CN114201933A - Method for simulating resource distribution condition of fish in early stage - Google Patents
Method for simulating resource distribution condition of fish in early stage Download PDFInfo
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- CN114201933A CN114201933A CN202111509579.0A CN202111509579A CN114201933A CN 114201933 A CN114201933 A CN 114201933A CN 202111509579 A CN202111509579 A CN 202111509579A CN 114201933 A CN114201933 A CN 114201933A
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Abstract
The invention discloses a method for simulating resource distribution conditions in an early stage of fishes, which comprises the following steps: step 1, driving a CROCO three-dimensional hydrodynamic model; step 2, operating a CROCO three-dimensional hydrodynamic model, simulating a marine dynamic process in real time, and outputting a marine hydrological element data file; step 3, inputting marine hydrological element data files output by the CROCO three-dimensional hydrodynamic model into an Ichhyop biological model, setting a range of a recruitment area of a fish spawning site, and simulating biological parameters related to spawning of the fish; step 4, operating an Ichhyop biological model to obtain a space-time position simulation result of roe distribution; and 5, carrying out data processing on the simulation result to obtain a coordinate curve of the fish spawning and juvenile fish related biological parameters changing along with time so as to predict the fish resource distribution condition. According to the invention, through dynamic coupling of the physical ocean model and the biological model, the space-time distribution of resources at the early stages of different fishes is simulated, and the defects of complex prediction process, poor timeliness and the like in the original resource simulation process are improved.
Description
Technical Field
The invention relates to marine fish resource prediction, in particular to a method for simulating resource distribution conditions in an early stage of fish.
Background
The fish resource amount and the supplement amount are easily influenced by the fluctuation of environmental factors, and great difficulty is brought to the effective management of the fish resources and the determination of the space-time distribution. It has been shown that in the ecological research of fish resources, the survival rate of roes and young fishes and the number of the rest population tend to determine apology the amount of the fish supplementing population resources. The roe and larval stages are the most vulnerable stages in the fish life cycle, and subtle changes in marine environmental factors will have a profound effect on the growth, survival and population replenishment of roe and larval fish. The individual fertility of the fish in the middle and upper layers is huge, the growth speed is high, the resource potential of the spawning parent fish can be more effectively excited by protecting the spawning parent fish, and the protection of the spawning parent and the juvenile fish is favorable for the reasonable utilization of fishery resource populations. Therefore, the space-time position of the resource distribution in the early stage of the fish obtained by simulation has very important significance and practical application value for rational utilization and effective management of fish resources.
At present, the resource distribution situation of the early stage of the calculation and simulation of fishes has fewer research directions in the application of marine fishery in China, and the programming requirement and the use difficulty are higher.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method for simulating the resource distribution condition of fish in the early stage.
The technical scheme adopted by the invention is as follows: a simulation method for the distribution condition of resources in the early stage of fishes comprises the following steps:
step 1, driving a CROCO three-dimensional hydrodynamic model;
step 2, operating the CROCO three-dimensional hydrodynamic model, simulating an ocean dynamic process in the CROCO three-dimensional hydrodynamic model in real time, and outputting an ocean hydrological element data file;
step 3, inputting marine hydrological element data files output by the CROCO three-dimensional hydrodynamic model into an Ichhyop biological model, setting a fish spawning site recruitment area range in the Ichhyop biological model, and simulating fish spawning related biological parameters;
step 4, operating the Ichhyop biological model to obtain a space-time position simulation result of roe distribution;
and 5, carrying out data processing on the simulation result obtained in the step 4 to obtain a coordinate curve of the biological parameters related to fish spawning and young fish change along with time so as to predict the distribution condition of fish resources.
Further, in step 1, the driving the bioco three-dimensional hydrodynamic model includes:
firstly, acquiring initial temperature data, salinity data and flow velocity data of the CROCO three-dimensional hydrodynamic model, and performing interpolation processing on the acquired data; secondly, establishing an initial field, a boundary field, a wind field and a thermal field of the CROCO three-dimensional hydrodynamic model according to the interpolated data, and adding 8 main tides on the boundary field; finally, the three-dimensional hydrodynamic model of the CROCO is driven with the 8 main partial tides on the wind field, the thermal force field, and the boundary field.
Further, the 8 main partial tides are respectively: m2Moisture separation, S2Moisture separation, K2Moisture, N2Moisture separation, K1Moisture and O1Moisture, P1Moisture, Q1And (5) moisture separation.
Further, in step 3, the biological parameters related to fish spawning include the number of fish eggs released by fish spawning, the release time of each fish egg, the release depth of each fish egg, and the killing condition of the fish egg under the influence of temperature.
Further, in step 5, the coordinate curve of the biological parameters related to fish spawning and young fish larval change with time includes: the distribution density of the fish eggs and the young fishes changes along with time, the depth of the fish eggs and the young fishes changes along with time, and the survival rate of the fish eggs and the young fishes changes along with time.
Further, in step 5, the data processing is performed in Matlab.
The invention has the beneficial effects that: the data of the terrain file, the wind field and the flow field required by the operation of the CROCO three-dimensional hydrodynamic model can be directly and quickly acquired from a HYCOM + NCODA global 1/12-degree data website, and an initial environment dynamic field is provided for the operation of the physical ocean model. Meanwhile, the visual Ichchop biological model is utilized to dynamically simulate the space-time distribution condition of fish resources, different influences of substance, heat, momentum and energy exchange in the ocean and numerous physical processes on the distribution of the fish resources can be obtained by changing the physical process of the ocean numerical value mode, the marine biological resources are protected, the business marine fish resource forecast is realized, and the thinking and the method for continuously utilizing the fish resources are provided with positive guidance.
Drawings
FIG. 1: the invention discloses a schematic diagram of a simulation method of early stage resource distribution condition of fish.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings:
in order to obtain the space-time conditions of the early-stage resource distribution of different fishes, the invention couples the physical ocean model and the biological model to construct an effective and simple fish resource distribution simulation method, and the method predicts the fish resource distribution conditions through the dynamic real-time coupling simulation of the physical ocean model and the biological model. The physical ocean model is a CROCO three-dimensional hydrodynamic model, and the biological model is an Ichhyop biological model.
CROCO three-dimensional hydrodynamic model
The three-dimensional hydrodynamic model of the CROCO is based on ROMS _ AGRIF, and is based on the non-hydrostatic core of SNH, and is gradually added to a novel ocean modeling system of MARS3D algorithm, so that the three-dimensional hydrodynamic model of the CROCO can solve very fine scales (particularly in coastal areas) and the interaction between the three-dimensional hydrodynamic model and larger-scale marine physical phenomena. It is a marine component of a complex coupled system, including many components of the atmosphere, surface waves, marine sediments, biogeochemical and ecological systems, etc. Is widely applied to the simulation of tide, wind current, mixed layers and duplex layers, hot salt circulation, ocean circulation and transportation by scholars at home and abroad.
The method for establishing the CROCO three-dimensional hydrodynamic model comprises the following steps:
in order to better fit the sea floor topography in shallow sea areas, a sigma coordinate system is adopted in the vertical direction, and the coordinate transformation is as follows:
wherein, σ is a vertical coordinate in a σ coordinate system, z is a vertical coordinate in a cartesian coordinate system, H is a bottom depth (relative to z being 0), ξ is a free water surface height, D is a total water depth, and the variation of the value of σ is from-1 of the sea bottom to 0 of the sea surface.
In the sigma coordinate system, the control equation set of the crosco three-dimensional hydrodynamic model includes a continuous equation (formula 2), a kinetic energy equation (formula 3 to formula 4), a temperature equation (formula 5), a salinity equation (formula 6), a turbulent kinetic energy transport equation (formula 7 and formula 8), and a state equation (formula 9):
ρ＝ρ(θ,s) (9)
wherein x is an east coordinate in a Cartesian coordinate system, and y is a north coordinate in the Cartesian coordinate system; u is a velocity component in the x coordinate direction, v is a velocity component in the y coordinate direction, and omega is a downward velocity in the sigma coordinate system; t is time; theta is the temperature; s is salinity; p is pressure; rho is the total density, equal to the perturbation density rho' and the reference density rho0Summing; g is the acceleration of gravity; f is a Coriolis force parameter; kmIs a vertical swirl viscosity coefficient, KhIs a thermal vertical swirl viscosity coefficient, KmAnd KhCan be calculated by a Mellon-Yamada2.5 stage turbulence closed submodel; fxIs the horizontal momentum in the x coordinate direction, FyIs the horizontal momentum in the direction of the y coordinate, FθAs a temperature diffusion term, FSAs salinity diffusion term, Fx、Fy、Fθ、FSMay be calculated by the Smagorinsky parameterization method; q. q.s2Is the turbulent kinetic energy; psConditions for generating shear forces of turbulent kinetic energy, PbBuoyancy generating conditions for turbulent kinetic energy; epsilon is the turbulent kinetic energy dissipation rate; kqIs the vertical vortex diffusion coefficient; fqHorizontal diffusion terms for turbulent kinetic energy, FlA horizontal diffusion term that is the turbulent mixing length; l is the turbulent mixing length; e1Is a constant term;
is a face wall approximation function.
② Ichthyop biological model
Ichthyop is a free Java tool aimed at studying the impact of physical and biological factors on fish plankton dynamics. It comprises the most important processes in early life of fish: oviposition, exercise, growth, death and replenishment. The tool uses archived flow rate, temperature and salinity fields of the ocean model as input time series. The tool provides two modes of operation: one is to provide a user-friendly visualization GUI for setting up and running simulated and visualized virtual egg and larva transportation. The second is batch mode, which gives full computing power. A NetCDF output file can be generated that stores simulated dynamic information (time, longitude, latitude, depth, length, etc.) about the individual.
As shown in fig. 1, a method for simulating the resource distribution of fish in early stage comprises the following steps:
step 1, firstly, acquiring initial temperature data, salinity data and flow velocity data of a CROCO three-dimensional hydrodynamic model from a HYCOM + NCODA global 1/12-degree data website, and performing interpolation processing on the acquired data; secondly, establishing an initial field, a boundary field, a wind field and a thermal field of the CROCO three-dimensional hydrodynamic model according to the interpolated data, and adding 8 main tides on the boundary field, wherein the 8 main tides are respectively: m2Moisture separation, S2Moisture separation, K2Moisture, N2Moisture separation, K1Moisture and O1Moisture, P1Moisture, Q1Moisture separating; and finally, driving the CROCO three-dimensional hydrodynamic model by using 8 main tides on the wind field, the thermal force field and the boundary field.
And 2, operating the CROCO three-dimensional hydrodynamic model, simulating an ocean dynamic process in the CROCO three-dimensional hydrodynamic model in real time, and stopping operation after outputting an ocean hydrological element data (the ocean hydrological element data comprise ocean temperature, salinity, flow velocity field and the like) file.
And 3, inputting the marine hydrological element data file output by the CROCO three-dimensional hydrodynamic model into the Ichhyop biological model, setting a fish spawning site recruitment area range in the Ichhyop biological model, and simulating fish spawning related biological parameters such as the number of fish spawns released by fish spawning, the release time of each fish spawn, the release depth of each fish spawn, the fatality rate of the fish spawns and young fish, and the like.
And 4, operating the Ichhyop biological model to obtain a space-time position simulation result of the distribution of the roes and the young fishes.
And 5, carrying out data processing presentation on the simulation result obtained in the step 4 by adopting Matlab to obtain a coordinate curve of the variation of fish spawning and young fish related biological parameters with time, such as the distribution density of fish eggs and young fish, the depths of the fish eggs and young fish, the survival rates of the fish eggs and young fish and the like, analyzing and summarizing the information obtained by simulation, and providing a powerful basis for the follow-up work of predicting the space-time situation of fish resource distribution, designing a sustainable utilization fish resource scheme and the like.
Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (6)
1. A method for simulating the resource distribution condition of fish in the early stage is characterized by comprising the following steps:
step 1, driving a CROCO three-dimensional hydrodynamic model;
step 2, operating the CROCO three-dimensional hydrodynamic model, simulating an ocean dynamic process in the CROCO three-dimensional hydrodynamic model in real time, and outputting an ocean hydrological element data file;
step 3, inputting marine hydrological element data files output by the CROCO three-dimensional hydrodynamic model into an Ichhyop biological model, setting a fish spawning site recruitment area range in the Ichhyop biological model, and simulating fish spawning related biological parameters;
step 4, operating the Ichhyop biological model to obtain a space-time position simulation result of roe distribution;
and 5, carrying out data processing on the simulation result obtained in the step 4 to obtain a coordinate curve of the biological parameters related to fish spawning and young fish change along with time so as to predict the distribution condition of fish resources.
2. The method of claim 1, wherein the driving the CROCO three-dimensional hydrodynamic model in step 1 comprises:
firstly, acquiring initial temperature data, salinity data and flow velocity data of the CROCO three-dimensional hydrodynamic model, and performing interpolation processing on the acquired data; secondly, establishing an initial field, a boundary field, a wind field and a thermal field of the CROCO three-dimensional hydrodynamic model according to the interpolated data, and adding 8 main tides on the boundary field; finally, the three-dimensional hydrodynamic model of the CROCO is driven with the 8 main partial tides on the wind field, the thermal force field, and the boundary field.
3. The method of claim 2, wherein the 8 main partial tides are respectively: m2Moisture separation, S2Moisture separation, K2Moisture, N2Moisture separation, K1Moisture and O1Moisture, P1Moisture, Q1And (5) moisture separation.
4. The method according to claim 1, wherein the biological parameters related to fish spawning comprise the number of fish eggs released by fish spawning, the release time of each fish egg, the release depth of each fish egg, and the killing conditions of the fish eggs under the influence of temperature in step 3.
5. The method according to claim 1, wherein the time-varying coordinate curve of the biological parameters related to fish spawning and larval fish in step 5 comprises: the distribution density of the fish eggs and the young fishes changes along with time, the depth of the fish eggs and the young fishes changes along with time, and the survival rate of the fish eggs and the young fishes changes along with time.
6. The method for simulating the distribution of resources in the early stage of fish in claim 1, wherein in step 5, the data processing is performed in Matlab.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117610303A (en) * | 2023-12-11 | 2024-02-27 | 中国人民解放军61540部队 | Fine simulation method and device for meteorological marine environment |
| CN117610303B (en) * | 2023-12-11 | 2024-05-10 | 中国人民解放军61540部队 | Fine simulation method and device for meteorological marine environment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117610303A (en) * | 2023-12-11 | 2024-02-27 | 中国人民解放军61540部队 | Fine simulation method and device for meteorological marine environment |
| CN117610303B (en) * | 2023-12-11 | 2024-05-10 | 中国人民解放军61540部队 | Fine simulation method and device for meteorological marine environment |
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