Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot

Marine aquaculture businesses are subject to a range of environmental conditions that can impact on day to day operations, the health of the farmed species, and overall production. An understanding of future environmental conditions can assist marine resource users plan their activities, minimise ri...

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Published in:Climate Risk Management
Main Authors: Spillman, CM, Hobday, AJ
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier Sci Ltd 2014
Subjects:
Online Access:https://doi.org/10.1016/j.crm.2013.12.001
http://ecite.utas.edu.au/119550
id ftunivtasecite:oai:ecite.utas.edu.au:119550
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spelling ftunivtasecite:oai:ecite.utas.edu.au:119550 2023-05-15T15:32:47+02:00 Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot Spillman, CM Hobday, AJ 2014 application/pdf https://doi.org/10.1016/j.crm.2013.12.001 http://ecite.utas.edu.au/119550 en eng Elsevier Sci Ltd http://ecite.utas.edu.au/119550/1/Dynamical seasonal ocean forecasts.pdf http://dx.doi.org/10.1016/j.crm.2013.12.001 Spillman, CM and Hobday, AJ, Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot, Climate Risk Management, 1 pp. 25-38. ISSN 2212-0963 (2014) [Refereed Article] http://ecite.utas.edu.au/119550 Agricultural and Veterinary Sciences Fisheries Sciences Aquaculture Refereed Article PeerReviewed 2014 ftunivtasecite https://doi.org/10.1016/j.crm.2013.12.001 2019-12-13T22:18:42Z Marine aquaculture businesses are subject to a range of environmental conditions that can impact on day to day operations, the 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, but 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. Based on twenty years of historical validation, the model shows useful skill (i.e., predictive ability) for all months of the year at lead-times of 0-1. months. 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 all months at lead-time 0. months for the upper tercile (warmest 33% of values) and exceeds 50% at a lead-time of 3. months. 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. Article in Journal/Newspaper Atlantic salmon eCite UTAS (University of Tasmania) Climate Risk Management 1 25 38
institution Open Polar
collection eCite UTAS (University of Tasmania)
op_collection_id ftunivtasecite
language English
topic Agricultural and Veterinary Sciences
Fisheries Sciences
Aquaculture
spellingShingle Agricultural and Veterinary Sciences
Fisheries Sciences
Aquaculture
Spillman, CM
Hobday, AJ
Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
topic_facet Agricultural and Veterinary Sciences
Fisheries Sciences
Aquaculture
description Marine aquaculture businesses are subject to a range of environmental conditions that can impact on day to day operations, the 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, but 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. Based on twenty years of historical validation, the model shows useful skill (i.e., predictive ability) for all months of the year at lead-times of 0-1. months. 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 all months at lead-time 0. months for the upper tercile (warmest 33% of values) and exceeds 50% at a lead-time of 3. months. 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.
format Article in Journal/Newspaper
author Spillman, CM
Hobday, AJ
author_facet Spillman, CM
Hobday, AJ
author_sort Spillman, CM
title Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
title_short Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
title_full Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
title_fullStr Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
title_full_unstemmed Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
title_sort dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot
publisher Elsevier Sci Ltd
publishDate 2014
url https://doi.org/10.1016/j.crm.2013.12.001
http://ecite.utas.edu.au/119550
genre Atlantic salmon
genre_facet Atlantic salmon
op_relation http://ecite.utas.edu.au/119550/1/Dynamical seasonal ocean forecasts.pdf
http://dx.doi.org/10.1016/j.crm.2013.12.001
Spillman, CM and Hobday, AJ, Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot, Climate Risk Management, 1 pp. 25-38. ISSN 2212-0963 (2014) [Refereed Article]
http://ecite.utas.edu.au/119550
op_doi https://doi.org/10.1016/j.crm.2013.12.001
container_title Climate Risk Management
container_volume 1
container_start_page 25
op_container_end_page 38
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