of water environment, and particularly to a coupled simulation method for the migration and transformation of DOM in shallow lakes. Background Art

[0002] Dissolved Organic Matter (DOM) is an important organic component widely present in lake water bodies. Its sources include watershed runoff input, endogenous release caused by sediment disturbance, and exogenous supply generated by algal death and lysis. DOM presents complex dynamic processes in lakes, including input and release, photodegradation and biodegradation, migration and diffusion, and sedimentation. These processes are jointly affected by multiple factors such as hydrodynamic conditions (such as water flow field, water depth, water temperature), light intensity, and microbial ecological processes. In shallow lakes, the water depth is relatively shallow and external disturbances are frequent. The superposition of various influencing factors makes the spatio-temporal variation and transformation mechanism of DOM extremely complex.

[0003] Existing studies mostly use hydrodynamic-water quality coupled models to simulate the lake water environment. However, the following problems are faced: 1) Data limitation: Traditional simulation methods mainly rely on limited measured data and lack dynamic input data with high spatio-temporal resolution, making it difficult to accurately characterize the spatio-temporal distribution characteristics of DOM. 2) Insufficient source and input: The description of the input process of DOM is insufficient, especially the endogenous release caused by sediment disturbance and the DOM contribution caused by algal death and lysis have not been fully dynamically simulated. 3) Simplification of degradation process: Existing models handle the degradation process of DOM too simply, often separating or linearly processing photodegradation and biodegradation, and failing to fully reflect the coupling effect and non-linear characteristics of the two under changing environmental conditions, resulting in insufficient simulation accuracy and applicability. Summary of the Invention

[0004] The purpose of the present invention is to: in order to solve the problem that the coupling effect and non-linear characteristics of photodegradation and biodegradation under changing environmental conditions are not reflected in the simulation method of the lake water environment for the degradation process of DOM, a coupled simulation method for the migration and transformation of DOM in shallow lakes is proposed, including the following steps:

[0005] S1. Obtain multi-source remote sensing data of the shallow lake and use the multi-source remote sensing data to invert lake parameters;

[0006] S2. Construct a hydrodynamic-water temperature coupled model and construct a DOM migration and transformation coupled model according to the lake parameters;

[0007] S3. Provide dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupled model and the DOM migration and transformation coupled model according to the inverted lake parameters;

[0008] S4. Perform a temporal solution for the hydrodynamic- water temperature coupling model and the DOM migration and transformation coupling model to obtain the DOM concentration field varying with time;

[0009] S5. Compare and match the DOM concentration field solved by the model with the remotely sensed inverted DOM concentration field in the corresponding time period within the entire space, and iteratively adjust the parameters of the DOM migration and transformation coupling model based on the parameter search algorithm.

[0010] Furthermore, the multi-source remote sensing data includes: water color remote sensing, thermal infrared remote sensing, and optical remote sensing data.

[0011] Furthermore, the lake parameters inverted using the multi-source remote sensing data include:

[0012] Invert the DOM concentration, chlorophyll-a concentration, and suspended sediment concentration through the water color remote sensing data; invert the water surface temperature field through the thermal infrared remote sensing data; obtain the water surface light intensity through the optical remote sensing data and the irradiance model.

[0013] Furthermore, the hydrodynamic- water temperature coupling model is expressed as:

[0014]

[0015]

[0016] where h represents the water depth, t represents time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, v represents the component of the water flow velocity in the y-axis direction of the rectangular coordinate system, x represents the x-axis of the rectangular coordinate system, y represents the y-axis of the rectangular coordinate system, g represents the acceleration due to gravity, η represents the water surface elevation, F u and F v are the volume force terms in the x and y directions respectively; T represents temperature, D T represents the thermal diffusion coefficient, Q T represents the heat source term determined by energy exchange, solar radiation, and atmospheric conditions, · represents the divergence operation, represents the vector differential operator, represents the gradient of temperature.

[0017] Furthermore, the DOM migration and transformation coupling model is expressed as:

[0018]

[0019] where h represents the water depth, C represents the DOM concentration, t represents time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, D C represents the horizontal diffusion coefficient of DOM, represents the DOM concentration gradient, f bed represents the DOM release rate under disturbance conditions, represents the suspended matter concentration, τ bed represents the bottom shear stress, k algae represents the contribution rate of algae lysis to DOM, represents the chlorophyll-a concentration, Indicates the contribution rate of photodegradation to DOM, represents the light intensity on the water surface, k bio (T) represents the contribution rate of biodegradation to DOM, and T represents water temperature.

[0020] Furthermore, the parameters that need to be iteratively adjusted in the DOM migration and transformation coupling model include: the contribution rate of algae lysis to DOM k algae , contribution rate of photodegradation to DOM Contribution rate of biodegradation to DOM k bio (T), DOM release rate under disturbance conditions f bed .

[0021] The present invention also proposes a shallow lake DOM migration and transformation coupled simulation system, comprising:

[0022] The data acquisition module is used to obtain multi-source remote sensing data of shallow lakes and invert lake parameters using multi-source remote sensing data;

[0023] The model building module is used to build a hydrodynamic-water temperature coupling model and a DOM migration and transformation coupling model based on lake parameters;

[0024] Model condition provision module, used to provide dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupling model and DOM migration and transformation coupling model based on the inverted lake parameters;

[0025] The model solving module is used to solve the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model in a time-series manner to obtain the DOM concentration field that changes with time;

[0026] The model optimization module is used to compare and match the DOM concentration field solved by the model with the remote sensing inversion DOM concentration field of the corresponding period in the entire space, and iteratively adjust the parameters of the DOM migration and transformation coupling model based on the parameter search algorithm.

[0027] The present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned shallow lake DOM migration and transformation coupled simulation method.

[0028] The present invention also provides an electronic device, including a processor and a memory, where the processor is connected to the memory. The memory is used to store a computer program, and the computer program includes computer-readable instructions. The processor is configured to call the computer-readable instructions to execute the above-mentioned coupled simulation method for DOM migration and transformation in shallow lakes.

[0029] The present invention also provides a computer program product, including a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the steps of the above-mentioned coupled simulation method for DOM migration and transformation in shallow lakes are implemented.

[0030] The beneficial effects brought by the technical solution provided by the present invention are:

[0031] The present invention obtains lake parameters by comprehensively using water color remote sensing, optical remote sensing, and thermal infrared remote sensing data, constructs a coupled simulation method for DOM dynamic migration and transformation in shallow lakes by coupling a hydrodynamic model, a DOM input / release model, and a photobiodegradation model, uses remote sensing data to invert lake parameters as key time-varying inputs and boundary conditions, and establishes a model system that can dynamically depict the processes of DOM input, photodegradation and biodegradation, migration and diffusion, and endogenous release. During the model simulation process, by comparing and evaluating the spatio-temporal deviation between the model results and multi-phase remote sensing inversion data, the model parameters are continuously optimized and corrected. After multiple iterative calibrations, the model parameters and structure tend to be stable, and fine simulation and prediction of DOM at a longer time series and higher spatio-temporal resolution can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flowchart of the coupled simulation method for DOM migration and transformation in shallow lakes according to an embodiment of the present invention;

[0033] Figure 2 is a block diagram of an electronic device in an exemplary embodiment of Embodiment 1 of the present invention;

[0034] Figure 3 is a schematic diagram of grid meshing of the research area of shallow lakes in an embodiment of the present invention;

[0035] Figure 4 is a schematic diagram of the distribution of DOM concentration simulation results obtained at time t = 30 days in a simulation period in an embodiment of the present invention;

[0036] Figure 5 is a schematic diagram of comparing the DOM remote sensing inversion results with the model simulation results in an embodiment of the present invention;

[0037] Figure 6 is a schematic diagram of the parameter calibration process and results in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0039] The flow chart of the method for coupling simulation of DOM migration and transformation in shallow lakes according to the embodiments of the present invention is as Figure 1 , and specifically includes the following steps:

[0040] S1. Obtain multi-source remote sensing data of shallow lakes and use the multi-source remote sensing data to invert lake parameters.

[0041] By obtaining multi-source remote sensing data of shallow lakes from Sentinel-2, Landsat, MODIS, etc., the multi-source remote sensing data includes: water color remote sensing, thermal infrared remote sensing and optical remote sensing data. Invert the DOM concentration chlorophyll-a concentration and suspended sediment concentration through the water color remote sensing data; invert the water surface temperature field T RS of the water body through the thermal infrared remote sensing data; and obtain the water surface light intensity

[0042] of the water body through the optical remote sensing data and the irradiance model.

[0043] Establish a two-dimensional hydrodynamic model based on the shallow water equations to describe the spatio-temporal variations of the water flow velocity field (u, v) and water depth (h):

[0044]

[0045] where h represents the water depth, t represents time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, v represents the component of the water flow velocity in the y-axis direction of the rectangular coordinate system, x represents the x-axis of the rectangular coordinate system, y represents the y-axis of the rectangular coordinate system, g represents the acceleration due to gravity, η represents the water surface elevation, F u and F v are the volume force terms in the x and y directions respectively, including external forces such as horizontal eddy diffusion, bottom friction, wind stress, and Coriolis force.

[0046] Construct a water temperature model to describe the dynamic distribution of water temperature (T):

[0047]

[0048] where h represents the water depth, T represents the temperature, t represents time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, v represents the component of the water flow velocity in the y-axis direction of the rectangular coordinate system, x represents the x-axis of the rectangular coordinate system, y represents the y-axis of the rectangular coordinate system, DT represents the thermal diffusivity, Q T represents the heat source term determined by energy exchange, solar radiation and atmospheric conditions, represents the divergence operation, represents the vector differential operator, Represents the temperature gradient.

[0049] The DOM migration and transformation coupling model is constructed through the following steps:

[0050] First, the DOM mass conservation equation is established with DOM concentration C as the state variable:

[0051]

[0052] Among them, D C is the DOM horizontal diffusion coefficient, S in is the input item, S deg For degradation items.

[0053] Input S in Including: endogenous release of S from sediment bed and algae lysis to release S algae :

[0054] S in =S bed +S algae ,

[0055]

[0056] Among them, f bed represents the DOM release rate under disturbance conditions, Characterizing the sediment disturbance release process can be achieved through The relationship between the field and the bottom shear force is determined. represents the suspended matter concentration, τ bed represents the bottom shear stress, k algae represents the contribution rate of algae lysis to DOM, Indicates the chlorophyll-a concentration.

[0057] Degradation item S deg Including photodegradation S photo With biodegradable S bio :

[0058] S deg =S photo +S bio ,

[0059]

[0060] S bio =k bio (T)C,

[0061] Among them, represents the contribution rate of photodegradation to DOM, represents the light intensity on the water surface, and k bio (T) represents the contribution rate of biodegradation to DOM, T represents the water temperature, and C represents the DOM concentration.

[0062] In summary, the coupled model of DOM migration and transformation can be obtained as follows:

[0063]

[0064] Among them, h represents the water depth, C represents the DOM concentration, t represents the time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, and D C represents the horizontal diffusion coefficient of DOM, represents the DOM concentration gradient, and f bed represents the DOM release rate under disturbance conditions, represents the suspended solid concentration, and τ bed represents the bed shear stress, and k algae represents the contribution rate of algal lysis to DOM, represents the chlorophyll-a concentration, represents the contribution rate of photodegradation to DOM, represents the light intensity on the water surface, and k bio (T) represents the contribution rate of biodegradation to DOM, and T represents the water temperature. Changes with the light intensity k bio (T) changes with the water temperature T, and both can be dynamic.

[0065] S3. Provide dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupled model and the DOM migration and transformation coupled model according to the inverted lake parameters. The inverted lake parameters T RS , and are updated in stages or continuously within the simulation period and used as the time-varying upper boundary or initial value of the model.

[0066] In the preferred embodiment of the present invention, the initial value of DOM: at t = 0, the inverted by remote sensing is used as the initial DOM distribution field.

[0067] Inflow boundary condition: Set the fixed or time-varying water temperature, chlorophyll, suspended solids, and DOM concentration of the inflowing water at the inflow port.

[0068] Update of water temperature and light intensity: Use the new T every 5 days RSUpdate the water temperature distribution boundary of the field and achieve continuous simulation of water temperature through interpolation.

[0069] Similarly, use to update the photodegradation rate coefficient of the field, and correct it every 5 days to reflect the changes in solar irradiance and atmospheric conditions.

[0070] Update of suspended solids and algae concentration: Use and to update the sediment disturbance release intensity and algae lysis input intensity every 5 days.

[0071] S4. Perform time-series solution on the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model to obtain the DOM concentration field varying with time.

[0072] Use the finite difference, finite volume or finite element method to perform time-series solution on the hydrodynamic-water temperature and DOM migration and transformation equations. The time step Δt can be set to the order of minutes to ensure numerical stability, and obtain the DOM concentration field C model (x, y, t) varying with time.

[0073] During the simulation process, at time periods t = 5, 10,..., 90 days, read the latest parameter field from remote sensing data and update the model input and boundaries. This dynamic assimilation strategy enables the model to continuously adapt to and respond to actual environmental changes throughout the research period.

[0074] At each time period, compare and match the simulation result C model (x, y, t) with the remotely sensed DOM concentration field corresponding to the time period in the entire spatial range, and measure the simulation accuracy and parameter suitability of the model by evaluating deviation, correlation coefficient or other statistical indicators.

[0075] S5. Compare and match the DOM concentration field solved by the model with the remotely sensed DOM concentration field corresponding to the time period in the entire spatial range, and iteratively adjust the parameters of the DOM migration and transformation coupling model based on the parameter search algorithm. The adjusted and optimized model realizes the simulation of DOM migration and transformation in shallow lakes.

[0076] The parameters of the photodegradation coefficient , the algae lysis parameter k algae , the biodegradation parameter k bio (T), and the endogenous release parameter f bedThe above are regarded as the set of parameters to be optimized. By using mature parameter optimization tools (such as PEST) and introducing parameter search algorithms based on advanced technologies such as machine learning or Bayesian optimization, the parameters are iteratively adjusted on the basis of spatial matching: the optimization objective function can be statistical quantities such as the root mean square error (RMSE) or Nash-Sutcliffe efficiency coefficient (NSE) of the simulated values and remote sensing values within the entire lake area; through multiple iterative calculations and full-space comparison, the parameter optimization algorithm continuously adjusts the parameters until they converge to the optimal solution within the acceptable error range.

[0077] When the parameter calibration reaches a steady state, the model parameters and structure have been fully verified, and the DOM migration and transformation process can be simulated and predicted under long-term and higher spatio-temporal accuracy. At this time, even if the subsequent remote sensing data input is limited, the model can still rely on the optimized parameter system and the predicted meteorological and hydrological conditions for scenario analysis and prediction, providing a scientific decision-making basis for lake water quality management, ecological restoration, and policy formulation.

[0078] The present invention also proposes a coupled simulation system for DOM migration and transformation in shallow lakes, including:

[0079] A data acquisition module, configured to acquire multi-source remote sensing data of a shallow lake and invert lake parameters using the multi-source remote sensing data;

[0080] A model construction module, configured to construct a hydrodynamic-water temperature coupled model and construct a DOM migration and transformation coupled model according to the lake parameters;

[0081] A model condition providing module, configured to provide dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupled model and the DOM migration and transformation coupled model according to the inverted lake parameters;

[0082] A model solving module, configured to perform time-series solution on the hydrodynamic-water temperature coupled model and the DOM migration and transformation coupled model to obtain the DOM concentration field varying with time;

[0083] A model optimization module, configured to compare and match the DOM concentration field obtained by model solution with the remotely sensed DOM concentration field corresponding to the corresponding time period in the full space range, and iteratively adjust the parameters of the DOM migration and transformation coupled model based on the parameter search algorithm.

[0084] In an exemplary embodiment, it includes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the above-mentioned coupled simulation method for DOM migration and transformation in shallow lakes.

[0085] Please refer to Figure 2 , in an exemplary embodiment, it further includes an electronic device, including at least one processor, at least one memory, and at least one communication bus.

[0086] Among them, a computer program is stored in the memory. The computer program includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus and executes the above-mentioned coupled simulation method for DOM migration and transformation in shallow lakes.

[0087] In an exemplary embodiment, there is also provided a computer program product, including a computer program / instructions, characterized in that when the computer program / instructions are executed by the processor, the steps of the above-mentioned coupled simulation method for DOM migration and transformation in shallow lakes are implemented.

[0088] This embodiment takes a certain shallow lake as the research object. The lake is about 5 km × 2 km, with an average water depth of about 2 m, and there is a main inflow port and a main outflow port. The research period is 90 days (such as from spring to summer), during which the lake is affected by periodic wind fields, temperature changes, and seasonal characteristics of algal growth.

[0089] The focus of the present invention is to use multi-source remote sensing data (including water color remote sensing, thermal infrared remote sensing, and optical remote sensing) to obtain key spatio-temporal distribution information such as DOM, chlorophyll-a, suspended solids, water temperature, and light intensity in the lake, and use them as dynamic inputs and calibration bases, and combine the hydrodynamic-water quality model to finely simulate and optimize the migration and transformation process of DOM.

[0090] Remote sensing data are obtained at discrete time intervals of t = 0, 5, 10,..., 90 days (such as once every 5 days), so that the key parameters of the lake (DOM, water temperature, algae, suspended solids, light) can be continuously monitored and updated in the time series.

[0091] Collect lake bottom topography data and construct a regional two-dimensional grid. As Figure 3 shown, Figure 3 is a schematic diagram of grid division of the research area of the shallow lake in an embodiment of the present invention, which is used to illustrate the two-dimensional discretization of the research lake area in the X-Y plane and provide a spatial basis for numerical solution. The lake area is discretized into 51 × 21 grids (Δx = 100 m, Δy = 100 m). According to the inflow and outflow boundary conditions (measured or reference data), the inflow rate and outflow boundary conditions are set.

[0092] Initial value and boundary condition setting:

[0093] (1) Initial value of DOM:

[0094] At t = 0, the remotely sensed inversion is used as the initial DOM distribution field. The T obtained by remote sensing RS is used for initial value and boundary update every 5 days.

[0095] (2) Inflow boundary condition:

[0096] Set the temperature, chlorophyll, suspended solids, and DOM concentration of the influent water body to be fixed or time-varying at the influent port.

[0097] (3) Water temperature and light intensity update:

[0098] Update the water temperature distribution boundary using the new T RS field every 5 days, and achieve continuous simulation of water temperature through interpolation.

[0099] Similarly, use the field to update the photodegradation rate coefficient, and correct it every 5 days to reflect the changes in solar irradiance and atmospheric conditions.

[0100] (4) Suspended solids and algae concentration update:

[0101] Use the and to update the sediment disturbance release intensity and algae lysis input intensity every 5 days.

[0102] Visualize the results in the MATLAB or Python environment. As Figure 4 shown, Figure 4 is a schematic diagram of the DOM concentration simulation result distribution obtained at the simulation time t = 30 days in an embodiment of the present invention, showing the spatial distribution characteristics of DOM concentration on a two-dimensional plane obtained by this method. Figure 5 is a schematic diagram comparing the DOM remote sensing inversion result with the model simulation result in an embodiment of the present invention. By separately showing the remote sensing inversion DOM concentration field, the model simulation result field, and their difference field, the model simulation accuracy and spatial distribution consistency are evaluated.

[0103] At key time periods (t = 10, 20, 30 days, etc.), calculate the global spatial error (such as RMSE) between the simulation result and the remote sensing result. Taking DOM as an example:

[0104]

[0105] where RMSE represents the root mean square error, N represents the number of grids, C model (x,y) represents the DOM concentration at the grid (x, y) of the calculation simulation, represents the DOM concentration at the grid (x, y) inverted from the remote sensing data.

[0106] Use parameter optimization tools such as PEST, Bayesian optimization, or genetic algorithms to optimize the photodegradation coefficient (k photo ), the algae lysis parameter (k algae ), the biodegradation parameter (k bio ), and the endogenous release parameter (fbed )Perform continuous iterative adjustment. In each iteration cycle, run the model with the current parameter combination, and calculate the full-space RMSE by comparing with the remotely sensed inversion DOM data for the corresponding time period. As the iteration progresses, continuously correct the parameters to gradually reduce the RMSE and make it tend to converge. Refer to Figure 6 , Figure 6 which is a schematic diagram of the parameter calibration process and results in an embodiment of the present invention, including the dynamic change trend of RMSE (root mean square error) with the number of iterations and k algae , k photo , k bio , f bed The convergence of parameters during the calibration process, demonstrating the effect of significantly reducing the simulation error and obtaining a stable parameter configuration through iterative optimization.

[0107] After the parameter calibration is completed, the obtained parameter combination can make the simulation results highly consistent with the remote sensing data at multiple time nodes and in the full space, significantly improving the accuracy and credibility of DOM simulation.

[0108] After completing the parameter calibration and optimization, the simulation accuracy of the DOM field within the 90-day simulation period is significantly improved. The results show that the method of the present invention uses multi-source and multi-phase remote sensing data to achieve a fine simulation of the DOM migration and transformation process, which can provide a scientific reference for the formulation of lake water quality management, nutrient control, and ecological regulation strategies. This technical solution can also be applied to the DOM simulation and research in other shallow water body environments.

[0109] It can be seen from this embodiment that the present invention uses dynamic remote sensing data to perform full-space constraint and calibration on the model, significantly improving the accuracy and spatio-temporal resolution of the model simulation, making up for the limitations of traditional calibration based on limited measured point data, and achieving a more comprehensive and fine description of the DOM migration and transformation mechanism.

[0110] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A coupled simulation method for DOM migration and transformation in shallow lakes, characterized in that, It includes the following steps: S1. Obtain multi-source remote sensing data of shallow lakes, and use the multi-source remote sensing data to invert lake parameters; S2. Construct a hydrodynamic-water temperature coupling model, and construct a DOM migration and transformation coupling model according to the lake parameters; S3. Provide dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model according to the inverted lake parameters; S4. Perform temporal solution on the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model to obtain the DOM concentration field varying with time; S5. Compare and match the DOM concentration field solved by the model with the remotely sensed inverted DOM concentration field in the corresponding time period in the full space range, and iteratively adjust the parameters of the DOM migration and transformation coupling model based on the parameter search algorithm.

2. The method for coupling simulation of DOM migration and transformation in a shallow lake according to claim 1, wherein The multi-source remote sensing data includes: water color remote sensing, thermal infrared remote sensing and optical remote sensing data.

3. A coupled simulation method for DOM migration and transformation in shallow lakes according to claim 1, characterized in that Using the multi-source remote sensing data to invert lake parameters includes: Inverting the DOM concentration, chlorophyll-a concentration and suspended solid concentration through water color remote sensing data; inverting the water surface temperature field of the water body through thermal infrared remote sensing data; obtaining the water surface light intensity of the water body through optical remote sensing data and irradiance model.

4. A method for coupling simulation of DOM migration and transformation in shallow lakes according to claim 1, characterized in that, The hydrodynamic-water temperature coupling model is expressed as: Among them, h represents the water depth, t represents time, u represents the component of the water flow velocity in the x-axis direction of the rectangular coordinate system, v represents the component of the water flow velocity in the y-axis direction of the rectangular coordinate system, x represents the x-axis of the rectangular coordinate system, y represents the y-axis of the rectangular coordinate system, g represents the acceleration due to gravity, η represents the water surface elevation, F u and F v are the volume force terms in the x and y directions respectively; T represents temperature, D T represents the thermal diffusion coefficient, Q T represents the heat source term determined by energy exchange, solar radiation and atmospheric conditions, · represents the divergence operation, represents the vector differential operator, represents the gradient of temperature.

5. A method for coupling simulation of DOM migration and transformation in shallow lakes according to claim 1, characterized in that The DOM migration and transformation coupling model is expressed as: Among them, h represents water depth, C represents DOM concentration, t represents time, u represents the component of water flow velocity in the x-axis direction of the rectangular coordinate system, D C represents the horizontal diffusion coefficient of DOM, represents the DOM concentration gradient, f bed represents the DOM release rate under disturbance conditions, represents the suspended solid concentration, τ bed represents the bottom bed shear stress, k algae represents the contribution rate of algal lysis to DOM, represents the chlorophyll-a concentration, represents the contribution rate of photodegradation to DOM, represents the light intensity at the water surface, k bio (T) represents the contribution rate of biodegradation to DOM, and T represents water temperature.

6. A method for simulating the coupling of DOM migration and transformation in a shallow lake according to claim 5, characterized in that The parameters that need to be iteratively adjusted in the DOM migration and transformation coupling model include: the contribution rate k of algal lysis to DOM algae , the contribution rate of photodegradation to DOM the contribution rate k of biodegradation to DOM bio (T), the DOM release rate f under disturbance conditions bed .

7. A coupled simulation system for DOM migration and transformation in shallow lakes, characterized in that, it includes: A data acquisition module for obtaining multi-source remote sensing data of shallow lakes and using the multi-source remote sensing data to invert lake parameters; A model construction module for constructing a hydrodynamic-water temperature coupling model and constructing a DOM migration and transformation coupling model according to the lake parameters; A model condition providing module for providing dynamic boundary conditions and initial conditions for the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model according to the inverted lake parameters; A model solution module for performing temporal solution on the hydrodynamic-water temperature coupling model and the DOM migration and transformation coupling model to obtain the DOM concentration field varying with time; A model optimization module for comparing and matching the DOM concentration field solved by the model with the remotely sensed inverted DOM concentration field in the corresponding time period in the full space range, and iteratively adjusting the parameters of the DOM migration and transformation coupling model based on the parameter search algorithm.

8. A computer-readable storage medium storing a computer program, characterized in that: The computer program, when executed by a processor, implements the method according to any one of claims 1-6.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor is connected to the memory, wherein the memory is used to store a computer program, the computer program includes computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program / instructions, characterized in that, The steps of the method according to any one of claims 1-6 are implemented when the computer program / instructions are executed by a processor.

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