Optimising the Australian Wave Observation Network

• Published in: Journal of Southern Hemisphere earth systems science Vol. 68; no. 1; pp. 184 - 200
• Main Authors: Greenslade, Diana, Zanca, Adriana, Zieger, Stefan, Hemer, Mark
• Format: Journal Article
• Language: English
• Published: Melbourne CSIRO 01.01.2018
• Subjects: Buoys; Climate models; Climate studies; Climate variability; Correlation; Locations (working); Population distribution; Significant wave height; Significant waves; Statistical methods; Variability; Wave climate; Wave data; Wave height
• ISSN: 2206-5865; 2206-5865
• DOI: 10.22499/3.6801.010

The Australian national in situ wave data network currently consists of 35 platforms distributed around the Australian coastline. At present, all except for five are directional waverider buoys. The spatial density of the observation locations is variable – at a glance, density is higher on the east coast compared to the rest of the coastline. This variability has resulted in some areas of the coastline being well observed and well accounted for in models and wave climate studies and other areas not being observed at all. This work aims to identify potential gaps in the existing wave observing network in order to provide guidance for prospective future deployments. In addition, the technique used allows us to easily identify which are the key locations in the existing network. The method is based on considering the spatial coherence of the wave field determined from a multi-decadal hindcast wave data set. For each modelled data point, correlations between monthly statistics (means and 95th percentiles) of modelled variables (significant wave height, mean period and mean direction) at that location and corresponding modelled variables at each observation site are calculated. Areas of low correlation provide an indication of the key network gaps, i.e. areas where climatological variability of the wave fields is poorly captured by existing observations. Removing locations individually from the network and repeating the analysis can also provide an indication of which are the most important locations in the network (and conversely, which are the least important) to capture the regional climatological variability. Several key gaps are identified, suggesting that most value can be gained by placing additional buoys in these areas. However, it is noted that other factors such as accessibility, areas of maritime industry, and population distribution are also important in selecting sites for new buoy deployments.