Super Speedy Reef Modelling

Coral reefs and the islands that they protect from flooding are in big trouble. This is a recurring theme on this blog, and now it’s time for the latest update. We are currently building towards the development of an early flood warning system for low-lying tropical islands fronted by coral reefs. Our previous work on this topic has focused on finding ways to do this accurately for a wide variety of coral reef shapes and sizes, as well as different wave and sea level conditions. However, it’s not enough to be accurate- to deliver timely early warnings, you also need to be fast.

That’s where the latest research of Vesna Bertoncelj comes in.

I am extremely proud to announce that Vesna Bertoncelj has successfully defended her MSc thesis, “Efficient and accurate modeling of wave-driven flooding on coral reef-lined coasts: On the interpolation of parameterized boundary conditions“. I had the great privilege of sitting on her graduation committee and working with her over the past year or so.

Vesna’s research provides us with new approaches for making highly accurate predictions of coastal flooding, at limited computational expense. The numerical models that we use to estimate flooding often take a long time to simulate, since they resolve many complex physical processes at high resolution in space and time. However, by paring down these models to only the most essential components for the task at hand, we can do this much faster. My colleagues at Deltares recently developed the SFINCS model, which has been successfully used to predict flooding in a fraction of the time that our standard models take. But how do we put all these different pieces together?

A schematic overview of Vesna’s research methodology. [Source].

First, Vesna established a baseline for model performance by running a computationally intensive XBeach Non-Hydrostatic model (XB-NH+), and a much faster SFINCS model. These models provide an estimate for runup (R2%), which can be taken as a proxy for coastal flooding. In the second step, she used a lookup table (LUT) of pre-computed XBeach model output and to derive the input for the SFINCS model. The crucial task is doing this quickly and accurately, so she experimented with different interpolation techniques for deriving that input. She then compared her new approach with the standard models to find the fastest and most accurate combination.

Her research gives us a useful methodology that we can implement to speed up our early flood warning system, saving time and hopefully someday saving lives.

Vesna’s quality of work is excellent and she has a fantastic attitude towards research and collaboration. Her curiosity, professionalism, and diligence will undoubtedly serve her well in the years to come. I hope that we will have other opportunities to collaborate in the future. If anybody out there needs a bright young coastal researcher and/or modeller, hire her!

Restoring Coral Reefs for Flood Protection

We frequently hear in the news about dying coral reefs, and also about the threats of sea level rise and climate change. But there is a key gap: what if we can hit two birds with one stone, and restore damaged ecosystems while providing vital protection against flooding? Our latest research demonstrates how coastal managers and ecologists can join forces to achieve both goals, which may help stretch limited funding further.

I am extremely proud to share Floortje Roelvink’s first scientific paper, “Coral reef restorations can be optimized to reduce coastal flooding hazards“, published in Frontiers of Marine Science. I had the great privilege of sitting on her MSc thesis committee two years ago, and she has been our colleague at Deltares ever since.

Continue reading Restoring Coral Reefs for Flood Protection

Can 3D-Printed Corals Help Us Prevent Flooding?

Coral reefs around the world are dying; that much is clear from the headlines we see in the news that grow increasingly distressed with each passing year. This is an ecological catastrophe, but are we also losing another key benefit of reefs? Coral reefs provide a form of natural protection against wave-driven flooding on tropical coastlines. This is partly because the physical form of the reef (often a big rocky shelf) serves as a sort of natural breakwater, but is also due to the frictional effects of the corals themselves.

Many species of coral have complex shapes that disrupt the flow of water across reefs, generating turbulence and dissipating energy. This has the effect of reducing the height of waves as they travel across the reef towards the shore. However, these effects are incredibly complex and poorly understood, so we usually just simplify them in our predictive models by considering a reef to be more “hydraulically rough” than a sandy beach, for example. But we need to do better: these models are used to forecast flooding and estimate the impact of future climate change on vulnerable coasts.

How can we improve this? In coastal engineering, we often conduct experiments in the laboratory to test our theories and understand the chaos of natural systems in more controlled settings. What if we could make a scale model of a coral reef and measure exactly how waves are dissipated?

I am extremely proud to announce the graduation of Paul van Wiechen, one of the Master’s students whom I have had the pleasure of supervising. Yesterday, he defended his thesis, “Wave dissipation on a complex coral reef: An experimental study“, where he built a tiny coral reef in the TU Delft wave flume (a 30-m long bathtub with a wave-making paddle at one end) using hundreds of 3D-printed coral models.

Paul’s thesis, “Wave dissipation on a complex coral reef: An experimental study“.

It was one of the coolest projects I have ever seen, and his research provides us with valuable measurements that give us a deeper understanding of the vital role that corals play in protecting our coasts.

He also did all of this in the middle of a global pandemic, and somehow managed to stay completely on schedule. We are very lucky, because Paul will be joining the Coastal Engineering department here at TU Delft to start a PhD on dune erosion this fall. We are all glad to have him on the team and eager to see what his research unveils next!

Clustering Coral Reefs for Coastal Flood Forecasting

Many of the world’s idyllic tropical coasts are facing threats on multiple fronts.  Rising seas threaten the very habitability of many low-lying islands, and the coral reefs that often defend these coasts from wave attack are dying, too.  Compounding this problem is the sheer number and variety of these islands: there are thousands of islands, and the coral reefs surrounding them come in all shapes and sizes.  Located around the globe, these islands are each exposed to a unique wave climate and range of sea level conditions. This variability in reef characteristics and hydrodynamic forcing makes it a big challenge to forecast how waves will respond when they approach the shore, something that is quite tricky even at the best of times.  Under these circumstances, how can we protect vulnerable coastal communities on coral reef coasts from wave-driven flooding?

This is the problem that my fantastic former student, Fred Scott (now at Baird & Associates in Canada), tackled in his paper, Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction, recently published in Frontiers in Marine Science.  Working in partnership with Deltares and the US Geological Survey for his master’s thesis, Fred came up with a new methodology for forecasting how waves transform in response to variations in the shape and size of coral reefs.

(A) This is a typical fringing coral reef cross-section, showing waves approaching from the right, with the shore on the left. (B) Fred came up with a number of algorithms for classifying and organizing the massive dataset of coral reef cross-sections. (C) The measurements used in our study are from sites around the globe. (D) This is what 30,000 reef cross-sections look like when you try to plot them all on top of one another- clearly we need to whittle those down a bit… [Source: Scott et al, 2020]
In our previous research on this topic, we tried to predict flooding on coral reef-lined coasts using a very simplified coral reef shape.  This was fine as a first guess, but most reefs are bumpy and jagged and bear little resemblance to the unnaturally straight lines in my model. We couldn’t help it though: there just wasn’t enough data available when I started my thesis four years ago, so we did the best we could with the information we had at the time. On the bright side, using a single simple reef shape meant that we could easily run our computer simulations hundreds of thousands of times to represent a wide range of wave and relative sea level conditions.

Fast forward three years to when Fred began his own thesis. We now had access to a mind-boggling dataset of over 30,000 measured coral reef cross-sections from locations around the world!  However, instead of too little data, we now had too much!  If we wanted to simulate a whole range of wave and sea level conditions on each of the reefs in our dataset, it might take months or even years to run our models! Fred had the daunting task of distilling that gargantuan database down to a more manageable number of reef cross-sections.

But how do we choose which cross-sections are the most useful or important to look at?  Even though every coral reef is, like a beautiful snowflake, utterly unique, surely there must be some general trends or similarities that we can identify, right?  This question lies at the heart of Fred’s research, and to answer it, he turned to many of the same powerful statistical and machine-learning techniques used by the likes of Google and Facebook to harvest your life’s secrets from the internet or power self-driving cars.  Maybe we can use some of this technology for good, after all!

The main approach that Fred used in this study was cluster analysis, a family of techniques that look for similarities or differences between entries in a dataset, and then group the entries accordingly into clusters.  The entries within one cluster should be more similar to each other than to the entries in other clusters.  In our case, this meant grouping the reefs into clusters by similar shape and size. This allowed us to increase efficiency and reduce redundancy by proceeding with 500 representative cross sections, instead of the entire database of 30,000.

These days when I’m lonely, I Zoom with 500 of my favourite coral reef profiles…

Other studies in our field have tried similar approaches (such as this Brazilian study of coral reef shape), but the innovative part of Fred’s technique was to also account for similarities in the hydrodynamic response of the waves to each reef via a second round of clustering. Wave transformation on coral reefs can be immensely complicated, so it is entirely possible that two reef profiles could look very different, but lead to the same amount of flooding in the end.  Since we are mainly concerned about the flooding (rather than a classification for ecological or geological purposes about coral reef formation and evolution), this suits us just fine!

In the end, Fred was able to distill this colossal dataset into between 50-312 representative cross sections that can forecast wave runup with a mean error of only about 10%, compared to predictions made using the actual cross sections.  This opens the door wide for a range of future applications, such as climate change impact assessments or coral reef restoration projects.  Right now, we are working on a new project that will apply Fred’s approach to the development of a simplified global early-warning system for wave-induced flooding on coral reef-fronted coasts.

Great work, Fred, and congratulations on your first publication! I am excited to see where this road takes us!


  1. Scott, F., Antolinez, J.A.A., McCall, R.C., Storlazzi, C.D., Reniers, A.J.H.M., & Pearson, S.G. (2020). Hydro-morphological characterization of coral reefs for wave-runup prediction. Frontiers in Marine Science. [Link]
  2. Scott, F. (2019). Data reduction techniques of coral reef morphology and hydrodynamics for use in wave runup prediction. [Link]. TU Delft MSc thesis in cooperation with Deltares and the US Geological Survey.
  3. Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reniers, A., and Pearson, S., 2020, Coral reef profiles for wave-runup prediction: U.S. Geological Survey data release [Link].

The Magical Figure that Changed My Entire PhD

It was Christmas 2016, and I felt like I had bitten off more than I could chew.  I’m not talking about turkey, though.  Four months into my PhD, I was feeling completely overwhelmed and starting to wonder what I had gotten myself into.

The goal of my project is to identify the pathways that sand takes as it moves in and around the Wadden Islands in the northern part of the Netherlands.  Since the Dutch coast has a chronic erosion problem, accurately accounting for the whereabouts of their sand is a matter of national security.  Right now, the Dutch deal with a deficit in their coastal sediment budget by adding more sand or “nourishing” wherever there is a shortfall.

Knowing when, where, and how much sand to add is especially challenging around these islands.  Here, the persistent push and pull of the tide competes with the chaotic brutality of the waves to move sediment in complex patterns.  These patterns are hard to predict with our usual box of tools, so we planned to throw everything we had at the problem: state-of-the-art field measurements, sophisticated computer models, reams of historical data, and a support team of experts from across the Netherlands.  As PhD students go, I felt [and still feel!] pretty darn lucky to be a part of such a large and well-conceived project.

The Spaghetti Problem

However, as I started reading more and more about my topic, my initial enthusiasm began to wane.  I was floored by just how much research had already been done on what I had thought was a fairly specific niche.  The Dutch have been scrutinizing their coast for centuries, and to my inexperienced eyes, it seemed like they had already thought of everything.

There was another problem: at the end of almost every study about sediment pathways, there seems to be a diagram summarizing all the paths with lots of curvy arrows flying all over the place. This veritable plate of spaghetti makes for a nice conceptual drawing, but how can you statistically compare two plates of spaghetti with one another? A “past spaghetti” and a “future spaghetti”, to help understand potential responses to climate change? A “Dutch spaghetti” and an “American spaghetti”, to make my findings more general and useful for other places? If I was going to get anywhere with my PhD, I needed a spaghetti system.

Spaghetti wound on a fork close-up
Mmm, sediment pathways… Also, to be clear, this is not The Magical Figure That Single-handedly Changed My PhD. [Source: Flickr]
By Christmas, I felt like I was in a weird purgatory between “it’s all been done before! I’ll never come up with anything original!” and “this is insurmountably complex and you’re foolish to think you’ll ever figure this out”. And just a dash of “how-did-I-get-here?” imposter syndrome, for good measure. I spent much of my holiday feeling overwhelmed and inadequate, like I couldn’t possibly live up to my own expectations, or (what I thought to be) the expectations of those around me.

But: new year, new start. On January 11th, 2017, my first day back in the Netherlands from holidays, a paper about coral reef hydrodynamics popped up in my Google Scholar alerts.  At that time, I was also finishing up a paper about predicting floods on tropical islands, and I liked to keep an eye on the latest developments in that topic.

A coupled wave-hydrodynamic model of an atoll with high friction: Mechanisms for flow, connectivity, and ecological implications“. Sounds promising, I like wave models.

In this paper, they wanted to understand how waves and ocean currents move water around Palmyra Atoll, a coral island in the middle of the Pacific. Coral reefs all around the world are in big trouble, and to help them we must first understand the physical processes governing the life and death of corals.

Palmyra Atoll, a coral island in the middle of the Pacific. Unfortunately not my PhD study site… [Source: Wikipedia]
This was all very interesting stuff, though not particularly relevant to my research about flood prediction, since they seemed more focused on the ecological impact of their results. It was seemingly even less relevant to my PhD topic on Dutch sand- stay focused and stop wasting your time, Stuart! But then I turned the page and there it was:

The Magical Figure That Singlehandedly Changed My PhD

The Magical Figure That Singlehandedly Changed My PhD, Figure 13 from Rogers et al (2017). “Connectivity between hydrodynamic zones. (a) connectivity matrix showing the probability a water parcel passing through a destination zone came from a given source zone, and (b) geographic connectivity of top 10% of pathways, where shading is relative importance as an overall source, width of line is relative strength of connection.”

Essentially, the authors had summarized the pathways that coral larvae can take around an island in a mathematically elegant way. This was pretty much identical to the goal of my PhD, if you substitute coral larvae for sand, and an idyllic Pacific island for a stormy estuary in Holland. They did it with a concept called “connectivity”, and it became immediately apparent that I had some homework to do.

So what the heck is connectivity?

So what exactly do they mean by connectivity, and how are we meant to interpret that magical diagram?  Let’s start at the top.  The upper panel is what we call an “adjacency matrix”, but you can think of it just like one of those mileage charts that you sometimes see in the corner of highway maps.

Image result for mileage chart
A mileage chart, which you can read in the same way as the adjacency matrix above. If you want to get between two points, just find the intersection between your row and column of choice. 290 miles from Birmingham to Edinburgh doesn’t sound too bad, until you encounter the menace that is Birmingham traffic… [Source: The Open University]
Instead of looking at the distance between two points like in a mileage chart, the authors of the reef paper consider the likelihood of water travelling from one point to another. Darker squares show a higher chance of connection, and lighter squares, a lower chance. For instance, if we look at the first column, water is more likely to flow from the point they call “WT FR NW” to point “WT W” than it is to flow to “WT FR SW”.

The second panel shows the same information as the matrix, but this time actually showing the connections on a map – a “network diagram”. The thickness of the blue lines on the network diagram indicate how strong a connection between two points is. If all this seems rather familiar, then that’s probably because you’ve already met our network diagram’s more famous cousin, the transit map:

Harry Beck’s famous map of the London Underground. This map is a network diagram, much like the coral atoll connectivity map shown earlier. It represents the stations as nodes, and the train lines between them as links. It shows the connections symbolically, rather than at their true geographical locations, but this makes it easier for use to focus on the important things, like knowing where we need to switch train lines to get back to our hotel. [Source: Transport for London]

Cool maps. So what?

After seeing the coral reef connectivity diagram, I started googling and soon realized that I had been woefully ignorant of an entire mathematical discipline. Network theory represents complex systems as a series of points and the links between them. Once you’ve done that, interesting patterns start to emerge, such as the “six degrees of separation” or “small-world” phenomenon. It has been used in neurology, sociology, ecology, epidemiology, geomorphology, and basically every kind of “-ology”, except for coastal science and engineering. As far as I can tell, we seem to be the last ones to the party.

The more I read, the more excited I became, and the more vital it seemed for our field to catch up. Connectivity could help us quantify and bring order to the chaotic spaghetti churned out by our models and measurements – if we could figure out how to adapt it.

The course of my PhD was changed instantly with the discovery of that figure. Not only did connectivity provide a potentially useful tool, but it jolted me out of my funk and got me excited about my PhD again.  It was an important finding for my research but not a “eureka moment” where everything was suddenly solved- far from it.  It has been a long uphill slog since then, but with the help of some very clever people, I think we have almost reached our first milestone. We presented our early findings at a conference in 2017, and right now we’re in the final stages of preparing a scientific article about our ideas.  That paper will then have to survive the woodchipper of peer-review, so it may still be many months before my work sees the light of day.  But I remain hopeful.

Would I have stumbled upon connectivity eventually, had I not seen The Magic Figure? Probably not if I had only stuck to reading papers about coastal sediment transport. This finding has shaped my attitude towards coastal engineering research- I believe that the next advances in our field will not come from developing a new bedload equation, but from adopting new tools and techniques from other disciplines. Not that we don’t need better bedload equations – I just don’t think I’m the guy to do it, and I think that we could all benefit from looking over the fence at our neighbours in other fields from time to time. As William Zinsser nicely put it:

“Think flexibly about the field you’re writing about. Its frontiers may no longer be where they were the last time you looked.”


Rogers, J. S., Monismith, S. G., Fringer, O. B., Koweek, D. A., & Dunbar, R. B. (2017). A coupled wave-hydrodynamic model of an atoll with high friction: Mechanisms for flow, connectivity, and ecological implications. Ocean Modelling, 110, 66-82.

Zinsser, W. K. (1989). Writing to learn (pp. 224). New York: Harper & Row.