Tag: small islands

Going Where No Cyclone Has Gone Before

Tropical cyclones or hurricanes threaten the lives of millions and cause billions of dollars in damage every year. To estimate flood risks at a particular location, scientists and engineers typically start by looking at the historical record of all previous storms there. From these records, they can statistically predict how likely a storm of a given size is (e.g., the biggest storm likely to occur there in 100 years).

There are two problems with this approach: (1) What if there isn’t much historical data in the records? This is often the case for Small Island Developing States (SIDS) and in the Global South. If you don’t have enough data points (particularly for rarer, more extreme events), your statistical estimates will be much more uncertain. (2) What if the historical record isn’t representative of the conditions we are likely to see in the present and future? This is also a big problem in light of climate change, which is expected to bring sea level rise and changes in storminess to coasts around the world.

To address these challenges, our team led by Tije Bakker came up with a new approach to estimating tropical cyclone-induced hazards like wind, waves, and storm surge in areas with limited historical data. Our findings are now published open-access in Coastal Engineering here!

Continue reading Going Where No Cyclone Has Gone Before

Rolling the Dice: Dealing with Uncertainty in Coastal Flood Predictions for Small Island Developing States

Small island developing states around the world are especially vulnerable to the hazards posed by sea level rise and climate change. As engineers, we have a number of tools in our toolbox for reducing the risk posed by coastal flooding and for planning adaptation measures. We often rely on predictive models which combine information about expected wave and sea level conditions, the topography of the coast, and vulnerable buildings and population to estimate potential flooding and expected damage.

However, to use these types of models, we first need to answer a lot of questions: what exactly are the expected wave and sea level conditions? What if detailed topographic measurements are unavailable? What if the population of a given coastal area increases? How are the local buildings constructed, and what are the consequences of that for estimating damage from flooding?

If our information is imperfect (which it almost always is), all is not lost: we can still make educated guesses or test the sensitivity of our models to a range of values. However, these uncertainties can multiply out of control rather quickly, so we need to be able to quantify them. There is no sense in spending the time to develop a detailed hydrodynamic model if your bathymetry data is crap. Can we get a better handle on which variables are the most important to quantify properly? Can we prioritize which data is the most important to collect? This would help us make better predictions, and to make better use of scarce resources (data collection is expensive, especially on remote islands!).

Matteo Parodi investigated these questions in his master’s thesis, and just published his first paper, “Uncertainties in coastal flood risk assessments in small island developing states“. I had the great privilege and joy of co-supervising Matteo during his thesis, and I am immensely proud of him and his work!

Based on a study of the islands of São Tomé and Príncipe, off the coast of Africa, Matteo found that topographic measurements and the relationship between flood depth and damage to buildings were the biggest uncertainties for predicting present-day flood damage. This means that measuring topography of vulnerable coastal areas in high resolution, and performing better post-disaster damage surveys will provide the best “bang for your buck” right now. However, for longer time horizons (i.e. the year 2100), uncertainty in sea level rise estimates become most important.

Matteo’s work will help coastal managers on vulnerable islands to better prioritize limited financial resources, and will improve the trustworthiness of our predictive models. Great job, Matteo!