Quantitative evaluation method for survival risk of viscidity roes

Abstract

The invention belongs to the technical field of environmental science and ecology, and discloses a visco-demersal roe survival risk quantitative evaluation method, which comprises the following steps: collecting multi-source environmental data of a spawning site, and constructing a water environment model of the spawning site; constructing an egg mass settlement model based on the settlement characteristics of the target viscidity fish eggs, and constructing a first survival risk assessment model of the egg mass in the settlement period according to the egg mass settlement model and the water environment model; constructing a second survival risk assessment model of the egg mass in the hatching period according to the water environment model; and evaluating the survival risk of the egg mass in the settling period according to the first survival risk evaluation model, and evaluating the survival risk of the egg mass in the hatching period according to the second survival risk evaluation model. According to the method disclosed by the invention, the biological characteristics of agglomeration and settlement of the viscidity roes are coupled with the environmental factors, so that the settlement risk of the viscidity roes is accurately evaluated, the incubation risk in the incubation period is accurately calculated by quantifying the contribution degree of each habitat factor to the risk, and targeted guidance is provided for formulating ecological restoration measures.

Description

Technical Field

[0001] This invention discloses a method for quantitatively assessing the survival risk of adhesive fish eggs, belonging to the fields of environmental science and ecology. Background Technology

[0002] In river ecosystems, the survival and hatching of adhesive fish eggs are highly dependent on the synergistic effects of hydrodynamic conditions, water temperature, and substrate environment. Their hatching success rate is significantly affected by the coupling of multiple factors such as water flow velocity, water depth, and water temperature.

[0003] Traditional methods for assessing the survival risk of fish eggs are mainly based on single-factor monitoring (such as water temperature and flow velocity thresholds) or qualitative judgments based on expert experience. They lack quantitative analysis of the dynamic coupling between the biological characteristics of viscous fish eggs and hydrodynamic processes, resulting in insufficient accuracy in risk identification and poor targeting of ecological restoration measures.

[0004] However, existing sedimentation models are generally designed for individual fish eggs, failing to consider the changes in physical properties after egg clusters aggregate, resulting in sedimentation velocity calculation errors exceeding 35%. Furthermore, traditional methods only classify risk levels based on absolute flow velocity values, neglecting the inherent sedimentation characteristics of the fish eggs themselves, leading to a 30% underestimation rate of egg scattering risk in high-velocity areas. In water temperature assessment, the lack of a nonlinear deviation coefficient to quantify the impact of extreme temperatures results in an accuracy rate of only 60% for hatching risk assessment under low or high temperature stress, far below actual requirements. Traditional assessments rely on subjective experience or simple threshold classifications, lacking calculable risk index models and standardized verification methods. Summary of the Invention

[0005] The purpose of this invention is to provide a method for quantitatively assessing the survival risk of adhesive fish eggs, addressing the lack of a standardized method in the prior art that dynamically couples aquatic environmental parameters with fish egg behavioral characteristics. This method aims to quantitatively assess the comprehensive survival risk of fish eggs hatching and settling in spawning grounds, providing a precise basis for decision-making in spawning ground protection and restoration. To achieve the above objective, this invention proposes a method for quantitatively assessing the survival risk of adhesive fish eggs, the specific scheme of which is as follows:

[0006] A method for quantitatively assessing the survival risk of adhesive fish eggs, comprising:

[0007] Step 1: Collect multi-source environmental data of the spawning grounds and construct an aquatic environment model of the spawning grounds based on the multi-source environmental data;

[0008] Step 2: Construct an egg mass sedimentation model based on the sedimentation characteristics of the target adhesive fish eggs, and construct a first survival risk assessment model for the egg mass during the sedimentation period based on the egg mass sedimentation model and the water environment model.

[0009] Step 3: Construct a second survival risk assessment model for the egg cluster during the incubation period based on the aforementioned aquatic environment model;

[0010] Step 4: Assess the survival risk of the egg mass during the settling period according to the first survival risk assessment model, and assess the survival risk of the egg mass during the hatching period according to the second survival risk assessment model.

[0011] Preferably, a sedimentation model for egg masses is constructed based on the sedimentation characteristics of the target viscous fish eggs, including:

[0012] Based on the settling characteristics of the target adhesive fish eggs, determine the porosity and structural looseness factor of the egg clusters formed.

[0013] Based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed based on the sedimentation model of a single fish egg.

[0014] Preferably, based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed using a sedimentation model of a single fish egg, specifically including:

[0015] Based on the sedimentation model of a single fish egg, the equivalent diameter of the egg mass is constructed according to the porosity and structural looseness factor of the egg mass.

[0016] The equivalent density of the egg cluster is corrected based on the porosity.

[0017] An egg mass sedimentation model is constructed based on the equivalent diameter and the equivalent density.

[0018] Preferably, constructing an egg cluster sedimentation model based on the equivalent diameter and the equivalent density includes:

[0019] Based on the settling characteristics of the target viscous fish eggs, determine the drag coefficient of the egg mass formed.

[0020] An egg mass settling model is constructed based on the drag coefficient, the equivalent diameter, and the equivalent density.

[0021] Preferably, a first survival risk assessment model for the egg mass during the settling period is constructed based on the egg mass settling model and the water environment model, including:

[0022] The vertical settling distance of the egg mass is determined based on the egg mass settling model described above;

[0023] The horizontal transport distance of the egg clusters was determined based on the aforementioned aquatic environment model;

[0024] A first survival risk assessment model for the egg cluster during the settling period is constructed based on the transport distance and the settling distance.

[0025] Preferably, step 3 includes:

[0026] The environmental dataset at the settling attachment point of the egg mass is determined based on the aforementioned water environment model;

[0027] A second survival risk assessment model for the egg cluster during the incubation period is constructed based on the environmental dataset.

[0028] Preferably, step 4 is followed by:

[0029] The spawning grounds are then gridded.

[0030] The first survival risk of each grid unit in the spawning ground is calculated based on the first survival risk assessment model, and a subsidence risk map of the spawning ground is drawn based on the first survival risk.

[0031] The second survival risk of each grid unit in the spawning ground is calculated based on the second survival risk assessment model, and the hatching risk map of the spawning ground is drawn based on the second survival risk.

[0032] Preferably, the multi-source environmental data includes topographic data, hydrological data, and meteorological data;

[0033] The water environment model includes a hydrodynamic module and a water temperature module; correspondingly, the water environment model of the spawning ground is constructed as follows:

[0034] Based on the principles of hydrodynamics, a hydrodynamic module is constructed using the topographic and hydrological data.

[0035] Based on thermodynamic principles, the heat exchange parameters in the meteorological data are analyzed, and a water temperature module is constructed based on these heat exchange parameters.

[0036] Preferably, based on hydrodynamic principles, a hydrodynamic module is constructed according to the topographic and hydrological data, including:

[0037] Based on the principles of hydrodynamics, and using the topographic and hydrological data, a hydrodynamic module is constructed by employing the three-dimensional incompressible Reynolds-averaged Navier-Stokes equations solution method, while satisfying the hydrostatic pressure assumption and the Boussinesq assumption.

[0038] Preferably, the heat exchange parameters in the meteorological data are analyzed based on thermodynamic principles, and a water temperature module is constructed based on these heat exchange parameters, specifically as follows:

[0039] Based on thermodynamic principles, the heat exchange parameters in the meteorological data are analyzed.

[0040] The heat transfer process at the water-atmosphere interface is quantified based on the heat exchange parameters.

[0041] A spatiotemporally continuous water temperature module is constructed based on the heat transfer process.

[0042] Beneficial Effects: This method constructs a quantitative risk assessment system, enhancing the scientific rigor and objectivity of the assessment. By establishing a hydrodynamic and water temperature module within an aquatic environment model, and combining it with a sedimentation model modified for the characteristics of viscous fish eggs, key habitat factors such as water flow velocity, fish egg settling rate, water temperature, and water depth are transformed into calculable risk indices. This precisely quantifies the dynamic impact of each factor on fish egg survival, overcoming the subjective limitations of traditional qualitative assessments, and forming a quantifiable and verifiable scientific assessment system, providing accurate mathematical evaluation criteria for survival risk analysis.

[0043] This technology accurately identifies the spatiotemporal distribution characteristics of risks, enabling refined risk localization. Based on the calculation results of the survival risk assessment model, a spatiotemporal distribution map of fish egg survival risk in the study area can be generated, clearly defining the risk differences between the settling and hatching periods. This technical solution represents a breakthrough in risk assessment, moving from qualitative description to quantitative spatial expression, providing clear spatiotemporal targets for risk management.

[0044] Establishing a threshold-driven precision restoration mechanism enhances the effectiveness of ecological protection. This method sets risk assessment thresholds and, combined with the spatiotemporal distribution characteristics of risks, formulates targeted ecological restoration and scheduling strategies: for high-risk areas during the settling period, water flow disturbance intensity can be reduced through water conservancy engineering regulation; for areas with abnormal water temperature and depth during the hatching period, water supply can be optimized or bottom sediment conditions can be improved. Compared to traditional extensive protection measures, this invention achieves "precise policy implementation" by quantifying the risk contribution of each habitat factor, significantly improving the targeting and resource utilization efficiency of restoration measures, and providing scientific decision-making support for fishery resource protection and biodiversity maintenance.

[0045] This study elucidates the multi-factor coupling mechanism, driving technological innovation in habitat protection. By introducing biological parameters such as structural loosening factors and porosity, as well as the risk response of multi-factor nonlinear coupling during the hatching period, into a fish egg settling model, it quantitatively couples the biological characteristics of clump settling of adhesive fish eggs with environmental factors such as hydrodynamics, water temperature, and water depth for the first time. This technique not only reveals the influence of individual factors on fish egg survival but also quantifies the interactions between factors (such as the dynamic balance effect between flow velocity and settling rate, and the synergistic stress effect between water temperature and water depth). It provides a new theoretical perspective and analytical tool for fish habitat protection research, promoting a technological upgrade from experience-driven to data-driven approaches in related fields.

[0046] In summary, this invention achieves a key breakthrough in assessing the survival risk of adhering fish eggs from qualitative to quantitative levels through a complete technical chain of "data collection, model building, risk quantification, and precise zoning." It provides a replicable scientific and technological solution for the protection of endangered fish species and the restoration of river ecosystems, and has significant application value for ecological protection and sustainable fisheries development. Attached Figure Description

[0047] Figure 1 A flowchart illustrating a method for quantitatively assessing the hatching risk of fish eggs in a fish spawning ground; Figure 2 This is a simulation result of fish egg settling in Example 2; Figure 3 This is a spatial distribution map of risk values ​​for fish eggs in the Yehuxia spawning grounds during the early incubation period, as shown in Example 2. Figure 4 This is a spatial distribution map of risk values ​​for fish eggs in the Yehuxia spawning grounds during the mid-incubation period, as shown in Example 2. Figure 5 This is a spatial distribution map of risk values ​​for fish eggs in the Yehuxia spawning grounds during the later stages of incubation, as shown in Example 2. Figure 6 This is a time series plot of risk values ​​at 10 representative locations during the incubation period of fish eggs from the Yehuxia spawning ground in Example 2. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of the invention.

[0049] Because existing sedimentation models are generally designed for individual fish eggs and do not consider the changes in physical properties after egg clusters aggregate, the calculation error of sedimentation velocity is relatively large. Furthermore, the risk level of fish egg dispersal is classified only by the absolute value of the flow velocity, without considering the inherent sedimentation characteristics of the fish eggs themselves, resulting in a false negative rate of up to 30% for fish egg dispersal risk in high-velocity areas. Moreover, the lack of comprehensive identification of the impact of the aquatic environment on fish eggs leads to significant bias in risk assessment. Based on this, this application proposes a quantitative assessment of the survival risk of adhesive fish eggs to accurately assess the risk of adhesive fish eggs at each stage. The practical application of this invention is illustrated below through two specific embodiments.

[0050] Example 1:

[0051] like Figure 1 As shown, a method for quantitatively assessing the survival risk of adhesive fish eggs includes:

[0052] Step 1: Collect multi-source environmental data of the spawning grounds and construct an aquatic environment model of the spawning grounds based on the multi-source environmental data;

[0053] Specifically, the multi-source environmental data includes topographic data, hydrological data, and meteorological data.

[0054] The topographic data includes Digital Elevation Model (DEM); the hydrological data includes flow and water level in the spawning grounds; and the meteorological data includes water temperature, precipitation, air temperature, and solar radiation data in the spawning grounds.

[0055] In this embodiment, to ensure the accuracy and consistency of the data, preprocessing of the aforementioned multi-source environmental data is also included. Specifically, the topographic data is gridded to generate a spawning ground topographic model with consistent spatial resolution, and the hydrological and meteorological data are processed using time series analysis and spatial difference analysis to eliminate spatiotemporal differences and provide a reliable data foundation for subsequent model construction.

[0056] Based on the preprocessed multi-source environmental data, a water environment model of the spawning grounds was further constructed. This model consists of two main parts: a hydrodynamic module and a water temperature module.

[0057] The hydrodynamic module is specifically based on hydrodynamic principles. Using the topographic and hydrological data, it employs the three-dimensional incompressible Reynolds-averaged Navier-Stokes equations for modeling. Under the conditions of satisfying the hydrostatic pressure assumption and the Boussinesq assumption, the hydrodynamic module within the MIKE model (Model for Integrated Catchment Modelling) is selected for concrete implementation. This module can accurately simulate the water flow state in the spawning grounds.

[0058] The main governing equations of the hydrodynamic module are as follows: (1)

[0059] Water flow equation ( (Direction) is as follows: (2)

[0060] Water flow equation ( (Direction) is as follows: (3)

[0061] In the formula: , , Represents the Cartesian coordinate system; , , They represent , , Velocity component in the direction;

[0062] Indicates the flow rate of the point source;

[0063] Indicates time;

[0064] Represents gravitational acceleration;

[0065] Indicates water level height;

[0066] Indicates atmospheric pressure;

[0067] Indicates the reference density of water;

[0068] Indicates the density of water;

[0069] It indicates that the still water is deep;

[0070] Indicates the total water depth;

[0071] Indicates the Coriolis parameter, and ( Indicates rotational angular velocity. (Indicates latitude);

[0072] , , and The components representing the radiation stress tensor;

[0073] Indicates the vertical eddy viscosity coefficient;

[0074] , Indicates the flow velocity of the source and sink water;

[0075] , This represents the horizontal stress term.

[0076] The water temperature module specifically analyzes the heat exchange parameters in the meteorological data based on thermodynamic principles. These parameters mainly include sensible heat flux, which is the heat flux caused by convection; latent heat flux, which is the heat loss caused by evaporation; net shortwave radiation; and net longwave radiation.

[0077] The heat transfer process at the water-atmosphere interface is quantified based on the heat exchange parameters; a spatiotemporally continuous water temperature module is constructed based on the heat transfer process.

[0078] The sensible heat flux has the following formula: (4)

[0079] In the formula:

[0080] This represents sensible heat flux;

[0081] This indicates air density (1.3 kg / m³). 3 );

[0082] This indicates the specific heat of air (1007 J / kg℃).

[0083] This represents the sensible heat transfer coefficient (0.00141).

[0084] This indicates the wind speed at a height of 10 meters above the water surface.

[0085] Indicates the absolute temperature of a body of water;

[0086] It represents the absolute temperature of the atmosphere.

[0087] The latent heat flux is expressed by Dalton's law, as shown in the following equation: (5)

[0088] In the formula:

[0089] Indicates latent heat flux;

[0090] This represents the latent heat of vaporization (2.5 × 10⁶ J / kg).

[0091] This indicates the humidity coefficient (0.00132).

[0092] This indicates the wind speed at a height of 2 meters above the water surface.

[0093] This indicates the density of water vapor near the water surface;

[0094] This represents the density of water vapor in the atmosphere, and is related to the relative humidity of the atmosphere. Proportional;

[0095] and These are Dalton constants, used to adjust for evaporation and evaporation affected by wind, respectively. They are user-defined and used to calibrate the evaporation rate.

[0096] Net shortwave radiation is an important parameter indicating how much solar shortwave radiation energy is absorbed by the water surface; solar shortwave radiation refers to the radiation energy emitted by the sun that can penetrate the atmosphere and reach the Earth's surface. However, not all solar shortwave radiation reaching the water surface is absorbed. Some energy is reflected back into the atmosphere by the water surface, while the remainder is absorbed or scattered by the water.

[0097] To quantify the actual solar shortwave radiation energy absorbed by the water surface, the concept of "net shortwave radiation" is introduced. Net shortwave radiation equals the solar shortwave radiation energy received by the water surface minus the energy reflected and scattered.

[0098] Net shortwave radiation is shown in the following formula: (6)

[0099] In the formula:

[0100] Indicates underwater Net shortwave radiation intensity at depth;

[0101] Indicates the intensity at the water surface;

[0102] This represents the ratio of the intensity absorbed by the water surface layer, with a value range of 0.2-0.6.

[0103] This represents the extinction coefficient, with a value range of 0.2-1.4m.

[0104] Net longwave radiation refers to the difference between longwave radiation emitted from the water surface to the atmosphere and longwave radiation emitted from the atmosphere to the water surface. Longwave radiation differs from solar shortwave radiation; it is primarily emitted from the Earth's surface (including water) and atmosphere. Longwave radiation has a longer wavelength and lower energy, and mainly occurs during heat exchange.

[0105] The net longwave radiation is shown in the following formula: (7)

[0106] In the formula:

[0107] , , , Denotes constants, respectively , , , ;

[0108] Indicates the actual number of hours of sunshine;

[0109] Indicates the maximum sunshine duration (daytime length);

[0110] Indicates atmospheric temperature;

[0111] This represents the Stefan Boltzmann constant (5.6697 × 10⁻⁶). -8 W / (m 2 ·K 4 ));

[0112] This indicates the vapor pressure at the measured dew point temperature. ;

[0113] And for In its formula Indicates atmospheric relative humidity; The saturated vapor pressure (kPa) is the pressure at 100% relative humidity between -51℃ and 52℃, which can be calculated by equation (8).

[0114] (8)

[0115] After obtaining the heat exchange parameters of the heat transfer process at the water-atmosphere interface, a spatiotemporally continuous water temperature module is constructed to simulate the water temperature changes in the spawning grounds.

[0116] A water environment model is constructed based on the water temperature module and the hydrodynamic module. Specifically, the water temperature module, representing the parameterized heat flux calculation, is integrated into the hydrodynamic module of the MIKE model. This water environment model not only provides a detailed simulation of water temperature and hydrodynamic changes but also considers the influence of hydrodynamic factors on water temperature.

[0117] Step 2: Construct an egg mass sedimentation model based on the sedimentation characteristics of the target adhesive fish eggs, and construct a first survival risk assessment model for the egg mass during the sedimentation period based on the egg mass sedimentation model and the water environment model.

[0118] Furthermore, a sedimentation model for egg masses is constructed based on the sedimentation characteristics of the target adhesive fish eggs, including:

[0119] Based on the settling characteristics of the target adhesive fish eggs, determine the porosity and structural looseness factor of the egg clusters formed.

[0120] Based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed based on the sedimentation model of a single fish egg.

[0121] Furthermore, based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed based on the sedimentation model of a single fish egg, specifically including:

[0122] Based on the sedimentation model of a single fish egg, the equivalent diameter of the egg mass is constructed according to the porosity and structural looseness factor of the egg mass.

[0123] The equivalent density of the egg cluster is corrected based on the porosity.

[0124] An egg mass sedimentation model is constructed based on the equivalent diameter and the equivalent density.

[0125] Specifically, based on the sedimentation model of a single fish egg, the equivalent diameter of the egg mass is constructed according to the porosity and structural looseness factor of the egg mass;

[0126] The expression for the equivalent diameter is as follows: (9)

[0127] In the formula:

[0128] Indicates the equivalent diameter of the egg mass;

[0129] This represents the average diameter of a single fish egg in the egg mass;

[0130] Indicates the structural looseness factor;

[0131] Indicates the porosity of the egg cluster;

[0132] This indicates the number of egg clusters of the target adhesive fish eggs, which is determined based on the type of fish eggs.

[0133] Considering that the porosity of the egg cluster has a dilution effect on the egg cluster density, this invention corrects the equivalent density of the egg cluster based on the porosity, as shown in the following formula: (10)

[0134] In the formula:

[0135] Indicates the equivalent density of the egg mass;

[0136] Indicates the density of water;

[0137] This indicates the density of a single fish egg, which is determined based on the type of fish egg.

[0138] Furthermore, a sedimentation model for the egg cluster is constructed based on the equivalent diameter and the equivalent density, including:

[0139] Based on the settling characteristics of the target viscous fish eggs, determine the drag coefficient of the egg mass formed.

[0140] An egg mass settling model is constructed based on the drag coefficient, the equivalent diameter, and the equivalent density.

[0141] Based on the resistance coefficient of loose egg masses, the egg mass settling model can be expressed as: (11)

[0142] In the formula:

[0143] Indicates the settling velocity of the egg mass;

[0144] The resistance coefficient of the egg mass is represented. ,in This indicates increased resistance due to the irregular shape of the loose egg mass, among which... This represents the single-particle drag coefficient.

[0145] Since the survival of viscous fish eggs is divided into two stages: post-spawning settling and hatching, the risk during the settling stage mainly comes from the stimulation of the fish eggs by excessive current velocity, which may even cause the fish eggs to be washed away; the risk during the hatching stage comes from the combined effects of water depth, current velocity and water temperature.

[0146] During the settling period, the main risk arises from the imbalance between water flow velocity and egg mass settling rate. When the horizontal drift velocity of the fish eggs propelled by the water flow exceeds their vertical settling velocity, the eggs will be transported long distances by the water flow, leaving the suitable spawning microenvironment, leading to a decrease in survival probability. Therefore, this invention constructs a first survival risk assessment model for egg masses during the settling period based on the egg mass settling model and the aquatic environment model, including:

[0147] The vertical settling distance of the egg mass is determined based on the egg mass settling model described above;

[0148] The horizontal transport distance of the egg clusters was determined based on the aforementioned aquatic environment model;

[0149] A first survival risk assessment model for the egg cluster during the settling period is constructed based on the transport distance and the settling distance.

[0150] The first survival risk assessment model is shown in the following formula: (12)

[0151] In the formula:

[0152] This indicates the survival risk index of the egg mass during the settling period;

[0153] Indicates the water flow velocity at the fish egg release point;

[0154] Indicates the sinking rate of fish eggs;

[0155] Represents the standardized coefficient;

[0156] Indicates the adjustment coefficient;

[0157] Indicates the actual drift distance;

[0158] Indicates the critical drift distance;

[0159] This represents the correction factor.

[0160] This invention introduces biological parameters such as structural loosening factor and porosity into the egg mass sedimentation model, and for the first time quantitatively correlates the sticky clustering characteristics of fish eggs with hydrodynamic, water temperature and water depth factors, revealing the laws of single and interactive effects. It provides a new theoretical perspective and analytical tool for fish habitat protection, and promotes the upgrading of this field from experience-driven to data-driven.

[0161] Step 3: Construct a second survival risk assessment model for the egg cluster during the incubation period based on the aforementioned aquatic environment model;

[0162] Furthermore, step 3 includes:

[0163] The environmental dataset at the settling attachment point of the egg mass is determined based on the aforementioned water environment model;

[0164] Specifically, these environmental datasets include key habitat factors that affect fish egg hatching, such as water temperature, water flow velocity, and water depth.

[0165] A second survival risk assessment model for the egg cluster during the incubation period is constructed based on the environmental dataset.

[0166] In this process, the environmental data first needs to be normalized to transform them into a comparable and analytical scale. To achieve this, this invention proposes using a deviation coefficient to measure the discrepancy between actual observed values ​​and habitat factor thresholds. Habitat factor thresholds are the thresholds of suitable habitat factors for the hatching of target fish eggs determined through literature review or experimental testing.

[0167] Specifically, in this embodiment, the formula for calculating the water temperature deviation coefficient is as follows: (13)

[0168] In the formula:

[0169] Indicates the water temperature deviation coefficient;

[0170] Indicates the water temperature sensitivity coefficient;

[0171] Indicates the current water temperature;

[0172] This represents the intermediate threshold within the suitable temperature range for the survival of target adhesive fish eggs;

[0173] This represents the highest threshold within the suitable temperature range for the survival of the target adhesive fish eggs;

[0174] This represents the lowest threshold within the suitable temperature range for the survival of target viscous fish eggs.

[0175] The formula for calculating the water flow velocity deviation coefficient is as follows: (14)

[0176] In the formula:

[0177] Indicates the deviation coefficient of water flow velocity;

[0178] Indicates the flow velocity sensitivity index (typically for fast-flowing fish). =2, slow-moving fish =1.5);

[0179] Indicates the current water flow velocity;

[0180] This represents the highest threshold within the suitable water flow range for the survival of target viscous fish eggs;

[0181] This represents the lowest threshold within the suitable water flow range for the survival of target viscous fish eggs.

[0182] The formula for calculating the water depth deviation coefficient is as follows: (15)

[0183] In the formula:

[0184] Indicates the water depth deviation coefficient;

[0185] Indicates the water depth sensitivity index;

[0186] This represents the water depth weighting adjustment factor;

[0187] Indicates the current water depth;

[0188] This represents the intermediate threshold within the suitable water depth range for the survival of target viscous fish eggs;

[0189] This represents the lowest threshold within the suitable water depth range for the survival of target viscous fish eggs;

[0190] This represents the highest threshold within the suitable water depth range for the survival of target viscous fish eggs.

[0191] Based on normalization, the second survival risk assessment model is further improved by introducing a nonlinear amplification mechanism and parameter coupling design. The nonlinear amplification mechanism considers that small environmental changes under certain conditions may have a disproportionately significant impact on fish egg hatching, reflecting the complexity and sensitivity of the ecosystem. The parameter coupling design connects multiple habitat factors through certain mathematical relationships or logical rules to form an interconnected and mutually influential system, simulating the interactions between factors in the real environment and their comprehensive impact on fish egg hatching.

[0192] Specifically, the expression for the second survival risk assessment model is as follows: (16)

[0193] In the formula:

[0194] This indicates the survival risk index of the egg mass during the incubation period;

[0195] , This represents the adjustment coefficient, used to control overall risk sensitivity and parameter coupling strength;

[0196] , , This represents a nonlinear exponent, used to amplify the effects of extreme deviations.

[0197] Step 4: Assess the survival risk of the egg mass during the settling period according to the first survival risk assessment model, and assess the survival risk of the egg mass during the hatching period according to the second survival risk assessment model.

[0198] This invention integrates an aquatic environment model (including hydrodynamic and water temperature modules) and an egg mass settling model for viscous fish eggs, transforming factors such as flow velocity, settling rate, water temperature, and water depth into calculable risk indices. By using nonlinear deviation coefficients and parameter coupling design, it quantifies the phased impact of habitat factors on egg masses, thereby forming a verifiable mathematical assessment system. This solves the subjectivity problem of traditional qualitative assessments and provides accurate and quantitative assessment basis for risk analysis.

[0199] Furthermore, step 4 is followed by:

[0200] The spawning grounds are then gridded.

[0201] The first survival risk of each grid unit in the spawning ground is calculated based on the first survival risk assessment model, and a subsidence risk map of the spawning ground is drawn based on the first survival risk.

[0202] The second survival risk of each grid unit in the spawning ground is calculated based on the second survival risk assessment model, and the hatching risk map of the spawning ground is drawn based on the second survival risk.

[0203] Specifically, the spawning grounds are gridded into several small units to facilitate a detailed assessment of the survival risk of each unit. Using the first survival risk assessment model, the primary survival risk (i.e., settling risk) of each grid unit within the spawning grounds is calculated. Based on these risk values, a settling risk map of the spawning grounds is drawn, visually illustrating the survival risks faced by egg masses in different areas during the settling period. Similarly, using the second survival risk assessment model, the secondary survival risk (i.e., hatching risk) of each grid unit within the spawning grounds is calculated. Based on these risk values, a hatching risk map of the spawning grounds is drawn, revealing the survival challenges that egg masses may encounter during the hatching period.

[0204] By analyzing the spatial distribution characteristics of sedimentation risk maps and hatching risk maps, high-risk and low-risk areas can be identified. Higher risk values ​​indicate harsher survival conditions for the egg masses in that area; conversely, lower risk values ​​indicate a more suitable environment for spawning and hatching. Areas with risk values ​​higher than 7 should be considered as high-risk areas. Targeted ecological management or artificial remediation measures are needed for these high-risk areas to improve the egg mass's living environment.

[0205] This invention generates a risk distribution map based on a survival risk model, clearly defining the risk differences between the settling and hatching periods. By using critical drift distance, flow velocity-settling rate balance, and environmental factor deviation coefficients, it accurately locates high-risk areas of fish egg dispersal, hypoxia, or inadequate hatching, achieving a breakthrough in risk assessment from qualitative description to quantitative spatialization.

[0206] Furthermore, this invention also includes generating settlement risk maps and hatching risk maps based on time series data, respectively, to obtain spatiotemporal settlement risk maps and spatiotemporal hatching risk maps with spatiotemporal distribution characteristics. This invention sets risk thresholds (e.g., initiating key restoration efforts if the risk value is ≥7) and formulates targeted measures based on spatiotemporal distribution. For example, it regulates water flow in high-velocity areas during the settlement period and optimizes substrate or water supply in areas with abnormal water temperature / depth during the hatching period. By quantifying the contribution of each habitat factor, it achieves "precise policy implementation," significantly improving restoration efficiency and resource utilization effectiveness compared to traditional extensive conservation methods, and providing scientific decision-making support for ecological scheduling and artificial restoration.

[0207] The present invention provides a method for quantitatively assessing the survival risk of viscous fish eggs, which achieves scientific assessment and targeted management of survival risk by constructing a quantitative model and a precise zoning mechanism.

[0208] In summary, this invention achieves a key breakthrough in assessing the survival risk of adhesive fish eggs from qualitative to quantitative levels through a technical chain of "model quantification - risk positioning - precise zoning". It provides a replicable scientific solution for the protection of endangered fish species, river ecological restoration, and ecological scheduling of water conservancy projects, and has significant application value for sustainable ecological and fisheries development.

[0209] Example 2:

[0210] Next, taking the spawning grounds of Yehuxia below Yangquba in the upper reaches of the Yellow River as an example, the technical solution of the present invention will be described in detail as follows:

[0211] DEM topographic data, upstream and downstream flow and water level, water temperature, precipitation, air temperature, evapotranspiration and long and short wave radiation data of the Yefuxia spawning ground downstream of Yangquba in the upper reaches of the Yellow River were obtained from April to July 2021. A water environment model was built, including hydrodynamic and water temperature modules, to obtain water environment parameter information of the Yefuxia spawning ground.

[0212] Based on ABM (Agent-Based Modeling) technology, a fish egg settling model is loaded onto an aquatic environment model. This embodiment uses the spotted naked carp as the target fish. Since late May is the peak breeding season for the spotted naked carp, simulated fish egg particles were uniformly deployed within the grid from May 20th to 31st. This allowed for the acquisition of the specific location information and spatial distribution of the passive settling of the fish eggs under water flow after spawning. (See...) Figure 2 .

[0213] Based on existing literature and experiments, the suitable habitat factor thresholds for hatching of spotted naked carp eggs were obtained, as shown in Table 1:

[0214] Table 1 Suitable range for fish egg hatching in spawning grounds

[0215] The ECOLab module of the MIKE software series was used as the development, implementation, and execution platform for the ABM framework. Based on the simulation of water flow variables, the settling velocity of fish eggs was calculated using factors such as density and particle size as inputs. Fish egg particles were uniformly distributed in each grid across the entire spawning area, and the simulation period was the peak spawning period of *Gymnocypris chinensis* (late May). Under an external flow field environment established using an Eulerian framework with a grid as the scale, the motion trajectory and aggregation effect of fish egg particles based on the Lagrangian framework were obtained.

[0216] Based on fish egg settling simulation, the relationship between the horizontal movement distance and flow velocity during the fish egg settling process is shown in Table 2:

[0217] Table 2. Horizontal movement distance of fish eggs in cross-section under different working conditions

[0218] Based on step 4 of Example 1, the survival risk of *Gymnocypris chinensis* eggs in the Yehuxia spawning ground was evaluated, taking into account the biological characteristics of *Gymnocypris chinensis*. The parameter values ​​in this example are shown in Table 3.

[0219] Table 3. Values ​​of fish egg survival risk parameters

[0220] Based on the above steps, the survival risk value of fish eggs during the settling period can be calculated. Survival risk value during the incubation period Specifically, this embodiment is based on the survival risk value during the incubation period at the Yehuxia spawning grounds. A survival risk map of fish eggs in the Yefuxia spawning grounds was drawn, such as... Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown.

[0221] The above description is merely a few embodiments of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any modifications or alterations made by those skilled in the art without departing from the scope of the technical solution of the present invention using the disclosed technical content are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A method for quantitatively assessing the survival risk of adhesive fish eggs, characterized in that, include: Step 1: Collect multi-source environmental data of the spawning grounds and construct an aquatic environment model of the spawning grounds based on the multi-source environmental data; Step 2: Construct an egg mass sedimentation model based on the sedimentation characteristics of the target adhesive fish eggs, and construct a first survival risk assessment model for the egg mass during the sedimentation period based on the egg mass sedimentation model and the water environment model. Step 3: Construct a second survival risk assessment model for the egg cluster during the incubation period based on the aforementioned aquatic environment model; Step 4: Assess the survival risk of the egg mass during the settling period according to the first survival risk assessment model, and assess the survival risk of the egg mass during the hatching period according to the second survival risk assessment model.

2. The survival risk quantification assessment method according to claim 1, characterized in that, A sedimentation model for egg masses was constructed based on the sedimentation characteristics of target viscous fish eggs, including: Based on the settling characteristics of the target adhesive fish eggs, determine the porosity and structural looseness factor of the egg clusters formed. Based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed based on the sedimentation model of a single fish egg.

3. The survival risk quantification assessment method according to claim 2, characterized in that, Based on the porosity and structural looseness factor of the egg mass, an egg mass sedimentation model is constructed based on the sedimentation model of a single fish egg, specifically including: Based on the sedimentation model of a single fish egg, the equivalent diameter of the egg mass is constructed according to the porosity and structural looseness factor of the egg mass. The equivalent density of the egg cluster is corrected based on the porosity. An egg mass sedimentation model is constructed based on the equivalent diameter and the equivalent density.

4. The survival risk quantification assessment method according to claim 3, characterized in that, Constructing an egg mass sedimentation model based on the equivalent diameter and the equivalent density includes: Based on the settling characteristics of the target viscous fish eggs, determine the drag coefficient of the egg mass formed. An egg mass settling model is constructed based on the drag coefficient, the equivalent diameter, and the equivalent density.

5. The survival risk quantification assessment method according to claim 1, characterized in that, Based on the described egg mass settling model and the described aquatic environment model, a first survival risk assessment model for egg masses during the settling period is constructed, including: The vertical settling distance of the egg mass is determined based on the egg mass settling model described above; The horizontal transport distance of the egg clusters was determined based on the aforementioned aquatic environment model; A first survival risk assessment model for the egg cluster during the settling period is constructed based on the transport distance and the settling distance.

6. The survival risk quantification assessment method according to claim 1, characterized in that, Step 3 includes: The environmental dataset at the settling attachment point of the egg mass is determined based on the aforementioned water environment model; A second survival risk assessment model for the egg cluster during the incubation period is constructed based on the environmental dataset.

7. The survival risk quantification assessment method according to claim 1, characterized in that, Step 4 is followed by: The spawning grounds are then gridded. The first survival risk of each grid unit in the spawning ground is calculated based on the first survival risk assessment model, and a subsidence risk map of the spawning ground is drawn based on the first survival risk. The second survival risk of each grid unit in the spawning ground is calculated based on the second survival risk assessment model, and the hatching risk map of the spawning ground is drawn based on the second survival risk.

8. The survival risk quantification assessment method according to claim 1, characterized in that, The multi-source environmental data includes topographic data, hydrological data, and meteorological data; The water environment model includes a hydrodynamic module and a water temperature module; correspondingly, the water environment model of the spawning ground is constructed as follows: Based on the principles of hydrodynamics, a hydrodynamic module is constructed using the topographic and hydrological data. Based on thermodynamic principles, the heat exchange parameters in the meteorological data are analyzed, and a water temperature module is constructed based on these heat exchange parameters.

9. The survival risk quantification assessment method according to claim 8, characterized in that, Based on hydrodynamic principles, a hydrodynamic module is constructed using the aforementioned topographic and hydrological data, including: Based on the principles of hydrodynamics, and using the topographic and hydrological data, a hydrodynamic module is constructed by employing the three-dimensional incompressible Reynolds-averaged Navier-Stokes equations solution method, while satisfying the hydrostatic pressure assumption and the Boussinesq assumption.

10. The survival risk quantification assessment method according to claim 9, characterized in that, Based on thermodynamic principles, the heat exchange parameters in the meteorological data are analyzed, and a water temperature module is constructed based on these parameters. Specifically: Based on thermodynamic principles, the heat exchange parameters in the meteorological data are analyzed. The heat transfer process at the water-atmosphere interface is quantified based on the heat exchange parameters. A spatiotemporally continuous water temperature module is constructed based on the heat transfer process.

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