Owing to a lack of resources, tools, and knowledge, the natural variability and distribution of Total Alkalinity (TA) has been poorly characterised in coastal waters globally, yet variability is known to be high in coastal regions due to the complex interactions of oceanographic, biotic, and terrestrially-influenced processes. This is a particularly challenging task for the vast Australian coastline, however, it is also this vastness that demands attention in the face of ocean acidification (OA). Australian coastal waters have high biodiversity and endemism, and are home to large areas of coral reef, including the Great Barrier Reef, the largest coral reef system in the world. Ocean acidification threatens calcifying marine organisms by hindering calcification rates, threatening the structural integrity of coral reefs and other ecosystems. Tracking the progression of OA in different coastal regions requires accurate knowledge of the variability in TA. Thus, estimation methods that can capture this variability at synoptic scales are needed. Multiple linear regression is a promising approach in this regard. Here, we compare a range of both simple and multiple linear regression models to the estimation of coastal TA from a range of variables, including salinity, temperature, chlorophyll-a concentration and nitrate concentration. We find that regionally parameterised models capture local variability better than more general coastal or open ocean parameterised models. The strongest contribution to model improvement came through incorporating temperature as an input variable as well as salinity. Further improvements were achieved through the incorporation of either nitrate or chlorophyll-a, with the combination of temperature, salinity, and nitrate constituting the minimum model in most cases. These results provide an approach that can be applied to satellite Earth observation and autonomous in situ platforms to improve synoptic scale estimation of TA in coastal waters.