AU2020103130A4 - Habitat Identification Method Based on Fish Individual Dynamic Simulation Technology - Google Patents
Habitat Identification Method Based on Fish Individual Dynamic Simulation Technology Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 41
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Abstract
The invention discloses a fish habitat diagnosis method based on fish individual dynamic
simulation technology, which includes the following steps: (1) observation and experiment of
fish individual behavior; (2) simulation of fish individual behavior; (3) habitat diagnosis
analysis method of target fish stocks. Based on the field parallel observation and experimental
data of target fish stocks, the correlation between fish behavior and environmental factors was
obtained, and it was transformed into a mathematical function to realize the simulation of fish
behavior based on individual movement trajectory of target fish stocks, and the habitat ecology
and environment of target fish stocks were diagnosed according to the comparison of individual
trajectory tracking and simulation results of target fish stocks. With the help of a small-volume
and high-precision underwater acoustic tracking system the invention uses dynamic
mathematical model and physical entity model for auxiliary and verification, and uses fish
behavior as the judgment basis for fish habitat diagnosis, thus making the habitat diagnosis
method more rigorous, reasonable and has more ecological significance.
Description
Habitat Identification Method Based on Fish Individual Dynamic Simulation
Technology
[01] The invention relates to a distinguishing method of fish habitat, in particular to a fish habitat identification method based on dynamic simulation of fish individuals.
[02] The construction of water conservancy project may change the river's hydrological situation, hydrodynamic force and natural state of water environment, and make the spatial distribution of river aquatic organisms habitat change to different extent. River aquatic organisms habitat can provide the complete habitat space and the environmental conditions needed for the complete ecological and life-history process for fish in the basin, and lay a solid foundation for maintaining the integrity of river ecological processes in the region, maintaining a certain amount of aquatic biological population resources, and maintaining the species and genetic diversity of aquatic organisms. In aquatic biological system, the relationship between fish and human is the most close, and fish is also the top living creature of river ecosystem. The change of fish stocks is closely related to river ecosystem. Many studies show that the change degree of fish diversity can reflect the health status of river system.
[03] In China, the protection of aquatic organisms and their habitats is mainly in the form of nature reserve construction or fish germplasm resource reserve construction, as well as corresponding protection and management schemes. In recent years, with the need of social and economic development, a large number of water conservancy and hydropower projects have been built, which affect fish habitat and make many wild fish in a state of stress. In view of this situation, people also protect fish habitat through various methods, and the establishment of fish nature reserve is the best measure to protect the fish habitat. Therefore, it is necessary to predict the change of fish habitat before and after the construction of water conservancy project reasonably and scientifically.
[04] Because of the high complexity and non-linearity of the ecosystem, the research on its evolution mechanism is often semi-empirical, while the ecological monitoring data are generally sparse. Therefore, the traditional ecological models are mainly based on semi-empirical aggregates, which usually ignore the behavior of individuals and their adaptation to the environment. Therefore, we should strengthen the investment in the key monitoring and research of fish behavior, systematically carry out long-term monitoring work, and use advanced acoustic marker positioning technology to track the fish migration trajectory, and real-time monitor the changes of river ecological factors and fish physiological and ecological behavior. However, with the help of small volume and high precision underwater acoustic tracking system, the water dynamic mathematical model and physical entity model are used for auxiliary and verification, and the results of fish habitat research based on fish behavior have not been reported. In view of the above problems, on the basis of the traditional ecological model to diagnose the habitat quality of target fish stocks, the present invention introduces the method of simulating and verifying the habitat distribution range of fish by the combination of fish individual behavior simulation and fish individual marker, and provides a more effective and scientific habitat diagnosis method.
[05] The purpose of the invention is to make up for the lack offish physiology and behavior in the existing habitat identification methods, thus providing a habitat identification method based on the dynamic simulation technology of fish individuals, which can scientifically evaluate the habitat changes quantitatively in combination with the elements of fish life history, and provide a basis for the protection and restoration of fish habitat, and improve the rationality of habitat identification.
[06] In order to achieve the above purpose, the invention adopts the following technical scheme:
[07] One kind of fish habitat diagnosis method based on fish individual dynamic simulation technology, including the following steps:
[08] (1) Observation and experiment of fish individual behavior;
[09] (2) Simulation of fish individual behavior;
[010] (3) Habitat diagnosis and analysis of target fish stocks.
[011] As a further scheme of the invention, the step (1) observation and experiment of fish individual behavior includes the following steps:
[012] (1-1) Determine the target fish stocks: First, investigate the status quo of fish resources in the study area, then determine the target fish stocks in the study area based on the historical data and visiting investigation, and obtain the variation law of the population and quantity of the target fish stocks;
[013] (1-2) Trajectory tracking: Based on the fish acoustic telemetry tracer system, a monitoring network is set up to study the three-dimensional motion trajectory of the target fish in different flow fields, pressure fields (water depth) and temperature fields through the monomer calibration (sensor implantation) and tracking during releasing, and establish the relationship between the occurrence frequency, velocity, residence time of the target fish and water temperature, water velocity, water pressure, longitude and latitude, circadian rhythm environment factors.
[014] (1-3) Relation establishment: Based on the outstanding ability of generalized additive model in solving the highly nonlinear and non-monotonic relationship between response variables and prediction factors, the response model of target fish stocks and water environment factors is established, and the relationship between data response variables and prediction factors is analyzed.
[015] As a further scheme of the invention, the step (2) simulation of fish individual behavior includes the following steps:
[016] (2-1) Model construction: Using shallow water equation to construct two dimensional hydrodynamic model of the river reach studied, simulating water depth, vertical average velocity and flow field velocity. Based on the two-dimensional water hydrodynamic model, the convection-diffusion equation is added in two-dimensional water environment model to consider the heat exchange process and simulate the change of key environmental factors, and the result of the model are used as input. According to the response relationship of fish to water environment factors established in step (1-3), the Eulerian-Lagrangian method is used to establish the simulation model of the target fish, and the real-time tracking trajectory of the target fish is used to provide the velocity at any time for the model to determine the number of the model parameters, calculate the position of the target fish in the next moment, simulate the individual behavior and growth state of fish, and propose an improved simulation model of fish individual behavior. On this basis, the particle swarm is established and the different properties of each particle are given, so that all the individuals can move through the above rules, the spatial distribution of the fish in the whole river section can be obtained with the dynamic change of the water environment condition, and the precise simulation of the growth, survival and reproduction behavior of the fish stocks in the study area can be realized, and the relationship between the real fish individual behavior and the stocks distribution can be reproduced accurately. The governing equations of the model mainly include:
[017] Water flow continuity equation:
S+ +Ow S
[018] ax ay Oz (1);
[019] Flow momentum equation (xdirection):
au au2 avu awu aq I OP, _ ga17OPd _+ + + =fv-g 1 dz at ax ay az ax p0 ax P0 ax 1 (as "?+ asJ, ''-F±+a (au vI+uS
[020] Poh ax ay " z ' z (2);
[021] Flow momentum equation (ydirection):
av _+ a2 + auv + awv =-fu-g a 7 A IOa 1 gaapa J dz at ay ax az ay p 0 ay P0 a
I asV as av
[022] Poh ax ay uz az (3);
[023] Temperature convection diffusion equation:
[024] or 2 Our ± Ov+ ± Ow r + a nDw+H =F,± O +s 0 (4); +
at Ox DyV Oz az ( Oz )
Dh Fr z= a a Dh +T_
[025] aTa aj (5);
[026] Water quality control equation:
ac ac ac a2C a 2C - +u +v- D +D
[027] at ax ay Y (6);
[028] Water quality degradation equation:
-9C KC
[029] 9t (7);
[030] In the above formula, t is time; x, y, and z are Cartesian coordinates; iis the
height of the water surface; d is the still water depth; h = 7 + d is the total water depth;
u, v and w are velocity components in the directions of x, y and z, respectively; f = 2Q sin# is the Coriolis parameter (Q is the angular velocity of rotation, # is the
latitude); g is the acceleration of gravity; p is the density of water; Sxx, Sxy, Syx and Syy are components of radiation stress tensor; vt is the vertical eddy viscosity coefficient; p, is the atmospheric pressure; po is the reference density of water; S is the flow rate
of the point source, and (us, vs) is the flow rate of the source and sink items; (F,, F,) is a horizontal stress term, which is described by pressure gradient correlation and T is water temperature.(Dh, D,) are the temperature diffusion coefficients in the horizontal
direction and the vertical direction respectively; H is the source term from atmospheric heat exchange; So is the other temperature source term; C: concentration in mg/l; D, Dy: is diffusion coefficients in x and y directions, K: attenuation coefficients, in units of s-1;
[031] (2-2) Model applicability: The model constructed by step (2-1) is applicable to most of the fish stocks in the river currently studied, and it is not limited to the target fish stocks. The reason is that the building module of the fish individual dynamic model is based on the combination of the basic model and the fish individual model. The basic model is the simulation construction of terrain, hydrology, water environment and hydraulic field, and the fish individual model is based on the Eulerian-Lagrangian framework. In order to obtain the growth, foraging and breeding behavior of the target fish stocks with different fish attributes, the applicability of the dynamic model of the individual fish stocks is mainly embodied in the transformation of the living habits of the target fish stocks into a mathematical function, and the rule is summarized. The growth function of the fish is derived by mathematical statistics, and the process of the fish movement is induced by fluid hydrodynamics and river dynamics.
[032] The growth of fish includes body weight growth and body length growth. Since the growth of fish exists in non-uniform linear in the life cycle, in order to describe the growth process of fish, mathematical equations can be used to describe the growth characteristics of fish, and von Bertalanffy growth equation based on metabolism theory is adopted.
It = L.[1 - e-k(t- t o )]
[033] (8);
Wt = W.[1 - e-k(t- to)]3
[034] (9);
[035] Where t is the age, the unit is day (d), It and Wt are the average body length
(cm) and body weight (kg) at time t, I is the average progressive body length (cm) and
W 1 is the average progressive body weight (kg), k is the growth coefficient (1/d), and to is the hypothetical theoretical growth starting age;
[036] The motion process in the fish individual model is actually the embodiment of Lagrangian algorithm in the basic model. It has accurate position (x, y, z), and is independent of the whole model network structure. The direction of motion, velocity and state variables (volume, mass) can be obtained by assignment. At the same time, the rules such as birth and death can be defined, and the feedback information between Euler and Langrange can be obtained.
[037] As a further scheme of the invention, the step (3) diagnosis and analysis of the habitat of the target fish stocks includes: reproducing the habitat distribution of the target fish stocks according to the simulation model of the fish individual behavior, and studying the data distribution characteristics from the data sample itself with the acoustic telemetry trajectory within the range of the predetermined "habitat", combining the kernel density estimation function to divide the high density and low density area, and then dividing the high density area into small area. The chi-square test theory is used to test whether the data change with the regional characteristics, so as to judge the specific location of the real habitat; at the same time, based on the psychological Q-matrix theory, an improvement is made and a diagnosis scheme is put forward to evaluate the small-scale behavior of the target fish stocks. Based on the on the-spot fishing of "habitat" and the behavior trajectory of target fish, the relationship between the probability of individual occurrence and the sex, body size, date, season and environment of the target fish is quantitatively analyzed, and a habitat diagnostic classification model is constructed to determine the physical attributes of the "habitat" of the native fish, including the spawning ground, wintering ground and feeding ground.
[038] The invention has the following advantages: the invention focuses on the behavior characteristics of various high-level biological populations including fish in the river ecosystem, adopts a variety of methods, such as physiological ecology, fluid hydrodynamics, river dynamics, fuzzy mathematics, computational fluid dynamics, etc. to reasonably and scientifically determine the suitable habitat of the target fish stocks, and fills the blank of the technology of fish habitat protection and recognition, improves the research system and has great significance for river ecosystem protection.
[039] The fish individual dynamic model of the invention is universal to river fish, and different fish movement trajectories will be obtained by changing the growth and movement functions of the particle fish in the model, which is of great significance to perfect the research of river ecological flow and habitat.
[040] With the help of a small-volume and high-precision underwater acoustic tracking system the invention uses dynamic mathematical model and physical entity model for auxiliary and verification, and uses fish behaviour as the judgment basis for fish habitat diagnosis, thus making the habitat diagnosis method more rigorous, reasonable and has more ecological significance.
[041] Figure 1 is a flowchart of the invention;
[042] Figure 2 shows a grid division diagram for the implementation embodiment study area model.
[043] Figure 3 is a simulation graph of individual fish trajectories (in which a is a graph of the fish aggregation effect; b is a graph of shoreline aggregation and sidewall migration trajectory of fish; c is a consistency graph of fish swimming tendency);
[044] Figure 4 is a distribution map of suitable spawning grounds by combining fish tracer map and simulated habitat map.
[045] The invention is further described in detail in combination with the attached drawing and the specific embodiment.
[046] As shown in the figure, a habitat diagnosis method based on dynamic simulation of individual fish in this embodiment includes the following steps:
[047] 1. Fish individual behavior observation and experimental study methods;
[048] (1-1) Determine the target fish stocks: First, investigate the status quo of the fish resources in the study area, then determine the target fish stocks in the study area, and obtain the variation law of the population and quantity of the target fish stocks;
[049] On the basis of on-the-spot investigation, visit to local residents and literature, the amount of fish resources, composition structure, distribution location and living habits in the study area were statistically summarized. Finally, the target stocks in the study area were Gymnocypris eckloni eckloni Herzenstein, which began to lay eggs in May each year. The spawning ground was located at the gravel bottom of the main stream of the Yellow River. Generally, it was located in the section where the water is clear and the water flow is relatively fast. They're demersal eggs and mature in the pit; both flowing water and still water can live, but most of the time they live in flowing water, usually scattered or concentrated in small groups in habitat, and during the breeding season they gather in large groups to the larger tributaries leading to the main stream, reservoir or lake. After hatching, the young fish are fed in the shallow water along with the flowing water into the main stream bay fork or lake and the banks of the reservoir; the main food is aquatic multi-vertebrates and they also eat small-scale loaches.
[050] (1-2) Trajectory tracking: Based on the fish acoustic telemetry tracer system, a monitoring network is set up to study the three-dimensional trajectory of the target fish in different flow field, pressure field (water depth) and temperature field; establish the relationship between the frequency, velocity and residence time of the target fish and water temperature, water velocity, water pressure, longitude, latitude and circadian rhythm.
[051] (1-3) Relationship establishment: Based on the outstanding ability of generalized additive model in solving the highly nonlinear and non-monotonic relationship between response variables and prediction factors, the response model of target fish stocks and water environment factors is established, and the relationship between data response variables and prediction factors is analyzed.
[052] By using the generalized additive model (GAM) coupling function, the functional relationship between the mathematical expectation value of the response variable and the prediction variable is established. Taking the presence or absence of the labeled sample as the observed variable, the response variable follows a binomial distribution with a value of 1 and no value of 0. The self-velocity and trajectory distribution density of the labeled samples are the dependent variables of the model, and the independent variables of the model include three sets of data: three-dimensional spatial variables, time variables and environment variables.
[053] 2. Research methods of fish individual behavior simulation;
[054] (2-1) Model construction: Using shallow water equation to build two dimensional hydrodynamic model of the river reach studied, simulating water depth, vertical average velocity and flow field velocity. Based on the two-dimensional water hydrodynamic model, the convection-diffusion equation is added in two-dimensional water environment model to consider the heat exchange process and simulate the change of key environmental factors, and the result of the model are used as input, using the established fish-water response relationship, calculate the position of the target fish in the next moment, i. e. the velocity u of the target fish at the time of t is synchronized into the Eulerian-Lagrangian formula by using the time, longitude and latitude, and the parameters are calculated and calibrated to obtain the position at the next moment:
[055] According to the response relationship of fish to water environment factors established in step (1-3), the Eulerian-Lagrangian method is used to establish the simulation model of the target fish, and the real-time tracking trajectory of the target fish is used to provide the velocity at any time for the model to determine the number of the model parameters, calculate the position of the target fish in the next moment, simulate the individual behavior and growth state of fish, and propose an improved simulation model of fish individual behavior. On this basis, the particle swarm is established and the different properties of each particle are given, so that all the individuals can move through the above rules, the spatial distribution of the fish in the whole river section can be obtained with the dynamic change of the water environment condition, and the precise simulation of the growth, survival and reproduction behavior of the fish stocks in the study area can be realized, and the relationship between the real fish individual behavior and the stocks distribution can be reproduced accurately. The governing equations of the model mainly include:
[056] Water flow continuity equation:
S+ +Ow S
[057] Ox 0y Oz (1);
[058] Flow momentum equation (x direction):
au + au-+2 - avu -+ awu -= fv-g aq 1 ap g~aJ--dz - fZ7O
at ax ay az ax p0 ax p0 ax
H-+F±+ a (au 1 (as "?+asJ, vI- uS
[059] Poh ax ay " z ' z (2):
[060] Flow momentum equation (ydirection):
[061] or + ouT + + = Fr + D + H + T, (4); at ax ay Oz az 'az
aaK aT FT = Dh + Dh
[062] ax ax ay ay (5);
[063] Water quality control equation:
ac ac ac a 2C a 2C +u-+v =D - +D
[064] at ax ay ay (6);
[065] Water quality degradation equation:
-9C KC
[066] 9t (7);
[067] In the above formula, t is time; x, y, and z are Cartesian coordinates; q is the
height of the water surface; d is the still water depth; h = q + d is the total water depth;
u, v and w are velocity components in the directions of x, y and z, respectively; f = 2Q sin# is the Coriolis parameter (Q is the angular velocity of rotation, # is the
latitude); g is the acceleration of gravity; p is the density of water; Sxx, Sxy, Syx and Syy are components of radiation stress tensor; vt is the vertical eddy viscosity coefficient; p, is the atmospheric pressure; po is the reference density of water; S is the flow rate
of the point source, and (us, vs) is the flow rate of the source and sink items; (F,, F,) is a horizontal stress term, which is described by pressure gradient correlation and T is water temperature.(Dh, D,) are the temperature diffusion coefficients in the horizontal
direction and the vertical direction respectively; H is the source term from atmospheric heat exchange; So is the other temperature source term; C: concentration in mg/l; D, Dy: is diffusion coefficients in x and y directions, K: attenuation coefficients, in units of s-1;
[068] (2-2) Model applicability: The model constructed by step (2-1) is applicable to most of the fish stocks in the river currently studied, and is not limited to the target fish stocks. The reason is that the building module of the fish individual dynamic model is based on the combination of the basic model and the fish individual model. The basic model is the simulation construction of terrain, hydrology, water environment and hydraulic field. The fish individual model is based on Eulerian-Lagrangian framework, which gives different fish attributes to the particle swarm to obtain the behavior of growth, foraging and reproduction of the target stocks. The applicability of the individual dynamic model of fish is mainly embodied in the transformation of the life habits of the target stocks into mathematical functions, summarizing the rules, the growth function of fish derived from mathematical statistics, and the process of fish movement induced by fluid hydrodynamics and river dynamics.
[069] The growth of fish includes body weight growth and body length growth. Since the growth of fish exists in non-uniform linear in the life cycle, in order to describe the growth process of fish, mathematical equations can be used to describe the growth characteristics of fish, and von Bertalanffy growth equation based on metabolism theory is adopted.
It = L [1 - e-k(t- to)]
[070] (8);
Wt = W.[1 - e k(t- to) 3
[071] (9);
[072] Where t is the age, the unit is day (d),it and Wt are the average body length
(cm) and body weight (kg) at time t, I is the average progressive body length (cm) and
W 1 is the average progressive body weight (kg), k is the growth coefficient (1/d), and to is the hypothetical theoretical growth starting age;
[073] The motion process in the fish individual model is the embodiment of Lagrangian algorithm in the basic model. It has accurate position (x, y, z), and is independent of the whole model network structure. The direction, velocity and state variables (volume, mass, etc.) can be obtained by assignment. At the same time, the rules such as birth, death, and the feedback information between Euler and Langrange can be defined.
[074] 3. Analysis methods for habitat diagnosis of target stocks:
[075] Reproducing the habitat distribution of the target fish stocks according to the simulation model of the fish individual behavior, and studying the data distribution characteristics from the data sample itself with the acoustic telemetry trajectory within the range of the predetermined "habitat", combining the kernel density estimation function to divide the high density and low density area, and then dividing the high density area into small area. The chi-square test theory is used to test whether the data change with the regional characteristics, so as to judge the specific location of the real habitat. On the basis of Q-matrix theory, an improved "habitat attribute" diagnosis scheme is proposed. The method of evaluating the small-scale behavior of the target stocks: Q matrix is Kxm matrix, K means the attributes of the target stocks, which is defined here as three behaviors of bait, breeding and overwintering; m means the items of the test, and the distribution of 16 tracks of the target stocks is defined here. Based on the on-the-spot fishing and small-scale behavior assessment of target fish, the relationship between the probability of individual occurrence and the sex, body size, date, season and environment of target fish is quantitatively analyzed. A classification model of habitat diagnosis is constructed to determine the physical attributes of the "habitat" of native fish (spawning ground, wintering ground and feeding ground).
[076] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms, in keeping with the broad principles and the spirit of the invention described herein.
[077] The present invention and the described embodiments specifically include the best method known to the applicant of performing the invention. The present invention and the described preferred embodiments specifically include at least one feature that is industrially applicable
Claims (4)
1. A fish habitat diagnosis method based on fish individual dynamic simulation technology is characterized in that following steps:
(1) Observation and experiment of fish individual behavior;
(2) Simulation of fish individual behavior;
(3) Habitat diagnosis and analysis of target fish stocks.
2. The method of fish habitat diagnosis based on fish individual dynamic simulation technology, as described in claim 1, is characterized by the following steps: (1) The observation and experiment of fish individual behavior includes the following steps:
(1-1) Determine the target fish stocks: First, investigate the status quo of fish resources in the study area, then determine the target fish stocks in the study area based on the historical data and visiting investigation, and obtain the variation law of the population and quantity of the target fish stocks;
(1-2) Trajectory tracking: Based on the fish acoustic telemetry tracer system, a monitoring network is set up to study the three-dimensional motion trajectory of the target fish in different flow fields, pressure fields (water depth) and temperature fields through the monomer calibration (sensor implantation) and tracking during releasing, and establish the relationship between the occurrence frequency, velocity, residence time of the target fish and water temperature, water velocity, water pressure, longitude and latitude, circadian rhythm environment factors.
(1-3) Relation establishment: Based on the outstanding ability of generalized additive model in solving the highly nonlinear and non-monotonic relationship between response variables and prediction factors, the response model of target fish stocks and water environment factors is established, and the relationship between data response variables and prediction factors is analyzed.
3. The fish habitat diagnosis method based on fish individual dynamic simulation technology, as described in claim 2, is characterized by the following steps: (2) The fish individual behavior simulation includes the following steps:
(2-1) Model construction: Using shallow water equation to construct two dimensional hydrodynamic model of the river reach studied, simulating water depth, vertical average velocity and flow field velocity. Based on the two-dimensional water hydrodynamic model, the convection-diffusion equation is added in two-dimensional water environment model to consider the heat exchange process and simulate the change of key environmental factors, and the result of the model are used as input. According to the response relationship of fish to water environment factors established in step (1-3), the Eulerian-Lagrangian method is used to establish the simulation model of the target fish, and the real-time tracking trajectory of the target fish is used to provide the velocity at any time for the model to determine the number of the model parameters, calculate the position of the target fish in the next moment, simulate the individual behavior and growth state of fish, and propose an improved simulation model of fish individual behavior. On this basis, the particle swarm is established and the different properties of each particle are given, so that all the individuals can move through the above rules, the spatial distribution of the fish in the whole river section can be obtained with the dynamic change of the water environment condition, and the precise simulation of the growth, survival and reproduction behavior of the fish stocks in the study area can be realized, and the relationship between the real fish individual behavior and the stocks distribution can be reproduced accurately. The governing equations of the model mainly include:
Water flow continuity equation:
au 0V __ + Ow =s ax ay az (1);
Flow momentum equation (x direction):
Bu -+-Bu2 +-aVu +---=fv-g- Owu 8q 1 1 a& g fZ7O@ -dz at ax ay az ax po ax P0 ax 1 as.+ asX+, a , au +uS __+ +±F±+ v Poh ax y z(' az (2);
Flow momentum equation (ydirection):
Dv Dy2 Duv Dwv aDq 1 DPa g fI17 a + + + =-fu-g D 1 Pdz at ay ax az ay po ay po ay 1 sY asY a av p+h+Dx+ " +F + - v +vS poh Ox ay z ' z(3);
Temperature convection diffusion equation:
DT7 DufT DvT DwT aD a DT~ + + =F,+ ,+ D + H+TS (4); at ax Dy az az V az)
FT= -QDh D-+- DhD T ax ax ay
( ay ()
Water quality control equation:
aC - +u aC -+v aC -= D 8 2C +D 0 2C at ax ay X ax2 Y aY 2 (6);
Water quality degradation equation:
C= -KC at (7);
In the above formula, t is time; x, y, and z are Cartesian coordinates;iis the
height of the water surface; d is the still water depth; h = q + d is the total water depth; u, v and w are velocity components in the directions of x, y and z, respectively; f = 2Q sin# is the Coriolis parameter (Q is the angular velocity of rotation, # is the latitude); g is the acceleration of gravity; p is the density of water; Sxx, Sxy, Syx and Syy are components of radiation stress tensor; vt is the vertical eddy viscosity coefficient; pa is the atmospheric pressure; po is the reference density of water; S is the flow rate of the point source, and (us, vs) is the flow rate of the source and sink items; (Fu, Fv) is a horizontal stress term, which is described by pressure gradient correlation and T is water temperature.(Dh, D) are the temperature diffusion coefficients in the horizontal direction and the vertical direction respectively; H is the source term from atmospheric heat exchange; So is the other temperature source term; C: concentration in mg/l; D,
Dy: is diffusion coefficients in x and y directions, K: attenuation coefficients, in units of s-1;
(2-2) Model applicability: The model constructed by step (2-1) is applicable to most of the fish stocks in the river currently studied, and it is not limited to the target fish stocks. The reason is that the building module of the fish individual dynamic model is based on the combination of the basic model and the fish individual model. The basic model is the simulation construction of terrain, hydrology, water environment and hydraulic field, and the fish individual model is based on the Eulerian-Lagrangian framework. In order to obtain the growth, foraging and breeding behavior of the target fish stocks with different fish attributes, the applicability of the dynamic model of the individual fish stocks is mainly embodied in the transformation of the living habits of the target fish stocks into a mathematical function, and the rule is summarized. The growth function of the fish is derived by mathematical statistics, and the process of the fish movement is induced by fluid hydrodynamics and river dynamics.
The growth of fish includes body weight growth and body length growth. Since the growth of fish exists in non-uniform linear in the life cycle, in order to describe the growth process of fish, mathematical equations can be used to describe the growth characteristics of fish, and von Bertalanffy growth equation based on metabolism theory is adopted.
It = LO.[1 - e--ktt- to)] (8):
Wt = W.[1 - e-k(t- to)1 3 (9);
Where t is the age, the unit is day (d), l and Wt are the average body length
(cm) and body weight (kg) at time t, 11 is the average progressive body length (cm) and
W 1 is the average progressive body weight (kg), k is the growth coefficient (1/d), and to is the hypothetical theoretical growth starting age;
The motion process in the fish individual model is actually the embodiment of
Lagrangian algorithm in the basic model. It has accurate position (x, y, z), and is independent of the whole model network structure. The direction of motion, velocity and state variables (volume, mass) can be obtained by assignment. At the same time, the rules such as birth and death can be defined, and the feedback information between Eulerian and Langrange can be obtained.
4. The method of fish habitat diagnosis based on the technique of fish individual dynamic simulation technology, as described in claim 2, is characterized by the following steps: (3) The diagnostic analysis of the habitat of the target fish stocks includes: reproducing the habitat distribution of the target fish stocks according to the simulation model of fish individual behavior, and studying the data distribution characteristics from the data sample itself with the acoustic telemetry trajectory within the range of the predetermined "habitat", combining the kernel density estimation function to divide the high density and low density area, and then dividing the high density area into small area. The chi-square test theory is used to test whether the data change with the regional characteristics, so as to judge the specific location of the real habitat; at the same time, based on the psychological Q-matrix theory, an improvement is made and a diagnosis scheme is put forward to evaluate the small-scale behavior of the target fish stocks. Based on the on-the-spot fishing of "habitat" and the behavior trajectory of target fish, the relationship between the probability of individual occurrence and the sex, body size, date, season and environment of the target fish is quantitatively analyzed, and a habitat diagnostic classification model is constructed to determine the physical attributes of the "habitat" of the native fish, including the spawning ground, wintering ground and feeding ground.
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