Use of POAMA in Fisheries

Jason Hartog,

Jason.Hartog@csiro.au

Acknowledgements

• CSIRO

• Alistair Hobday

• BOM

• Claire Spillman

• Debbie Hudson

• Oscar Alves

• POAMA team

Use of Forecasting in aquaculture

Weatherforecasting

Seasonal forecasting

Climateforecasting

1-5 days Weeks to months Decades to centuries

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• Future environmental information can aid decision making

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Hobday unpublished

Planning timescales

Business case for seasonal forecasting

• Seasonal timescale (1-4 months) is useful for managers• Range of decisions made on this time scale

• Provides an early window for implementation of management strategies to minimise impacts

• Not all decisions can be made instantly – e.g. stocking date

• Business performance could be improved with predictions about the future• Match growth/harvest to market needs

• Anticipation of climate variability and change is proactive adaptation• Preparing for longer time scales too

• Better managed marine resources have improved resilience under climate change

Benefit of seasonal forecast

• With forecast information - in a “good” year do better

• With forecast information - in a “bad” year lose less

Average

$

ForecastNo Forecast

Average

$

ForecastNo Forecast

Who is using POAMA based seasonal forecasts?

• Tuna (Eastern Australia) • Fishery managers zone fishing areas

• Hobday et al 2010; 2011

• Atlantic salmon (Tasmanian) • Summer water temperature

• Prawn (Queensland) • Rainfall and air temperature

Management issue:

• Salmon grown towards upper thermal limit. Temperatures linked to fish growth and health

Management need:

• Reduce vulnerability to temperature extremes

• Forecast goal: water temperature

Management options:

• Freshwater bathing (disease), stocking density, diet modifications (variety)

Example 1: Salmon in Tasmania

Australia’s most valuable “fishery”

• ~380 million per year

• Major regional employer

• Growth of 50% planned over next decade

• Fish in ocean cages for 12-18 months

Managementdecision

POAMA SST forecast

Farm specific salmon forecasts

Farm forecast

Farm data

Image: C

SIR

O

V. Lyne

Delivered last 2 summers• 4 sites in 2011/12• Lead times of up to 4 months•ITAS index to increase number of POAMA cells used

Example 2: Southern Bluefin Tuna Habitat

Management issue:

• SBT quota managed fishery

Management need:

• Restrict interactions with SBT to fishers that hold quota

Management options:

• Partition fishery into zones:

• Core SBT habitat – quota and observers

• Buffer – limited quota and random observers

• OK – no quota, observers as part of regular ETBF observer program

Predicting tuna habitat for spatial fisheries management using electronic tags and ocean models

Managementdecision

+

Habitat preferences

3D ocean info

Habitat prediction

Hobday and Hartmann (2006)Hobday et al 2009Hartog et al, in reviewHobday et al, in review

Southern Bluefin Tuna Habitat

Jan Apr Jul Oct Jan-42

-40

-38

-36

-34

-32

-30

-28

-26

-24

Month

Latitu

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Scenario 1 Core and Buffer Zone Edges

Buffer Zone Climatology (1994-2011)

Buffer Zone 2012

Maximum Extent Any Year

Example 3: Prawns in Queensland

Management issue:

• Optimisation of prawn growth and yield. Sensitive to hot or cold pond temperatures

Management need:

• Reduce vulnerability to temperature & rainfall extremes

Management options:

• Flushing ponds, timing of stocking & harvesting, probiotics, diet, ordering supplies in advance, (variety)

Australian prawn farming industry

• Farms located in New South Wales and Queensland.

• Produces more than 4,000 tonnes (2009) of product annually

• Farm gate value ~ $70 million.

• Provides more than 1000 direct jobs and 1800 indirect jobs.

• The Australian industry is one of the smaller volumetric producers in the world but leads the world in productivity with an average yield of more than 8,000 kg per hectare.

Managementdecision

POAMA forecasts

Prawn forecasts

Local data

Project began late 2011

Farm forecasts

Methods: Developing targeted forecasts for prawn farms

• Identified environmental variables that matter to prawn farmers

• Rainfall, max and min temperature

• Investigated time series from prawn farms (Temp)

• Long period is needed to develop strong relationship

• Used a seasonal forecast model

• Test model for these variables over Queensland at lead-times (1-4 months ahead of today)

• Test model for projecting local weather station (long local time series)

• Test relationship between local weather and prawn pond conditions

• Generate forecasts at national, regional, and local (farm) scale

• Deliver via web for national to regional forecasts

• Deliver via “email” for local scale forecasts for selected farms

Results: Prawn forecasts – national to local scale

• http://poama.bom.gov.au/marine_mw/prawn_project.shtml

National scaleRegional scale

Other links

A. National scale forecasts

(also tropical cyclones – updated in October)

ENSOLikely to be slight El Nino

(plumes representdifferent model runs)

B. Regional forecasts

Select here

Weeks 1 & 2

Rainfall Maximum air temperature

Weeks 3 & 4

Rainfall Maximum air temperature

C. Local forecast: down-scale from regional

QLD 1

QLD 2

QLD 3

POAMA -> Region (AWAP) -> Local (closest weather station)

Replace with farm

Replace with farm

Min Temp

Max Temp

Mossman Cardwell

Ayr

MackayGC

Rainfallabove the upper tercile

Fortnight 1

Fortnight 2

Month 1

(for 10-31 day leads)

Season 1

(for 10-31 day leads)

Tminbelow the lower tercile

Tmaxabove the upper tercile

KEY: INDEX REGIONS

N_QLD

C_QLD

S_QLD

Note: Tmax,Tmin, & rainfall are area-averaged over the index regions and then the skill is determined

N_QLD = 0.75 C_QLD = 0.75S_QLD = 0.74

N_QLD = 0.73C_QLD = 0.71S_QLD = 0.69

N_QLD = 0.70C_QLD = 0.72S_QLD = 0.71

N_QLD = 0.64 C_QLD = 0.62S_QLD = 0.62

N_QLD = 0.63C_QLD = 0.62S_QLD = 0.60

N_QLD = 0.62C_QLD = 0.62S_QLD = 0.62

N_QLD = 0.64C_QLD = 0.63S_QLD = 0.67

N_QLD = 0.64C_QLD = 0.63S_QLD = 0.59

N_QLD = 0.62C_QLD = 0.61S_QLD = 0.65

Verification data: BoM AWAP

N_QLD = 0.69C_QLD = 0.68S_QLD = 0.71

N_QLD = 0.65 C_QLD = 0.64S_QLD = 0.62

N_QLD = 0.68C_QLD = 0.67S_QLD = 0.66

Accuracy score

(all months 1981-2010)

Can POAMA forecast AWAP historical?

Example targeted farm report

Maximum Air Temperature (average daily max for the time period)

Engaging at a relevant timescale….

• Does thinking more about the future lead to better long term skills?

Skill in using seasonal forecasts

Long-t

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Testing planned

Summary

• Stakeholder liaison key to understanding seasonal forecasting

• Web delivery great, but need to provide interpretation.

• How to deliver efficiently, and not lose contact / value add to product.