Marine and Atmospheric Research
Hobart Laboratories: Castray Esplanade
GPO Box 1538 Hobart TAS 7001 Australia
Telephone +61 3 6232 5222 • Facsimile +61 3 6232 5000 • ABN 41 687 119 230
Project Title: Seasonal forecasting to improve resilience of prawn farms to future climate events
Principle Investigators: Alistair Hobday (CSIRO), Jason Hartog (CSIRO), Claire Spillman
(BOM), Debra Hudson (BOM)
Alistair.hobday@csiro.au (03 62 32 5310)
Date: June 22, 2011
Project description
The Australian prawn farming industry now produces more than 4,000 tonnes (2009) of product
annually with a farm gate value estimated to be in excess of $70 million, providing more than
1000 direct jobs and 1800 indirect jobs, and is dominated by production from Queensland. This
project will assist economic recovery and future climate risk preparedness for the prawn farming
industry by developing industry-specific seasonal forecasts. While the focus will be on farms
within the Severe Tropical Cyclone Yasi Category D region, the approach will also be tested for
farms outside this region which may encounter extreme events in coming years. This project
addresses the terms of the assistance funds; it will improve the potential for future climate risk
management by dissemination of information raising awareness of best practice (use of seasonal
forecasts) among the prawn farming industry, and will improve future resilience to extreme
environmental events by promoting more profitable businesses (overall resilience) and improved
risk management in the face of extreme event risk (minimizing losses).
Seasonal forecasting is used widely in terrestrial farming systems to set crop timing, fertilization
schedules, harvest periods, rotation schedules etc (e.g. see Hudson et al 2011). Recent
development of forecast models that also include marine variables such as ocean temperature
have seen operational forecasting systems developed for marine fisheries (southern bluefin tuna;
Hobday et al 2011), cage aquaculture (Atlantic salmon; Hobday et al, FRDC project)and
biodiversity management (coral bleaching; Spillman and Alves 2009; Spillman 2011). Seasonal
forecasting has the potential to reduce costs in bad years, and maximize profits in good years.
Any business which has to make decisions that are influenced by the future environment have
the potential to improve profitability by using seasonal forecast information. There are two
strong environmental influences on prawn farming that seasonal forecasting may help to manage.
Profitability is influenced by typical conditions during the growing season (September to May)
and by extreme events, such as cyclones. With regard to “typical conditions”, if you knew it was
to be a warm year, could you increase profits or reduce costs? Similarly, information regarding
risk of extreme events in the coming months could reduce the risk of loss.
The objectives of this project are to test the skill of forecast methods for both “typical
conditions” and “extreme events” for the pond-based prawn industry in both coastal Queensland
Category D areas (e.g. Cardwell) and more generally across the major prawn farming region
(Cairns to Brisbane) which also needs to be prepared for extreme events in future. In the first
year, we would have two main objectives:
Objective 1. Forecasts of “typical conditions”. We will investigate the forecast skill for
air temperatures (which approximates pond temperature) for the first and second
fortnights, as well as the upcoming season at lead times of 0-1 months, for the period
September (depending on when project commenced) to May. These forecasts would be
downscaled to individual prawn farms in 3-4 coastal locations across the prawn farming
region. We would need local historical and current data on pond and air temperature from
these participating farms. We would perform historical validation to determine the value
and skill of the forecast for each region (say years 1990-2010). We have done some
preliminary evaluation of the POAMA model, and are confident that there is some skill in
the model for the prawn region in terms of forecasting seasonal air temperatures. For
some regions and months, only shorter time scales may be useful, while for other regions
and months forecasts may be skilful further into the future. There will be critical points in
the production cycle for which forecasts may be particularly useful, say for September
and May, and we will work with industry to identify these periods.
The seasonal forecast model (POAMA) can also represent and skillfully forecast tropical sea
surface temperatures (SST) and thus ENSO indexes. This is useful as ENSO has been linked
with tropical cyclone frequency and cyclone tracks off the eastern Australian coast. Cyclones
tend to have different tracks in La Nina and El Nino years, which means different sections of the
coast are likely to be impacted in each ENSO phase. In La Niña years tropical cyclones have
tended to track towards Queensland’s coast and then deteriorated into rain depressions. In
contrast, cyclones paths in El Niño years have been generally south or east (Figure 1). Thus, the
second objective is:
Figure 1: Cyclone tracks for La Nina (left) and El Nino (right) from Hastings 1990 (source:
http://cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_figs/figure5_3.htm and
http://cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_figs/figure5_2.htm)
Objective 2. Forecasts of “extreme conditions”. We will explore existing statistical
relationships between tropical ocean SST and tropical cyclone activity off the
Queensland coast. We will then drive these derived relationships with POAMA forecasts
of tropical ocean SST to give a prediction of the likely tropical cyclone frequency for the
region. These forecasts would be for the region and may be refined to smaller latitudinal
bands along the Queensland coast, depending on results. It is important to note that
POAMA does not resolve individual cyclones. These cyclone risk forecasts might help
prepare the industry decide on the level of investment for cyclone management in a
particular year.
We will work with industry/farm representatives to develop targeted forecasts that deliver
maximum value to the farms. Site visits by the research team to the farm sites will be undertaken
through the project. The first preliminary regional forecasts could be delivered 2-3 months after
the project commenced (this time is needed to start up the forecasting process). These will be
steadily refined over the course of the project, as the methods are developed further. The benefit
would be immediate on receipt of forecasts. Decisions regarding business planning can begin to
consider the upcoming seasonal environment based on the forecasts.
Knowledge transfer
Forecasts will be targeted at each farm site, and in the case of the salmon forecasts were
delivered by email as a single page document indicate the projected temperatures at each site for
the current month, next month, etc (see Attachment 1). Feedback via phone or meetings during
the project will be important. A final presentation on the forecasting at an industry meeting
would be appropriate. In the operational phase after year 1, web-based delivery would be
appropriate. The coral bleaching forecasts (http://poama.bom.gov.au/gbr/gbr_coral.shtml)
prepared by Spillman and her team include knowledge building elements, and we could design
similar for the prawn industry.
The benefit will depend on how each business uses the forecast. In some years, a forecast will
suggest to a farmer to increase the stocking density and thus maximize profits, while in other
years the forecasts will suggest that reducing the stocking density might be required. For
example, warm years may lead to increased growth rates, and knowing that ahead of time may be
an advantage.
The final stages of the project will include discussion with industry on how to deliver ongoing
seasonal forecasts to an increased number of sites, or to those sites for which the forecasting is
considered valuable.
Reference material supporting proposal
References:
Hastings, P. A. (1990) Southern Oscillation influences on tropical cyclone activity in the
Australian/southwest Pacific region. Int J. Climatol. 10 291-298
Hobday AJ, Hartog J, Spillman C, Alves O (2011) Seasonal forecasting of tuna habitat for
dynamic spatial management. Canadian Journal of Fisheries and Aquatic Sciences 68, 898–
911.
Hudson D, Alves O, Hendon HH, Marshall AG (2011) Bridging the gap between weather and
seasonal forecasting: intraseasonal forecasting for Australia. Quarterly Journal of the Royal
Meteorological Society 137, 673-689.
Spillman C (2011) Operational real-time seasonal forecasts for coral reef management. Journal
of Operational Oceanography 4, 13-22.
Spillman C, Alves O (2009) Dynamical seasonal prediction of summer sea surface temperatures
in the Great Barrier Reef. Coral Reefs 28, 197-206.
Similar project forecasting water temperatures around Tasmania for coastal salmon farms:
 FRDC project 2010/217: Atlantic Salmon Aquaculture Subprogram: Forecasting ocean
temperatures for salmon at the farm site
Coral bleaching forecasts: http://poama.bom.gov.au/gbr/gbr_coral.shtml)