CN112801381A - Jellyfish disaster early warning method - Google Patents
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Abstract
The invention relates to the technical field of ocean monitoring and early warning, in particular to an early warning method for jellyfish disasters, which comprises the following steps of establishing a jellyfish drift path prediction model: data collection and arrangement, meteorological mode research and development, marine physical mode research and development: by utilizing a double nesting technology, simulating typical sea area hydrodynamic environment elements, jellyfish physical-ecological model research and development and jellyfish drift path prediction model research and development by adopting an ROMS marine mode, then establishing a jellyfish disaster risk level early warning method, then participating in data integration and system construction, undertaking research and development of a jellyfish disaster drift early warning module, and finally performing operation demonstration. In the invention, a collective forecasting method is independently developed, living habits such as the autonomous movement of jellyfishes and the like are considered, and an external jellyfish emergency drift collective prediction model is considered; according to indexes such as the variety of the disaster jellyfish, the toxicity of the jellyfish, the distribution density of the jellyfish and the like, a jellyfish disaster risk level early warning method is researched.
Description
Technical Field
The invention relates to the technical field of ocean monitoring and early warning, in particular to an early warning method for jellyfish disasters.
Background
Global changes and human activities affect the structure and functions of the marine ecosystem greatly, the frequency of large jellyfish disasters and the types of disasters are increasing, and the marine fishery, coastal industry, coastal tourism industry and marine ecosystem are seriously affected. In norway, jellyfishes have been listed as one of the important factors affecting the national economic support industries of the breeding industry, the fishery industry, the tourism industry and the like. In japan, the outbreak of giant jellyfishes has, since 2000, placed japanese fishery resources and fishing at the edge of a breakdown. In the sea areas of the Bohai sea in the Liaodong gulf, the south of the yellow sea and the north of the east sea in China, jellyfishes are intensively exploded in 2003, and jellyfishes are wound to block nets, so that the amount of harvested fish is reduced, people are injured in bathing beaches, and drainage port taking events such as coastal power plants, desalination water plants, nuclear power plants and the like are blocked; in order to ensure sustainable development of coastal economy and ecological environment, relevant departments in China pay high attention to and start a plurality of scientific research and application projects, such as national science foundation 'life history of large jellyfish in yellow and east China sea and regulation and control effect on plankton', 973 project 'key process, mechanism and ecological environment effect of offshore jellyfish outbreak in China' and national sea bureau public welfare project 'typical jellyfish disaster monitoring and early warning technology business application and demonstration research in sea area'.
Scholars at home and abroad do much work on the research of jellyfish drifting and gathering mechanism, and weather and hydrologic dynamic conditions are considered to possibly influence the gathering of jellyfish. Meteorological conditions may affect the drift accumulation of large jellyfishes. For example, fishermen experience is that jellyfish is difficult to see in rainy days. The peak hours of the jelly jellyfish and white cyanea population just passed in 2011 at 8/11/gulf jellyfish, but significant reductions in the numbers of these two jellyfishes were observed at 8/16/19, which may be related to rainfall during this period. Jellyfishes of Hydra, Pot Hymenomycetes and tubular Hymenomycetes generally have the phenomenon of day-night vertical migration, namely the jellyfishes sink to the bottom layer of a water body in the daytime and float to the surface layer of the water body at night; the jellyfishes of the Cteno acaleph class are rare. However, in the open sea area of yellow sea in 2006, 10&11 months (8&9 months), continuous trawling for 24 hours at two stations (50 sampling stations) in a voyage is extensively investigated, and it is found that the change of the number of jellyfishes continuously investigated within 24 hours is not caused by the diurnal vertical movement of the jellyfishes, and may be caused by the change of the water mass during the trawling investigation. The jellyfish vertical movement regularity is not obvious in jellyfish investigation in 8-9 months in Bay of Jiaozhou, the jellyfish in south China of Japan is mainly distributed in a shallow water layer with the depth of 40m, and the night is usually deeper than the day, so that the vertical migration conditions of different types of jellyfish in different sea areas are different, and the jellyfish is possibly related to phototropism or illumination intensity of the jellyfish and also possibly not related to the phototropism or illumination intensity, and the mechanism is still not clear, so that the jellyfish disaster early warning method is provided for solving the problems.
Disclosure of Invention
The invention aims to provide an early warning method for jellyfish disasters, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an early warning method for jellyfish disasters comprises the following steps:
the method comprises the following steps: establishing a jellyfish drift path prediction model:
a. data collection and sorting: collecting and arranging topographic data, collecting data such as sea charts with different scales and water depth actual measurement data, and extracting water depth data of a target sea area; collecting and sorting various hydrometeorological data, collecting hydrometeorological data such as sea temperature, sea current, salinity, wind, air temperature, heat flux, evaporation, precipitation, water level and harmonic constant of a coastal tidal observation station and the like; then mother monitoring data are processed, multi-source jellyfish monitoring data such as net mining, aerial remote sensing and ROV are integrated, and relevant data are provided for a jellyfish drift path prediction model;
b. and (3) meteorological model research and development: data such as a sea surface wind field, sea air flux and the like required by the jellyfish drift path prediction model are established on the basis of a WRF meteorological model, and aiming at the characteristics of the project, a grid nesting technology is adopted to carry out encryption calculation in a typical sea area, so that the description of optimizing the sea-land wind phenomenon is improved in the aspect of a physical process;
c. marine physical model research and development: simulating typical sea area hydrodynamic environment elements by using a dual nesting technology and an ROMS marine mode;
d. researching and developing a jellyfish physical-ecological model: according to the life habits of jellyfishes in different sea areas, analyzing ecological parameters influencing jellyfish drift in different sea areas by adopting a statistical method, selecting main influence factors, carrying out ecological parametric coupling on a marine physical model, and finally establishing a physical-ecological model of the jellyfishes in the sea areas;
e. researching and developing a jellyfish drift path prediction model: the method comprises the steps of establishing a prediction numerical model of jellyfish drift paths in a target peripheral sea area by utilizing a Lagrange particle tracking method based on a jellyfish physical-ecological model, quickly predicting the drift paths of jellyfishes by taking jellyfish position, distribution area, density and other data provided by a jellyfish disaster monitoring system as an initial field, providing the jellyfishes and analyzing related information such as jellyfish drift tendency, drift speed, time required for reaching a sensitive sea area and the like according to different requirements of governments and related departments.
Step two: establishing a jellyfish disaster risk level early warning method: and establishing a target sea area jellyfish disaster risk grade early warning model based on the information provided by the jellyfish database, the jellyfish disaster risk grade classification standard and the jellyfish disaster monitoring result, so as to realize early warning on the jellyfish disaster risk grade. Dividing jellyfish forecast alarms into 4 levels according to indexes such as the scale and the influence degree of jellyfish generation, wherein the early warning degree of each level of jellyfish forecast alarms is represented by four colors such as red, orange, yellow and blue, and the normal forecast alarms are not represented by colors;
step three: participate in data integration and system construction, undertake jellyfish disaster drift early warning module research and development: and (3) jellyfish disaster early warning and prediction: early warning and forecasting of jellyfish disasters are realized; the method has the advantages that the rapid prediction of the drift track and direction of the jellyfish is realized, the influence of the jellyfish on a sensitive target is predicted in a focused manner, and the time required by the jellyfish to reach a sensitive area is calculated; early warning of jellyfish disaster risk levels is achieved, and different levels correspond to different map display forms;
step four: during the demonstration operation, once more jellyfishes are found in a monitoring area, jellyfish distribution area, density and hydrological meteorological conditions provided by a jellyfish disaster monitoring system are used as an initial field, a jellyfish drift path prediction numerical model is used for rapidly predicting drift tracks and directions of the jellyfishes, influences of the jellyfishes on sensitive sea areas are analyzed, the time of arrival of the jellyfishes at a target sea area is predicted, and early warning reports are issued in time.
Preferably, when the data is sorted in the first step, in the aspect of chart digitization, due to the fact that the charts are different in scale and mean sea level, methods such as reference surface conversion and linear interpolation are adopted to obtain reasonable water depth required by the mode, typical sea area shorelines extracted from the high-resolution SAR satellite images are combined with Google Earth shorelines to correct the existing shorelines.
Preferably, when collecting the hydrometeorology data in the first step, the accuracy and the error of various instruments and equipment are mainly estimated from buoys, navigation observation, marine satellites and the like, and the multi-source observation data are fused based on technologies such as a weighted average method, a correlation analysis method, a step-by-step correction method, a statistical dynamics method, an optimal interpolation method and the like.
Compared with the prior art, the invention has the beneficial effects that:
in the invention, a collective forecasting method is independently developed, living habits such as the autonomous movement of jellyfishes and the like are considered, and an external jellyfish emergency drift collective prediction model is considered; according to indexes such as the variety of the disaster jellyfish, the toxicity of the jellyfish, the distribution density of the jellyfish and the like, a jellyfish disaster risk level early warning method is researched.
Detailed Description
Example 1: the invention provides a technical scheme that:
an early warning method for jellyfish disasters comprises the following steps:
the method comprises the following steps: establishing a jellyfish drift path prediction model:
a. data collection and sorting: collecting and arranging topographic data, collecting data such as sea charts with different scales and water depth actual measurement data, and extracting water depth data of a target sea area; collecting and sorting various hydrometeorological data, collecting hydrometeorological data such as sea temperature, sea current, salinity, wind, air temperature, heat flux, evaporation, precipitation, water level and harmonic constant of a coastal tidal observation station and the like; then mother monitoring data are processed, multi-source jellyfish monitoring data such as net mining, aerial remote sensing and ROV are integrated, and relevant data are provided for a jellyfish drift path prediction model;
b. and (3) meteorological model research and development: data such as a sea surface wind field, sea air flux and the like required by the jellyfish drift path prediction model are established on the basis of a WRF meteorological model, and aiming at the characteristics of the project, a grid nesting technology is adopted to carry out encryption calculation in a typical sea area, so that the description of optimizing the sea-land wind phenomenon is improved in the aspect of a physical process;
c. marine physical model research and development: simulating typical sea area hydrodynamic environment elements by using a dual nesting technology and an ROMS marine mode;
d. researching and developing a jellyfish physical-ecological model: according to the life habits of jellyfishes in different sea areas, analyzing ecological parameters influencing jellyfish drift in different sea areas by adopting a statistical method, selecting main influence factors, carrying out ecological parametric coupling on a marine physical model, and finally establishing a physical-ecological model of the jellyfishes in the sea areas;
e. researching and developing a jellyfish drift path prediction model: the method comprises the steps of establishing a prediction numerical model of jellyfish drift paths in a target peripheral sea area by utilizing a Lagrange particle tracking method based on a jellyfish physical-ecological model, quickly predicting the drift paths of jellyfishes by taking jellyfish position, distribution area, density and other data provided by a jellyfish disaster monitoring system as an initial field, providing the jellyfishes and analyzing related information such as jellyfish drift tendency, drift speed, time required for reaching a sensitive sea area and the like according to different requirements of governments and related departments.
Step two: establishing a jellyfish disaster risk level early warning method: and establishing a target sea area jellyfish disaster risk grade early warning model based on the information provided by the jellyfish database, the jellyfish disaster risk grade classification standard and the jellyfish disaster monitoring result, so as to realize early warning on the jellyfish disaster risk grade. Dividing jellyfish forecast alarms into 4 levels according to indexes such as the scale and the influence degree of jellyfish generation, wherein the early warning degree of each level of jellyfish forecast alarms is represented by four colors such as red, orange, yellow and blue, and the normal forecast alarms are not represented by colors;
step three: participate in data integration and system construction, undertake jellyfish disaster drift early warning module research and development: and (3) jellyfish disaster early warning and prediction: early warning and forecasting of jellyfish disasters are realized; the method has the advantages that the rapid prediction of the drift track and direction of the jellyfish is realized, the influence of the jellyfish on a sensitive target is predicted in a focused manner, and the time required by the jellyfish to reach a sensitive area is calculated; early warning of jellyfish disaster risk levels is achieved, and different levels correspond to different map display forms;
step four: during the demonstration operation, once more jellyfishes are found in a monitoring area, jellyfish distribution area, density and hydrological meteorological conditions provided by a jellyfish disaster monitoring system are used as an initial field, a jellyfish drift path prediction numerical model is used for rapidly predicting drift tracks and directions of the jellyfishes, influences of the jellyfishes on sensitive sea areas are analyzed, the time of arrival of the jellyfishes at a target sea area is predicted, and early warning reports are issued in time.
In the invention, a collective forecasting method is independently developed, living habits such as the autonomous movement of jellyfishes and the like are considered, and an external jellyfish emergency drift collective prediction model is considered; according to indexes such as the variety of the disaster jellyfish, the toxicity of the jellyfish, the distribution density of the jellyfish and the like, a jellyfish disaster risk level early warning method is researched.
During data arrangement in the first step, in the aspect of chart digitization, due to the fact that the charts are different in scale and average sea level, reasonable water depth required by a mode is obtained by means of methods such as reference surface conversion and linear interpolation, typical sea area shorelines extracted from high-resolution SAR satellite images are combined with Google Earth shorelines to correct the existing shorelines, accuracy and errors of various instruments and equipment are evaluated mainly from buoys, navigation observation and ocean satellites when hydrological meteorological data are collected in the first step, observation data are fused based on technologies such as a weighted average method, a correlation analysis method, a gradual correction method, a statistical dynamics method and an optimal interpolation method, and the accuracy of information collection and arrangement is effectively guaranteed through the arrangement.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present invention. The foregoing is only a preferred embodiment of the present invention, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present invention, and the technical features described above may be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention using its spirit and scope, as defined by the claims, may be directed to other uses and embodiments.
Claims (3)
1. An early warning method for jellyfish disasters is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a jellyfish drift path prediction model:
a. data collection and sorting: collecting and arranging topographic data, collecting data such as sea charts with different scales and water depth actual measurement data, and extracting water depth data of a target sea area; collecting and sorting various hydrometeorological data, collecting hydrometeorological data such as sea temperature, sea current, salinity, wind, air temperature, heat flux, evaporation, precipitation, water level and harmonic constant of a coastal tidal observation station and the like; then mother monitoring data are processed, multi-source jellyfish monitoring data such as net mining, aerial remote sensing and ROV are integrated, and relevant data are provided for a jellyfish drift path prediction model;
b. and (3) meteorological model research and development: data such as a sea surface wind field, sea air flux and the like required by the jellyfish drift path prediction model are established on the basis of a WRF meteorological model, and aiming at the characteristics of the project, a grid nesting technology is adopted to carry out encryption calculation in a typical sea area, so that the description of optimizing the sea-land wind phenomenon is improved in the aspect of a physical process;
c. marine physical model research and development: simulating typical sea area hydrodynamic environment elements by using a dual nesting technology and an ROMS marine mode;
d. researching and developing a jellyfish physical-ecological model: according to the life habits of jellyfishes in different sea areas, analyzing ecological parameters influencing jellyfish drift in different sea areas by adopting a statistical method, selecting main influence factors, carrying out ecological parametric coupling on a marine physical model, and finally establishing a physical-ecological model of the jellyfishes in the sea areas;
e. researching and developing a jellyfish drift path prediction model: the method comprises the steps of establishing a prediction numerical model of jellyfish drift paths in a target peripheral sea area by utilizing a Lagrange particle tracking method based on a jellyfish physical-ecological model, quickly predicting the drift paths of jellyfishes by taking jellyfish position, distribution area, density and other data provided by a jellyfish disaster monitoring system as an initial field, providing the jellyfishes and analyzing related information such as jellyfish drift tendency, drift speed, time required for reaching a sensitive sea area and the like according to different requirements of governments and related departments.
Step two: establishing a jellyfish disaster risk level early warning method: and establishing a target sea area jellyfish disaster risk grade early warning model based on the information provided by the jellyfish database, the jellyfish disaster risk grade classification standard and the jellyfish disaster monitoring result, so as to realize early warning on the jellyfish disaster risk grade. Dividing jellyfish forecast alarms into 4 levels according to indexes such as the scale and the influence degree of jellyfish generation, wherein the early warning degree of each level of jellyfish forecast alarms is represented by four colors such as red, orange, yellow and blue, and the normal forecast alarms are not represented by colors;
step three: participate in data integration and system construction, undertake jellyfish disaster drift early warning module research and development: and (3) jellyfish disaster early warning and prediction: early warning and forecasting of jellyfish disasters are realized; the method has the advantages that the rapid prediction of the drift track and direction of the jellyfish is realized, the influence of the jellyfish on a sensitive target is predicted in a focused manner, and the time required by the jellyfish to reach a sensitive area is calculated; early warning of jellyfish disaster risk levels is achieved, and different levels correspond to different map display forms;
step four: during the demonstration operation, once more jellyfishes are found in a monitoring area, jellyfish distribution area, density and hydrological meteorological conditions provided by a jellyfish disaster monitoring system are used as an initial field, a jellyfish drift path prediction numerical model is used for rapidly predicting drift tracks and directions of the jellyfishes, influences of the jellyfishes on sensitive sea areas are analyzed, the time of arrival of the jellyfishes at a target sea area is predicted, and early warning reports are issued in time.
2. The early warning method for jellyfish disasters according to claim 1, characterized in that: during data arrangement in the first step, in the aspect of chart digitization, due to the fact that the charts are different in scale and mean sea level, reasonable water depth required by the mode is obtained by adopting methods of datum plane conversion, linear interpolation and the like, typical sea area shorelines extracted from high-resolution SAR satellite images are combined with Google Earth shorelines to correct the existing shorelines.
3. The early warning method for jellyfish disasters according to claim 1, characterized in that: in the first step, when collecting the hydrometeorology data, the accuracy and the error of various instruments and equipment are mainly estimated from buoys, navigation observation, marine satellites and the like, and the multi-source observation data are fused based on technologies such as a weighted average method, a correlation analysis method, a step-by-step correction method, a statistical dynamics method, an optimal interpolation method and the like.
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| CN116721363B (en) * | 2023-08-07 | 2023-11-03 | 江苏省地质调查研究院 | Ecological disaster identification and motion prediction method and system |
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| CN117113796B (en) * | 2023-10-24 | 2024-02-27 | 国家海洋局北海预报中心((国家海洋局青岛海洋预报台)(国家海洋局青岛海洋环境监测中心站)) | Large jellyfish medium-term drift set forecasting method considering autonomous movement |
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