A refined drift prediction method for Ulva prolifera green tide considering the obstruction effect

Abstract

This invention discloses a refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect, belonging to the field of *Ulva prolifera* green tide drift forecasting methods. The method includes the following steps: a) selecting a forecast area and obtaining the distribution of obstacles and environmental elements within the forecast area; b) acquiring historical satellite remote sensing images of the forecast area and obtaining the distribution location, shape, and coverage area of ​​the *Ulva prolifera* green tide based on the satellite remote sensing images; c) constructing a refined drift forecasting model for *Ulva prolifera* green tides considering the obstruction effect; d) training the constructed refined drift forecasting model for *Ulva prolifera* green tides considering the obstruction effect; e) determining the distribution location of the *Ulva prolifera* green tide as the initial drift location using satellite remote sensing images, and using the trained refined drift forecasting model to perform drift forecasting, obtaining the particle positions of the *Ulva prolifera* green tide at different times. This method can provide accurate drift early warning results for raft aquaculture areas and key disaster protection areas.

Description

Technical Field

[0001] This invention relates to the field of methods for predicting the drift of Ulva prolifera green tides, and more specifically to a refined method for predicting the drift of Ulva prolifera green tides that takes into account the blocking effect of obstacles. Background Technology

[0002] Ulva prolifera green tides are characterized by high biomass, long-distance transport, and significant impact. Marine salvage, nearshore interception, and shoreline cleanup constitute the three lines of defense against Ulva prolifera green tides. Currently, publicly available methods for forecasting Ulva prolifera green tide drift primarily rely on forecasting drift in unobstructed open waters. In recent years, large-scale aquaculture raft areas have emerged nearshore; and due to the influence of Ulva prolifera green tides, most key nearshore protection sections have been equipped with Ulva prolifera interception nets.

[0003] Current monitoring indicates that obstacles such as aquaculture rafts and seaweed interception nets intercept most of the seaweed under conditions of low wind and waves. Under high wave conditions, some seaweed can cross the rafts and nets; in this case, the large-scale obstacles, such as the aquaculture raft area, can slow the seaweed's drift. Seaweed surges may accumulate in the aquaculture raft area, and these surges may, under unfavorable wind conditions, cause large-scale beaching and impact the shoreline. Clearly, traditional drift forecasting methods are not applicable when obstacles such as aquaculture rafts and nets are present. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a refined drift forecasting method for Ulva prolifera green tides that takes into account the obstruction effect of obstacles.

[0005] The technical solution adopted in this invention is:

[0006] A refined drift forecasting method for *Ulva prolifera* green tides that considers the blocking effect of obstacles includes the following steps:

[0007] a. Select the forecast area and obtain the distribution of obstacles and environmental factors within the forecast area;

[0008] b. Obtain historical satellite remote sensing images of the forecast area, and obtain the distribution location, shape, and coverage area of ​​the green tide of Ulva prolifera based on the historical satellite remote sensing images;

[0009] c. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles;

[0010] Considering the dragging effect of wind and current on the particles of *Ulva prolifera* (a type of algae) and the weakening effect of obstacles on the drift velocity of these particles; and taking into account the directional distribution of obstacles, the velocity of *Ulva prolifera* particles is decomposed into the velocity along the direction of the obstacle and the velocity perpendicular to the obstacle. Therefore, a refined drift prediction model for *Ulva prolifera* tides considering the obstacle blocking effect is constructed, as follows:

[0011] ;

[0012] (1)

[0013] In equation (1), τ i and n i These represent the positions of the i-th *Ulva prolifera* green tide particle at time t, parallel and perpendicular to the obstacle, respectively; u a u is the x-axis component of the surface flow velocity. d The x-component of the wind at 10 m above sea level; v a v is the y-axis component of the surface flow velocity. d R1 represents the y-axis component of the wind at 10 m above the sea surface; R2 is the ocean current coefficient and R1 is the wind coefficient. The angle between the wind and the x-axis, in degrees; θ is the wind-driven deflection angle, in degrees; θ is the angle between the direction of the obstacle distribution and the x-axis; D is the reduction coefficient of the obstacle on the drift velocity of the seaweed tide.

[0014] d. Determine the distribution location of the green tide of Ulva prolifera using satellite remote sensing images as the initial drift location. Use the refined drift prediction model of Ulva prolifera green tide constructed in step c, which considers the obstruction effect, to predict the drift and obtain the position of Ulva prolifera green tide particles at different times.

[0015] The beneficial technical effects of the present invention are as follows:

[0016] This invention proposes a refined drift forecasting method for Ulva prolifera green tides that considers the obstruction effect of obstacles. This method innovatively considers the obstruction effect of obstacles such as nearshore raft aquaculture areas and interception nets on the drift of Ulva prolifera. It addresses the attenuation or obstruction effect of obstacles on the drift speed of Ulva prolifera under different wind and wave conditions, and constructs a refined drift forecasting model based on the spatial distribution characteristics of obstacles (including height, location, and distribution direction). This model can achieve refined drift forecasting of Ulva prolifera green tides and provide accurate drift warning results for Ulva prolifera green tides.

[0017] This invention provides a refined drift forecasting method for *Ulva prolifera* green tides, considering both single-layer and multi-layer obstacle blocking effects, overcoming the limitations of traditional models in nearshore obstacle scenarios. Key innovations include:

[0018] (1) Quantifying the dynamic blocking effect perpendicular to the distribution direction of obstacles: Based on the physical mechanism that the blocking effect of obstacles mainly occurs in the vertical direction, the velocity of the green tide particles of Ulva prolifera is decomposed into components parallel and perpendicular to the obstacles. The dynamic drift velocity reduction coefficient of wind speed-wave height coupling is innovatively introduced to accurately quantify the blocking effect in the vertical direction, effectively solving the modeling problem of single-layer and multi-layer obstacles such as aquaculture rafts and interception nets.

[0019] (2) Refined forecasting and risk prediction capabilities: Practical application in prevention and control shows that this method can achieve high-precision and refined drift forecasting of Ulva prolifera green tides (up to 72 hours in advance). Its advantages lie not only in accurately forecasting the hotspot areas and times when Ulva prolifera approaches obstacles or shorelines, but also in quantifying the biomass accumulation intensity in the obstacle distribution area and predicting the risk of secondary release of accumulated Ulva prolifera, significantly improving the practicality and accuracy of the forecast. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0021] Figure 1 This is a flowchart illustrating the refined drift forecasting method for *Ulva prolifera* green tides that takes into account the obstruction effect of the present invention.

[0022] Figure 2 This is a schematic diagram of the drift trajectory of the green tide of *Ulva prolifera* in the aquaculture raft area and the non-aquaculture raft area in a specific application example of the present invention;

[0023] Figure 3 The results show the predicted location and biomass of the green tide of *Ulva prolifera* in the aquaculture raft area and the non-aquaculture raft area at different times in a specific application example of this invention. Detailed Implementation

[0024] like Figure 1 As shown, a refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect includes the following steps:

[0025] a. Select the forecast area and obtain the distribution of obstacles (aquaculture rafts, interception nets, etc.) and environmental factors within the forecast area. This includes the following steps:

[0026] a1. Select the forecast area.

[0027] a2. Collect at least 10 satellite remote sensing or UAV images covering the entire sea area where the obstacles are located during the period of Ulva prolifera green tide impact. There is a significant difference in the distribution shape of Ulva prolifera green tide in raft-supported areas and areas without raft-supported areas. Generally, in raft-supported areas, the distribution direction of Ulva prolifera green tide is similar to the shape of the raft, and high biomass accumulation occurs; while in areas without raft-supported areas, the shape of Ulva prolifera green tide is generally consistent with the flow field direction, appearing as elongated strips. Collect high-resolution satellite or UAV remote sensing monitoring images of the raft-supported areas during the period of Ulva prolifera green tide impact to determine the specific distribution of obstacles, such as the latitude and longitude of distribution, distribution direction (angle with the x-axis), and for multi-layered obstacles, the interval between obstacles. Simultaneously, collect the water height of obstacles such as aquaculture rafts or interception nets.

[0028] a3. Collect observational data or numerical simulation data of the environmental field (wind field, flow field and ocean waves) in the forecast area to obtain the spatiotemporal distribution characteristics of the environmental field in the area.

[0029] a4. Analyze the timing and main distribution of the green tide of seaweed near the obstacle.

[0030] b. Obtain historical satellite remote sensing images of the forecast area, and obtain the distribution location, shape, and coverage area of ​​the green tide of Ulva prolifera based on the satellite remote sensing images.

[0031] Collect satellite remote sensing images of green seaweed tides near obstacles during the period of green seaweed tides, especially satellite remote sensing images under different wind speeds and different wave heights, particularly during strong wind processes with wind speeds greater than the average wind speed.

[0032] After acquiring satellite remote sensing images, scattered point extraction of Ulva prolifera green tides was carried out. First, the remote sensing images were preprocessed, including radiometric calibration and atmospheric correction, to obtain atmospheric low-level radioactivity products. Then, the Ulva prolifera green tide remote sensing detection methods, such as the Normalized Diffusion Vibration Index (NDVI), were used to calculate the preprocessed images. Finally, the calculated images were visually interpreted, and boundary values ​​that could be identified as Ulva prolifera green tide pixels were selected. The detection index of these boundary values ​​was used as thresholds for Ulva prolifera green tide correlation extraction.

[0033] ;

[0034] In the formula and These are the reflectance values ​​in the near-infrared and infrared bands, respectively. The presence or absence of *Ulva prolifera* is determined by setting a threshold T0. The T value is jointly determined by visual interpretation using NDVI threshold segmentation and false-color composite images. The theoretical value of the threshold T0 is 0, but it fluctuates due to various factors (shallow water, water depth, etc.).

[0035] The location, shape, and coverage area of ​​the green tide of Ulva prolifera were obtained based on satellite remote sensing imagery.

[0036] c. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles;

[0037] (1) Model construction and requirements;

[0038] Based on the Lagrange particle tracking method, without considering the growth and extinction process of *Ulva prolifera*, the floating *Ulva prolifera* green tide patches are discretized into a certain number of *Ulva prolifera* green tide particles. The dragging effect of wind and current on the *Ulva prolifera* green tide particles, as well as the weakening effect of obstacles on the drift velocity of the particles, are considered. Furthermore, considering the directional distribution of obstacles such as aquaculture rafts, the velocity of the *Ulva prolifera* green tide particles is decomposed into the direction along the obstacle and the direction perpendicular to the obstacle. The particles drift unimpeded along the obstacle direction, while the drift perpendicular to the obstacle direction is blocked. Therefore, a refined drift prediction model for *Ulva prolifera* green tides considering the obstacle blocking effect is constructed, as follows:

[0039] ;

[0040] (1)

[0041] The two formulas above are for calculating the drift position along and perpendicular to the obstacle, respectively.

[0042] In the formula, τ i and n i These represent the positions of the i-th *Ulva prolifera* green tide particle at time t, parallel and perpendicular to the obstacle, respectively; u a u is the x-axis component of the surface flow velocity. d The x-component of the wind at 10 m above sea level; v a v is the y-axis component of the surface flow velocity. d R1 represents the y-axis component of the wind at 10 m above the sea surface; R2 is the ocean current coefficient and R1 is the wind coefficient. The angle between the wind and the x-axis, in degrees; θ is the wind-driven deflection angle in degrees; θ is the angle between the direction of the obstacle distribution and the x-axis; and D is the reduction coefficient of the obstacle on the drift velocity of the seaweed tide.

[0043] (2) Model setup;

[0044] Grid resolution: Based on the distribution of obstacles (distribution intervals, etc.), set the grid for the refined drift forecast model of Ulva prolifera green tide. For multi-layer obstacles, the grid resolution is generally set to be smaller than the obstacle distribution interval; for single-layer obstacles, the grid resolution is generally set to be no more than 1 / 4 of the obstacle length. The model should depict the nearshore topography, shoreline and obstacle distribution characteristics.

[0045] Model Land, Sea and Obstacle Mask Settings: The model uses masks to set the land, sea and obstacles, set to 0, 1 and 2 respectively, to distinguish land, sea and obstacles.

[0046] (3) Obtain hydro-meteorological data on surface currents, sea surface winds and wave heights in the forecast area.

[0047] Hydrometeorological data include sea surface wind speed at 10 m, surface current velocity, and significant wave height (WaveH), sourced from measured data or numerical simulation results of the forecast area. For multi-layered obstacles, the grid resolution of the refined drift forecast model is generally smaller than or close to the obstacle width, characterizing the shoreline and obstacle distribution features; for single-layered obstacles, the grid resolution is generally set no greater than 1 / 4 of the obstacle length. The duration of hydrometeorological data is no shorter than the duration of the drift numerical simulation; the spatial range of the data is greater than or equal to the simulation area.

[0048] The reduction factor D of the drift velocity of the green tide of *Ulva prolifera* due to obstacles (such as aquaculture rafts or interception nets) was determined using the following steps:

[0049] When the significant wave height is higher than the critical value for the green tide of *Ulva prolifera* to overcome the obstacle, the green tide will overcome the obstacle. When the significant wave height is less than or equal to the critical value for the green tide of *Ulva prolifera* to overcome the obstacle, the green tide will be blocked by the obstacle and will not be able to overcome it; when the green tide of *Ulva prolifera* reaches the obstacle, it will stick to the obstacle, and under the action of wind and current away from the obstacle, the green tide particles can leave the obstacle.

[0050] The significant wave height is represented by WaveH, and the critical value for the green tide of Ulva lactuca to overcome obstacles is represented by WaveCR.

[0051] c1. If the obstacle is a single-layer obstacle (such as a single-layer interception net);

[0052] When WaveH is greater than WaveCR, the green tide of Ulva prolifera directly crosses the single-layer obstacle: D=1;

[0053] When WaveH is less than or equal to WaveCR, the green tide of Ulva prolifera is blocked by an obstacle and remains stationary near a single layer of obstacle: D=0.

[0054] c2. If the obstacle is a multi-layered obstacle (such as an aquaculture raft);

[0055] When WaveH is greater than WaveCR:

[0056] (2)

[0057] Where Uwind is the sea surface wind speed, f(Uwind) is the drift attenuation coefficient function fitted based on the sea surface wind speed; Wcr is the critical wind speed; Coe is the reduction coefficient of the drift speed of the seaweed green tide when the sea surface wind speed is greater than or equal to the critical wind speed, which is generally a constant.

[0058] When WaveH is less than or equal to WaveCR: D=0.

[0059] Furthermore, the aforementioned WaveCR (wave threshold) for the green tide of *Ulva prolifera* overcoming obstacles can be determined using the following method:

[0060] Based on satellite remote sensing data of Ulva prolifera green tide distribution and drift over different time periods (including key periods such as when the green tide crosses or fails to cross obstacles at different wave heights), as well as wave distribution (wind and wave environment data such as buoys), and combined with the empirical understanding that "generally, if the effective wave height is greater than twice the obstacle height, floating objects at sea can cross the obstacle," a preliminary threshold for Ulva prolifera green tide to cross obstacles was determined. Then, based on multi-time period Ulva prolifera green tide satellite remote sensing data, drift prediction models, wave height numerical simulation data, or measured data, the WaveCR was corrected.

[0061] Furthermore, for multi-layered obstacles, the critical wind speed (Wcr) is determined, and a drift attenuation coefficient function is fitted.

[0062] c21. Identify cases under strong wind conditions and, based on satellite remote sensing of the drift of the seaweed green tide, determine the critical wind speed and the drift velocity reduction coefficient Coe under conditions greater than or equal to the critical wind speed.

[0063] c22. When the wind speed is less than the critical wind speed, the attenuation coefficient of the green tide of *Ulva prolifera* is a linear function, as shown in the following formula:

[0064] D = aUwind + b (3)

[0065] In the formula: a and b are undetermined coefficients.

[0066] Based on the numerical simulation or observation data of the sea surface wind field and flow field for each case, as well as the drift distance of the seaweed green tide, the least squares method is used to obtain a and b, and the fitting function for the drift attenuation coefficient is determined.

[0067] From more than 10 clear GOCI satellite remote sensing images (including strong wind processes), based on the distribution pattern of the green tide, feature points of the green tide within a time span of two days were extracted to form no less than 15 sets of valid samples, and the time series of the distribution location of the Ulva prolifera green tide was obtained. No less than 10 sets of numerical simulation data of sea surface wind field and flow field and Ulva prolifera green tide drift location sequence were selected from the valid samples. The drift attenuation coefficient D in formula (1) was obtained by using the velocity fitting method and trajectory similarity optimization method, and then the critical wind speed, the reduction coefficient Coe of Ulva prolifera drift speed and the drift attenuation coefficient fitting function were determined.

[0068] The specific steps are as follows:

[0069] (1) Calculate the true displacement of the characteristic points of the green tide particles of Ulva prolifera during the time interval from t0 to t1. Decompose the true displacement into the direction perpendicular to and parallel to the obstacle. Divide the displacement perpendicular to the obstacle direction by the time interval to obtain the average true vertical drift velocity.

[0070] (2)Based on the environmental field and obstacle direction data, in the case of different groups of cases, by adopting a hypothetical drift attenuation coefficient D, using formula (1), calculate to obtain the simulated vertical drift velocity and drift trajectory.

[0071] (3)Establish a drift velocity error objective function, and use the sum of squares of the difference between the predicted vertical drift velocity and the true vertical drift velocity as the evaluation index; set 0 < D < 1, and adopt the gradient descent method, genetic algorithm or grid search method to solve for the drift attenuation coefficient D that minimizes the value of the drift velocity error objective function; use the drift attenuation coefficient calculated in the previous step as the initial value of the trajectory similarity method to construct a trajectory similarity evaluation function; by calculating the root mean square value of the spatial position deviation between the predicted drift trajectory and the observed drift trajectory, also adopt the gradient descent method to seek the optimal D value, so as to determine the drift velocity attenuation coefficient under different wind speeds.

[0072] (4)Plot a scatter plot or trend graph of D changing with wind speed, and observe its changing pattern. When the value of D tends to be stable as the wind speed increases (that is, converges to a stable value, which is Coe), the wind speed corresponding to this convergence point can be determined as the critical wind speed.

[0073] (5)Based on the data below the critical wind speed, use a unary linear regression model (formula 3) to fit the parameters a and b.

[0074] (6)Verify and determine the parameters;

[0075] Use two groups of satellite Enteromorpha prolifera green tide characteristic point data and environmental field data (verification set) that did not participate in the fitting to test the prediction effect after the model uses the parameters, and finally determine the parameters.

[0076] When determining the drift velocity attenuation coefficient D using the above steps, it is necessary to use the obstacle distribution and environmental elements in the predicted area obtained in step a, as well as the Enteromorpha prolifera green tide distribution positions, shapes and coverage areas at different times obtained in step b.

[0077] d. Determine the Enteromorpha prolifera green tide distribution position through satellite remote sensing images as the initial drift position, adopt the refined Enteromorpha prolifera green tide drift prediction model considering the obstacle blocking effect constructed in step c, combine with hydrological and meteorological simulation data, conduct drift prediction, and obtain the Enteromorpha prolifera green tide particle positions at different times. Since each Enteromorpha prolifera green tide particle carries a certain amount of biomass, and the biomass is calculated based on the satellite remote sensing coverage area and biomass per unit area, it is also possible to further simulate the residence time, distribution, biomass of Enteromorpha prolifera green tide in the sea area near the obstacle, and the time and biomass that may affect the coastal sea area.

[0078] Specifically:

[0079] 1) Determine the residence time of Enteromorpha prolifera green tide in the sea area near the obstacle;

[0080] The obstacle distribution area is determined, and the spatial position of the Ulva prolifera green tide particles in the obstacle distribution area at different times is statistically analyzed. The time from the entry to the exit of a single Ulva prolifera green tide particle from the obstacle distribution area is calculated, and finally the average value of the Ulva prolifera green tide particle set is taken.

[0081] 2) Determine the distribution of the green tide of *Ulva prolifera*;

[0082] For single-layer obstacles, the distribution location of the Ulva green tide obstacle is obtained based on the coverage area of ​​different drifting positions of the Ulva green tide, and the coverage area of ​​the Ulva green tide inside and outside the obstacle is counted.

[0083] For multi-layered obstacles, the range of Ulva prolifera green tide accumulation in the obstacle distribution area is dynamically obtained, and the coverage area of ​​Ulva prolifera green tide in the obstacle distribution area at different times or at the time of interest is statistically analyzed; the density is calculated based on the coverage area of ​​Ulva prolifera green tide in the obstacle distribution area.

[0084] 3) Determine the biomass of *Ulva prolifera* in the obstacle distribution area;

[0085] Since each Ulva prolifera green tide particle carries a certain biomass, and its growth is not considered in the short term, for a single-layer obstacle, the total biomass of the Ulva prolifera green tide in a parallel strip area extending m kilometers to both sides of the obstacle is counted (the sum of the biomass of particles in each grid). For multi-layer obstacles, the biomass of the Ulva prolifera green tide in the distribution area of ​​the obstacle can be counted.

[0086] The area where obstacles are distributed is divided into several grid cells. The sum of the biomass of the green tide of Ulva prolifera in each grid cell during the period of interest is calculated. Then, the biomass density of the green tide of Ulva prolifera in the i-th grid cell is the sum of the biomass of the green tide of Ulva prolifera in each grid cell divided by the area of ​​the grid cell. At the same time, a biomass density map can be further generated.

[0087] 4) Determine the nearshore impact;

[0088] To determine the earliest time when the green tide of *Ulva prolifera* affects sensitive nearshore areas, and the time when large-scale biomass affects nearshore areas, etc.

[0089] If the shoreline of the area of ​​interest is used as a benchmark, the total biomass of Ulva prolifera in the area within m kilometers of the shoreline is statistically analyzed.

[0090] The following example, using the forecast of the drift of seaweed green tide in the raft aquaculture area near Rushan City, further illustrates the invention.

[0091] 1. Collect and investigate the distribution of aquaculture rafts and related environmental factors;

[0092] (1) Collect 10 GOCII satellite remote sensing images of clear weather during the period of Ulva prolifera green tide from 2022 to 2024. Each set of images contains impact data at more than 8 time points, which are used for fitting the velocity attenuation coefficient function. Collect 20 high-resolution satellite remote sensing images and UAV images of the sea area where the images are located. The images cover the aquaculture rafts in the sea area near Rushan City.

[0093] (2) Based on high-resolution satellite or UAV remote sensing images of the Sentinel and Environmental Satellites, and considering the significant difference in the shape of the green tide distribution of Ulva prolifera in the raft area and the non-raft area, machine learning methods or human visual identification were used, combined with the collected height of the aquaculture rafts above the water, to obtain the distribution of Ulva prolifera green tide aquaculture rafts, such as the latitude and longitude of the distribution, the distribution interval between rafts, and the distribution direction (angle with the x-axis). The distribution interval between rafts in this sea area is approximately (20m-50m), and the distribution is generally parallel to the shoreline. The height of the buoys in the rafts above the water surface is approximately 0.3m.

[0094] (3) Collect observation data and numerical simulation data of wind field, current field and waves in the sea area near the aquaculture raft in the forecast area to obtain the average wind speed and significant wave height of the simulated sea area.

[0095] (4) The timing and main distribution of the green tide of seaweed near Rushan aquaculture area were sorted out and analyzed. The green tide of seaweed in 2024 was from June 10 to July 20.

[0096] 2. Obtain historical satellite remote sensing monitoring data on green tides of *Ulva prolifera* in the forecast area;

[0097] High-resolution satellite remote sensing images of *Ulva prolifera* green tides under different wind speeds and wave heights in aquaculture areas affected by *Ulva prolifera* green tides from 2022 to 2025 were acquired. These images included HY series (HY-1C, HY-1D, and HY-1E, etc.), GF series (GF3, GF4, etc.), sentinel and environmental data, as well as GOCI data with continuous time series, particularly those showing wind speeds greater than or less than the average wind speed (especially during strong wind events). After acquiring the satellite remote sensing images, interpretation was performed based on operational green tide information inversion algorithms. The NDVI index was used to extract *Ulva prolifera* green tide information, obtaining the distribution location and coverage area of ​​the green tides.

[0098] 3. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles;

[0099] (1) Model setup;

[0100] Based on the model requirements, a refined numerical model of the drift of Ulva prolifera green tides considering the raft barrier effect was constructed. The specific model settings are as follows:

[0101] a) Grid resolution: Based on the distribution of aquaculture rafts in the forecast area, the grid resolution of the refined drift forecast model for Ulva prolifera green tide is set to 20m, which is smaller than or close to the width of obstacles, to depict the nearshore topography, shoreline features and aquaculture area distribution characteristics.

[0102] b) Setting up land, sea and obstacle masks in the model: The model uses masks to set up masks for land, sea and obstacles, which are set to 0, 1 and 2 respectively.

[0103] (2) Obtain hydrometeorological data on surface currents, sea surface winds, and significant wave heights in the forecast sea area;

[0104] Hydrometeorological data, including sea surface wind speed at 10 m, surface current velocity, and significant wave height, are derived from historical refined WRF meteorological models, three-dimensional temperature-salinity-current (ROMS) models, SWAN wave simulation results, and buoy observation data. The duration of sea surface wind, ocean current, and significant wave height data is no shorter than the duration of the drift numerical simulation; the spatial range of the data is greater than or equal to the simulation area.

[0105] (3) Construct a parameterized scheme for the drift of Ulva prolifera green tides and calibrate the parameters;

[0106] Calibration parameters were determined based on satellite remote sensing monitoring data and hydrological and meteorological data.

[0107] Step 1: Determine the WaveCR (Wave Threat) threshold for the green tide of seaweed to overcome obstacles;

[0108] This study analyzes the distribution and drift of *Ulva prolifera* (seaweed) green tides from June 10th to July 20th, particularly during key periods such as when *Ulva prolifera* crossed or failed to cross obstacles under different wave heights. The spatiotemporal distribution characteristics of wave monitoring data near the aquaculture rafts were analyzed. Based on the established understanding that "generally, if the significant wave height is greater than twice the obstacle height, floating objects can cross the obstacle," a preliminary threshold for *Ulva prolifera* green tides to cross obstacles was determined. Using satellite remote sensing data of multiple *Ulva prolifera* green tides in the waters near Rushan during this period (when weather was clear) and measured significant wave height data near the aquaculture rafts, a preliminary wave height threshold (WaveCR) was determined. Combined with a refined drift prediction model, the final WaveCR was determined to be 0.5m.

[0109] Step 2: Determine the critical wind speed (Wcr) and fit the drift attenuation coefficient function;

[0110] 1) From 10 clear GOCI satellite remote sensing images (including strong wind processes), based on the distribution pattern of the green tide, feature points of the green tide within a time span of two days were extracted to form 20 sets of valid samples, and the time series of the distribution location of the green tide of Ulva prolifera was obtained.

[0111] 2) Select the numerical simulation data of sea surface wind field and current field and the drift position sequence of Enteromorpha prolifera green tides in 15 groups out of the above 20 groups. Using the velocity fitting method and the trajectory similarity optimization method, obtain the drift attenuation coefficient D in formula (1), and then determine the critical wind speed, the reduction coefficient Coe of the Enteromorpha prolifera drift speed, and the fitting function of the drift attenuation coefficient. Specifically as follows:

[0112] 2.1 Calculate the true displacement of the characteristic points of the Enteromorpha prolifera green tide particles within the time interval from t0 to t1, decompose the true displacement into the vertical and parallel directions of the obstacle, and divide the displacement perpendicular to the obstacle by the time interval to obtain the average true vertical drift speed;

[0113] 2.2 Using formula (1), based on the environmental field data and the obstacle direction, under the conditions of different groups of cases, with a hypothetical drift reduction coefficient D, calculate and obtain the simulated vertical drift speed and drift trajectory;

[0114] 2.3 Establish a drift speed error objective function, and use the sum of squares of the difference between the predicted vertical drift speed and the true vertical drift speed as the evaluation index; set 0 < D < 1, and use the gradient descent method to solve the drift attenuation coefficient D that minimizes the value of the drift speed error objective function; use the drift attenuation coefficient calculated in the previous step as the initial value of the trajectory similarity method to construct a trajectory similarity evaluation function; by calculating the root mean square value of the spatial position deviation between the predicted drift trajectory and the observed drift trajectory, also use the gradient descent method to find the optimal D value, so as to determine the drift speed attenuation coefficient under different wind speeds.

[0115] 2.4 Plot the trend graph of D changing with the wind speed and analyze its change law. When the value of D tends to be stable as the wind speed increases, the wind speed corresponding to this convergence point can be determined as the critical wind speed. Based on the case analysis result graph, it can be seen that when the critical wind speed is greater than 8 m / s, the speed reduction coefficient tends to 0.8. Therefore, the wind speed of 8 m / s is determined as the critical wind speed. When the wind speed is greater than the critical wind speed, the reduction coefficient Coe of the Enteromorpha prolifera drift speed is 0.8.

[0116] 2.5 Use the data with wind speed lower than the critical wind speed and fit the parameters a and b using the unary linear regression model (formula 3).

[0117] 2.6 Verify and determine the parameters;

[0118] Use 5 groups of satellite Enteromorpha prolifera green tide characteristic point data and environmental field data (verification set) that did not participate in the fitting to test the prediction effect after using the model parameters. Finally, obtain that a is 0.1 and b is -0.05.

[0119] 3) Reduction coefficient of Enteromorpha prolifera drift speed:

[0120] ;

[0121] 4) Furthermore, combining this with the refined drift prediction model for *Ulva prolifera* green tides that takes into account the blocking effect of obstacles, the details are as follows:

[0122] ;

[0123] ;

[0124] In the formula, τ i and n i These represent the positions of the i-th *Ulva prolifera* green tide particle at time t, parallel and perpendicular to the obstacle, respectively; u a u is the x-axis component of the surface flow velocity. d The x-component of the wind at 10 m above sea level; v a v is the y-axis component of the surface flow velocity. d R1 represents the y-axis component of the wind at 10 m above the sea surface; R2 is the ocean current coefficient and R1 is the wind coefficient. The angle between the wind and the x-axis, in degrees; θ is the wind-driven deflection angle in degrees; θ is the angle between the direction of the obstacle distribution and the x-axis; and D is the reduction coefficient of the obstacle on the drift velocity of the seaweed tide.

[0125] 4. Conduct refined drift forecasting of Ulva prolifera green tides obstructed by obstacles;

[0126] Using the distribution location and coverage area monitored by HY-1E satellite remote sensing on June 28, 2024 as the initial field of the model, based on the constructed model and parameter settings, and using hydrological and meteorological simulation data, the drift trajectory, residence time, distribution, biomass of the raft area, and nearshore impact of the green tide of Ulva prolifera in the sea area near the aquaculture rafts are predicted.

[0127] from Figure 2 and Figure 3 It is evident that the drift speed of the seaweed tide is significantly slowed down by the obstruction of the rafts, and the speed attenuation coefficient is dynamic; moreover, a large amount of seaweed is retained in the raft area due to the obstruction. By forecasting the accumulated biomass and future impact in the aquaculture raft area, key technical support can be provided for ship deployment and interception and salvage, which can mitigate the secondary risk of the retained seaweed tide concentrating on the beach.

Claims

1. A method for fine-scale drift prediction of Enteromorpha prolifera green tide considering barrier effect of obstacles, characterized in that The method comprises the following steps: a. Selecting a forecast area, and obtaining the distribution of obstacles and environmental factors in the forecast area; b. Obtaining historical satellite remote sensing images of the forecast area, and obtaining the distribution position, shape and coverage area of the green tide based on the historical satellite remote sensing images; c. Constructing a fine green tide drift prediction model considering the blocking effect of obstacles; Considering the drag effect of wind and current on green tide particles, and the weakening effect of obstacle blocking on the drift speed of green tide particles, and considering that the distribution of obstacles has a certain directionality, the green tide particle velocity is decomposed into the direction along the obstacle and the direction perpendicular to the obstacle, and then a fine green tide drift prediction model considering the blocking effect of obstacles is constructed, as follows: ； （1） In formula (1), τ i and n i are the positions of the i th Enteromorpha prolifera green tide particle in parallel and perpendicular to the barrier direction at time t, respectively; u a is the x-axis direction component of the surface current velocity, u d is the x-axis direction component of the 10 m wind on the sea surface; v a is the y-axis direction component of the surface current velocity, v d is the y-axis direction component of the 10 m wind on the sea surface; R1 is the sea current action coefficient, and R2 is the wind action coefficient; is the angle between the wind and the x-axis direction, in degrees; is the wind drag deflection angle, in degrees; θ is the angle between the barrier distribution direction and the x-axis direction, and D is the barrier reduction coefficient for the drift velocity of the Enteromorpha prolifera green tide. d. Determining the distribution position of the green tide as the initial drift position through satellite remote sensing images, and using the fine green tide drift prediction model considering the blocking effect of obstacles constructed in step c to perform drift prediction and obtain the position of the green tide particles at different times; In step c, the obstacle reduction coefficient D of the drift speed of the green tide is determined by the following steps: When the wave height is higher than the critical value of the green tide overtopping the obstacle, the green tide overtops the obstacle; when the wave height is less than or equal to the critical value of the green tide overtopping the obstacle, the green tide is blocked by the obstacle and cannot overtop the obstacle; when the green tide reaches the obstacle, it sticks to the obstacle, and under the action of wind and current away from the obstacle, the green tide can leave the obstacle; The wave height is represented by the effective wave height WaveH, and the critical value of the green tide overtopping the obstacle is represented by WaveCR; c1. If the obstacle is a single-layer obstacle; When WaveH is greater than WaveCR, the green tide directly overtops the single-layer obstacle: D=1; When WaveH is less than or equal to WaveCR, the green tide is blocked by the obstacle and is stationary near the single-layer obstacle: D=0; c2. If the obstacle is a multi-layer obstacle; When WaveH is greater than WaveCR: （2） Wherein, Uwind is the sea surface wind speed, f(Uwind) is the drift attenuation coefficient function based on the sea surface wind speed; Wcr is the critical wind speed; Coe is the obstacle reduction coefficient of the drift speed of the green tide when the sea surface wind speed is greater than or equal to the critical wind speed; When WaveH is less than or equal to WaveCR: D=0.

2. The method according to claim 1, wherein, In step a: According to the high-resolution satellite or unmanned aerial vehicle remote sensing monitoring images during the influence period of the green tide, and the different shapes of the green tide blocked by the obstacle, the distribution of the green tide obstacle is determined, including the distribution longitude and latitude, distribution direction, and the height of the obstacle from the water; The environmental factors are the environmental field observation data of the forecast area, including the wind field, the current field and the sea wave.

3. The method of claim 1, wherein the method is characterized by, In step c2: The multi-layer obstacle is a culture raft; for the multi-layer obstacle, the critical wind speed is determined, and the drift attenuation coefficient function is fitted; c21. Based on the satellite remote sensing green tide drift, the critical wind speed is determined, and the drift speed reduction coefficient of the green tide when the sea surface wind speed is greater than or equal to the critical wind speed is determined. c22、When the sea surface wind speed is less than the critical wind speed, the reduction coefficient of the obstacle to the drifting speed of Enteromorpha prolifera green tide is a linear function, as follows: D=aUwind+b (3) In the formula: a and b are undetermined coefficients.

4. The method according to claim 3, wherein, The critical wind speed and the reduction coefficient of the obstacle to the drifting speed of Enteromorpha prolifera green tide are determined by the following steps: According to step b, more than 10 GOCI satellite remote sensing images of sunny weather are selected, the distribution form of green tide is determined according to the GOCI satellite remote sensing image, the characteristic points of green tide with a time span of less than two days are extracted, and more than 15 groups of effective samples are formed to obtain the time sequence of the distribution position of Enteromorpha prolifera green tide; According to step a, more than 10 groups of sea surface wind field and flow field numerical simulation data are selected, combined with the time sequence of the distribution position of Enteromorpha prolifera green tide obtained in the above step, the speed fitting method and the trajectory similarity optimization method are used to obtain the reduction coefficient of the obstacle to the drifting speed of Enteromorpha prolifera green tide in formula (1), and then the critical wind speed is determined, when the sea surface wind speed is greater than or equal to the critical wind speed, the reduction coefficient Coe of the obstacle to the drifting speed of Enteromorpha prolifera green tide is determined, and when the sea surface wind speed is less than the critical wind speed, the drifting decay coefficient function is determined.

5. The method of claim 4, wherein the method is characterized by, Specifically, the following steps are included: (1) Calculate the real displacement of the characteristic points of Enteromorpha prolifera green tide particles in the time interval from t0 to t1, and decompose the real displacement into the vertical and parallel directions of the obstacle, and divide the displacement perpendicular to the direction of the obstacle by the time interval to obtain the average real vertical drifting speed; (2) Based on the environmental field and the direction of the obstacle, in different groups of cases, an assumed reduction coefficient D of the obstacle to the drifting speed of Enteromorpha prolifera green tide is used to calculate the simulated vertical drifting speed and drifting trajectory by using formula (1); (3) An error objective function of the drifting speed is established, and the sum of the squares of the difference between the predicted simulated vertical drifting speed and the average real vertical drifting speed is used as an evaluation index; 0<D<1 is set, and the gradient descent method, genetic algorithm or grid search method is used to solve the drifting decay coefficient that minimizes the error objective function value of the drifting speed; the drifting decay coefficient calculated in the above step is used as the initial value of the trajectory similarity method, and a trajectory similarity evaluation function is constructed; The root mean square value of the spatial position deviation of the predicted drifting trajectory and the observed drifting trajectory is calculated, and the gradient descent method is also used to seek the optimal D value, so as to determine the drifting speed decay coefficient under different sea surface wind speeds; (4) Draw a scatter plot or trend graph of D with respect to the sea surface wind speed, and observe its change rule; when the D value tends to be stable with the increase of the sea surface wind speed, the sea surface wind speed corresponding to the convergence point is determined as the critical wind speed; the D value converges to a stable value with the increase of the sea surface wind speed, which is Coe; (5) Based on the data below the critical wind speed, the undetermined coefficients a and b are fitted by using formula 3.

6. The method of claim 1, wherein the method is a method of fine-scale drift prediction of green tide of Enteromorpha considering the blocking effect of obstacles. Each Enteromorpha prolifera green tide particle carries a certain biomass, and the biomass is calculated based on the satellite remote sensing coverage area and the biomass per unit area; in step d, the trained fine drifting prediction model of Enteromorpha prolifera green tide considering the blocking effect of the obstacle can be used to determine the length of time, distribution, biomass and time and biomass of the Enteromorpha prolifera green tide in the nearshore sea area when the Enteromorpha prolifera green tide is retained near the obstacle.

7. The method of claim 6, wherein the method comprises: (1) determining the residence time of the green tide in the sea area near the obstacle; determining the distribution area of the obstacle, calculating the time for a single green tide particle to enter and exit the distribution area of the obstacle by counting the spatial position of the green tide particle in the distribution area of the obstacle at different times, and finally taking the average value of the collection of green tide particles; (2) determining the distribution of the green tide; for a single-layer obstacle, obtaining the distribution position of the green tide obstacle based on the coverage area of the green tide at different drift positions, and counting the coverage area of the green tide inside and outside the obstacle; for a multi-layer obstacle, dynamically obtaining the aggregation range of the green tide in the distribution area of the obstacle, counting the coverage area of the green tide in the distribution area of the obstacle at different times or at the time of interest, and calculating the density of the green tide in the distribution area of the obstacle based on the coverage area of the green tide; (3) determining the biomass of the green tide in the distribution area of the obstacle; since each green tide particle carries a certain biomass and growth is not considered in the short term, the biomass on the nearshore and offshore sides of a single-layer obstacle can be counted, and the biomass of the green tide in the area of a multi-layer obstacle can be counted; (4) determining the nearshore influence; determining the earliest time for the green tide to affect the nearshore sensitive area, and the time for large-scale biomass to affect the nearshore. ​ ​ ​ ​ ​ ​ ​ ​ ​

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