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Coral reefs are at risk due to climate change, specifically extreme ocean warming events. Understanding how water temperature varies vertically in the Great Barrier Reef (GBR) lagoon is important for understanding the potential threat to coral reef ecosystems. Predictive modelling can be used to assess extreme temperature high risk areas. This study had two aims: (1) to compare vertical temperature profiles predicted by the eReefs 1 km resolution (GBR1) hydrodynamic model with observed temperature collected by Slocum gliders on the Great Barrier Reef (GBR); (2) to create a simple statistical regression model to quickly predict subsurface temperature on the GBR during the wet season down to 40 m given sea surface temperature (SST). First, profiles of eReefs and glider temperature from different regions, seasons and time of day were compared using bias, Root Mean Square Error (RMSE) and Willmott’s Skill Score. Results show that temperature profiles predicted by the eReefs GBR1 hydrodynamic model are sufficiently accurate for the purpose of estimating impacts on corals. We then developed a new statistical model, Generalised Additive Modelling (GAM) was selected due to the nonlinear relationships between the subsurface temperatures and the explanatory variables. The GAM model built used five variables: SST, depth, time (days since October 1st) and location (as latitude and longitude) to predict temperature. The model produced RMSE values below 0.5°C and Pearson’s correlation coefficients between predicted and observed temperatures above 0.90. This study provides a simple and accurate statistical model allowing prediction of subsurface sea temperature from observed or modelled surface temperature.

Predicting subsurface water temperature from sea surface temperature in the Great Barrier Reef