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One of the hazards associated with tropical cyclones (TCs) is a storm surge, which leads to coastal inundation and often results in loss of life and damage to infrastructure. In this study, we used GIS-based bathtub models and tide-gauge-derived water levels to assess coastal inundation scenarios for the landfall region of TC Debbie. The three scenarios modelled what could have happened if the TC's maximum storm surge had coincided with the maximum storm tide for that day, month, or TC season, where the water levels were determined through analysis of tide gauge data, using a new method called the variable enhanced Bathtub Model. Additionally, this study analysed the impact of excluding the correction of water levels with the Australian Height Datum. Our study found that between the least and most severe scenarios, with the input water-level difference for the model along the coastline being 0.43 m, the observed inundation depth of the analysed populated region increased from 0.25 m to 1 m. Ultimately, it was found that in the worst-case scenario, the study region could have experienced coastal inundation 0.63 m higher than it did, inundating 72.53 km2 of the coast. The results of this study support the consensus that coastal inundation is highly dependent on the characteristics of the terrain, and that coastal inundation modelling, such as that completed in this study, needs to be performed to better inform decision makers and communities of the potential impacts of TC-induced storm surges. [ABSTRACT FROM AUTHOR]

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Ni˜no-Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products. [ABSTRACT FROM AUTHOR]