Enteromorpha green tide refined drift forecasting method considering obstacle blocking effect

Abstract

The invention discloses an enteromorpha green tide fine drift forecasting method considering an obstacle blocking effect, and belongs to the field of enteromorpha green tide drift forecasting methods. The method comprises the following steps: a, selecting a forecast area, and obtaining obstacle distribution conditions and environmental elements in the forecast area; b, acquiring a historical satellite remote sensing image of the forecast area, and acquiring the green tide distribution position, shape and coverage area of enteromorpha on the basis of the satellite remote sensing image; c, constructing an enteromorpha green tide fine drift forecasting model considering the obstacle blocking effect; d, training the constructed enteromorpha green tide fine drift forecasting model considering the obstacle blocking effect; and e, determining an enteromorpha green tide distribution position as an initial drift position through a satellite remote sensing image, and carrying out drift forecasting by adopting the trained refined drift forecasting model to obtain enteromorpha green tide particle positions at different moments. The method can provide accurate drifting early warning results for raft frame culture 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: A refined drift forecasting method for *Ulva prolifera* green tides that considers the blocking effect of obstacles includes the following steps: a. Select the forecast area and obtain the distribution of obstacles and environmental factors within the forecast area; 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; c. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles; 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: ; (1) 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. 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.

[0006] The beneficial technical effects of the present invention are as follows: 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.

[0007] 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: (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.

[0008] (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

[0009] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: 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. 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; 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

[0010] like Figure 1 As shown, a refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect includes the following steps: 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: a1. Select the forecast area.

[0011] 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.

[0012] 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.

[0013] a4. Analyze the timing and main distribution of the green tide of seaweed near the obstacle.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] ; 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.).

[0018] The location, shape, and coverage area of ​​the green tide of Ulva prolifera were obtained based on satellite remote sensing imagery.

[0019] c. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles; (1) Model construction and requirements; 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: ; (1) The two formulas above are for calculating the drift position along and perpendicular to the obstacle, respectively.

[0020] In the formula, τ iand 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.

[0021] (2) Model setup; 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.

[0022] 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.

[0023] (3) Obtain hydro-meteorological data on surface currents, sea surface winds and wave heights in the forecast area.

[0024] 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.

[0025] 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:

[0026] 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.

[0027] 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.

[0028] c1. If the obstacle is a single-layer obstacle (such as a single-layer interception net); When WaveH is greater than WaveCR, the green tide of Ulva prolifera directly crosses the single-layer obstacle: D=1; 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.

[0029] c2. If the obstacle is a multi-layered obstacle (such as an aquaculture raft); When WaveH is greater than WaveCR: (2) 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. When WaveH is less than or equal to WaveCR: D=0.

[0030] Furthermore, the aforementioned WaveCR (wave threshold) for the green tide of *Ulva prolifera* overcoming obstacles can be determined using the following method: 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.

[0031] Furthermore, for multi-layered obstacles, the critical wind speed (Wcr) is determined, and a drift attenuation coefficient function is fitted.

[0032] 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.

[0033] 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: D = aUwind + b (3) In the formula: a and b are undetermined coefficients.

[0034] Based on the numerical simulation data or observational data of the sea surface wind field and current field for each case, as well as the drift distance of the Enteromorpha prolifera green tide, the least squares method is used to obtain a and b, and the fitting function of the drift attenuation coefficient is determined.

[0035] From more than 10 scenes of GOCI satellite remote sensing images with clear weather (including strong wind processes), according to the distribution pattern of the green tide, characteristic points of the green tide within a time span of two days are extracted to form no less than 15 groups of effective samples, and a time series of the distribution positions of the Enteromorpha prolifera green tide is obtained. Among the effective samples, no less than 10 groups of numerical simulation data of the sea surface wind field and current field and the drift position sequence of the Enteromorpha prolifera green tide are selected. The velocity fitting method and the trajectory similarity optimization method are used to obtain the drift attenuation coefficient D in formula (1), and then the critical wind speed, the reduction coefficient Coe of the Enteromorpha prolifera drift speed, and the fitting function of the drift attenuation coefficient are determined.

[0036] The specific steps are as follows: (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 to the obstacle. Divide the displacement perpendicular to the obstacle direction by the time interval to obtain the average true vertical drift speed.

[0037] (2) Based on the environmental field and obstacle direction data, in the case of different groups of cases, with a hypothetical drift reduction coefficient D, use formula (1) to calculate and obtain the simulated vertical drift speed and drift trajectory.

[0038] (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, genetic algorithm or grid search 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 seek the optimal D value, so as to determine the drift speed attenuation coefficient under different wind speeds.

[0039] (4) Plot a scatter plot or trend chart of D changing with the wind speed, and observe its change law. 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.

[0040] (5) Based on the data below the critical wind speed, use the unary linear regression model (formula 3) to fit the parameters a and b.

[0041] (6) Verify and determine the parameters; Two sets of satellite data on Ulva prolifera green tide features and environmental field data (validation set) that were not used in the fitting were used to test the prediction effect of the model after using the parameters, and the parameters were finally determined.

[0042] When determining the drift velocity reduction coefficient D using the above steps, it is necessary to use the distribution of obstacles and environmental elements in the forecast area obtained in step a, as well as the distribution location, shape, and coverage area of ​​the seaweed green tide at different times obtained in step b.

[0043] d. Using satellite remote sensing imagery to determine the distribution location of the *Ulva prolifera* green tide as the initial drift location, the refined drift forecasting model for the *Ulva prolifera* green tide, which considers the obstruction effect, constructed in step c, is used in conjunction with hydrological and meteorological simulation data to predict the drift and obtain the position of *Ulva prolifera* green tide particles at different times. Since each *Ulva prolifera* green tide particle carries a certain biomass, calculated based on the satellite remote sensing coverage area and biomass per unit area, it is also possible to further simulate the duration of *Ulva prolifera* green tide's stay in the sea area near the obstacle, its distribution, biomass, and the time and biomass that may affect the nearshore sea area.

[0044] Specifically: 1) Determine the duration of the green tide of seaweed near the obstacle; 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.

[0045] 2) Determine the distribution of the green tide of *Ulva prolifera*; 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.

[0046] 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.

[0047] 3) Determine the biomass of *Ulva prolifera* in the obstacle distribution area; 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.

[0048] 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.

[0049] 4) Determine the nearshore impact; 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.

[0050] 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.

[0051] 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.

[0052] 1. Collect and investigate the distribution of aquaculture rafts and related environmental factors; (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.

[0053] (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.

[0054] (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.

[0055] (4) The impact time and main distribution of the green tide of seaweed near Rushan aquaculture area were sorted out and analyzed. The impact time of the green tide of seaweed in 2024 was from June 10 to July 20.

[0056] 2. Obtain historical satellite remote sensing monitoring data on green tides of *Ulva prolifera* in the forecast area; 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.

[0057] 3. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles; (1) Model setup; 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: 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.

[0058] 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.

[0059] (2) Obtain hydrometeorological data on surface currents, sea surface winds, and significant wave heights in the forecast sea area; 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.

[0060] (3) Construct a parameterized scheme for the drift of Ulva prolifera green tides and calibrate the parameters; Calibration parameters were determined based on satellite remote sensing monitoring data and hydrological and meteorological data.

[0061] Step 1: Determine the WaveCR (Wave Threat) threshold for the green tide of seaweed to overcome obstacles; Analyze the satellite remote sensing distribution and drift of Enteromorpha prolifera green tides from June 10th to July 20th, especially during key time periods such as when Enteromorpha prolifera climbs over and does not climb over obstacles under different wave heights. Analyze the spatio-temporal distribution characteristics of the wave monitoring data near the aquaculture rafts. Combining with the empirical understanding that "when the general effective wave height is greater than twice the height of the obstacle, floating objects at sea can climb over the obstacle", preliminarily determine the critical value of Enteromorpha prolifera green tides climbing over obstacles. Based on the satellite remote sensing data of Enteromorpha prolifera green tides in the sea area near Rushan with clear weather during this period and the measured data of the effective wave height near the aquaculture rafts, preliminarily determine the wave height critical value WaveCR. Combining with the refined drift prediction model, finally determine that WaveCR is 0.5m.

[0062] Step 2: Determine the critical wind speed (Wcr) and fit the drift attenuation coefficient function; 1) From 10 scenes of GOCI satellite remote sensing images with clear weather (including strong wind processes), according to the distribution pattern of the green tide, extract the characteristic points of the green tide within two days of time span to form 20 groups of effective samples, and obtain the time series of the distribution positions of Enteromorpha prolifera green tides.

[0063] 2) Select 15 groups of numerical simulation data of the sea surface wind field and flow field and the drift position sequence of Enteromorpha prolifera green tides from the above 20 groups. Adopt the velocity fitting method and the trajectory similarity optimization method to 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 drift attenuation coefficient fitting function. Specifically as follows: 2.1 Calculate the true displacement of the characteristic points of Enteromorpha prolifera green tide particles within the time interval from t0 to t1. Decompose the true displacement into the directions perpendicular and parallel to the obstacle. Divide the displacement perpendicular to the obstacle by the time interval to obtain the average true vertical drift speed. 2.2 Use 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 to obtain the simulated vertical drift speed and drift trajectory. 2.3 Establish a drift speed error objective function, with 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 adopt 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 adopt the gradient descent method to seek the optimal D value, so as to determine the drift speed attenuation coefficient under different wind speeds.

[0064] 2.4 Plot the trend of D versus wind speed and analyze its variation pattern. When the D value tends to stabilize with increasing wind speed, the wind speed corresponding to this convergence point can be determined as the critical wind speed. Based on the case analysis results, it can be seen that when the critical wind speed is greater than 8 m / s, the velocity attenuation 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 attenuation coefficient Coe of the drift velocity of Ulva lactuca is 0.8.

[0065] 2.5 Using data with wind speeds below the critical wind speed, parameters a and b are fitted using a univariate linear regression model (Formula 3).

[0066] 2.6 Verification and determination of parameters; Five sets of satellite data on Ulva prolifera green tide features and environmental field data (validation set) that were not involved in the fitting were used to test the prediction effect of the model after using the parameters. Finally, a was 0.1 and b was -0.05.

[0067] 3) Coefficient for reducing the drift speed of the green tide of *Ulva prolifera*: ; 4) Furthermore, combining this with the refined drift prediction model for *Ulva prolifera* green tides that considers the blocking effect of obstacles, the details are as follows: ; ; 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.

[0068] 4. Conduct refined drift forecasting of Ulva prolifera green tides obstructed by obstacles; 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.

[0069] 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 refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect, characterized in that... Includes the following steps: a. Select the forecast area and obtain the distribution of obstacles and environmental factors within the forecast area; 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; c. Construct a refined drift forecast model for Ulva prolifera green tides that takes into account the blocking effect of obstacles; 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: ； （1） 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. 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.

2. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 1, characterized in that, In step a: Based on high-resolution satellite or UAV remote sensing images during the period of Ulva green tide impact, and the different shapes of Ulva green tide blocked by obstacles, determine the distribution of Ulva green tide obstacles, including distribution latitude and longitude, distribution direction, and obtain the height of obstacles above the water. The environmental elements refer to environmental field observation data of the forecast area, including wind field, flow field, and ocean waves.

3. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 1, characterized in that, In step c, the reduction coefficient D of the obstacle's effect on the drift velocity of the green tide of *Ulva prolifera* is determined using the following steps: When the wave height is higher than the critical value for the seaweed tide to overcome the obstacle, the seaweed tide will overcome the obstacle; when the wave height is less than or equal to the critical value for the seaweed tide to overcome the obstacle, the seaweed tide will be blocked by the obstacle and will not be able to overcome the obstacle; when the seaweed tide reaches the obstacle, it will stick to the obstacle, and under the action of wind and current away from the obstacle, the seaweed tide will be able to leave the obstacle. Wave height is represented by effective wave height WaveH, and the critical value for the green tide of seaweed to overcome obstacles is represented by WaveCR; c1. If the obstacle is a single-layer obstacle; When WaveH is greater than WaveCR, the green tide of Ulva prolifera directly crosses the single-layer obstacle: D=1; When WaveH is less than or equal to WaveCR, the green tide of Ulva prolifera is blocked by the obstacle and remains stationary near the single-layer obstacle: D=0; c2. If the obstacle is a multi-layered obstacle; When WaveH is greater than WaveCR: （2） 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 caused by the obstacle 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.

4. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 3, characterized in that, In step c2: the multi-layered obstacle is an aquaculture raft; for the multi-layered obstacle, the critical wind speed is determined, and the drift attenuation coefficient function is fitted; c21. Based on satellite remote sensing of the drift of Ulva prolifera green tide, determine the critical wind speed, and the reduction coefficient of the drift speed of Ulva prolifera green tide by obstacles when the sea surface wind speed is greater than or equal to the critical wind speed. c22. When the sea surface wind speed is less than the critical wind speed, the reduction coefficient of the drift speed of the Enteromorpha prolifera green tide by the obstacle is a linear function, as shown in the following formula: D = aUwind + b (3) Where: a and b are undetermined coefficients.

5. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 4, characterized in that, The critical wind speed and the reduction coefficient of the drift speed of the Enteromorpha prolifera green tide by the obstacle are determined by the following steps: Select more than 10 scenes of GOCI satellite remote sensing images with clear weather according to step b. Determine the distribution pattern of the green tide based on the GOCI satellite remote sensing images, extract the characteristic points of the green tide within a time span of two days, form no less than 15 groups of effective samples, and obtain the time series of the distribution positions of the Enteromorpha prolifera green tide; Select no less than 10 groups of numerical simulation data of the sea surface wind field and flow field according to step a. Combine the time series of the distribution positions of the Enteromorpha prolifera green tide obtained in the above steps, and use the velocity fitting method and the trajectory similarity optimization method to obtain the reduction coefficient of the drift speed of the Enteromorpha prolifera green tide by the obstacle in formula (1), and then determine the critical wind speed. When the sea surface wind speed is greater than or equal to the critical wind speed, the reduction coefficient Coe of the drift speed of the Enteromorpha prolifera green tide by the obstacle and the drift attenuation coefficient function when the sea surface wind speed is less than the critical wind speed.

6. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 5, characterized in that, Specifically, it includes the following steps: (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 directions perpendicular and parallel to the obstacle. Divide the displacement perpendicular to the obstacle by the time interval to obtain the average true vertical drift speed; (2) Based on the environmental field and obstacle direction data, in the case of different groups of cases, use a hypothetical reduction coefficient D of the drift speed of the Enteromorpha prolifera green tide by the obstacle, and use formula (1) to calculate the simulated vertical drift speed and drift trajectory; (3) Establish a drift speed error objective function, and use the sum of squares of the differences between the predicted simulated vertical drift speed and the average true vertical drift speed as the evaluation index; set 0 < D < 1, and use the gradient descent method, genetic algorithm or grid search method to solve the drift attenuation coefficient 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, and 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, and also using the gradient descent method, seek the optimal D value, so as to determine the drift speed attenuation coefficient under different sea surface wind speeds; (4) Plot a scatter diagram or trend diagram of D changing with the sea surface wind speed, and observe its change law; when the value of D tends to be stable as the sea surface wind speed increases, the sea surface wind speed corresponding to this convergence point is determined as the critical wind speed; the value of D converges to a stable value as the sea surface wind speed increases, which is Coe; (5) Based on the data below the critical wind speed, use formula 3 to fit the undetermined coefficients a and b.

7. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 1, characterized in that: Each Enteromorpha prolifera green tide particle carries a certain amount of biomass, which is calculated based on the satellite remote sensing coverage area and the biomass per unit area; in step d, it is also possible to determine the residence time, distribution, biomass of the Enteromorpha prolifera green tide in the sea area near the obstacle, and the time and biomass affecting the nearshore sea area through the trained refined drift prediction model of the Enteromorpha prolifera green tide considering the obstacle blocking effect.

8. The refined drift forecasting method for *Ulva prolifera* green tides considering the obstruction effect according to claim 7, characterized in that: (1) Determine the duration of the green tide of seaweed near the obstacle; The obstacle distribution area is determined. By statistically analyzing the spatial positions of the Ulva prolifera green tide particles in the obstacle distribution area at different times, 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. (2) Determine the distribution of the green tide of *Ulva prolifera*; For a single-layer obstacle, 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. For multi-layered obstacles, the range of green tide accumulation of Ulva prolifera in the obstacle distribution area is dynamically obtained, and the coverage area of ​​Ulva prolifera in the obstacle distribution area at different times or the time of interest is statistically analyzed; the density is calculated based on the coverage area of ​​Ulva prolifera in the obstacle distribution area. (3) Determine the biomass of *Ulva prolifera* in the area where obstacles are distributed; Since each Ulva prolifera green tide particle carries a certain biomass, and its growth is not considered in the short term, it is possible to count the biomass on the nearshore and offshore sides of a single-layer obstacle; and for a multi-layer obstacle, it is possible to count the biomass of Ulva prolifera green tide within the obstacle area. (4) Determine the nearshore impact; 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.

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