San Francisco Bay is a massive estuary, with over six million people living nearby. In addition to San Francisco, Silicon Valley sits on its shores. Some of the biggest tech companies in the world like Google and Facebook have their head offices right next to the Bay. For over 150 years, the ecological health of the bay has deteriorated, in part due to land reclamations and contaminated sediment from gold mining. The dynamics of San Francisco thus have a huge economic, social, and environmental impact.
Laurie’s work focused on calibrating and improving a sediment transport model of the bay, in order to track the pathways of fine sediment (i.e., mud). She worked with a notoriously fickle model (DELWAQ) and succeeded in greatly improving its calibration.
Another cool thing about her work is that Laurie was the first person to apply the coastal sediment connectivity framework that I have been developing! She was able to use this to identify key transport pathways and critical locations in the bay. It was extremely helpful for my research, as it gives us a proof of concept that our framework is applicable to multiple sites and can tell us something useful.
Her work was also accepted for a presentation at the NCK Days conference, which was meant to be held this week in Den Helder, but was cancelled due to ongoing societal chaos. Great job, Laurie!
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 almosteverystudyaboutsedimentpathways, 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.
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.
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…
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.
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:
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 techniquesfrom 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.”
Some really cool sand that my friend Claudia brought back from France. I especially like the purple shell fragments. This image is magnified 40x from the actual size. If anyone else goes to the beach on holiday, please bring me back some sand!
Sword Beach is located on the coast of Normandy in northern France, and is also where the British landed on D-Day in World War II.
This is a poem about the Laser In-Situ Scattering and Transmissometry or LISST instrument, which we use for measuring sand and mud floating through the water. I wrote it in response to a challenge to rap about what we learned during a workshop on estuaries last summer. I had some fun with it so I thought I’d share…
And now a poem about the LISST
It is a great solution
To measure stuff that’s floating
And its grain size distribution
When processing your measurements
You must beware the floc!
Since if you don’t account for it
You’re in for quite a shock
If there seems like too much mud
We should have some suspicion
Before all else, we have to check
The optical transmission
“We have an awful lot of sand!”
Is this hallucination?
First thing’s first: we should have checked
Our background concentration
We sometimes see before our eyes
Large particles appearin’
When gradients of salt are high
It is the fault of Schlieren
So from the depths of Ameland,
A lesson that does matter:
When working with a fancy LISST
Don’t blindly trust your data!