Enteromorpha-microalgae competitive growth process simulation method

Abstract

The invention discloses an enteromorpha-microalgae competitive growth process simulation method, and relates to the technical field of marine ecology and environmental science. The method comprises the following steps: taking remote sensing image monitoring data of a research area, constructing an enteromorpha green tide coverage area three-dimensional space-time distribution matrix, and determining the distribution range of the matrix; dividing and numbering according to the size of a preset grid unit to obtain an enteromorpha block and a spatial position, and performing dimension reduction processing; the method comprises the following steps: dividing a research area into three subareas, and respectively constructing nutritive salt-enteromorpha-microalgae-debris ecological dynamics box-type models; determining a cross-regional shunting coefficient according to the drifting speed of the enteromorpha between the partitions, and connecting the three box-type models in series; and reconstructing and simulating a time-varying simulation value of the enteromorpha coverage area by using the serially connected models, determining an environmental element modulation effect value, and further determining the key control degree of each environmental element on the competitive growth process of the enteromorpha.

Description

Technical Field

[0001] This application relates to the fields of marine ecology and environmental science, and in particular to a method for simulating the competitive growth process of Ulva prolifera and microalgae. Background Technology

[0002] Enteromorpha ( Ulva prolifera As the main green algae causing green tides, the large-scale outbreak and rapid expansion of *Ulva prolifera* has brought about a series of serious consequences. The proliferation of *Ulva prolifera* not only significantly degrades water quality and damages the marine ecological environment, but also has a huge impact on fishery resources, seriously affecting the balance and sustainable development of the marine ecosystem. In-depth research has found that excessive nutrients in the water, especially the continuous supply of nitrogen and phosphorus, are key factors leading to the abnormally rapid growth of *Ulva prolifera*. Furthermore, the rampant growth of *Ulva prolifera* green tides also affects the normal growth of microalgae in the water, disrupting the original balance of the aquatic ecosystem.

[0003] In the existing technology, a variety of ecological models have been applied to simulate the growth process of Ulva prolifera and microalgae. However, most of the existing models have failed to comprehensively and systematically consider the competitive growth relationship between Ulva prolifera and microalgae in different growth stages in the actual complex environment, and the characterization of the nutrient biogeochemical processes in the competition between Ulva prolifera and microalgae is not accurate enough.

[0004] Therefore, there is an urgent need for a method to simulate the competitive growth process of Ulva prolifera and microalgae, so as to accurately simulate the nutrient biogeochemical processes during the competition between Ulva prolifera and microalgae. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for simulating the competitive growth process of Ulva prolifera and microalgae to address the above-mentioned technical problems.

[0006] The present invention adopts the following technical solution: This invention provides a method for simulating the competitive growth process of *Ulva prolifera* and microalgae, comprising: Remote sensing image monitoring data of Ulva prolifera and microalgal biomass were obtained for the study area; and three-dimensional spatiotemporal data of Ulva prolifera coverage area were constructed based on the remote sensing image monitoring data. The distribution area of ​​the three-dimensional spatiotemporal data of the coverage area of ​​*Ulva prolifera* is divided into grids according to the preset grid cell size; the grid cell number is used to replace the latitude and longitude of the three-dimensional spatiotemporal data of the coverage area of ​​*Ulva prolifera* green tide, and the three-dimensional spatiotemporal distribution matrix is ​​reduced to two-dimensional or one-dimensional data to obtain the dimensionality-reduced data of the coverage area of ​​*Ulva prolifera*. Convert the dimensionality-reduced data of Ulva prolifera coverage area into Ulva prolifera biomass; The simulation value of the coverage area of Enteromorpha over time is obtained by simulating the growth and decline process of the biomass of Enteromorpha and the biomass of microalgae through the ecological dynamics box model of nutrients-Enteromorpha-microalgae-debris; the simulation value of the coverage area of Enteromorpha over time under different numerical values of each environmental factor variable is obtained by adjusting the numerical value of each environmental factor variable in the ecological dynamics box model of nutrients-Enteromorpha-microalgae-debris one by one through the control variable method and simulating the growth and decline process of the biomass of Enteromorpha and the biomass of microalgae. The modulation effect value of each environmental factor is determined according to the simulation value of the coverage area of Enteromorpha over time and the simulation value of the coverage area of Enteromorpha over time under different numerical values of each environmental factor variable; and the key degree of each environmental factor in controlling the competitive growth process of Enteromorpha is determined according to the modulation effect value.

[0007] Preferably, according to the remote sensing image monitoring data of Enteromorpha, a three-dimensional spatio-temporal data of the coverage area of Enteromorpha is constructed, which specifically includes: The coverage area of the Enteromorpha green tide disaster block is determined by performing normalized difference vegetation index calculation on the remote sensing image monitoring data of Enteromorpha; The longitude and latitude of the centroid point of the Enteromorpha green tide disaster block are extracted as the spatial position of the Enteromorpha green tide disaster block; According to the spatial position of the Enteromorpha green tide disaster block, a three-dimensional spatio-temporal distribution matrix of the coverage area of the Enteromorpha green tide is determined, and the formula is: ; In the formula, CA U represents the coverage area of the Enteromorpha green tide, x lon and y lon are the longitude and latitude of the centroid point of the Enteromorpha block, respectively, and t is the time when the Enteromorpha block is monitored by the satellite.

[0008] Preferably, the ecological dynamics box model of nutrients-Enteromorpha-microalgae-debris includes an Enteromorpha sub-model and a microalgae sub-model; both the Enteromorpha sub-model and the microalgae sub-model include a nitrogen and phosphorus nutrient direct conversion module, a nitrogen and phosphorus nutrient biological migration and conversion module, and an algae growth and decline module; and the construction process of the ecological dynamics box model of nutrients-Enteromorpha-microalgae-debris specifically includes: The research area of the reduced dimension data of the coverage area of Enteromorpha is divided into three sub-areas according to the cross section of the research area of the reduced dimension data of the coverage area of Enteromorpha as the division limit, and each sub-area corresponds to a box model; The cross-region diversion coefficient is determined according to the drift speed of the Enteromorpha green tide across the sub-area boundary; The three box models are connected in series through the cross-region diversion coefficient between adjacent sub-area boxes to obtain the ecological dynamics box model of nutrients-Enteromorpha-microalgae-debris.

[0009] Preferably, the preset grid cell size is the minimum increment of the daily coverage area in the three-dimensional spatiotemporal data of the coverage area of green tide of Enteromorpha.

[0010] Preferably, the longitude and latitude of the three-dimensional spatiotemporal data of the coverage area of green tide of Enteromorpha is replaced by the serial number of the grid cell to reduce the three-dimensional spatiotemporal distribution matrix to two-dimensional or one-dimensional data, specifically including: The longitude and latitude of the three-dimensional data of the Enteromorpha block is replaced by the serial number of the grid cell in which the Enteromorpha block is located to reduce the three-dimensional data to two-dimensional data, and the formula is: ; In the formula, CA U represents the coverage area of green tide of Enteromorpha, S represents the serial number of the grid cell, and t is the time when the Enteromorpha block is monitored by the satellite. Nonlinear curve fitting and linear fitting are applied to the two-dimensional data to obtain the adaptability function of the spatial position and time of the Enteromorpha block, and the formula is: ; In the formula, S represents the serial number of the grid cell of the spatial position of the Enteromorpha block, t is the time when the Enteromorpha block appears, a is the maximum value, k is the growth rate, and c is the inflection point.

[0011] If the spatial position S changes monotonically with time t, the two-dimensional data is reduced to one-dimensional data, and the formula is: ; If the spatial position S does not change monotonically with time t, the two-dimensional data is not reduced.

[0012] Preferably, the conversion formula of the reduced dimension of the coverage area of Enteromorpha to the biomass of Enteromorpha is: ; In the formula, B U (t) is the biomass of Enteromorpha at time t, ξ is the conversion coefficient of the biomass of Enteromorpha and the coverage area, and CA U (t) represents the coverage area of Enteromorpha at time t.

[0013] Preferably, the numerical simulation formulas of the growth and decline processes of the biomass of Enteromorpha and the biomass of microalgae are respectively: ; ; In the formula, is the chlorophyll content of microalgae, is the chlorophyll content of Enteromorpha, and are the maximum growth rate constants of microalgae and Enteromorpha, respectively; and respectively are the maximum mortality rate constants of microalgae and Enteromorpha, is the light limitation coefficient in the growth and decline process of microalgae, is the temperature limitation coefficient in the growth and decline process of microalgae, is the nitrogen and phosphorus nutrient limitation coefficient in the growth and decline process of microalgae, is the limitation coefficient in the respiration process of microalgae, is the light limitation coefficient in the growth and decline process of Enteromorpha, is the temperature limitation coefficient in the growth and decline process of Enteromorpha, is the limitation coefficient in the respiration process of Enteromorpha, is the nitrogen and phosphorus nutrient limitation coefficient in the growth and decline process of Enteromorpha.

[0014] Preferably, the environmental factor variables in the nutrient-Enteromorpha-microalgae-detritus ecological dynamics box model include: Enteromorpha initial appearance block coverage area, sea surface temperature, light intensity and nitrogen and phosphorus nutrient concentration.

[0015] Preferably, the modulation effect value of each environmental factor is determined according to the change range between the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time and the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time under different values of each environmental factor variable, and specifically includes: selecting the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time and the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time under different values of each environmental factor variable; determining the modulation effect value of the Enteromorpha green tide disaster control factor according to the change range between the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time and the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time under different values of each environmental factor variable, and the formula is: ; In the formula, Re represents the modulation effect value of the environmental factor, S Re_max represents the simulated value of the annual change of the Enteromorpha coverage area with time under different values of each environmental factor variable, S Ac_max represents the maximum value of the simulated value of the annual change of the Enteromorpha coverage area with time.

[0016] Preferably, the modulation effect value is proportional to the key degree of the environmental factor in controlling the competitive growth process of Enteromorpha.

[0017] The above-mentioned at least one technical scheme adopted by the present application can achieve the following beneficial effects: In the simulation method of the Enteromorpha-microalgae competitive growth process provided by the application, the simulation of the Enteromorpha growth and decline process is realized through multidimensional data processing and complex model construction. The three-dimensional space-time distribution matrix construction, grid division and data dimension reduction operation are adopted to simplify the Enteromorpha coverage area data from complex three-dimensional space-time information to two-dimensional or one-dimensional data convenient for analysis, and the numerical simulation of the data after dimension reduction is carried out by combining the marine nutrient-Enteromorpha-microalgae-detritus ecological dynamics model (NUPD, Nutrient Ulva prolifera Phytoplankton Detritus) process coupling box model, which comprehensively considers the migration and transformation of nitrogen and phosphorus nutrients, the growth and decline of algae and other processes, so that the model can accurately simulate the Enteromorpha growth and decline process and calculate the simulation value of the time variation of the Enteromorpha coverage area, thereby providing scientific and accurate data support and model basis for the prediction and prevention and control of the Enteromorpha green tide disaster and assisting the relevant departments in planning the response strategies in advance and reducing the disaster loss.

[0018] Among them, the NUPD box model is used for numerical test, and the analysis focuses on the influence of environmental factors on the growth of Enteromorpha. The method has the characteristics of comprehensively considering multiple control factors. This method can scientifically and systematically analyze the regulation of each environmental factor in the growth and decline process of the Enteromorpha disaster, and clearly determine the influence degree and law of different factors on the growth of Enteromorpha, so as to formulate targeted prevention and control measures for the Enteromorpha green tide disaster, inhibit the excessive growth of Enteromorpha, reduce the possibility and harm degree of the disaster, and protect the marine ecological environment and the production and living order of the coastal area. BRIEF DESCRIPTION OF DRAWINGS

[0019] The drawings described herein are used to provide further understanding of the present application, constitute a part of the present application, the illustrative embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 The flowchart of the simulation method of the Enteromorpha-microalgae competitive growth process provided by the application is shown in the figure; Figure 2 The NUPD process coupling box model logic framework diagram of the simulation method of the Enteromorpha-microalgae competitive growth process provided by the application is shown in the figure; Figure 3 The Enteromorpha green tide coverage area monitoring and data reconstruction result graph of the simulation method of the Enteromorpha-microalgae competitive growth process provided by the application is shown in the figure; Figure 4 The effect of the environmental factors on the Enteromorpha green tide coverage area of the simulation method of the Enteromorpha-microalgae competitive growth process provided by the application is shown in the figure. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in connection with the embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the specification, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0022] The technical solutions provided by the embodiments of the present application will be described in detail below in connection with the drawings.

[0023] Figure 1 The flowchart of the method for simulating the competitive growth process of Enteromorpha and microalgae in the present application specifically comprises the following steps: S101: Obtain the Enteromorpha remote sensing image monitoring data and the microalgae biomass of the research area; and construct the three-dimensional spatio-temporal data of the Enteromorpha coverage area according to the Enteromorpha remote sensing image monitoring data.

[0024] Optionally, the three-dimensional spatio-temporal distribution matrix of the Enteromorpha green tide coverage area is constructed according to the remote sensing image monitoring data, specifically comprising: performing normalized difference vegetation index calculation on the remote sensing image monitoring data to determine the coverage area of the Enteromorpha green tide disaster block; extracting the longitude and latitude of the centroid point of the Enteromorpha green tide disaster block as the spatial position of the Enteromorpha green tide disaster block; determining the three-dimensional spatio-temporal distribution matrix of the Enteromorpha green tide coverage area according to the spatial position of the Enteromorpha green tide disaster block, and the formula is: ; In the formula, CA U represents the coverage area of the Enteromorpha green tide, x lon and y lon are the longitude and latitude of the centroid point of the Enteromorpha block, respectively, and t is the time when the Enteromorpha block is monitored by the satellite.

[0025] Specifically, based on the Enteromorpha remote sensing image monitoring data, the normalized difference vegetation index method is applied to estimate the coverage area of the Enteromorpha green tide disaster block. The longitude and latitude of the centroid point of the Enteromorpha block in the remote sensing image are obtained by using ArcGIS, and the longitude and latitude of the centroid point of the Enteromorpha block are used to represent the spatial position of the Enteromorpha, and the three-dimensional spatio-temporal matrix of the Enteromorpha coverage area is constructed.

[0026] S102: Perform grid division on the distribution area of the three-dimensional spatio-temporal data of the Enteromorpha coverage area according to a preset grid unit size; replace the longitude and latitude of the three-dimensional spatio-temporal data of the Enteromorpha green tide coverage area with the serial number of the grid unit, reduce the dimension of the three-dimensional spatio-temporal distribution matrix to two-dimensional or one-dimensional data, and obtain the dimension-reduced data of the Enteromorpha coverage area.

[0027] Optionally, the preset grid unit size is the minimum increment of the daily coverage area in the three-dimensional spatio-temporal distribution matrix of the Enteromorpha green tide coverage area.

[0028] Optionally, the three-dimensional data of the multiple Enteromorpha blocks is reduced in dimension according to the number of the spatial position, to obtain Enteromorpha block reduced dimension data, specifically including: using the grid cell number where the Enteromorpha block is located to replace the longitude and latitude in the three-dimensional data of the Enteromorpha block, reducing the three-dimensional data into two-dimensional data, the formula is: ; In the formula, CA U represents the coverage area of the Enteromorpha green tide, S represents the grid cell serial number, and t is the time when the Enteromorpha block is monitored by the satellite; Nonlinear curve fitting and linear fitting are applied to the two-dimensional data, to obtain the adaptability function of the spatial position and time of the Enteromorpha block, the formula is: ; In the formula, the parameters are explained, S represents the grid cell serial number of the spatial position of the Enteromorpha block, t is the time when the Enteromorpha block appears, a is the maximum value, k is the growth rate, and c is the inflection point.

[0029] If the spatial position S is monotonically changed with time t, the two-dimensional data is reduced into one-dimensional data, the formula is: ; If the spatial position S is not monotonically changed with time t, the two-dimensional data is not reduced in dimension.

[0030] Specifically, the distribution range of the Enteromorpha is determined according to the superposition of the Enteromorpha coverage area in previous years, which is approximately between 29°-38°N to 119°-128°E. The Enteromorpha coverage range is divided into grids, and the minimum increment of the daily coverage area of the Enteromorpha is used as the grid cell division standard, and the grid cell is about 0.05°x0.05°. Combined with the drifting characteristics of the Enteromorpha, 29°N128°E is defined as the initial point, numbered as N1E1, and the remaining grid cells are sequentially numbered as N1E1-N180E180. The grid cells are sorted, and the sorting rule is that the product S of i and j in the grid number NiEj is positioned as the spatial position of the Enteromorpha block.

[0031] Specifically, the grid cell where the centroid point of the Enteromorpha block is located is used as the spatial position of the Enteromorpha block, and the grid cell serial number S is used to replace the longitude and latitude, to reduce the three-dimensional data into two-dimensional data; according to the two-dimensional data of the Enteromorpha block occurrence date and spatial position serial number, nonlinear curve fitting and linear fitting are applied to different years, to obtain the adaptability of the Enteromorpha occurrence time and spatial position; the corresponding relationship between the Enteromorpha occurrence time and spatial position since 2008 is compared and analyzed. If the spatial position of the Enteromorpha is monotonically changed with time, the two-dimensional spatiotemporal variation of the Enteromorpha coverage area can be reduced into one-dimensional time sequence variation.

[0032] S103: convert the Enteromorpha coverage area reduction dimension data into Enteromorpha biomass; through the nutrient salt-Enteromorpha-microalgae-debris ecological dynamic box model, the growth and consumption processes of Enteromorpha biomass and microalgae biomass are numerically simulated, and the simulation value of the change of Enteromorpha coverage area with time is obtained; by adjusting the numerical value of the environmental factor variable in the box model one by one through the control variable method, and simulating the growth and consumption processes of Enteromorpha biomass and microalgae biomass, the simulation value of the change of Enteromorpha coverage area with time under different numerical values of each environmental factor variable is obtained.

[0033] Specifically, the logical architecture of the NUPD process coupled box model is constructed, including two sub-models of Enteromorpha model and microalgae model, each sub-model including three modules of nitrogen and phosphorus nutrient salt direct conversion, nitrogen and phosphorus nutrient salt biological migration conversion, and algae growth and consumption. The main variables include nitrogen and phosphorus nutrients, Enteromorpha biomass, microalgae biomass and debris, and temperature and light environmental constraints.

[0034] Specifically, the target ecosystem / nitrogen and phosphorus nutrient enrichment culture bottle field experiment is carried out, including two series: (1) algae removal / nutrient enrichment culture bottle series corresponding to the nitrogen and phosphorus nutrient direct conversion process; (2) Enteromorpha and microalgae / nutrient enrichment culture bottle series corresponding to the growth and consumption of Enteromorpha and microalgae and the biological migration conversion process of nitrogen and phosphorus nutrients. The nitrogen and phosphorus nutrient enrichment culture bottle field experiment site can be set in Nantong, Rizhao and Qingdao coastal areas. The measured variables include NH4-N, NO2-N, NO3-N, TDN, TPN, PO4-P, TDP, TPP and SiO3-Si. The uncertainty and accidental deviation of the field experiment data are quantitatively evaluated by the relative standard deviation (RSD) of double samples and the volatility of TN and TP. The smaller the relative standard deviation and volatility, the more precise the experimental results.

[0035] Specifically, the direct transformation of nitrogen and phosphorus nutrients, the biological migration transformation process, the growth and death of Enteromorpha and microalgae in the algae removal / enriched nutrient culture bottles and in the Enteromorpha and microalgae / enriched nutrient culture bottles can all be in different segmental forms. Therefore, it is necessary to identify their segmental nodes and comprehensively apply statistical methods such as Partial Mann-Kendall trend analysis to determine the significance of each process. For the selection of the best kinetic equation form for significant kinetic processes, in the direct transformation of nitrogen and phosphorus nutrients, NH4-N nitration, NO2-N nitration, and NO3-N denitrification often apply first-order kinetic equations. In the biological migration transformation process of nitrogen and phosphorus nutrients, the Monod equation is often used for the absorption of Enteromorpha and microalgae nutrients. The growth process of Enteromorpha and microalgae is closely related to the process of absorbing nitrogen and phosphorus nutrients, and it is generally believed that they both conform to the "positive S-type" Logistic equation; while the death process of Enteromorpha and microalgae is closely related to the degradation of intracellular nitrogen and phosphorus, and presents the characteristics of the "inverse S-type" Logistic equation. As for the phenomena of DOM released by Enteromorpha and microalgae cells, biodebris generated, degradation, and DOM mineralization produced by cells, they can usually be described by first-order kinetic equations.

[0036] Specifically, the NUPD process-coupled box model is a multi-process box model, which needs to be optimized by parameter calibration. The steps of parameter calibration include regional correction of process kinetics equation, parameter sensitivity analysis by Monte Carlo method, setting initial values of parameters according to the results of parameter sensitivity analysis, step-by-step simulation calculation and accuracy evaluation of simulation results. Among them, the low-sensitivity parameters can use the authoritative values of NOVECOM model or the parameter values verified by sea investigation, while the high-sensitivity parameters need to be determined according to the results of nonlinear fitting of field experiments. When evaluating the accuracy of simulation results, the relative standard deviation (RSD) and similarity coefficient (SI) are used to measure the degree of agreement between simulation results and experimental results. In order to simulate the time variation of different forms of nitrogen and phosphorus nutrients in the target ecological system-enriched nutrient culture bottles, the Runge-Kutta simulation calculation method is used for parameter calibration. Since multiple processes may occur in different series of culture bottles, the model parameter calibration is carried out in the order from simple to complex, and the experimental results are adjusted step by step. In the initial first round of experimental result simulation calculation, the model parameters of the growth and decay of Enteromorpha and microalgae and the migration and transformation of nitrogen and phosphorus nutrients are set to 0, and the parameters of the direct conversion process of nitrogen and phosphorus nutrients are calibrated. Then, in the second round of experimental result simulation calculation, the experimental results of the Enteromorpha and microalgae enriched nutrient culture bottle series are considered comprehensively, and the parameters of all processes are calibrated comprehensively. Finally, through multiple cycles of approximation, the simulation results that are consistent with the experimental data in all aspects and have an error less than 20% and a time variation similarity greater than 80% are obtained, and the parameters of each process are determined. The accuracy of the NUPD process-coupled model is evaluated by the degree of agreement between the simulation results of the time variation of Enteromorpha / microalgae biomass in other "independent" field experiments and the actual experimental results.

[0037] Specifically, the NUPD model is used to simulate the growth and decay process of Enteromorpha, and according to the occurrence, development and extinction process of Enteromorpha, three boxes of the NUPD model are set, namely the first partition, the second partition and the third partition. The cross-regional diversion coefficients between regions are used to connect the NUPD three-box model in series. The cross-regional diversion coefficient is the proportion of the Enteromorpha biomass crossing the first partition-second partition and second partition-third partition interfaces to the total Enteromorpha biomass. The cross-regional drift coefficient is mainly determined by the drift speed of the Enteromorpha block, and the average cross-regional drift coefficients of the first partition-second partition and second partition-third partition interfaces are 0.01 and 0.005, respectively. According to the conversion coefficient (ξ) of Enteromorpha biomass and coverage area, the simulation value of the time variation of Enteromorpha green tide coverage area can be calculated, and the formula is:

[0038] ; where B U (t) is the biomass of Enteromorpha prolifera at time t (gC); ξ is the conversion coefficient of biomass to covered area (gC / m 2 ).

[0039] The time series of Enteromorpha prolifera biomass (i.e. covered area) was calculated according to the environmental factors during the green tide outbreak in the target year. The three-box NUPD model was used to reconstruct the biomass of Enteromorpha prolifera under the ModelMaker 4.0 software tool environment. The relative deviation (RD) and similarity index (SI) between the simulation results and the marine survey monitoring results were used to test and evaluate the uncertainty of the numerical reconstruction simulation.

[0040] Specifically, in the nutrient-Enteromorpha prolifera-microalgae-detritus ecological dynamics model, the environmental limiting functions mainly include light intensity, temperature, and nutrient limitation, which can be described as: (1) Light limiting function ; ; where I is the light intensity (W / m 2 ); is the optimum light intensity of microalgae (W / m 2 ); is the optimum light intensity of Enteromorpha prolifera (W / m 2 ).

[0041] (2) Temperature limiting function ; ; where T is the temperature (℃); TG is the temperature coefficient (℃ -1 ); and are the maximum and minimum temperatures for the growth of Enteromorpha prolifera (℃), respectively.

[0042] (3) Nitrate nitrogen limiting function ; ; where , and are the concentrations of nitrate nitrogen, ammonia nitrogen, and inorganic nitrogen nutrients (μmol·L -1 ); , and are the half-saturation constants for the absorption of nitrate nitrogen by microalgae, the absorption of ammonia nitrogen by microalgae, and the absorption of inorganic nitrogen by Enteromorpha prolifera (μmol·L​-1 ).

[0043] (4) Ammonium limitation function ; ; (5) Dissolved organic nitrogen limitation function ; ; ; ; where, , and are the concentrations of terrestrial dissolved organic nitrogen, terrestrial refractory dissolved organic nitrogen and labile dissolved organic nitrogen (pmol L -1 ); , , and are the half-saturation constants for microalgal uptake of terrestrial dissolved organic nitrogen, microalgal uptake of terrestrial refractory dissolved organic nitrogen, microalgal uptake of labile dissolved organic nitrogen and microalgal uptake of labile dissolved organic nitrogen (pmol L -1 ).

[0044] (6) Maximum uptake / symbiosis limitation function for different forms of nitrogen ; ; (7) Phosphate limitation function ; ; where, is the concentration of phosphate nutrient (pmol L -1 ); and are the half-saturation constants for microalgal uptake of phosphate and microalgal uptake of phosphate (pmol L -1 ).

[0045] (8) Dissolved organic phosphorus limitation function ; where, and are the concentrations of terrestrial dissolved organic phosphorus, terrestrial refractory dissolved organic phosphorus (pmol L -1 ); is the half-saturation constant for microalgal uptake of terrestrial dissolved organic phosphorus (pmol L-1 ).

[0046] (9) Silicate limitation function where, is the nutrient salt concentration of silicate (pmol L -1 ); is the half-saturation constant of silicate uptake by microalgae (pmol L -1 ).

[0047] (10) Nutrient salt constraint function ; ; ; ; where, and are the minimum nitrogen and phosphorus concentrations (pmol L -1 ) for Enteromorpha maintenance growth, respectively; and are the particulate nitrogen and phosphorus contents (pmol L -1 ) of Enteromorpha, respectively; is the chlorophyll content of Enteromorpha (pmol L -1 ).

[0048] (11) Respiration limitation function ; ; where, and are the particulate nitrogen and chlorophyll contents (pmol L -1 ) of microalgae, respectively; and are the respiration half-saturation constants of microalgae and Enteromorpha, respectively; is the nitrogen to chlorophyll ratio of Enteromorpha.

[0049] In this model, the nitrogen, phosphorus, and silicate nutrient salt transfer and conversion processes of the Enteromorpha and microalgae modules are described using first-order differential kinetics equations, and each state variable can be described using the following equation: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; wherein, , , and are the detrital nitrogen and phosphorus contents (μmol·L -1 ) produced by the degradation of microalgae and Enteromorpha; are the nitrification and denitrification rate constants (d -1 ); are the nitritation and reduction rate constants (d -1 ); and are the mineralization rate constants of terrestrial dissolved organic nitrogen and phosphorus (d -1 ); and are the mineralization rate constants of algal-derived labile dissolved organic nitrogen and phosphorus (d -1 ); and are the aging rate constants of labile dissolved organic nitrogen and phosphorus (d -1 ); is the mineralization rate constant of dissolved organic silicate (d -1 ); , and are the maximum uptake rate constants of nitrogen, phosphorus and silicate by microalgae (d -1 ); and are the maximum uptake rate constants of nitrogen and phosphorus by Enteromorpha (d -1 ); , , and are the nitrogen and phosphorus release rate constants of microalgae and Enteromorpha (d-1 are the detritus production rate constants of microalgae and Enteromorpha prolifera (d -1 are the detritus degradation rate constants of microalgae and Enteromorpha prolifera (d -1

[0050] The NUPD process coupling box model of the growth and death processes of Enteromorpha prolifera and microalgae is expressed by the following differential equations: are the maximum growth rate constants of microalgae and Enteromorpha prolifera (d -1 are the maximum death rate constants of microalgae and Enteromorpha prolifera (d -1

[0051] S104: determining the modulation effect value of each environmental factor according to the simulated values of the change of the coverage area of Enteromorpha prolifera over time and the simulated values of the change of the coverage area of Enteromorpha prolifera over time at different numerical values of each environmental factor variable; and determining the key degree of each environmental factor in controlling the competitive growth process of Enteromorpha prolifera according to the modulation effect value.

[0052] Optionally, the modulation effect value of each environmental factor is determined according to the change amplitude between the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year and the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year at different numerical values of each environmental factor variable, and specifically includes: selecting the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year and the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year at different numerical values of each environmental factor variable; and determining the modulation effect value of the Enteromorpha prolifera green tide disaster control factor according to the change amplitude of the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year and the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year at different numerical values of each environmental factor variable, and the formula is: In the formula, Re represents the modulation effect value of the environmental factor, S Re_max represents the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year at different numerical values of each environmental factor variable, and S Ac_max represents the maximum value of the simulated value of the change of the coverage area of Enteromorpha prolifera over time each year. ​​​​​​​​​​​​​​​​​

[0053] Optionally, the environmental factor variables in the nutrient-sargassum-microalgae-detritus ecological dynamic box model include: sargassum initial occurrence block coverage area, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient salt concentration.

[0054] Optionally, the modulation effect value is directly proportional to the key degree of the environmental factor in controlling the competitive growth process of sargassum.

[0055] Specifically, due to the reasons such as that the growth of sargassum is regulated by multiple factors, the changes of the control factors such as sargassum initial occurrence block coverage area, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient salt concentration will usually cause the change of sargassum coverage area. The control factor modulation effect refers to the regulation effect of sargassum disaster in the process of generation and disappearance, which can be characterized by the change range of the maximum coverage area of sargassum disaster. Through statistical analysis of the interannual variation of the key control factors in the sargassum occurrence sea area, the annual average values of the environmental factors in the sargassum occurrence sea area since 2008 are used as the scenario of numerical test. In this way, the numerical test of the NUPD box model can be applied, and the change range of the maximum coverage area of sargassum disaster under the condition of the annual average of the control factors can be used to test and evaluate the modulation effect of the control factors of sargassum green tide disaster.

[0056] The technical features of the above embodiments can be combined in any manner. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the range disclosed by the present application.

Claims

1. A method for simulating the competitive growth process of *Ulva prolifera* and microalgae, characterized in that, include: Acquire remote sensing image monitoring data of Ulva prolifera and microalgal biomass in the study area; Based on remote sensing image monitoring data of Ulva prolifera, a three-dimensional spatiotemporal data of Ulva prolifera coverage area was constructed; The distribution area of ​​the three-dimensional spatiotemporal data of the coverage area of ​​*Ulva prolifera* is divided into grids according to the preset grid cell size; the grid cell number is used to replace the latitude and longitude of the three-dimensional spatiotemporal data of the coverage area of ​​*Ulva prolifera* green tide, and the three-dimensional spatiotemporal distribution matrix is ​​reduced to two-dimensional or one-dimensional data to obtain the dimensionality-reduced data of the coverage area of ​​*Ulva prolifera*. Convert the dimensionality-reduced data of Ulva prolifera coverage area into Ulva prolifera biomass; The biomass generation and degradation processes of *Ulva prolifera* and microalgae were numerically simulated using a nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model, and the simulated values ​​of *Ulva prolifera* coverage area over time were obtained. By adjusting the values ​​of environmental element variables in the nutrient-Ulva-microalgae-detritus ecodynamic box model one by one using the controlled variable method, and simulating the generation and decay processes of Ulva biomass and microalgae biomass, the simulated values ​​of the Ulva coverage area changing over time under different values ​​of each environmental element variable were obtained. Based on the simulated values ​​of the change in Ulva prolifera coverage area over time and the simulated values ​​of the change in Ulva prolifera coverage area over time under different values ​​of each environmental factor variable, the modulation effect value of each environmental factor is determined; based on the modulation effect value, the criticality of each environmental factor in controlling the competitive growth process of Ulva prolifera is determined.

2. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The construction of three-dimensional spatiotemporal data of Ulva prolifera coverage area based on Ulva prolifera remote sensing image monitoring data specifically includes: Normalized vegetation index was calculated from remote sensing image monitoring data of Ulva prolifera to determine the coverage area of ​​Ulva prolifera green tide disaster blocks; The latitude and longitude of the centroid of the Ulva prolifera green tide disaster area are extracted as the spatial location of the Ulva prolifera green tide disaster area; Based on the spatial location of the Ulva prolifera green tide disaster area, the three-dimensional spatiotemporal distribution matrix of the Ulva prolifera green tide coverage area is determined by the following formula: ； In the formula, CA U x represents the area covered by the green tide of seaweed. lon and y lon , respectively, are the longitude and latitude of the centroid of the Ulva prolifera block, and t is the time when the Ulva prolifera block was monitored by satellite.

3. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model includes an Ulva prolifera sub-model and a microalgae sub-model; both the Ulva prolifera sub-model and the microalgae sub-model include: a nitrogen and phosphorus nutrient direct conversion module, a nitrogen and phosphorus nutrient biomigration and conversion module, and an algal biogenesis and decomposition module; the construction process of the nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model specifically includes: Based on the cross-section of the study area where the dimensionality reduction data of Ulva prolifera coverage area is located, the study area where the dimensionality reduction data of Ulva prolifera coverage area is located is divided into three partitions, and each partition corresponds to a box model. The cross-regional diversion coefficient is determined based on the drift speed of the green tide of *Ulva prolifera* across the zoning boundaries; By connecting the three box models in series using the cross-regional diversion coefficient between adjacent partition boxes, a nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model is obtained.

4. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The preset grid cell size is the minimum daily increment of the coverage area in the three-dimensional spatiotemporal data of the Ulva prolifera green tide coverage area.

5. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The method of replacing the latitude and longitude of the three-dimensional spatiotemporal data of the Ulva prolifera green tide coverage area with the grid cell index, thereby reducing the three-dimensional spatiotemporal distribution matrix into two-dimensional or one-dimensional data, specifically includes: The grid cell number of the Ulva prolifera patch is used to replace the longitude and latitude in the 3D data of the Ulva prolifera patch, thus reducing the 3D data to 2D data. The formula is as follows: ； In the formula, CA U The area covered by the green tide of *Ulva prolifera* is represented by S, which represents the grid cell number, and t is the time when the *Ulva prolifera* block was detected by satellite. Applying nonlinear curve fitting and linear fitting to the two-dimensional data, the spatial location and temporal fit function of the *Ulva prolifera* patches are obtained, as shown in the formula: ； In the formula, S represents the grid cell number of the spatial location of the Ulva prolifera block, t is the time of appearance of the Ulva prolifera block, a is the maximum value, k is the growth rate, and c is the inflection point. If the spatial location S changes monotonically with time t, then the two-dimensional data can be reduced to one-dimensional data using the following formula: ； If the spatial location S does not change monotonically with time t, then dimensionality reduction of the two-dimensional data is not performed.

6. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The formula for converting the dimensionality reduction of Ulva prolifera coverage area into Ulva prolifera biomass is as follows: ； In the formula, B U (t) represents the biomass of *Ulva prolifera* at time t, ξ is the conversion factor between *Ulva prolifera* biomass and its coverage area, and CA U (t) represents the area of ​​vegetation coverage at time t.

7. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The numerical simulation formulas for the generation and degradation processes of *Ulva prolifera* biomass and microalgal biomass are as follows: ； ； In the formula, This refers to the chlorophyll content of microalgae. The chlorophyll content of *Ulva prolifera* and These are the maximum growth rate constants for microalgae and Ulva prolifera, respectively. and These are the maximum mortality rate constants for microalgae and Ulva prolifera, respectively. This is the light limitation coefficient during the growth and decay of microalgae. This is the temperature limiting coefficient in the microalgae growth and digestion process. The nitrogen and phosphorus nutrient limitation coefficients during the microalgal growth and digestion process. This represents the limiting factor in the respiration process of microalgae. This is the light limitation coefficient during the growth and decay of *Ulva prolifera*. This is the temperature limiting coefficient during the growth and decay of *Ulva prolifera*. This is the limiting factor during the respiration process of *Ulva prolifera*. The limiting coefficients for nitrogen and phosphorus nutrients during the growth and decay of Ulva prolifera.

8. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The environmental variables in the nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model include: the coverage area of ​​the initial Ulva prolifera patch, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient concentrations.

9. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The modulation effect value of each environmental factor is determined by the amplitude of the variation between the maximum simulated value of the annual Ulva prolifera coverage area over time and the maximum simulated value of the annual Ulva prolifera coverage area over time under different values ​​for each environmental factor variable. Specifically, this includes: The maximum value of the simulated annual change in Ulva prolifera coverage area over time and the maximum value of the simulated annual change in Ulva prolifera coverage area over time for each environmental factor variable under different values ​​were selected. Based on the maximum value of the simulated annual variation of Ulva prolifera coverage area over time and the amplitude of the variation of the maximum value of the simulated annual variation of Ulva prolifera coverage area over time for each environmental factor variable under different values, the modulating effect value of the control factor for Ulva prolifera green tide disaster is determined, using the following formula: ； In the formula, Re represents the modulation effect value of environmental factors, and S Re_max S represents the simulated value of the annual Ulva prolifera coverage area over time for each environmental variable at different values. Ac_max This represents the maximum value of the simulated annual change in the area covered by *Ulva prolifera* over time.

10. The method for simulating the competitive growth process of *Ulva prolifera* and microalgae as described in claim 1, characterized in that, The modulation effect value is proportional to the degree to which environmental factors control the competitive growth process of Ulva prolifera.

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