Fisheries Management and Red Tide Early Warning System for

Project WATERMAN Public Lecture Series

Fisheries Management and Red Tide Early Warning System for Hong Kong

Project WATERMAN Public Lecture Series

Fisheries Management and Red Tide Early Warning System for Hong Kong

Project WATERMAN Public Lecture SeriesFisheries Management and Red Tide Early Warning System for Hong Kong


Lecture Title: Fisheries Management and Red Tide Early Warning System for Hong Kong

:

Speakers: Professor Joseph Hun-wei Lee

Principal Investigator of Project WATERMAN

Project WATERMAN



WATERMAN system

www.waterman.hku.hk



A New Environmental Knowledge BaseA New Environmental Knowledge Base

A New Environmental Knowledge Base

Outline

MarineFishFarminginHongKongandChallenges

WATERMANFisheriesModule

HowtoQuantifyCarryingCapacity

Whatisalgalbloom/redTide?HowcanWATERMANpredictalgalblooms?/

Mariculture inHongKong

Highdemandonlive/freshfish(forhighqualityfood)

300and100tonnesofmarinefishandfreshfishconsumedeveryday(top5fishconsumptioninAsia;4xworldaverage)4

Commonculturedspecies(grouper,snapper,seabream):,

BanningoffishtrawlinginHKby2012

7m

FishCultureZone(FCZ)

Familyoperated

Fishraftsarelocatedatshelteredcoastalwaters(weaklyflushed)

Stockingdensityvariesfromafewfishperm2 tohundredfishperm2~/

26FCZs

IncreaseinTotalInorganicNitrogen(TIN)inPearlRiverflow

HongKongSAR1,072km2oflandarea

1,800km2ofcoastalwatersPopulation6.7M

HongKongSAR1,072km2oflandarea

1,800km2ofcoastalwatersPopulation6.7M

1000

1500

2000

2500

3000

3500

4000

4500

1990 1992 1994 1996 1998 2000 2002 2004

Population of PRD (ten thousand )

0

0.1

0.2

0.3

0.4

0.5

0.6

1986 1988 1990 1992 1994 1996 1998 2000

TIN (mg/L) at SM7

1000

1500

2000

2500

3000

3500

4000

4500

1992 1994 1996 1998 2000 2002 2004

waste water discharge(106 t)

PearlRiverDeltaPearlRiverDelta

Pearl River - 2200 km; Annual precipitation 1470 mm; wet season flow 20,000 m3/s

2 1 4 1 19

411

232928

3340

88

3936

2519

10

202325

19

3731

4540

2120

3441

1413151617

0

10

20

30

40

50

60

70

80

90

100

75 7677 7879 8081 828384 8586 8788 8990 9192 9394 9596 9798 9900 010203 0405 0607 0809 10

Year

No.

of R

ed T

ides

Eutrophication,algalbloomsandredtides

Increased frequencies of harmful algal blooms, red tides, and fish kills around

the world recent decades

ChallengestoHKFisheries

EstimatedmariculturelossoverHK$315Million

(NewspapercuttingsfromSCMPandMingpaoDaily,1998)

Pollutionandpoorwaterquality Redtides Oxygendepletion Fishdiseaseandparasites Coldspell

MassiveRedTidein1998

ProjectWATERMANOnlineFisheriesManagementandRedTideEarlyWarningForecasting

ScienceBasedDecisionSupportPlatform1) Carryingcapacityforfishmanagement

sustainableandprofitablefishfarming Healthyfishproduct Healthyecosystem

2)Redtideearlywarningsystem

Dailyforecastofredtideriskforcurrentweek Predictionofredtidemovementsandmitigations

Marine fish farming causes local nutrient enrichment but can also be a victim of existing pollution. A robust quantitative methodology is needed for mariculture management (site selection; impact assessment; determine carrying capacity)

Organic loading Flushing rate Hydro-meteorological condition

Environmental Management of Mariculture

WaterQualityObjectivesforMarineWater

DissolvedOxygen 4mg/L

NH3 Annualmean 0.021mg/L

Chla

runningarithmeticmeanof5dailymeasurementsforanylocationanddepth

20mg/m3

Totalinorganicnitrogen(TIN)

Annualmeandepthaveragedtotalinorganicnitrogen

0.3mg/L

Cannot find

CarryingcapacityofFishFarm

CarryingcapacityofFishFarm

What is the capacity (stocking density) of the farm

With sustainable fish farming Meet water quality objectives Healthy fish product

High stocking Density Increase waste excretions, hence pollution Reduces growth rates (for a number of species)

Potential/existing fish culture zone(FCZ)

Carrying capacity of FCZ

Hydrodynamics model

Mass transport model

Water quality model (sediment water exchange)

Numerical tracer experiments

Flushing time

Pollution loading, ambient water quality

Bathymetry, tidal conditions, salinity

TidalFlushing FlushingTime=thetimerequiredtoexchangetheentirevolumeofthegivenwaterbodybynewoceanwateror theaveragelifetimeofaparticleinthatsystem

Insemienclosedcoastalwaters,theflushingtimecanbequitelong,intheorderof1040days.Anyalgaespeciesintroducedintotheareahasachancetogrowandbloom.

Pollutantsreleasedintothecoastalenvironmentaremixedandflushedbythehighlyvariablehydrodynamiccirculation.

pollutants

clean water

contaminated water

mixing

Semi-enclosed tidal inlet

Ocean/estuary

tidal flushing by ocean current

openboundary

Numerical determination of Flushing time: Release tracer in fishfarm and track changes

Computation via3D hydrodynamic and mass transport (particle tracking)model

Flushing time =average lifetime of a particle in the given water system

System = fish farm

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 0.5 1 1.5 2 2.5 3

Time (day)

Elev

atio

n ab

ove

MSL

(m)

0

1f

oT tdM

M

0

0.2

0.4

0.6

0.8

1

Mas

s re

mai

ned

NESTRandom WalkDouble exponential curve f it

Tf 10 days

TracermassintheFishCultureZone

Numericaltracerexperimentfordeterminingthetidalflushingtimeinasemienclosedbay(YungShuAu)

1 2

1fT k k

tktko

eeMM

21 1

3D models and laboratory experiments show that the tracer mass removal process due to tidal flushing can be approximated by a double-exponential decay curve that is described by 3 flushing parameters only, from which the flushing time (or flushing rate) can be uniquely determined.

Tracer mass

Flushingtime

The three flushing parameters , k1 and k2 can be interpreted in terms of the size of the fish farm, the exchange flows between the fish farm and its surrounding and that between the water system and the external clean ocean using a two-segment model

dryseasonwetseason

dryseason

time(days)

M/M

o

YimTinTsaifishculturezone

Yim TinTsaiFCZ

NumericalTracerexperimentfordeterminingtheflushingtime

ChoiandLee,J.ofMarineSystems,2004;Leeetal,MarinePollutionBulletin,2002

Tolo Harbour

Flushing time of some representative eutrophic water bodies -

LocationTolo Harbour

Sok Kwu Wan Ma WanYim Tin Tsai Yung Shu Au Lo Fu Wat

flushing time (d) 38.0 23.6 15.8 25.8 3.1

Tolo Harbour

Dry season (Nov Feb)

Wet season (May Aug)

flushing time (d) 14.4 14.2 7.1 3.5 1.5

Salinity and Velocity distribution in a partially mixed estuary

Density-induced circulation due to salinity gradients

Numerical simulation of salinity intrusion in a rectangular estuary (with fresh runoff Qf and 2.8Qf)

S/So

Tolo HarbourResidence time = mass weighted average of the time taken by individual particles to leave the system through the open boundary = system-wide flushing time

Movement track for individual particles

Wet season Dry season

Flushing time (days) 3.5 25.8

Flushing rate (day-1) 0.2857 0.0388

Sok Kwu Wan

the wet season flushing time is about an order of magnitude smaller than that for dry season;

For other coastal bays in Hong Kong, the wet season flushing rate is only about 2~3 times that for dry season

Sok Kwu Wan

Wet Season

Dry Season

Tidal Flushing in Sok Kwu Wan

Vertical profiles of tidally averaged longitudinal velocity

three-layer structure (diffusion-induced circulation)

two-layer structure (commonly observed in stratified and partially mixed estuaries)

Flushing Time (dry / wet season) of FCZs (days)

Schematic diagram of the water quality model and sediment-water interactions

Dissolved Oxygen

Biochemical Oxygen Demand

Phytoplankton

Inorganic Nutrient

Organic Nutrient

Algal Nutrient

Inorganic Nutrient

Organic Nutrient

Sediment Oxygen Demand

Re-aeration

deoxygenationphotosynthesis

respiration

nitrification

decay

uptake

hydrolysis

Diffusion & settling

settlingsettlingsettling uptake

settling

Water Column

Sediment Layer

Water Quality Model

decay

decay mineralization

/

Introduced feed (100%)

Ingested nutrients

Nutrients in waste feed

Non & slow-settleableparticulates

Soluble nutrients

(15%-30%)

Settles out

Fecal nutrients

Near-field advection

(42%-51%)

Dissolves in water column

(10%-13%)

Indigestible nutrients

Digested nutrients

Retained(18%-21%)

FishFarmPollution

(70%-85%)

Main Physical Processes

Diagenesis (Decay)

Deposition

Diffusive fluxes

Exchange due to tides

Pollution Load

PavaSODh

CCk

CCkPTrgNgaNkaLkdtdC

WVPrafVNhfv

kkdt

dNV

WVNkVNkVPrfNgadtdNV

VPdkhvTrgNg

dtdPV

cpsocffsf

saopnond

NnpONpNs

fn

NnfONnp

pfs

1

1

11

1

11

1

growth respiration settling flushing nonpredatorymortality

Phytoplankton

Dissolved Inorganic Nitrogen

Organic Nitrogen

Dissolved Oxygen

uptake regeneration hydrolysis loading

deoxygenation nitrification re-aeration

maricultureactivities

decay ofsettled algae

algal production

Algal Growth Modelling

g(I) g(N) g(T)

sI

hesIoIsIoI

eeh

Ig

ZeoII

sIIesI

IIg

1

0

01718.2)(

1)(

Saturating intensity

ISI (ly/day) Nutrient Concentration (ug/l)

KN

Temperature (OC)

NK

NNK

NNg )(

Half-saturation constant

Limiting nutrient =NITROGEN

20)( TTg

0 20 400

1

2

3

4

0 100 200 300 400 5000

0.5

1

0 500 1000 1500 2000 25000

1 Rate of Photosynthesis

g(N)

Phytoplankton growthLimiting Factors

Light Nutrient Temperature

Or Multiple-nutrient limitation using LiebigsLaw of Minimum

Best Fit Curve

Riley Eq.

Riley Eq. r(p)=0.24+0.0088*p+0.054*p2/3

Best Fit Eq. r(p)=0.24-0.0057*p+0.145*p1/2

EXTINCTION COEFF. AS A FUNCTION OF CHL-A

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 10 20 30 40 50 60

CHL-A(UG/L)

EXTINCTION COEFF.(M-1 )

Field Data

Effect of Self-shading by algae

Changes in nutrient concentration and cell countsduring the growth of Porocentrum minimum in alaboratory experiment

1.0E+03

1.0E+04

1.0E+05

0 50 100 150

Time (HR)

CellCountsPerml

0

100

200

300

0 20 40 60 80 100Time(HR)

Ammoniaor

NitrateNitrogen(ug/l)

NH3

NO3

Preference of ammonia nitrogenin nutrient uptake (lab)

Field observationDec 1987 diatom bloomTIN change = 65 ug/LNH3 change = 60 ug/L

0

10

20

30

40

31-Dec-99 20-Jan-00 09-Feb-00 29-Feb-00 20-Mar-00 09-Apr-00 29-Apr-00 19-May-00

Chl

orop

hyll-

a (

g/L)

Surface MountedSurfaceMiddleBottom

Alarming Level

0

2

4

6

8

10

12

14

31-Dec-99 20-Jan-00 09-Feb-00 29-Feb-00 20-Mar-00 09-Apr-00 29-Apr-00 19-May-00

Dis

solv

ed O

xyge

n (m

g/L)

SurfaceMiddleBottom

Chlorophylla

DissolvedOxygen

Algal bloom dynamics:

fishkill

Fishkill due to Oxygen Depletion, Three Fathoms Cove, July87

Dissolved Oxygen

High water temperature and prolonged sunny clear skies led to high algal production; significant DO consumption during period of overcast skies and neap tide

Three Fathoms Cove, Tolo Harbour, Hong Kong

Comparison of model prediction of Chl-a (solid line) with field data (symbols) for 1987 and 1998

Less algal blooms after introduction of Tolo Harbour Effluent Export Scheme

Two-layer Diagenetic Water Quality Model for Tolo Harbour

Comparison of model prediction of DO (solid line) with field data (symbols) for 1987 and 1998

Low DO (below 2 mg/L) even after introduction of the Tolo HarbourEffluent Export Scheme!El Nino Effect? (1998 warmestyear on record)

Two-layer Diagenetic Water Quality Model for Tolo Harbour

Oxygen Consumption at Night

FISHSODh

CCkPTrgaNkaLkdtdC

saopnond 1

TBOD algal respiration re-aeration sediment oxygen demand + fish consumption

PDODCPLDO

PavaSODh

CCk

CCkPTrgaNkaLkPDOD

cpsocffsf

saopnond

1

)(

Potential Dissolved Oxygen Drop (PDOP)

Potential Lowest Dissolved Oxygen (PLDO)

Carrying Capacity Indicator

Organic loads, flushing times and key water quality indicators at six representative fish culture zones

Time variation of the water quality parameters in the fish farm in response to a constant loading

simulated by a box model

Predicted potential lowest DO level (PLDO) under different loading conditions

Potential LowestDissolved oxygenLevel (PLDO) as a measure of carrying capacity Potential DO dropOn a day of

Negligible Photosynthetic Production

YTTE - Poor flushingHigh loading

Sok Kwu Wan- Good flushing

in wet seasonModerate loading

Ma Wan- strong flushing

High loading

T = 30 C and kf = 0.1 day-1

Carrying capacity for: DO > 4 mg/L Chl-a < 20 ug/L

Carrying capacity (tonne fish) of the 26 FCZs

Recommended stocking density(e.g. Lo Tik Wan)

0

1000

2000

3000

4000

5000

6000

0.5 1 1.5 2

Size of Fish (kg)M

ax. N

umbe

r of F

ish

Lo Tik Wan FCZ

Raft Size

6mx6m

3mx3m4mx4m5mx5m

FCZ Carrying Capacity = 1,900 tonne

Recommended stocking densityCarrying Capacity

Licensed Area

Max Number of fishRecommended density raft area

fish size

= = 78kg/m2

=

surface

bottom

Why not 3D WQ Model?Uncertainties in the model parameters and the forcing conditions including loadings from other sources, the spatial and temporal variations

Computed and observed long-term (1990-2001) water quality for six fish culture zones

Observed and computed long term water quality via Box Model and Delft3d-WAQ Model in 4 FCZs of Tolo Harbour: DO, Chlorophyll-a, and organic nitrogen for dry season

Red tide - red discoloration of the sea by micro-organisms (mainly micro-algae)

Algal Bloom - rapid growth/ germination of micro-algae (phytoplankton) to concentration as high as 100,000 cells per ml

Red Tide and Harmful Algal Bloom (HAB)

HarmfulAlgalBloom(HAB) Oxygen depletion Cause shellfish to contain toxins Cause mass mortality of fish,

invertebrates etc. Cause fish to contain toxins Cause skin or respiratory irritations

Dynamicsofalgalbloomsandredtidesinsubtropicalcoastalwaters(RGCGroupResearchProject19992004)

Kat O

Lo Tik Wan

Estuarine

Oceanic

Objective: To develop real time forecasting model of algal bloom dynamics

Red Tide Research and Mariculture Management

Anemometer Pyranometer

Field Monitoring

Alarming Level

Red Tide Early Detection

0

10

20

30

40

31-Dec-99 30-Jan-00 29-Feb-00 30-Mar-00 29-Apr-00 29-May-00 28-Jun-00 28-Jul-00 27-Aug-00 26-Sep-00 26-Oct-00

Chl

orop

hyll-

a (

g/L)

Surface MountedBottomMiddleSurface

Alarming Level

Red Tide Early Detection

0

10

20

30

40

31-Dec-99 30-Jan-00 29-Feb-00 30-Mar-00 29-Apr-00 29-May-00 28-Jun-00 28-Jul-00 27-Aug-00 26-Sep-00 26-Oct-00

Chl

orop

hyll-

a (

g/L)

Surface MountedBottomMiddleSurface

TelemetrySystem

M a g n e tic v a lv e s

R e la y b o x

P u m p

P e rso n a lC o m p u te r

M o d e m

T e le p h o n e L in e

M o d e m

M ic ro lo g g e r

C o n tro l M e a s u re m e n t

F L U O R I M E T E R T H E R M IS T O R SD O M E T E R

P Y R A N O -M E T E R

A N E M O -M E T E R

A C O U S T I C D O P P L E R C U R R E N T

M E T E R

L A B O R A T O R Y

F IE L D

D a ta R e tr ie v a l, A n a ly s is & S to r a g e

D a ta C o lle c tio n

D a ta T r a n sm is s io n

C H L O R O P H Y L L D IS S O L V E D O X Y G E N

T ID A L L E V E L &

C U R R E N T

S O L A R R A D IA T IO N

W A T E R T E M P E R A T U R E

W IN D

When do algal blooms occur?

Based on field observations and theoretical modeling, we propose the first quantitative model for forecast algal blooms

FactorsAffectingAlgalGrowth

Irradiance

Water Temperature

Nutrient

Growth Rate = 0G(I)G(T)G(N)

T

G(T)summer species

winter speciesN

G(N)

I

G(I) algae acclimatized to high and low irradiance levels

photo-inhibition

0 optimal growth rate at reference temperature

Aug2000 - Chlorophyll-a

0102030405060

17-Aug 19-Aug 21-Aug 23-Aug 25-Aug

Read

ing

(inst

rum

ent v

alue

)

BottomMiddleSurfaceS.Mounted

Aug2000 - temp

23

25

27

29

31


Tem

pera

ture

(OC)

SurfaceMiddleBottomAir

Aug2000 - Predicted Tidal Level

0

1

2

3

17-Aug 19-Aug 21-Aug 23-Aug 25-AugTi

dal L

evel

(m C

.D.)Aug2000 - Wind speed at 3m height

0123456


Win

d Sp

eed

(m/s

)

Field observations show that algal blooms are highly correlated with the stability of the water column (tide, wind, stratification)

Chla

Wind

Temperature

Tide

Field observations before and during an Algal Bloom

RedTideForecast

Model-DataIntegration

Model-DataIntegration

Red TideForecast and Investigation

Hydro-meteorologicalData

-

Dissolved OxygenLevel

Water Quality Data

NO2NO3NH3

NutrientConcentration

Predicted Red Tide Risk and

Impacted Areas

Biological Knowledge

Past Experience

Hydrodynamic Knowledge Tidal Mixing

Solar Radiation

Two-layer model for vertical biomass (Wong et al., 2007)

Ct = 0 Ct < 0,

Ct > 0 )

irradiance

surface photic zone (thickness = l)

non-productive lower segment

simplified growth function

depth z

net growth rate =

lossrate = d

sinking velocity v

turbulent diffusivity E

irradiance

surface

(thickness = l)

non-productive lower segment

simplified growth function

depth z

net growth rate =

lossrate = d

sinking velocity v

turbulent diffusivity E

Considertheeffectofturbulentdiffusion,sinkingandgrowth/mortalityonthealgalconcentration

Ct = E

2Cz2 - v

Cz + kC

underwhatconditionscould:

(andalso

:

()

0.00001

0.0001

0.001

0.01

0.00001 0.0001 0.001 0.014l2/2

Diff

usiv

ity E

(m 2 s

-1)

Motile species

Non-motile species

Dinof lagellate(YSA)Diatom (YSA)

P. mican (Kat O2004)Dinof lagellate(YTT)Diatom (YTT)

Blooms unlikely

Blooms likely

Critical Turbulence (m2s-1) Critical turbulence

E < Ec = 4l2

(1)

Ec (m2s-1)

E(m2s-1)

start

Diffusivity < Critical Turbulence?

blooms likely

Nutrient > threshold?

blooms unlikely

Y

N

N

Y

Decision model for predicting algal bloom occurrences -

?

?

Environmental and hydro-meteorological conditions

Bloom Triggering Factor for Lamma Island Period Species10Aug00-11Aug00 Mixed diatoms

18Aug00-24Aug00 Mixed diatoms16Jun01-20Jun01 Thalassiosira subtilis20Jun02-26Jun02 Thalassiosira subtilis01Jul02-06Jul02 Skeletonema Costatum24Jul02 Chaetoceros spp.12Aug03 Pseudonitszchia pseudodelicatissima

1)StabilityRiskFactor (R)=CriticalTurbulence(EC)/EstimatedDiffusivity(E)

R 1:stablewatercolumnfavouringalgalblooms

2)Nutrientthreshold sufficientnutrientconcentration

100mgm3 forinorganicnitrogen 15mgm3 forphosphate

In any given coastal water, there is a risk of algal blooms if both criteria are fulfilled.

Algal Bloom Risk Map

Algal bloom risk map (October1995)

Bloom Risk Observed Chl-a

Sufficient nutrient

Mirs Bay

Tolo Harbour

Port Shelter

Victoria Harbour

Lamma Island

Mirs Bay

Tolo Harbour

Port Shelter

Victoria Harbour

Lamma Island

(mg/m3)

Bloom : Chl-a > 10 mg/m3

(mg/m3)

: > 10 mg/m3

Predictability of the Risk Map

Bloom Not Bloom Total

Bloom 86 24 110

Not Bloom 39 321 360

Total 125 345 470

VerificationofAlgalBloomRiskForecastPerformanceagainstwaterqualitydatainHongKongforyears88,93,95,9804

The algal bloom forecast is accurate for (86+321)/470 = 87% of the time: 87%

Model Predictions

Field Observations

0%

20%

40%

60%

80%

100%

Per

cent

age

Blo

omed

Field ObservationApproximated Sigmoid Function

0.1 10.05.02.01.00.50.2

Nutrient Criterion Fulfilled

Stability Factor R

0%

20%

40%

60%

80%

100%

Per

cent

age

Blo

omed

Field ObservationApproximated Sigmoid Function

50 30020015010070Nutrient Concentration (mg/L)

Stability Criterion Fulfilled

Index Probability Red Tide Risk1 < 30% Low2 30% ~ 70% Medium3 >70% High

Red Tide Prognostic Forecast/

P(Bloom) = P(Stability) P(Nutrient)

P(Nutrient |High Stability) P(Stability |High Nutrient)

Historical data in past 20 years

2005

Feb 2009

Oct 2009

Sep 2011

First generation of red tide risk map forecast using historical water quality data in HK

Daily trial of probabilistic red tide forecast for six water zones

Field surveys based on predicted red tide risk and red tide reports

Pilot red tide early warning system with daily red tide risk forecast

Milestones

Dynamics of algal blooms and red tides in sub-tropical coastal waters (RGC Group Research Project )

1999-2004

Harmonic Analysis

HKO Daily Meteorological Data

AFCD Biweekly Monitoring Data

MeteorologyDatabase

Water QualityDatabase

Email + manual input

FTP transfer +manual checking

DailyRedTideForecastSystem/

Nutrient Data

Tidal Range

Temperature

Salinity

Wind Speed

Secchi DepthNutrient vs threshold

Stability RiskFactor

/

Probability ofred tide occurrencein coming week

High

Medium

Low

Red Tide Risk Map -

Red tide occurrencein past week

EasternWaters

SoutheasternWaters

SouthernWaters

Western Waters

Tolo Harbour

NortheasternWaters

Clear Water Bay Gonyaulax Polygramma2009-03-25 2009-03-26

Clear Water Bay Gonyaulax Polygramma2009-03-25 2009-03-26

Silver Mine Bay Beach Karenia Digitata

2009-03-23 2009-03-28

Silver Mine Bay Beach Karenia Digitata

2009-03-23 2009-03-28

Fish culture zone

Red tide risk forecast map

TrackingfortheAugust2011RedTide

PilotTestingSince20092009

NE E TOL SE S W Total

2009 0/0 0/1 0/2 5/5 3/4 0/2 8/14

2010 5/5 0/0 1/2 4/5 0/4 0/1 10/17

2011 6/8 1/2 4/7 6/6 3/9 0/2 20/34

Successfully Predicted Red Tides

Collaboration withtheAgriculturalFisheriesandConservationDepartment(AFCD) Red tide risk prediction is available in the intranet. Alert (phone call /

email) is sent to AFCD in case of predicted high red tide risk to increase the vigilance on the field condition.

When a red tide is spotted, AFCD will send us an alert on the location and the extent of the bloom, and information on the red tide causative species.

Red tide tracking will then be run to predict the possibly affect area and the information will be send back to AFCD. Announcement on the red tide news will be made on the WATERMAN intranet as well.

AFCD will keep updates through local network. If necessary, field investigation will be carried out jointly with AFCD.

Recommendations will be given to fishermen on actions to be taken (pumping DO, pulling fish raft to safer location).

Prediction on 2011-8-16

TemporaryFishRaftRelocation

No go zones

Fish Raft Move Away from Culture Zone

EquipmentsuppliedtofishfarmertomonitorDO

EfficiencyAeration(Airblower+microporetubing)

DOMonitoringandAerationatFishCultureZone

Micropore air tubingElectric Air blower

Conclusions Redtidesandharmfulalgalbloomsarehighlydynamicandnon

linearphenomonagovernedbycomplexphysicalbiologicalinteractions

Redtidesoccurinweaklyflushedandeutrophicwaterbodiesunderfavorabletemperature,sunlightandhydrodynamicconditions

Itispossibletodevelopshorttermforecastsofredtiderisksbydatamodelintegration

Redtideandfishkillsdisasterscanbepreventedormitigatedthroughnutrientreduction,flowaugmentation,monitoringandeducation,andsimpleearlywarningsystems

Conclusions

ThankYou

HydrodynamicEffect Inweaklyflushedsemienclosedcoastal

waters,theoptimalalgalgrowthrate(~1perday)ismuchgreaterthantheloss/flushingrate

Henceunderfavorablelightandnutrientconditions,analgalbloomwillbeabletotakeoff

However,thisisnegatedbytheverticalmixingoutoftheeuphoticzone,sinking,andotherlosses(e.g.flushing,respiration)

Irradiance

Nutrient

Turbulence

Algae

Relation between Eutrophication & Red Tides in Tolo Harbour -

Rising population, increase of nutrient (annual mean inorganic N), and corresponding increase of red tides (1982-87)

1982 1983 1984 1985 1986 19870.00

0.03

0.06

0.09

0.12

0.15

0.18 Inorganic N

Ann

ual M

ean

inor

gani

c N

(mg/

L)

0.0

0.2

0.4

0.6

0.8

1.0

Population

Pop

ulat

ion

(milli

on)

Red tide number

3 11 15 16 19 19

1998

Temperature: Effect of Global Warming ()Mean annual air temperature in Hong Kong (1997-2008)

1998

Temperature anomalyin April 2008 = 2.3 deg C

Documents

Fisheries Management and Red Tide Early Warning System for