At the onset of the semester, myself along with Taylor, Xu and Erick committed to setting up and running a 3d hydrographic model of Apalachicola Bay. Since the primary goal of these weekly meetings was to learn how to take a modelling project from start to finish, we needed to be deliberate about our choice of model and to glean a working knowledge of all of its components/features. While it has taken several weeks, we have finally taken our first stride into this project.
To see the second article in this series, please checkout this post where we interpolate our bathymetry data to the mesh.
Since each hydrographic model is unique, and often the learning curve between different models is steep, it is not surprising that researchers often become tied to their ‘model of choice’ at the exclusion of all other models. We wanted to stay away from this temptation and to make sure that we picked the best modeling package available even if we were unfamiliar with it. To accomplish this, we started off the first couple weeks of the semester going through bullet point lists of features and aspects of all the models we had heard of (i.e. MitGCM, HYCOM, ROMs, Selfe, POM, FVCOM); and when we came to an aspect that we hadn’t heard of before, we took time to figure out what it did and what benefit it may or may not bring our modeling project. It was in this way that we became familiar with all the models and, more importantly, became able to make an informed decision as to which model would work best in our area.
Since the choice of the best tool often requires an understanding of the problem or question to be tackled, we had to start discussing possible research-level questions to be pursued. For this, we decided that a sampling of the existing literature would serve us best and might naturally lead us in the right direction.
After discussing a few papers and brainstorming questions that we would be interested in pursuing, we decided that–at least for now–the most reasonable goal would be to develop a model to study the variability of physical properties within Apalachicola Bay and the impact of physical forcings on that variability. Ultimately these results may find direct or indirect applications in modeling oyster populations, climate change impact and generally improves our understanding of the estuary.