CN109657200B - Method for determining burst probability of cyanobacterial bloom in lake reservoir - Google Patents
Method for determining burst probability of cyanobacterial bloom in lake reservoir Download PDFInfo
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- CN109657200B CN109657200B CN201811479966.2A CN201811479966A CN109657200B CN 109657200 B CN109657200 B CN 109657200B CN 201811479966 A CN201811479966 A CN 201811479966A CN 109657200 B CN109657200 B CN 109657200B
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Abstract
The invention discloses a method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs, which comprises the following steps: obtaining algae species, density and biomass data in the water body through field sampling and indoor identification; measuring by a water quality instrument to obtain water quality data; obtaining dominance of each alga according to species, density and biomass data; determining main driving factors of dominant species according to the algae dominance degree and water quality data; and predicting the blue algae outbreak probability according to the main driving factors of the dominant species. The method utilizes the actually measured algae and water quality data, identifies dominant species, determines main driving factors, and finally predicts the blue algae outbreak probability according to a blue algae outbreak probability prediction model. The invention does not depend on the measured data of the blue algae outbreak, has wide application range and plays an important role in the prevention and control of the blue algae bloom.
Description
Technical Field
The invention relates to a method, in particular to a method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs.
Background
With the development of industry and agriculture, a large amount of nutrient substances flow into rivers and lakes, the eutrophication degree of water bodies is increased continuously, and blue algae outbreaks happen frequently under the promotion of global warming. The mass propagation and the decay of the blue algae affect the dissolved oxygen content of the water body, so that aquatic animals die greatly and the water ecosystem is seriously damaged. Cyanobacterial bloom also produces various biological toxins, particularly microcystins which are the most common and most harmful and can damage organ tissues such as liver, kidney, heart, gonad and the like and nervous systems of vertebrates. The outbreak of the cyanobacterial bloom not only harms the health of the water ecosystem, but also seriously harms the drinking water safety and the life health of human beings.
At present, a plurality of blue-green algae outbreak prediction models mainly use uncertain data driving models such as a neural network and the like, the models focus on critical judgment of blue-green algae outbreak, and the outbreak risk and main driving factors of the blue-green algae outbreak cannot be analyzed for the environmental conditions without the outbreak of the water bloom. And the method is limited by regions and cannot be used in areas where blue-green algae do not burst, so that the application range of the blue-green algae is limited, the burst range and the burst time of the blue-green algae cannot be effectively forecasted, and the capability of human beings for prejudging and controlling the water bloom algae is weakened. Therefore, analyzing dominant species of algae based on statistical data of the algae species, screening main driving factors based on the dominant species, establishing a blue algae outbreak probability prediction model which is not limited by regions, and determining blue algae outbreak risk has important significance for preventing and controlling blue algae bloom.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs comprises the following steps:
1) obtaining algae species, density and biomass data in the water body through field sampling and indoor identification;
2) measuring by a water quality instrument to obtain water quality data including water temperature, pH, conductivity, dissolved oxygen, total nitrogen and total phosphorus;
3) obtaining dominance of each alga according to species, density and biomass data;
4) determining main driving factors of dominant species according to the algae dominance degree and water quality data;
5) and predicting the blue algae outbreak probability according to the main driving factors of the dominant species.
In step 3), the density is represented by D, the biomass is represented by B, and the dominance I of each species is calculated by the formula II:
in the formula, i represents the ith species, and a and b are weights.
Further, step 3) determines the weights a, b according to formula three:
wherein i is the ith species, D is density, and B is biomass.
And 4) determining dominant species based on the dominance degree, and further analyzing the relation between the dominance degree of the algae and water quality data by adopting a canonical correspondence analysis method to determine main driving factors of the dominant species.
The step 5) is specifically as follows:
a. calculating the blue algae outbreak probability according to a formula I:
wherein P represents the burst probability of blue algae, c is weight, n is the total number of main driving factors, and SjFor the j-th main driving factor measured value,
the optimal value of the j main driving factor;
b. equally dividing the monitoring range of the water quality factor into five grades, calculating the habitat suitability index of each grade, determining the optimal value range of the main driving factor, and taking the middle value of the range as the optimal value of the main driving factor;
c. and performing canonical correspondence analysis by using the main driving factors and the species data, when the value of the first sequencing axis is more than 50%, normalizing by using the absolute value of the Biplot scores value of the first sequencing axis to obtain the weight of each driving factor, and when the value of the first sequencing axis is less than 50%, normalizing by using the root mean square of the Biplot scores values of the first sequencing axis and the second sequencing axis to obtain the weight of each driving factor.
In conclusion, the outbreak probability of the blue algae in the water body can be determined according to the sampling data according to the steps.
The invention has the beneficial effects that:
compared with the prior art, the method for determining the burst probability of the blue algae in the water body analyzes dominant species and main driving factors through algae and water quality sampling data, calculates the burst probability of the blue algae according to a blue algae burst probability prediction model, has wide application range and does not depend on the measured data of the blue algae burst.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
A method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs comprises the following steps:
the method comprises the following steps: data acquisition:
(1) obtaining algae species, density and biomass data in the water body through field sampling and indoor identification;
(2) water quality data including water temperature, pH, conductivity, dissolved oxygen, total nitrogen and total phosphorus are obtained through measurement of a water quality instrument.
Step two: determining the dominance degree:
(1) and (3) obtaining the density and biomass of various algae according to the sampling data, wherein the density is represented by D, the biomass is represented by B, and the dominance degree of each species is calculated by a formula II and is represented by I.
The formula II is as follows:
in the formula, i represents the ith species, and a and b are weights.
(2) And determining weights a and b according to a formula III.
The formula III is as follows:
wherein i is the ith species, D is density, and B is biomass.
Step three: dominant species major driver determination:
determining dominant species based on the dominance degree, further adopting a canonical correspondence analysis method to analyze the relationship between the algae dominance degree and the water quality data, and determining the main driving factors of the dominant species.
Step four: predicting the blue algae outbreak probability:
(1) and calculating the blue algae outbreak probability according to the formula I.
The formula I is as follows:
wherein P represents the burst probability of blue algae, c is weight, n is the total number of main driving factors, and SjFor the j-th main driving factor measured value,
the optimum value of the j-th main driving factor.
(2) The monitoring range of the water quality factor is divided into five grades, the habitat suitability index of each grade is calculated, the optimal value range of the main driving factor is determined, and the middle value of the range is used as the optimal value of the main driving factor.
(3) And performing canonical correspondence analysis by using the main driving factors and the species data, when the value of the first sequencing axis is more than 50%, normalizing by using the absolute value of the Biplot scores value of the first sequencing axis to obtain the weight of each driving factor, and when the value of the first sequencing axis is less than 50%, normalizing by using the root mean square of the Biplot scores values of the first sequencing axis and the second sequencing axis to obtain the weight of each driving factor.
In conclusion, the outbreak probability of the blue algae in the water body can be determined according to the sampling data according to the steps.
The method fully utilizes the algae and water quality data obtained by sampling, identifies dominant species representative algae communities to determine main driving factors, calculates the blue algae outbreak probability according to the blue algae outbreak probability prediction model, and can guide the blue algae prevention and treatment work. The method can determine and research the blue algae outbreak probability of the water body without actually measured data of blue algae outbreak, and the application range is not limited by whether the blue algae outbreak of the water body is researched.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs is characterized by comprising the following steps: the method comprises the following steps:
1) obtaining algae species, density and biomass data in the water body through field sampling and indoor identification;
2) measuring by a water quality instrument to obtain water quality data including water temperature, pH, conductivity, dissolved oxygen, total nitrogen and total phosphorus;
3) obtaining dominance of each alga according to species, density and biomass data;
4) determining main driving factors of dominant species according to the algae dominance degree and water quality data;
5) predicting the blue algae outbreak probability according to the main driving factors of the dominant species; the step 4) adopts a canonical correspondence analysis method to analyze the relationship between the algae dominance and the water quality data and determine the main driving factors of dominant species, and the step 5) specifically comprises the following steps:
a. calculating the blue algae outbreak probability according to a formula I:
wherein P represents the burst probability of blue algae, c is weight, n is the total number of main driving factors, and SjFor the j-th main driving factor measured value,
the optimal value of the j main driving factor;
b. equally dividing the monitoring range of the water quality factor into five grades, calculating the habitat suitability index of each grade, determining the optimal value range of the main driving factor, and taking the middle value of the range as the optimal value of the main driving factor;
c. and performing canonical correspondence analysis by using the main driving factors and the species data, when the value of the first sequencing axis is more than 50%, normalizing by using the absolute value of the Biplot scores value of the first sequencing axis to obtain the weight of each driving factor, and when the value of the first sequencing axis is less than 50%, normalizing by using the root mean square of the Biplot scores values of the first sequencing axis and the second sequencing axis to obtain the weight of each driving factor.
2. The method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs according to claim 1, which comprises the following steps: in the step 3), the density is represented by D, the biomass is represented by B, and the dominance I of each species is calculated by the formula II:
wherein i is the species i,
and b is a weight.
3. The method for determining the burst probability of cyanobacterial bloom in lakes and reservoirs according to claim 2, which comprises the following steps: step 3) determining the weight according to the formula III
、b：
Wherein i is the ith species, D is density, and B is biomass.
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| CN110414051B (en) * | 2019-06-27 | 2023-04-18 | 长江水资源保护科学研究所 | Ecological water demand accounting method for inhibiting river water bloom |
| CN110991047B (en) * | 2019-12-04 | 2023-04-25 | 珠江水利委员会珠江水利科学研究院 | Rapid early warning method for blue algae bloom in water source area reservoir |
| CN112345473B (en) * | 2020-10-23 | 2021-08-24 | 中国水利水电科学研究院 | Method for identifying dissolved oxygen control factors of thermal stratification reservoir |
| CN115795367A (en) * | 2023-01-29 | 2023-03-14 | 湖南大学 | Algal bloom outbreak prediction method based on machine learning and application |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010077922A1 (en) * | 2008-12-17 | 2010-07-08 | LiveFuels, Inc. | Systems and methods for reducing algal biomass |
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN102141517A (en) * | 2011-01-05 | 2011-08-03 | 中国科学院南京地理与湖泊研究所 | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake |
| CN107292436A (en) * | 2017-06-16 | 2017-10-24 | 北京工商大学 | Blue-green alga bloom Forecasting Methodology based on nonlinear kinetics temporal model |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101533000B (en) * | 2009-03-05 | 2012-07-25 | 重庆大学 | Method for constructing water eutrophication risk analysis model |
| CN104112080B (en) * | 2014-07-29 | 2016-03-02 | 武汉大学 | The quick method for early warning of water bloom of water body risk |
-
2018
- 2018-12-05 CN CN201811479966.2A patent/CN109657200B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010077922A1 (en) * | 2008-12-17 | 2010-07-08 | LiveFuels, Inc. | Systems and methods for reducing algal biomass |
| CN102135531A (en) * | 2010-12-24 | 2011-07-27 | 中国科学院南京地理与湖泊研究所 | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours |
| CN102141517A (en) * | 2011-01-05 | 2011-08-03 | 中国科学院南京地理与湖泊研究所 | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake |
| CN107292436A (en) * | 2017-06-16 | 2017-10-24 | 北京工商大学 | Blue-green alga bloom Forecasting Methodology based on nonlinear kinetics temporal model |
Non-Patent Citations (2)
| Title |
|---|
| A simple tool for the early prediction of the cyanobacteria Nodularia spumigena bloom biomass in the Gulf of Finland;Madis-Jaak Lilover et al;《OCEANOLOGIA》;20061231;第48卷(第S期);第8-10页 * |
| 北京城市水华预警指标体系研究;黄振芳 等;《人民黄河》;20100531;第32卷(第5期);第213-229页 * |
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