Salmon farm management
Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
Abstract
Marine aquaculture businesses are subject to a range of environmental conditions that can impact on day to day operations, health of the farmed species, and overall production. An understanding of future environmental conditions can assist marine resource users plan their activities, minimise risks due to adverse conditions, and maximise opportunities. Short-term farm management is assisted by weather forecasts, whereas longer term planning may be hampered by an absence of useful climate information at relevant spatial and temporal scales. Here we use dynamical seasonal forecasts to predict water temperatures for south-east Tasmanian Atlantic salmon farm sites several months into the future. High summer temperatures pose a significant risk to production systems of these farms, and even more so in the future given projected temperature increases under climate change in this climate hotspot.
Based on twenty years of historical validation, the model shows useful skill (i.e. predictive ability) for all months of the year 1-2 months ahead. Model skill is highest when forecasting for winter months, and lowest for December and January predictions. The poorer performance in summer may be due to increased variability due to the convergence of several ocean currents offshore from the salmon farming region. Accuracy of probabilistic forecasts exceeds 80% for the first forecast month for the upper tercile (warmest 33% of values) and exceeds 50% at 4 months into the future. This analysis shows that useful information on future ocean conditions up to several months into the future can be provided for the salmon aquaculture industry in this region. Similar forecasting techniques can be applied to other marine industries such as wild fisheries and pond aquaculture in other regions. This future knowledge will enhance environment-related decision making of marine managers and increase industry resilience to climate variability.