CN102141517B - Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake - Google Patents
Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake Download PDFInfo
- Publication number
- CN102141517B CN102141517B CN2011100006767A CN201110000676A CN102141517B CN 102141517 B CN102141517 B CN 102141517B CN 2011100006767 A CN2011100006767 A CN 2011100006767A CN 201110000676 A CN201110000676 A CN 201110000676A CN 102141517 B CN102141517 B CN 102141517B
- Authority
- CN
- China
- Prior art keywords
- blue
- lake
- green algae
- green
- bloom
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Micro-Organisms Or Cultivation Processes Thereof (AREA)
Abstract
The invention discloses a method for predicting the water area where water bloom of blue algae occurs first next year in a large shallow lake, which is characterized by comprising: based on the area and characteristics of water, setting a monitoring point for sampling water and bottom sediment; characterizing the time and spatial distribution of blue algae by using pigment analysis; characterizing the activity of blue algae in all lake areas by using photosynthesis activity and esterase activity of cells; acquiring meteorological data of the lake areas, and determining an effective wind field; and building an ecological dynamic module to predict the area where the water bloom of blue algae occurs first next year. In the method disclosed by the invention, the biomass and growth activity of the blue algae and the effective wind field are regarded as the common triggering factors of the formation of water bloom, the growth and transport of the blue algae and the driving factors of the formation of the water bloom are analyzed to estimate the change law of the ecological pattern of overwintering and resurrection of blue algae, the formation of water bloom of blue algae is predicted from multiple angles, parameters are integrated by the module, and the prediction result is directly displayed in a blue algae biomass contour map mode according to a time sequence.
Description
Technical field
The present invention relates to a kind of large-scale shallow water lake blue algae wawter bloom method for early prediction; Particularly relate to a kind of large-scale shallow water lake blue-green alga bloom in next year the Forecasting Methodology in waters takes place first; Specifically be through pigment analysis, algae photosynthesis and activity analysis, effective wind field analysis etc.; Combination model prediction, to next year blue-green alga bloom the waters takes place at first carries out early prediction.
Background technology
Because the lake eutrophication problem is difficult to solved in a short time at all; For a comparatively long period of time; Be difficult to stop the generation of the annual blue-green alga blooms of water body such as lake, therefore cutting down lake nutritive salt, when fundamentally blocking the nutriment source of blue-green alga bloom growth; Development blue-green alga bloom forecasting techniques will help to reduce ecological hazard and the health risk that blue-green alga bloom brings.
Domestic and international research is primarily aimed at the blue-green alga bloom that has formed and carries out short time yardstick early warning prediction, analyzes the relation of algal bloom formation and meteorological hydrologic condition, nutritive salt level.For example, the research of the detection that European Union carried out blue-green alga bloom in 1999, monitoring and forecast has developed the approach that prediction through remote sensing technology algal tufa is taken place.The expert on Japan crosses artificial neural network and finds that the concentration of chlorophyll a can characterize the total biomass of algae, and can predict algal bloom.U.S. ocean and weather bureau have developed red tide short-term forecasting system, and the red tide in the monitoring and the forecast Gulfian forecast weekly 2 times in the season that red tide is arranged.In China, Chinese Academy of Sciences Nanjing geographical with lake research institute in 2007 beginnings in Taihu Lake summer wawter bloom formation carry out wawter bloom short-term forecasting and issue period, the Distribution Pattern of important water head site in Taihu Lake and big Taihu Lake chlorophyll concentration in following 3 days of every half cycle prediction.
Yet the research for prediction wawter bloom in coming year formation trend on the long period yardstick does not at present also have relevant report.The long time scale prediction need to consider the phase of surviving the winter provenance Distribution Pattern, recovery phase algae grows, a series of processes such as migrate, also higher to the requirement of weather forecast, the problem that relates to is more more complicated than short-term forecasting.Yet the long time scale prediction will help to improve the decision-making capability of environmental management department, the technology in the raising lake treatment and the specific aim and the science of engineering measure.Therefore, in lake ecological diaster prevention and control system, be necessary to introduce new and more effective detection and analytical approach, set up the blue-green alga bloom method for early prediction of a cover science.
Summary of the invention
The present invention provides a kind of large-scale shallow water lake blue-green alga bloom in next year that the Forecasting Methodology in waters takes place first; Utilization biology, ecological detection method; In conjunction with meteorological, remote sensing correlation technique; As far as possible truly the survive the winter whole ecological general layout of recovery of blue-green algae is carried out omnibearing monitoring, and through blue algae growth, migration rule, prediction blue-green alga bloom in the coming year initial scene.
In brief, the present invention with pigment analysis, algae photosynthesis and activity analysis, effectively wind field analysis etc. be basis, analyzes the driving factors of blue algae growth, migration and wawter bloom formation, thereby the blue-green algae ecological general layout Changing Pattern of recovering of surviving the winter is assessed; In conjunction with the ecodynamics forecast model, the waters is taken place next year in blue-green alga bloom first carry out early prediction.
Above-mentioned purpose of the present invention is achieved in that
The Forecasting Methodology in waters takes place in a kind of large-scale shallow water lake blue-green alga bloom in next year first, it is characterized in that: based on coverage of water and feature-set monitoring point water body and bed mud are taken a sample; Utilize pigment analysis to characterize the spatial and temporal distributions of blue-green algae, it is active to utilize cell photosynthesis activity, esterase active to characterize each lake region blue-green algae; Gather the lake region weather data, confirm effective wind field; Setting up the ecodynamics model next year waters takes place first to blue-green alga bloom and predicts.
Forecasting Methodology of the present invention may further comprise the steps:
1), in the monitoring phase, respectively water body and bed mud are taken a sample in each monitoring point based on coverage of water and feature-set monitoring point;
Carry out the sampling of full waters April October to next year of annual Winter-Spring, every month at least once.The top layer bed mud that mud appearance cuts the 2cm of the superiors left and right thickness moves in the sealing bag.Water column is put in order in water sampling, mixing, and measure each item water quality parameter immediately.Gather three parts of parallel sample at each sampled point.
2) the algocyan content in mensuration water body and the bed mud utilizes pigment analysis to characterize the spatial and temporal distributions general layout that blue-green algae survives the winter and recovers;
The mensuration of algocyan can adopt AAS to measure.Before the algocyan assay, earlier sample to be handled, sample treatment is:
The processing of water sample: measure 100ml water sample filter paper suction filtration.Filter paper is shredded adding 0.05 M pH7.0 Tris damping fluid grind, in 4 ℃ of following dark, leave standstill and extract 8-10h.Sample is centrifugal and supernatant is transferred in the volumetric flask.Last constant volume is measured algocyan content.
The processing of bed mud sample: take by weighing the bed mud sample after a certain amount of the thawing, calculate water percentage.Take by weighing the ground sample in the 5g left and right sides and put into mortar, add 0.05 M pH, 7.0 Tris damping fluids and grind, under 4 ℃ of dark conditions, leave standstill 8-10h.Sample is centrifugal and supernatant is settled to 10mL with 90% acetone.This liquid to be measured is used to measure algocyan content.
Through fluorescent spectrophotometer assay fluorescent intensity, with fluorescence intensity algocyan concentration is made working curve, calculate final content according to bed mud example weight or volume of water sample again.
According to algocyan content in water body and the bed mud, can confirm the biomass of different waters blue-green algae in different times water body and bed mud, the spatial and temporal distributions general layout of reflection blue-green algae.Can know that from the description of hereinafter in the ecodynamics model, algocyan content can be used as the provenance amount and brings forecast model into.
3) photosynthetic activity and the esterase active of frustule in the mensuration water body, the time series of sign blue-green algae recovery process is revised the growth in situ amount of blue-green algae in conjunction with the provenance amount;
The sample of each collection is measured the photosynthesis ability and the esterase active of frustule in the water body respectively, wherein carry out the analysis of algae photosynthetic activity, analyze algae cell activity through the esterase detection method through the pigment fluorescence analyser.
The concrete operations that the algae photosynthetic activity is analyzed are following: get the water sample of just adopting the back measuring cup of packing in right amount; Dark adatpation 15min opens measuring light (ML) earlier; Treat that initial fluorescence obtains Fo after stable; Open saturated light intensity (ST), obtain the maximum photosynthetic efficiency of the Fm and the photosystem II of sample, and measure the quick optical response curve.
The concrete grammar of esterase detection method analysis algae cell activity is following: in 1ml algae liquid, add an amount of fluorescent dye (Fluorescence dieastrate), after the dyeing of room temperature lucifuge, detect the intensity that enzyme is lived through flow cytometer.
The Fv/Fm, the maximum photosynthetic efficiency of photosystem II and the photosynthesis ability that the quick optical response curve can characterize frustule comprehensively that obtain through the photosynthetic activity analysis, and esterase active detects through the power of the back FL1 passage fluorescence signal that dyes and representes the esterase active size.The both characterizes the activity sequence in time of frustule from different perspectives; Through defining the activity of blue-green algae; In conjunction with the provenance amount growth in situ amount of blue-green algae is revised, in the ecodynamics model prediction, can be confirmed growth in situ amount shared weight in forecast model of blue-green algae according to the activity of blue-green algae.
4) gather the lake region weather data, confirm effective wind field, can predict the migration in the blue-green algae recovery process according to meteorological condition;
(a) collection of data: utilize automatic weather monitoring station to collect various weather datas, comprise wind speed and direction, illumination, temperature etc.
(b) analyze effective wind field blue-green algae survive the winter and the recovery process in driving action.Method is following: different waters are according to the actual observation data; Confirm that blue-green algae lateral transfer main wind speed in lake region is interval, for example Taihu Lake is generally 1.9-3.1m/s, screens the frequency of the interval different wind directions of this wind speed; Make effective wind direction wind rose diagram frequently, confirm to monitor every month effective wind field in waters with this.
5) based on ELCOM-CAEDYM mode construction blue-green alga bloom ecodynamics model, the formation of blue-green alga bloom is predicted, being comprised:
(a) collection of data: Parameterization Scheme comprises lake terrain data, weather data, lake physicochemical data (comprising that nutritive salt distributes).
(b) foundation of ecodynamics model and prediction: because the formation of initial blue-green alga bloom is to be the basis with certain blue-green algae biomass (provenance amount); Under the common influence of the migration under its growth activity and the wind field condition, form; Therefore for the object of the invention, blue-green algae migration and blue-green algae growth in situ (provenance and active factors) that effective wind field is caused are regarded as the common triggering factor that wawter bloom forms.According to Monitoring Data calibration model parameter, select grid, according to model the blue-green algae biomass in each grid is predicted.
In the actual numerical value model construction, select measured value of experiment, similar system reference value and literature value as the model parameter initial value respectively according to the difference of parameter region degree, application data assimilation technology is optimized then.For high strength region parameter, adopt field experiment method acquisitions such as monitoring lake original position ecological experiment; Blue algae growth speed for example, optimum growth temperature etc.For medium tenacity region parameter, generally adopt the similar system reference value; Suspended particle degradation rate constant for example, nutrient remineralization coefficient etc.In addition, for indivedual free parameters, for example suspended particle transfer rate constant etc. on modeling result and in-situ observation data basis, adopts the Model Parameter Optimization method finally to confirm.
(c) result output: with time series pattern output result stage by stage, the shortening of predicted time will improve prediction accuracy.Can use surfer software to draw the output of blue-green algae biomass contour map, also can under the support of Arcgis condition, export blue-green algae biomass contour map or the directly output of programming back.
Advantage of the present invention and effect: the present invention gathers water sample, mud appearance through lay the monitoring point, many places in the lake region; Carry out multinomial physics and chemistry, cell biology analysis; And combination weather data; Start with from recovery amount and these two key factors of migration amount of blue-green algae, to blue-green algae survive the winter the recovery ecological general layout Changing Pattern assess.The inventive method is regarded as the common trigger factor that wawter bloom forms with blue-green algae biomass, growth activity and effective wind field; Analyze the driving factors that blue algae growth, migration and wawter bloom form; And through model each parameter is integrated, the form that predicts the outcome with blue-green algae biomass contour map is represented by the time sequence intuitively.The inventive method is predicted the formation of blue-green alga bloom from different perspectives, through mutual checking (as: pigment and the activity analysis of correlation analysis; Photosynthesis and esterase active or the like), thus prediction accuracy improved.The inventive method can be predicted the large-scale shallow water lake initial scene of blue-green alga bloom in next year, makes environmental management department carry out corresponding preparation in advance, and feasibility and the specific aim for lake treatment engineering measure (like the dredging in winter) provides foundation simultaneously.
Describe the present invention below in conjunction with specific embodiment.Protection scope of the present invention is not exceeded with embodiment, but is limited claim.
Description of drawings
Fig. 1 Taihu Lake monitoring site distribution schematic diagram;
Survive the winter in Fig. 2 A, 2B 2007-2008 Taihu Lake water body and bed mud time of blue-green algae distributes;
Survive the winter in Fig. 3 A, 3B 2008-2009 Taihu Lake water body and bed mud time of blue-green algae distributes;
The variation of Fig. 4 2008-2009 Taihu Lake water body blue-green algae photosynthetic activity;
Survive the winter in the water body of Fig. 5 2008-2009 Taihu Lake N5 point position variation of blue-green algae esterase active;
Fig. 6 2007-2009 survives the winter in Taihu Lake in earlier stage and effective wind field of recovery phase;
The satellite remote sensing figure of Fig. 7 initial blue-green alga bloom formation in Taihu Lake in 2008;
The satellite remote sensing figure of Fig. 8 initial blue-green alga bloom formation in Taihu Lake in 2009;
Fig. 9 2008-04-25 Taihu Lake MODIS monitoring result;
Figure 10 A-10C is different, and zero-time is simulated predicting the outcome of wawter bloom on the 25th in April in 2008 stage by stage;
Wherein 10A:2008-01-20 is the initial value analog result; 10B:2008-02-20 is the initial value analog result; 10C:2008-03-20 is the initial value analog result.
Embodiment
Adopt the inventive method; Through surviving the winter and the analysis of its provenance quantity of recovery phase, growth activity and migratory movement to the Taihu Lake blue-green algae; Specifically comprise pigment analysis, algae photosynthesis and esterase active analysis, effective wind field analysis etc.; Can judge the most probable territory that just floods of blue-green alga bloom in the coming year, combine the ecodynamics forecast model again, to next year blue-green alga bloom the waters takes place first carries out early prediction.
Sampled point is provided with: based on coverage of water and feature-set monitoring point, guarantee northern Taihu Lake, southwestern Taihu Lake reaches blue-green algae such as each arm of lake zone that takes place frequently and comprises wherein, and Taihu Lake, southeast pasture and water are intensive, wawter bloom seldom occurs, do not monitor.
1. measure algocyan content, utilize pigment analysis that the spatial and temporal distributions general layout of the blue-green algae provenance of surviving the winter winter is described, and the blue-green algae provenance is estimated the influence that next year, the waters took place blue-green alga bloom first and predicted
Set monitoring point, 14 place (as shown in Figure 1) based on coverage of water and characteristic in Tai Lake, carry out the sampling of full lake annual April October to next year, sampling in every month at least once.When mud appearance is gathered, use internal diameter to gather the bed mud sample as the column bottom sampler of 62mm, the top layer bed mud that cuts the 2cm of the superiors left and right thickness moves in the sealing bag.During water sampling, with the whole water column of the long PVC column hydrophore collection of self-control 2.5m, mixing.Three parts of parallel sample of each sample collecting, wherein (Yellow Spring Instruments USA) measures water quality parameter to water sample with multi-functional water quality appearance (YSI6600-V2) immediately.
Measure the 100ml water sample, with Whatman GF/C glass fiber filter paper suction filtration.Filter paper is shredded adding 0.05 M pH7.0 Tris damping fluid grind, in 4 ℃ of following dark, leave standstill and extract 8-10h.Centrifugal 5min under the 4000g rotating speed is transferred to supernatant in the volumetric flask then.Last constant volume is measured algocyan content in the water body.Equally, take by weighing the bed mud sample after a certain amount of the thawing, to constant weight, and take by weighing dry weight, calculate water percentage in room-dry.Take by weighing the ground sample in the 5g left and right sides and put into mortar, add 0.05 M pH, 7.0 Tris damping fluids, carefully grind 2-5min, under 4 ℃ of dark conditions, leave standstill 8-10h.With the centrifugal 5min of the rotating speed of 4000g, supernatant is transferred in the 10mL volumetric flask then, is settled to 10mL with 90% acetone at last and is used for measuring bed mud algocyan content.
Measure algocyan content (Yan Rong, 2004) through Tianjin, island fluorospectrophotometer RF-5301, excitation wavelength is 620nm, wavelength of transmitted light 647nm, and sweep velocity 60nm/min makes working curve with fluorescence intensity to algocyan concentration.Thereby the fluorescence intensity of working sample extract is tried to achieve algocyan content in the sample on working curve under the condition identical with standard series, calculates final content according to bed mud example weight or volume of water sample again.
According to pigment analysis result can obtain surviving the winter time and the Spatial Distribution Pattern of blue-green algae.Time, space distribution with the blue-green algae that survives the winter in 2007-2008 Taihu Lake water body (Fig. 2 A) and the bed mud (Fig. 2 B) are example; Can know by figure: after going into the winter; Northern waters bed mud in Taihu Lake and water body medium blue algae biomass are all less, and the blue-green algae biomass mainly concentrates on Central-South Taihu Lake and western Taihu Lake.The blue-green algae distribute bits is in the southwestern lake region and the southern middle of a lake in the bed mud, and that water body distributes is more changeable.And subtract gathering of water body blue-green algae after getting into January, make 1-3 in the month water body do not have tangible blue-green algae accumulation area.Dec, water body and bed mud blue-green algae distributed areas had significant correlation property (p < 0.01), single month all the other each months water body and the bed mud blue-green algae distribute and do not have significant correlation property.
But carry out statistical study for all time point positions in the cycle of surviving the winter, the result shows, survive the winter stage bed mud and water body blue-green algae distribute and have significant correlation property (p < 0.01).Go up from the time in addition, for the time interval of the rapid recovery that is formed with material impact of wawter bloom, the March that blue-green algae in the bed mud is distributed with February point analyze and to show, both have significant correlation property (p < 0.01).Similarly; Analysis result to time of the blue-green algae that survives the winter in different monitoring points 2008-2009 Taihu Lake water body (Fig. 3 A) and the bed mud (Fig. 3 B) distributes can be known; The blue-green algae that survives the winter in the water body of Taihu Lake mainly is distributed in northern Taihu Lake and western Taihu Lake, winter the water body blue-green algae sinking and death, make the also corresponding growth of blue-green algae content in the bed mud of Taihu Lake; And has a regular hour hysteresis quality, Taihu Lake bed mud blue-green algae recover quick growth region and wintering ground distribute closely related (p < 0.01).
Conclusion: through water body and bed mud algocyan pigment analysis, discovery next year blue-green algae recovers regional fast and the blue-green algae wintering ground distributes has tangible correlativity, also consistent with the initial generation area of blue-green alga bloom.This just explanation and combine its transition process (, can predict) according to zone of surviving the winter (promptly " provenance ") of blue-green algae and defining of recovery amount through meteorological condition like hereinafter, can to the coming year blue-green alga bloom intensity predict with initial wawter bloom with forming.
2. utilize the cytoactive analysis that the time series of the recovery process of blue-green algae is predicted
Carry out water sample full lake sampling annual April October to next year in each monitoring point, and sampling in every month at least once.With the whole water column of the long PVC column hydrophore collection of self-control 2.5m, mixing is gathered three parts of parallel sample at each sampled point.Get the water sample of just adopting the back measuring cup of packing in right amount, select for use Phyto-PAM (WALZ Germany) to carry out the analysis of algae photosynthetic activity, Control Software is Phyto win1.47 (Walz).Sample dark adatpation 15min opens measuring light (ML) earlier, treats that initial fluorescence obtains Fo after stable, opens saturated light intensity (ST), obtains the Fm of sample, and passes through formula:
Fv/Fm?=?(Fm?-?F0)/Fm
Thereby obtain the maximum photosynthetic efficiency of photosystem II.Open actinic light (AL) when measuring the quick optical response curve, the light application time of each intensity is 20s, obtains a series of relative electron transport speed, and match obtains the quick optical curve.
Analyze algae cell activity through esterase detection method (Franklin, 2001).In 1ml algae liquid (filtering), add 25ml Fluorescence dieastrate through 300 mesh sieve thin,tough silk; After the dyeing of room temperature lucifuge; At flow cytometer (BD; The U.S.) (530+15nm) can detect fluorescence signal, characterize enzyme through fluorescence signal and live strong and weak to go up FL1 passage after 488nm laser excitation.Each preceding standard bead (Caltag, the U.S.) that passes through of esterase active that detects is 200mW, and the signal intensity of FL1 passage is proofreaied and correct and is definite value after the 488nm laser excitation, makes the fluorescence intensity of each detection have comparability.
Said determination can know that the time and space of monitoring waters blue-green algae photosynthetic capacity and esterase active distributes, like Fig. 4 and shown in Figure 5.Can know by Fig. 4, the Fv/Fm value of Taihu Lake water body medium blue algae from October, 2008-Dec Taihu Lake blue-green algae photosynthesis ability stronger, but numerical value reduces gradually, and green alga is relatively low, possibly there be the inhibition of blue-green alga bloom to chlorella growth in this.January-March, the photosynthesis ability of green alga strengthened gradually, and the photosynthesis ability of blue-green algae almost disappears, and had reflected the blue-green algae status of processes of surviving the winter.April, the photosynthesis of blue-green algae recovered rapidly, and wawter bloom takes place.This shows that the survive the winter different phase of recovery of blue-green algae photosynthesis and blue-green algae is corresponding, and the fast quick-recovery wawter bloom initial of blue-green algae photosynthesis ability with it be formed with very strong time consistency.The contrast May we can find out that diatom disappears basically, blue-green algae accounts for absolute advantage.
The change in time and space of water body N5 point position Microcystis aeruginosa esterase active of surviving the winter in the cycle is as shown in Figure 5, and the fluorescence intensity of FL1 passage has characterized the power of esterase active.Can find out that Taihu Lake water body blue-green algae esterase active is the trend of reduction after the October on the whole; Last till February always; Touch the bottom to February, show that low temperature has inhibiting effect to the power of blue-green algae esterase active, since of the rising of February next year owing to water temperature; Blue-green algae begins recovery, and the frustule esterase active begins slow rising. and we find that also the higher regional esterase active of blue-green algae biomass on the contrary can be active lower than closing on the lower zone of biomass.Generally speaking, the peak period of esterase active appears at the 3-4 month of October and next year.The time of the formation of this and blue-green alga bloom also has tangible correlativity.
Conclusion: utilize the blue-green algae cytoactive of surviving the winter that the recovery time course of blue-green algae is predicted.The variation of blue-green algae cytoactive in the recovery process, particularly blue-green algae photosynthesis grows out of nothing, and the process that increases gradually is with the tangible synchronism of being formed with of initial blue-green alga bloom.Through the activity of blue-green algae, in conjunction with the provenance amount growth in situ amount of blue-green algae is revised, confirm growth in situ amount shared weight in model of blue-green algae in other words according to the activity of blue-green algae.When for example the blue-green algae activity was extremely low, although the provenance amount is abundant, the growth in situ amount still can be ignored, and the distribution of blue-green algae is mainly determined by its migration amount; Along with the active increase of blue-green algae, the growth in situ amount constantly increases and then distribution is exerted an influence.
3. meteorological condition is predicted the recovery process of blue-green algae
Utilize annual April November to next year automatic weather monitoring station (U.S. NOVALYNX system) whenever to collect surface, lake weather data, comprise wind speed and direction, illumination, temperature etc. at a distance from 5 minutes.In the wind speed and direction data; The data (being effective wind speed) of the main wind speed of screening Taihu Lake blue-green algae lateral transfer interval (1.9-3.1m/s); On average divide 16 zones by wind direction; By the frequency of calculating 16 wind direction zone effective wind speeds month, make effective wind direction wind rose diagram frequently, roughly confirm effective wind field of lake surface every month with 4-5 the highest direction of the frequency.Consider that sinkage can appear in the phase of surviving the winter (January-February) blue-green algae, the content in water body is lower, wind field to the driving action of blue-green algae migration a little less than, therefore ignore.And in the description of following ecodynamics model, day part wind field data all are applied, and the two is basically identical as a result, and this has also confirmed wind rose map, and to ignore the phase wind field of surviving the winter be rational.
Survive the winter shown in early stage and the effective wind field of recovery phase according to Fig. 6; 2007-2008 survive the winter in Taihu Lake blue-green algae mainly receive northwest and northerly to influence; Distributed areas progressively southwester with middle of a lake regional change, the southwestern lake region and the middle of a lake become the main wintering ground of blue-green algae in 2007.In conjunction with in February, 2008 and March water body and the bed mud algocyan distribute (seeing Fig. 2 A, 2B), southwestern Taihu Lake and the middle of a lake are that algocyan increases comparatively fast, also are the highest zones of concentration.To April 18 (Fig. 7) in 2008, formed obvious blue-green alga bloom aggregation zone in southwestern lake region.It is visible to form the wawter bloom process from blue-green algae recovery spring in 2008, and blue-green alga bloom was risen in southwestern lake region in 2008, and this 07 year blue-green algae zone of surviving the winter just, provenance is abundant.The recovery of blue-green algae takes place and increases the zone that forms wawter bloom promptly is last one year of the wawter bloom zone of surviving the winter.The situation of 2008-2009 is also similar, the early stage of surviving the winter with the recovery phase mainly with southeaster to being main, wawter bloom is survived the winter regional and the quick growth region of blue-green algae in the coming year also concentrates on the north and northwest corner.According to remote sensing image (Fig. 8), also one band formation at first of blue-green alga bloom in by the end of April, 2009 in the Zhu Shan gulf.Thereby grasp the survive the winter variation in zone of blue-green algae in winter and can judge the zone that the coming year, blue-green algae recovered the earliest, thereby improve early stage blue-green alga bloom accuracy of the forecast.
Interpretation of result: utilize effective wind field to analyze, comprise the accumulation of the provenance in early stage of surviving the winter the migration of blue-green algae, and the migration of blue-green algae in the recovery process, thus to bloom blue algae recovery in spring and the coming year initial formation time waters predict.
4. set up the ecodynamics model early prediction is carried out in the formation of blue-green alga bloom.
According to above blue-green algae recovery influencing factors, based on the ELCOM-CAEDYM pattern, use lake, Taihu Lake terrain data, weather data, lake physicochemical data, make up Taihu Lake blue-green alga bloom ecodynamics model.Terrain data comprises the depth of water and water front.Weather data comprises wind speed, wind direction, solar radiation, temperature, relative humidity, atmospheric pressure, rainfall amount etc.The lake physicochemical data comprises lake water flow velocity, the flow direction, lake water concentration of nitrogen and phosphorus (solubilised state, particulate form), water temperature, dissolved oxygen, suspension, transparency, lake mud nitrogen and phosphorus content etc.; The pigment data of algae such as water body and bed mud blue-green algae.In flow, concentration of nitrogen and phosphorus (solubilised state, particulate form), water temperature, dissolved oxygen, suspension of going into the lake runoff in addition etc. is also included within.
The blue-green algae migration that blue-green algae biomass, growth activity and effective wind field are caused is regarded as the common trigger factor that wawter bloom forms, thereby the relation between each factor of forecast model is following:
F=?1?(Nt)×?2（L）×?3（V）
Wherein 1 (Nt) is the wawter bloom probability of happening that t is caused by amount of algae constantly; The wawter bloom probability of happening of 2 (L) for causing by the blue algae growth activity; The wawter bloom probability of happening that 3 (V) cause for effective wind field condition.
According to Monitoring Data; Analyze the gained threshold value in conjunction with above-mentioned pigment, cytoactive, meteorological condition etc.; Verification model operation result reliability; And each parameter of calibration model, according to the time series statistical data and the weather prognosis data of Taihu Lake physicochemical data, weather data, predict the change in time and space of blue-green algae biomass in the following certain hour.Model meshes is selected 250m ' 250m, and time step is 5 minutes, operation under the windows environment.According to the length of predicted time, calculate the biomass data in following each grid stage by stage.
Use model of the present invention and simulate the initial result who forms of wawter bloom on April 25th, 2008 stage by stage, and contrast with the MODIS monitoring result (see figure 9) of reality in different zero-times.Visible like Figure 10 A-C, analog result and actual monitoring result that initial value is respectively in January, 2008, February, March have certain similarity, and along with the shortening accuracy of predicted time also increases.This result has further verified the operability of the blue-green alga bloom early prediction of long time scale, has shown that also the inventive method has certain application value in the blue-green alga bloom early prediction of Taihu Lake simultaneously.
Claims (6)
1. the Forecasting Methodology in waters takes place in large-scale shallow water lake blue-green alga bloom in next year first, it is characterized in that: based on coverage of water and feature-set monitoring point water body and bed mud are taken a sample; Utilize pigment analysis to characterize the spatial and temporal distributions of blue-green algae; It is active to utilize cell photosynthesis activity, esterase active to characterize each lake region blue-green algae; Gather the lake region weather data, confirm effective wind field; Setting up the ecodynamics model next year waters takes place first to blue-green alga bloom and predicts;
Said method comprising the steps of:
1) based on coverage of water and feature-set monitoring point, carry out the sampling of full waters annual April October to next year, and every month at least once, respectively water body and bed mud taken a sample in each monitoring point;
2) measure algocyan content in water body and the bed mud, confirm the biomass of different waters blue-green algae in different times water body and bed mud, the survive the winter spatial and temporal distributions general layout of recovery phase provenance of sign blue-green algae;
3) measure the photosynthetic activity and the esterase active of frustule in the water body, and combine the provenance amount that the growth in situ amount of blue-green algae is revised, characterize the time series of blue-green algae recovery process;
4) gather the lake region weather data, confirm effective wind field, the migration amount in the blue-green algae recovery process is predicted according to meteorological condition;
5), the waters is taken place next year in blue-green alga bloom first predict based on ELCOM-CAEDYM mode construction blue-green alga bloom ecodynamics model;
Step 5) is set up the ecodynamics model and blue-green alga bloom is carried out forecast method may further comprise the steps:
(a) collection comprises lake terrain data, weather data, lake physicochemical data parameter;
(b) foundation of ecodynamics model and prediction: blue-green algae migration and blue-green algae growth in situ that blue-green algae biomass, effective wind field are caused are regarded as the common triggering factor that wawter bloom forms; The calibration model parameter; Select grid, the biomass in each grid is predicted;
(c) result output: draw blue-green algae biomass contour map, with time series pattern output result stage by stage;
The common trigger factor that blue-green algae moves and the blue-green algae growth in situ forms as wawter bloom that described ecodynamics model causes blue-green algae biomass, effective wind field, the relation between each factor is shown below,
F=1?(Nt)×?2（L）×?3（V）
Wherein 1 (Nt) is the wawter bloom probability of happening that t is caused by amount of algae constantly; The wawter bloom probability of happening of 2 (L) for causing by the blue algae growth activity; The wawter bloom probability of happening that 3 (V) cause for effective wind field condition.
2. the Forecasting Methodology in waters takes place large-scale shallow water according to claim 1 lake blue-green alga bloom in next year first, it is characterized in that: algocyan content adopts AAS to measure.
3. the Forecasting Methodology in waters takes place large-scale shallow water according to claim 1 lake blue-green alga bloom in next year first, it is characterized in that: the photosynthetic activity of frustule is measured and is adopted the pigment fluorescence analyser to carry out.
4. the Forecasting Methodology in waters takes place large-scale shallow water according to claim 1 lake blue-green alga bloom in next year first, it is characterized in that: the esterase active detection method is with after the frustule dyeing, detects the intensity of esterase active through flow cytometer.
5. the Forecasting Methodology in waters takes place in large-scale shallow water according to claim 1 lake blue-green alga bloom in next year first; It is characterized in that: effectively definite method of wind field is; According to the actual observation data; Confirm that blue-green algae lateral transfer main wind speed in lake region is interval, the frequency of screening the interval different wind directions of this wind speed is made effective wind direction wind rose diagram frequently.
6. the Forecasting Methodology in waters takes place in large-scale shallow water according to claim 1 lake blue-green alga bloom in next year first; It is characterized in that: described model parameter selects measured value of experiment, similar system reference value and literature value as the model parameter initial value respectively according to the difference of parameter region degree, after be optimized.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100006767A CN102141517B (en) | 2011-01-05 | 2011-01-05 | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100006767A CN102141517B (en) | 2011-01-05 | 2011-01-05 | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102141517A CN102141517A (en) | 2011-08-03 |
| CN102141517B true CN102141517B (en) | 2012-11-14 |
Family
ID=44409186
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100006767A Expired - Fee Related CN102141517B (en) | 2011-01-05 | 2011-01-05 | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102141517B (en) |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735671B (en) * | 2012-07-02 | 2014-07-02 | 中国科学院南京地理与湖泊研究所 | Method for determining point of dredging site of species sources of over-wintering cyanobacterial in large shallow lake |
| CN103439303B (en) * | 2013-08-09 | 2014-10-22 | 中国长江三峡集团公司 | Alga anabiosis and vertical migration monitoring device and monitoring method for early warning of algal bloom |
| CN103616490B (en) * | 2013-12-03 | 2015-09-23 | 中国科学院南京地理与湖泊研究所 | The method of a kind of large-scale shallow water lake bloom blue algae total inventory estimation |
| CN103743700B (en) * | 2014-01-17 | 2016-07-06 | 中国科学院南京地理与湖泊研究所 | A kind of large-scale shallow water lake blue algae wawter bloom MODIS satellite high-precision monitoring method |
| CN105606075B (en) * | 2015-12-18 | 2017-11-28 | 中国科学院南京地理与湖泊研究所 | A kind of method of discrimination of the large-scale shallow water lake wawter bloom Microcystis aeruginosa vertical characteristics pattern based on local wind speed |
| CN107292436B (en) * | 2017-06-16 | 2020-07-17 | 北京工商大学 | Cyanobacterial bloom prediction method based on nonlinear dynamics time sequence model |
| CN108675454B (en) * | 2018-04-28 | 2020-10-30 | 中国科学院南京地理与湖泊研究所 | Method for controlling whole process of blue algae bloom development in large shallow lake |
| CN108593849B (en) * | 2018-04-28 | 2021-03-09 | 中国科学院南京地理与湖泊研究所 | Method for judging overwintering period and blue algae control window period of lake blue algae |
| CN109657200B (en) * | 2018-12-05 | 2020-10-23 | 北京师范大学 | Method for determining burst probability of cyanobacterial bloom in lake reservoir |
| CN109711066B (en) * | 2018-12-28 | 2023-01-31 | 南开大学 | Shallow water type small lake and reservoir water bloom prediction method and prediction model |
| CN110686999B (en) * | 2019-10-08 | 2022-04-22 | 南京信息工程大学 | Extreme value measurement and calculation method for biomass of marine ecological disaster-causing macroalgae |
| CN112062236A (en) * | 2020-09-08 | 2020-12-11 | 中国科学院南京地理与湖泊研究所 | In-situ cleaning method for overwintering blue algae seed source |
| CN112735511B (en) * | 2021-01-13 | 2023-11-03 | 西北农林科技大学 | Cold damage cucumber PSII potential activity prediction method based on QGA-SVR |
| CN114324796B (en) * | 2021-12-28 | 2023-03-10 | 中国科学院南京地理与湖泊研究所 | Prediction method for attached filamentous algae blooms on aquatic plants |
| CN114821977B (en) * | 2022-06-27 | 2022-09-02 | 环球数科集团有限公司 | Ecological monitoring disaster early warning system based on satellite remote sensing monitoring |
| CN116819024B (en) * | 2023-06-28 | 2024-04-19 | 同济大学 | Simulation optimization method and device for lake and reservoir eutrophication ecological dynamic model |
| CN117113735B (en) * | 2023-10-25 | 2024-01-02 | 湖南大学 | Algal bloom intelligent early warning method and system based on multilayer ecological model |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101441206A (en) * | 2009-01-05 | 2009-05-27 | 中国环境科学研究院 | Test analysis method for lake eutrophication algae and sample collecting apparatus thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7132254B2 (en) * | 2004-01-22 | 2006-11-07 | Bowling Green State University | Method and apparatus for detecting phycocyanin-pigmented algae and bacteria from reflected light |
-
2011
- 2011-01-05 CN CN2011100006767A patent/CN102141517B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101441206A (en) * | 2009-01-05 | 2009-05-27 | 中国环境科学研究院 | Test analysis method for lake eutrophication algae and sample collecting apparatus thereof |
Non-Patent Citations (1)
| Title |
|---|
| 孔繁翔,等.《太湖蓝藻水华的预防、预测和预警的理论与实践》.《湖泊科学》.2009,第21卷(第3期),第314-328页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102141517A (en) | 2011-08-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102141517B (en) | Method for predicting water area where water bloom of blue algae occurs first next year in large shallow lake | |
| Werner et al. | Hydrogeology and management of freshwater lenses on atoll islands: Review of current knowledge and research needs | |
| Jiang et al. | Geomorphic evolution of the Yellow River Delta: Quantification of basin-scale natural and anthropogenic impacts | |
| Ibrakhimov | Spatial and temporal dynamics of groundwater table and salinity in Khorezm (Aral Sea Basin), Uzbekistan | |
| Thorarinsdottir et al. | Wind erosion of volcanic materials in the Hekla area, South Iceland | |
| Wang et al. | Distributions of recent gullies on hillslopes with different slopes and aspects in the Black Soil Region of Northeast China | |
| Tramblay et al. | Estimation of local extreme suspended sediment concentrations in California Rivers | |
| Gonzaga et al. | Development of a low-cost ultrasonic sensor for groundwater monitoring in coastal environments: validation using field and laboratory observations | |
| Wren et al. | Using lake sedimentation rates to quantify the effectiveness of erosion control in watersheds | |
| Mirramazani et al. | The Origin of River Sediments, the Associated Dust and Climate Change | |
| Mohamed et al. | Evaluation of the soil moisture content using GIS technique and SWAT model,(Wadi Al-Naft region: as a case study) | |
| Punwong et al. | Documenting a thousand years of environmental and anthropogenic changes on mangroves on the Bangkok coast, the upper Gulf of Thailand | |
| Chen et al. | Waterlogging risk assessment for winter wheat using multi-source data in the middle and lower reaches of Yangtze River | |
| DURDURAN et al. | Monitoring of groundwater level change with geographical information system (GIS), the case of Konya Altınekin Basin | |
| Albano et al. | Baseline Assessment of Groundwater Dependent Vegetation in Relation to Climate and Groundwater Levels in Select Hydrographic Basins of Nevada: Oasis Valley | |
| CN102735671B (en) | Method for determining point of dredging site of species sources of over-wintering cyanobacterial in large shallow lake | |
| Zema et al. | Prediction of Surface Runoff and Soil Erosion at Watershed Scale: Analysis of the AnnAGNPS Model in Different Environmental Conditions | |
| Mugabe | Temporal and spatial variability of the hydrology of semi-arid Zimbabwe and its implications on surface water resources | |
| Kumar | Estimation of Soil Erosion Using Rusle And Arcgis In Dholpur District... | |
| Sreeja | Evolution of Karamana River Basin and its Implications on Hydrogeology: A Geomatics Approach Using Morphometric Analysis | |
| Koja | Sand dune movement and its impact on human activities in the North Western coast region of Libya. An analysis of the sediment characteristics of sand dunes, and their movement using satellite images, and the effects of encroachment on farms assessed by a questionnaire survey. | |
| Zhao et al. | Research on Identification and Supervision of Ecological Background of Subtropical Mountainous Highways Based on Multi-source Data | |
| Schaffrath | Surface-water salinity in the Gunnison River Basin, Colorado, water years 1989 through 2007 | |
| Kundu et al. | Geo-Spatial Assessment of Irrigation-Induced Groundwater Depletion and Groundwater Prospect in an Alluvial River Basin of West Bengal | |
| Talebiniya et al. | Investigating the role of meteorological drought and geodetic factors on land subsidence vulnerability using fuzzy overlay |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20121114 Termination date: 20220105 |