A method for predicting salinity in oyster aquaculture waters

Abstract

The present invention discloses a method for predicting the salinity of oyster breeding sea areas, which relates to the technical field of big data analysis, and includes the following steps: determining a salinity prediction study area, obtaining GSHHG shoreline data and ETOPO1 water depth data, and combining the measured water depth data to produce a high-precision study area terrain file; based on the high-precision terrain file, producing an initial input file and a tidal drive file, and then establishing a hydrodynamic model; based on the ocean current results provided by the hydrodynamic model, combining the upper forcing field provided by ERA5, the river runoff and the temperature-salinity field provided by CEMES to construct a numerical model of the salinity of the study area; based on the salinity numerical model, using the forecast data provided by GFS, the salinity value of the study area is predicted, and the predicted salinity values ​​of different latitudes of the study area are intuitively displayed using visualization technology. The present invention predicts the salinity changes in the oyster breeding sea area, so that breeders can timely understand the dynamic changes of the breeding environment, which helps to prevent the risk of disease and death of oysters due to salinity fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a method for predicting the salinity of oyster culture sea areas. Background Art

[0002] Studies have shown that long-term exposure to high-salinity stress may be the main cause of the large-scale death of oysters in spring. Therefore, it is crucial to closely monitor the salinity changes in oyster farming areas and adjust the farming methods in a timely manner.

[0003] At present, environmental monitoring of oyster farming areas mainly relies on survey sampling and testing, which has disadvantages such as low efficiency, small samples, and small coverage. It is also unable to quickly and accurately predict salinity changes based on the hydrological and meteorological conditions of oyster farming areas, which greatly affects the rapidity and accuracy of adjusting oyster farming methods. In the prediction of salinity, numerical simulation is a very important means, but the current numerical simulation mainly focuses on estuaries and offshore areas, and pays less attention to oyster farming areas. Therefore, how to establish a salinity prediction model for oyster farming based on the FVCOM model and predict the salinity variation range at different latitudes in oyster farming areas is a problem we need to solve. To this end, a prediction method for salinity in oyster farming areas is proposed. Summary of the invention

[0004] The present invention aims to provide a method for predicting the salinity of oyster culture waters to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for predicting salinity in oyster culture waters, comprising the following steps:

[0007] Step 1: Determine the salinity prediction study area, obtain GSHHG shoreline data and ETOPO1 water depth data, and combine the measured water depth data to produce a high-precision study area terrain file;

[0008] Step 2: Based on the high-precision terrain file, the initial input file and tidal drive file are prepared, and the hydrodynamic model of the study area is established using the FVCOM model. The simulated tide data are compared with the measured tide data to ensure that the simulated tide value is basically the same as the observed value, and the simulated tidal field is close to the measured tidal field.

[0009] Step 3: Collect the measured river runoff, temperature and salinity data in the study area to create a river file, and extract the evaporation data, precipitation data, heat flux data and atmospheric wind field data in the study area to create an upper forcing field file;

[0010] Step 4: According to the start and end time of the hydrodynamic model, download the data of the starting time point of the hydrodynamic model, make the initial temperature-salinity field file of the model, obtain the CMEMS ocean monthly average data based on the start and end time of the hydrodynamic model, provide the open boundary temperature-salinity field, and combine the hydrodynamic simulation data to make the open boundary file of salinity simulation;

[0011] Step 5, establish a salinity model for the study area based on the FVCOM model, compare the simulated salinity with the measured salinity data, analyze the comparison between the salinity simulation value and the observed value, and ensure that the salinity simulation value is basically the same as the observed value;

[0012] Step 6, obtain GFS ocean forecast data, process the GFS ocean forecast data and the associated data of the upper forcing field file, input them into the salinity model for simulation, obtain the predicted salinity value, and determine the risk warning level of oyster farming.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the process of making the high-precision terrain file of the study area is:

[0014] Step 101, determine the location and range of the salinity prediction study area, download the GSHHG shoreline data of the study area, set the longitude and latitude range in the GEODAS-NG software to match the study area, draw the corresponding coastline, and export the shoreline data of the study area;

[0015] Step 102, processing the shoreline data into a file format usable by SMS software in MATLAB;

[0016] Step 103, download ETOPO1 water depth data, set the longitude and latitude range in GEODAS-NG software, generate water depth data of the study area, including the longitude and latitude of the water depth points and the water depth values, and perform smoothing in MATLAB;

[0017] Step 104, load the shoreline data in the SMS software, generate an unstructured triangular mesh that meets the horizontal resolution and perform quality control, load the smoothed water depth data and the measured water depth data, fuse the two and interpolate them into the triangular mesh, and complete the production of a high-precision terrain file for the study area.

[0018] A further improvement of the technical solution of the present invention is that in step 2, the construction process of the hydrodynamic model is:

[0019] Step 201, based on the high-precision terrain file of the research area that has been prepared, prepare the input file required for the hydrodynamic model, and set the vertical layer type and quantity;

[0020] Step 202, determining the simulation start and end time of the hydrodynamic model, and using the TPXO data set to create an initial input file and a tidal drive file;

[0021] Step 203, given the initial field of the study area, import the input file and the tidal drive file into the FVCOM model, set the internal model and external model step size, run the positive pressure model, and obtain the output result;

[0022] Step 204, compare the simulated tide level and tidal current with the measured values, adjust the bottom friction coefficient according to the comparison result, and repeat the calculation until the calculated tide level result at the observation point is basically consistent with the tide level observation result.

[0023] A further improvement of the technical solution of the present invention is that the hydrodynamic model in step 2 is:

[0024] The initial equation of motion and continuity equation are:

[0025]

[0026] Where t is time; x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system respectively; u, v, w are the velocity components in the x, y, z directions respectively; f is the Coriolis force parameter; g is the gravitational acceleration; ρ is the seawater density; ρ0 is the reference density; K m is the vertical vortex viscosity coefficient; F u 、F v is the horizontal momentum diffusion term.

[0027] A further improvement of the technical solution of the present invention is that the hydrodynamic model under the σ coordinate in step 2 is as follows:

[0028] The σ coordinate transformation is used to obtain a smooth expression for the irregular variable bottom terrain vertically. The σ coordinate transformation is defined as:

[0029]

[0030] D = z + ζ;

[0031] Among them, ζ is the free surface height, D is the total water depth, and the value range of σ varies from -1 at the seabed to 0 at the sea surface;

[0032] The momentum equation and continuity equation are transformed into:

[0033]

[0034] ρ=ρ(T,S);

[0035] The horizontal diffusion term is defined as:

[0036]

[0037] Among them, A mrepresents the horizontal eddy viscosity coefficient, t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, f is the Coriolis force parameter, g is the gravitational acceleration, ρ is the seawater density, ρ0 is the reference density, K m is the vertical vortex viscosity coefficient.

[0038] A further improvement of the technical solution of the present invention is that in step 3, the process of making the river file and the upper forced field file is as follows:

[0039] Step 301, collecting the monthly average runoff, temperature, and salinity data of the main rivers in the study area;

[0040] Step 302, determine the number of river bifurcations, interpolate the runoff to the daily average value in MATLAB according to the start and end time of the hydrodynamic model, the start and end time of the river file is less than or equal to the start and end time of the hydrodynamic model, and generate an nc file containing time, river runoff, and river name information;

[0041] Step 303, determine all the node points of the river input in the triangular mesh, set the river vertical stratification coefficient, and make the river.nml file required for the model;

[0042] Step 304, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, the evaporation data, precipitation data, heat flux data and atmospheric wind field data of the study area are downloaded from the ERA5 dataset website, the downloaded data files are preprocessed to ensure the integrity and correctness of the data, and the data are interpolated into the triangular grid in MATLAB to generate the upper forced field file.

[0043] A further improvement of the technical solution of the present invention is that the salinity model in step 3 is as follows:

[0044] The original temperature and salinity equations are:

[0045]

[0046] ρ=ρ(T,S);

[0047] Among them, T is the seawater temperature, S is the seawater salinity, F T 、F S are the temperature and salinity diffusion terms, respectively, which are calculated by the Smagorinsky eddy parameterization method;

[0048] The salinity model of the σ coordinate in step 3 is:

[0049] In the σ coordinate, the original temperature and salinity equations are transformed into:

[0050]

[0051] The horizontal diffusion term is

[0052]

[0053] Where t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, ρ is the seawater density, D is the total water depth, is the absorbed downward short-wave irradiance; A h represents the horizontal thermal eddy diffusion coefficient, σ ranges from -1 at the sea bottom to 0 at the sea surface, q and l represent the turbulent kinetic energy and turbulent kinetic energy scale, respectively, q 2 Represents turbulent kinetic energy.

[0054] A further improvement of the technical solution of the present invention is that in step 4, the process of making the initial temperature-salinity field file and the open boundary file is as follows:

[0055] Step 401, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data at the start time point of the hydrodynamic model, interpolate it to the triangular grid in MATLAB, and generate an initial temperature-salinity field file;

[0056] Step 402, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data during the operation of the hydrodynamic model, superimpose the sea surface height on the tide level of the open boundary point in MATLAB, and superimpose the temperature and salinity on the tidal current of the open boundary point to generate an open boundary file.

[0057] A further improvement of the technical solution of the present invention is that in step 5, the process of establishing the salinity model is:

[0058] Step 501, input the initial temperature-salinity field of the study area into the model, turn on the wind forcing, heat flux forcing, evaporation-precipitation forcing, and temperature-salinity control switches, set the inner model and outer model step sizes, turn off the tidal current drive switch, run the baroclinic model, and obtain the output results;

[0059] Step 502, the simulated salinity is compared with the measured value, and the parameters are adjusted continuously according to the comparison result to repeat the calculation until the calculated salinity result of the observation point is basically consistent with the salinity observation result.

[0060] A further improvement of the technical solution of the present invention is that in step 6, the process of making the salinity model is as follows:

[0061] Step 601, select the prediction time according to the requirements of the study area, download the ERA5 data, perform dimensionality reduction processing, and generate the upper forcing field data;

[0062] Step 602, downloading GFS ocean forecast data, interpolating it into a triangular grid, inputting the upper forcing field data and ocean forecast data into a salinity model, and obtaining salinity prediction data for the study area;

[0063] Step 603, draw salinity prediction curves at different latitudes in the study area, and divide the risk levels according to the degree of harm caused by salinity values ​​to oysters. The risk levels are divided into four levels, namely, level I risk level, level II risk level, level III risk level, and level IV risk level. Set salinity thresholds for the four risk levels, and use different colors to mark different risk levels on the salinity prediction curve. Level IV risk level is green, level III risk level is yellow, level II risk level is orange, and level I risk level is red.

[0064] The multiple risk levels correspond to the multiple salinity thresholds, and the multiple risk levels and the multiple salinity thresholds satisfy the following relationship:

[0065] The salinity threshold for risk level I is 21-22;

[0066] The salinity threshold for risk level II is 23-24;

[0067] The salinity threshold for risk level III is 24-25;

[0068] The salinity threshold for risk level IV is greater than 25.

[0069] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0070] 1. The present invention provides a method for predicting the salinity of oyster culture sea areas. By predicting the salinity changes in oyster culture sea areas, breeders can timely understand the dynamic changes of the culture environment, so as to take corresponding management measures. When the salinity is within an appropriate range, the growth and reproduction of oysters will be smoother, the culture cycle will be shortened, and the survival rate and yield will be improved. This not only directly increases the economic benefits of breeders, but also promotes the healthy development of the entire aquaculture industry. At the same time, accurate salinity prediction also helps breeders to prevent the risks of disease and death that may be caused by salinity fluctuations in advance, further reducing the cost of aquaculture.

[0071] 2. The present invention provides a method for predicting the salinity of oyster farming areas. By predicting the salinity changes in oyster farming areas, farmers can make preparations in advance, reduce the farming risks caused by salinity fluctuations, ensure the stability of the farming environment, and thus ensure the healthy growth of oysters.

[0072] 3. The present invention provides a method for predicting the salinity of oyster culture waters. The salinity prediction technology provides scientific data support for breeders, which helps breeders make more reasonable management decisions. By comparing historical data and real-time prediction data, the changing trend of the culture environment can be more accurately judged. Through the salinity prediction technology, the changing laws of the culture environment can be more accurately grasped, thereby formulating a more scientific and reasonable culture plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0074] Figure 1 is a flow chart of the method of the present invention;

[0075] Figure 2 This is a flow chart for making high-precision terrain files of the study area of ​​the present invention;

[0076] Figure 3 It is a flow chart for constructing the hydrodynamic model of the present invention;

[0077] Figure 4 It is a flow chart for making river files and upper forced field files of the present invention;

[0078] Figure 5 This is a salinity verification curve diagram of the present invention;

[0079] Figure 6 This is the salinity forecast result diagram for the 17th to 19th day at the latitude and longitude (108.8403, 21.6941) of the present invention;

[0080] Figure 7 This is the 20-22 day salinity forecast result diagram of the latitude and longitude (108.8403, 21.6941) of the present invention;

[0081] Figure 8 This is the 23-25 ​​day salinity forecast result diagram of the latitude and longitude (108.8403, 21.6941) of the present invention;

[0082] Fig. 9 This is the salinity forecast result diagram for the 17th to 19th day at the latitude and longitude (108.8587, 21.7513) of the present invention;

[0083] Fig.10 This is the 20-22 day salinity forecast result diagram of the latitude and longitude (108.8587, 21.7513) of the present invention;

[0084] Fig.11This is the 23-25 ​​day salinity forecast result diagram of the latitude and longitude (108.8587, 21.7513) of the present invention;

[0085] Fig.12 This is the salinity forecast result diagram for the 17th to 19th day at the latitude and longitude (108.8789, 21.6646) of the present invention;

[0086] Fig.13 This is the 20-22 day salinity forecast result diagram of the latitude and longitude (108.8789, 21.6646) of the present invention;

[0087] Fig.14 This is the 23-25 ​​day salinity forecast result diagram of the latitude and longitude (108.8789, 21.6646) of the present invention;

[0088] Fig.15 This is the salinity forecast result diagram for the 17th to 19th day at the latitude and longitude (108.8991, 21.6094) of the present invention;

[0089] Fig.16 This is the 20-22 day salinity forecast result diagram of the latitude and longitude (108.8991, 21.6094) of the present invention;

[0090] Fig.17 This is the 23-25 ​​day salinity forecast result diagram of the latitude and longitude (108.8991, 21.6094) of the present invention;

[0091] Fig.18 This is the salinity forecast result diagram for the 17th to 19th day at the latitude and longitude (108.9120, 21.6338) of the present invention;

[0092] Fig.19 This is the 20-22 day salinity forecast result diagram of the latitude and longitude (108.9120, 21.6338) of the present invention;

[0093] Fig. 20 This is the 23-25 ​​day salinity forecast result diagram of the longitude and latitude (108.9120, 21.6338) of the present invention. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] Embodiment 1, as Figure 1-20As shown, the present invention provides a method for predicting the salinity of oyster culture waters, comprising the following steps:

[0096] Step 1: Download GSSH shoreline data and ETOPO1 water depth data, combine with measured water depth data, use SMS software to make a high-precision terrain file of the study area, determine the location and scope of the salinity prediction study area, download GSHHG shoreline data of the study area, set the longitude and latitude range in GEODAS-NG software to match the study area, and draw the corresponding coastline, export the shoreline data of the study area, process the shoreline data into a file format available to SMS software in matlab, download ETOPO1 water depth data, set the longitude and latitude range in GEODAS-NG software, generate water depth data of the study area, including the longitude and latitude of the water depth point and the water depth value, and perform smoothing in matlab, load the shoreline data in SMS software, generate an unstructured triangular mesh that meets the horizontal resolution and perform quality control, load the smoothed water depth data and measured water depth data, fuse the two and interpolate them into the triangular mesh, and complete the production of high-precision terrain files of the study area;

[0097] Step 2: Prepare the initial input file and tidal drive file, and use the FVCOM model to establish the hydrodynamic model of the study area. Compare the simulated tides with the measured tidal data to ensure that the simulated tidal values ​​are basically the same as the observed values, and the simulated tidal field is close to the measured tidal field. According to the high-precision terrain file of the study area that has been prepared, prepare the input file required for the hydrodynamic model, set the vertical layer type and number, determine the simulation start and end time of the hydrodynamic model, use the TPXO data set to prepare the initial input file and tidal drive file, give the initial field of the study area, import the input file and tidal drive file into the FVCOM model, set the internal and external model step sizes, run the positive pressure model, and obtain the output results. Compare the simulated tidal level and tidal current with the measured values, adjust the bottom friction coefficient according to the comparison results, and repeat the calculation until the calculated tidal level result of the observation point is basically consistent with the tidal level observation result.

[0098] Furthermore, the hydrodynamic model in step 2 is:

[0099] The initial equation of motion and continuity equation are:

[0100]

[0101] Where t is time; x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system respectively; u, v, w are the velocity components in the x, y, z directions respectively; f is the Coriolis force parameter; g is the gravitational acceleration; ρ is the seawater density; ρ0 is the reference density; P is the atmospheric pressure on the seawater surface; K m is the vertical vortex viscosity coefficient; F u 、Fv is the horizontal momentum diffusion term;

[0102] Furthermore, the hydrodynamic model under the σ coordinate in step 2 is as follows,

[0103] The σ coordinate transformation is used to obtain a smooth expression for the irregular variable bottom terrain vertically. The σ coordinate transformation is defined as:

[0104]

[0105] D = z + ζ;

[0106] Among them, ζ is the free surface height, D is the total water depth, and the value range of σ varies from -1 at the seabed to 0 at the sea surface;

[0107] The momentum equation and continuity equation are transformed into:

[0108]

[0109] ρ=ρ(T,S);

[0110] The horizontal diffusion term is defined as:

[0111]

[0112] Among them, A m represents the horizontal eddy viscosity coefficient, t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, f is the Coriolis force parameter, g is the gravitational acceleration, ρ is the seawater density, ρ0 is the reference density, K m is the vertical vortex viscosity coefficient.

[0113] Embodiment 2, as Figure 4As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, in step 3, using the measured river runoff, temperature and salinity data to prepare a river file; downloading evaporation data, precipitation data, heat flux data and atmospheric wind field data from the ERA5 data set website to prepare an upper forced field file; according to the model start time, downloading the data at the model start time point to prepare the model initial temperature and salt field file, collecting the monthly average runoff, temperature, and salinity data of the main rivers in the study area, determining the number of river bifurcations, and interpolating the runoff to the daily average in matlab according to the start and end time of the hydrodynamic model, and the start and end time of the river file The time is less than or equal to the start and end time of the hydrodynamic model, and an nc file containing time, river runoff, and river name information is generated. The node points of all river inputs are determined in the triangular mesh, and the river vertical stratification coefficient is set. The river.nml file required by the model is made. According to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, the evaporation data, precipitation data, heat flux data, and atmospheric wind field data of the study area are downloaded from the ERA5 dataset website. The downloaded data files are preprocessed to ensure the integrity and correctness of the data, and interpolated to the triangular mesh in MATLAB to generate the upper forced field file.

[0114] Furthermore, the salinity model in step 3 is as follows,

[0115] The original temperature and salinity equations are:

[0116]

[0117] ρ=ρ(T,S);

[0118] Among them, T is the seawater temperature, S is the seawater salinity, F T 、F S are the temperature and salinity diffusion terms, respectively, which are calculated by the Smagorinsky eddy parameterization method;

[0119] The salinity model of the σ coordinate in step 3 is:

[0120] In the σ coordinate, the original temperature and salinity equations are transformed into:

[0121]

[0122] The horizontal diffusion term is

[0123]

[0124] Where t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, ρ is the seawater density, D is the total water depth, is the absorbed downward short-wave irradiance; Ah represents the horizontal thermal eddy diffusion coefficient, σ ranges from -1 at the sea bottom to 0 at the sea surface, q and l represent the turbulent kinetic energy and turbulent kinetic energy scale, respectively, q 2 Represents turbulent kinetic energy.

[0125] Step 4: According to the start and end time of the model, download the CMEMS ocean monthly average data to provide the open boundary temperature and salinity field; combine the hydrodynamic simulation data to make an open boundary file for salinity simulation; according to the longitude and latitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data at the start time of the hydrodynamic model, interpolate it to the triangular grid in MATLAB, generate the initial temperature and salinity field file; according to the longitude and latitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data during the operation of the hydrodynamic model, superimpose the sea surface height on the tide level at the open boundary point in MATLAB, and superimpose the temperature and salinity on the tidal current at the open boundary point to generate the open boundary file;

[0126] Step 5: Use the FVCOM model to establish a salinity model for the study area, compare the simulated salinity and measured salinity data, ensure that the simulated salinity value is basically the same as the observed value, input the initial temperature and salinity field of the study area into the model, turn on the wind forcing, heat flux forcing, evaporation precipitation forcing, and temperature and salinity control switches, set the internal and external model step sizes, turn off the tidal drive switch, run the baroclinic model, obtain the output results, compare the simulated salinity with the measured value, and adjust the parameters according to the comparison results to repeat the calculation until the calculated salinity result of the observation point is basically consistent with the salinity observation result;

[0127] Step 6, perform dimensionality reduction processing on the ERA5 data, process the GFS forecast field data, input it into the salinity model for simulation, obtain the predicted salinity value, provide risk warning for oyster farming, select the prediction time according to the requirements of the study area, download the ERA5 data, generate the upper forced field data after dimensionality reduction processing, download the GFS ocean forecast data, interpolate it into the triangular grid, input the upper forced field data and the ocean forecast data into the salinity model, obtain the salinity prediction data of the study area, draw the salinity prediction curve of different latitudes in the study area, and divide the risk level according to the degree of harm caused by the salinity value to the oyster. The risk level is divided into four levels, namely, level I risk level, level II risk level, level III risk level and level IV risk level. Set salinity thresholds for the four risk levels, and use different colors to mark different risk levels on the salinity prediction curve. Level IV risk level is green, level III risk level is yellow, level II risk level is orange, and level I risk level is red;

[0128] Furthermore, multiple risk levels correspond to multiple salinity thresholds, and the multiple risk levels and the multiple salinity thresholds satisfy the following relationship:

[0129] The salinity threshold for risk level I is 21-22;

[0130] The salinity threshold for risk level II is 23-24;

[0131] The salinity threshold for risk level III is 24-25;

[0132] The salinity threshold for risk level IV is greater than 25.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting salinity in oyster culture waters, characterized in that: The following steps are involved: Step 1: Determine the salinity prediction study area, obtain GSHHG shoreline data and ETOPO1 water depth data, and combine the measured water depth data to produce a high-precision study area terrain file; Step 2: Based on the high-precision terrain file, the initial input file and the tidal drive file are prepared, and the hydrodynamic model of the study area is established using the FVCOM model to compare the simulated tides with the measured tide data. In step 2, the construction process of the hydrodynamic model is as follows: Step 201, based on the high-precision terrain file of the research area that has been prepared, prepare the input file required for the hydrodynamic model, and set the vertical layer type and quantity; Step 202, determining the simulation start and end time of the hydrodynamic model, and using the TPXO data set to create an initial input file and a tidal drive file; Step 203, given the initial field of the study area, import the input file and the tidal drive file into the FVCOM model, set the internal model and external model step size, run the positive pressure model, and obtain the output result; Step 204, comparing the simulated tide level and tidal current with the measured values, adjusting the bottom friction coefficient according to the comparison result, and repeating the calculation until the calculated tide level result at the observation point is basically consistent with the tide level observation result; Step 3: Collect the measured river runoff, temperature and salinity data in the study area to create a river file, and extract the evaporation data, precipitation data, heat flux data and atmospheric wind field data in the study area to create an upper forcing field file; Step 4: According to the start and end time of the hydrodynamic model, download the data of the starting time point of the hydrodynamic model, make the initial temperature-salinity field file of the model, obtain the CMEMS ocean monthly average data based on the start and end time of the hydrodynamic model, provide the open boundary temperature-salinity field, and combine the hydrodynamic simulation data to make the open boundary file of salinity simulation; Step 5, establish a salinity model for the study area based on the FVCOM model, compare the simulated salinity with the measured salinity data, and analyze the comparison between the simulated salinity value and the observed value; Step 6, obtain GFS ocean forecast data, process the GFS ocean forecast data and the associated data of the upper forcing field file, input them into the salinity model for simulation, obtain the predicted salinity value, and determine the risk warning level of oyster farming.

2. A method for predicting salinity in oyster aquaculture waters according to claim 1, characterized in that: In step 1, the process of making a high-precision terrain file of the study area is as follows: Step 101, determine the location and range of the salinity prediction study area, download the GSHHG shoreline data of the study area, set the longitude and latitude range in the GEODAS-NG software to match the study area, draw the corresponding coastline, and export the shoreline data of the study area; Step 102, processing the shoreline data into a file format usable by SMS software in MATLAB; Step 103, download ETOPO1 water depth data, set the longitude and latitude range in GEODAS-NG software, generate water depth data of the study area, including the longitude and latitude of the water depth points and the water depth values, and perform smoothing in MATLAB; Step 104, load the shoreline data in the SMS software, generate an unstructured triangular mesh that meets the horizontal resolution and perform quality control, load the smoothed water depth data and the measured water depth data, fuse the two and interpolate them into the triangular mesh, and complete the production of a high-precision terrain file for the study area.

3. A method for predicting salinity in oyster aquaculture waters according to claim 2, characterized in that: The hydrodynamic model in step 2 is: The initial equation of motion and continuity equation are: Where t is time; x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system respectively; u, v, w are the velocity components in the x, y, z directions respectively; f is the Coriolis force parameter; g is the gravitational acceleration; ρ is the seawater density; ρ0 is the reference density; K m is the vertical vortex viscosity coefficient; F u 、F v is the horizontal momentum diffusion term.

4. A method for predicting salinity in oyster aquaculture waters according to claim 3, characterized in that: The hydrodynamic model under the σ coordinate in step 2 is as follows: The σ coordinate transformation is used to obtain a smooth expression for the irregular variable bottom terrain vertically. The σ coordinate transformation is defined as: D = z + ζ; Among them, ζ is the free surface height, D is the total water depth, and the value range of σ varies from -1 at the seabed to 0 at the sea surface; The momentum equation and continuity equation are transformed into: ρ=ρ(T,S); The horizontal diffusion term is defined as: Among them, A m represents the horizontal eddy viscosity coefficient, t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, f is the Coriolis force parameter, g is the gravitational acceleration, ρ is the seawater density, ρ0 is the reference density, K m is the vertical vortex viscosity coefficient.

5. The method for predicting salinity in oyster aquaculture waters according to claim 1, characterized in that: In step 3, the process of making the river file and the upper forced field file is as follows: Step 301, collecting the monthly average runoff, temperature, and salinity data of the main rivers in the study area; Step 302, determine the number of river bifurcations, interpolate the runoff to the daily average value in MATLAB according to the start and end time of the hydrodynamic model, the start and end time of the river file is less than or equal to the start and end time of the hydrodynamic model, and generate an nc file containing time, river runoff, and river name information; Step 303, determine all the node points of the river input in the triangular mesh, set the river vertical stratification coefficient, and make the river.nml file required for the model; Step 304, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, the evaporation data, precipitation data, heat flux data and atmospheric wind field data of the study area are downloaded from the ERA5 dataset website, the downloaded data files are preprocessed, and interpolated into the triangular grid in MATLAB to generate the upper forcing field file.

6. A method for predicting salinity in oyster culture waters according to claim 5, characterized in that: The salinity model in step 3 is as follows: The original temperature and salinity equations are: ρ=ρ(T,S); Among them, T is the seawater temperature, S is the seawater salinity, F T 、F S are the temperature and salinity diffusion terms, respectively, which are calculated by the Smagorinsky eddy parameterization method; The salinity model of the σ coordinate in step 3 is: In the σ coordinate, the original temperature and salinity equations are transformed into: The horizontal diffusion term is Where t is time, x, y, z are the coordinates of the east, north and vertical directions in the Cartesian coordinate system, u, v, w are the velocity components in the x, y, z directions, ρ is the seawater density, D is the total water depth, is the absorbed downward short-wave irradiance; A h represents the horizontal thermal eddy diffusion coefficient, σ ranges from -1 at the sea bottom to 0 at the sea surface, q and l represent the turbulent kinetic energy and turbulent kinetic energy scale, respectively, q 2 Represents turbulent kinetic energy.

7. The method for predicting salinity in oyster aquaculture waters according to claim 1, characterized in that: In step 4, the process of making the initial temperature-salinity field file and the open boundary file is as follows: Step 401, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data at the start time point of the hydrodynamic model, interpolate it to the triangular grid in MATLAB, and generate an initial temperature-salinity field file; Step 402, according to the latitude and longitude range of the study area and the start and end time of the hydrodynamic model, download the CMEMS data during the operation of the hydrodynamic model, superimpose the sea surface height on the tide level of the open boundary point in MATLAB, and superimpose the temperature and salinity on the tidal current of the open boundary point to generate an open boundary file.

8. The method for predicting salinity in oyster aquaculture waters according to claim 1, characterized in that: In step 5, the process of establishing the salinity model is as follows: Step 501, input the initial temperature-salinity field of the study area into the model, turn on the wind forcing, heat flux forcing, evaporation-precipitation forcing, and temperature-salinity control switches, set the inner model and outer model step sizes, turn off the tidal current drive switch, run the baroclinic model, and obtain the output results; Step 502, the simulated salinity is compared with the measured value, and the parameters are adjusted continuously according to the comparison result to repeat the calculation until the calculated salinity result of the observation point is basically consistent with the salinity observation result.

9. The method for predicting salinity in oyster culture waters according to claim 1, characterized in that: In step 6, the process of making the salinity model is as follows: Step 601, select the prediction time according to the requirements of the study area, download the ERA5 data, perform dimensionality reduction processing, and generate the upper forcing field data; Step 602, downloading GFS ocean forecast data, interpolating it into a triangular grid, inputting the upper forcing field data and ocean forecast data into a salinity model, and obtaining salinity prediction data for the study area; Step 603, draw salinity prediction curves at different latitudes in the study area, and divide the risk levels according to the degree of harm caused by salinity values ​​to oysters. The risk levels are divided into four levels, namely, level I risk level, level II risk level, level III risk level, and level IV risk level. Set salinity thresholds for the four risk levels, and use different colors to mark different risk levels on the salinity prediction curve. Level IV risk level is green, level III risk level is yellow, level II risk level is orange, and level I risk level is red. The multiple risk levels correspond to the multiple salinity thresholds, and the multiple risk levels and the multiple salinity thresholds satisfy the following relationship: The salinity threshold for risk level I is 21-22; The salinity threshold for risk level II is 23-24; The salinity threshold for risk level III is 24-25; The salinity threshold for risk level IV is greater than 25.

Images