Category Archives: science communication

Grow me a River

I need a river. Not a real one but a model one. As I develop my YouTube channel, Model Life, I want to be able to demonstrate the playability of numerical models by doing experiments and letting viewers decide what to do next. Think of the EmRiver mini-flumes but in a computer and made of numbers instead.

People playing with an EmRiver mini-flume - a shallow metal tank filled with shredded plastic sand. Water is pumped into it to simulate the development of rivers.

An EmRiver mini-flume demonstrated by the Earth Arcade for the British Science Festival in Hull, 2018.

The easiest thing to do would be to use data from a real river. However, whenever you do anything with real world data you risk playing games in a way that affects real people and their property. No, I needed something made from scratch. I need to grow a river from nothing.

Rivers are complex things and growing one takes a while. I’m not really sure how long it takes for a river to ‘mature’ but I decided 500 years would be a good start. Obviously, I’m not growing a real river, I’m growing one in a numerical model called CAESAR-Lisflood – it won’t take 500 years as models tend to be quicker than real life but still a long time, 100 days to be exact.

What are numerical models, on Model Life

Starting on January 1st with a featureless plain and shallow straight channel to get it going, I will be flowing virtual water through the model. Each day, the model will process 5 years’ worth of data, simulating the flow of water and the processes of geomorphology – the erosion, transport, and deposition of mud and rocks.

You can follow along on my FloodSkinner YouTube channel, a support channel for Model Life – there will be a new video every day for 100 days. You can join the conversation on YouTube or via the Fediverse or Twitter – I’d love to see your predictions of how you think the river will change next.

Cut it out: How behavioural psychology can help us improve environmental modelling

I am speaking to the environmental modellers now. Imagine, you have been asked to make your model better, to improve its performance, and generally make it a more useful tool for decision makers. You have got a generous budget and free reign to do whatever you want. Just take a short moment to think about what you would do.

When you read the paragraph above, what did you think about? I am going to guess it was something along the lines of “Amazing, I’m going to add in representation of that process the model currently doesn’t have”. Maybe it was how you would increase the resolution of the model or how you would collect more data to add into it.  I am also going to guess that you did not think about what you would take away from your model.

A recent study by Adams et al (2021), published in Nature, found that we are hard wired to solve solutions by adding things in rather than looking at taking things away, despite the fact that taking something away would have been the better and more efficient way. I really encourage you to watch the video below that nicely summarises this work.

I know when I have approached modelling problems, my go to has been to add something in, rather than to consider what could be taken away. Yet, often when we add in new processes or increase the resolutions we may improve our outputs but we also increase the complexity, resulting in slower processing speeds and increased uncertainties. When assessing the models on how useful they are to decision makers, we may have actually made them worse.

The European Centre for Medium Weather Forecasts (ECMWF) have recently upgraded their Integrated Forecast System. One of the improvements they made is a great example of taking something away to solve a problem. Previously, they had stored numbers using 64-bits of memory within their computers. Using 64-bit over 32-bit allows you to store bigger numbers, i.e., use more decimal places and increase the precision of the output. This sounds like it is better, it sounds like if you had the option to go to 128-bit you ought to as you could have even bigger numbers and even greater precision still. The flipside is that storing and computing with bigger numbers takes a tiny bit longer to do each time and when multiplied over the vast number of sums the supercomputers at ECMWF do, this adds up. They realised that they did not need that level of precision and, for many processes, using 32-bit instead of 64-bit made little different to the output. Making the switch reduced the computational load by 40%, meaning swifter, and therefore more useful, results.

Photo by Gabriela Palai on Pexels.com

This is not anything new in numerical modelling and reduced-complexity approaches are popular and long established. However, these were designed with a conscious effort to take things away and it is when we stop making this conscious effort that we default back to adding things in as a first option. This is especially true, as the video tells us, when our cognitive load is high. Next time you sit down to solve a modelling problem make sure to remind yourself to stop and think – what can I take away to make this better?

Chris

Fridays are my non-work day so I try to write a short blog post on my thoughts about environmental modelling, games, or really anything else that is on my mind. The purpose is for nothing more than the love of writing and for practice but I do hope you enjoy them. For the avoidance of any doubt, all of the views and opinions I express in these blogs are very much my own and not those of my employer.

On the useful-ness of models

One thing I’d really like to do in 2021 is get back into writing just for fun. Although I have written a lot academically in the last few years, my space and time to just write my thoughts had become really squeezed. I hope to use some spare time on Friday mornings to quickly put a few words together about what’s on my mind at the time and re-engage with the craft. These are my own personal views and opinions.

On the useful-ness of models

Most numerical modellers will be familiar with mathematician George Box’s quote “All models are wrong, but some are useful”. I love this quote, as even though I don’t think it was intended for numerical simulations, it strikes right at the heart of many of the issues our research community are trying to address.

Photo by Genaro Servu00edn on Pexels.com

Too often though, we don’t consider how ‘useful’ our models are. How wrong they are? Yes, we look at that all the time. We develop new ways to calculate, express, and communicate how wrong they are. We work hard on new methods and at collecting new, more, and better data so we can make the models less wrong. When we’ve done this, we have models that are either less wrong, which is good as they will be right more often, or are able to show us how wrong they might be, which is also good as it allows people to make better informed choices about risks.

When we do consider how useful a model is, it’s often in the ways discussed above. Providing decision makers with the information about how wrong a model is lets them make a better informed decision. It is more useful to them. Great, box ticked. But, in my opinion, the model does not stop there.

Photo by ThisIsEngineering on Pexels.com

In a recent post for CIWEM, Phiala Mehring, a floodie, research director, and PhD researcher, discussed how we communicate with communities affected, or at risk of being affected, by flooding. It’s a really important post so please go read it here. There was one paragraph that really stood out for me:

“Imagine having lived in your home for three decades, to have a complete stranger knock on your door to say you are at risk of flooding “because the flood model says so”. What do you believe; a model that simulates the area – or your lived experience of more than 30 years?”

In this situation, to this audience, it does not matter how precise and accurate that model had been made. All the effort and hours put in developing methods to communicate how wrong the model might be do not matter either. It also does not matter how useful decision makers found it. Here, in this situation, the model is useless.

How we utilise model results when working out in the real-world communicating flood risk is a crucial facet of the model’s development and its use. It’s just as important as finding reliable and accurate rainfall information to input into it right at the start of the chain. And it’s the reason we should always measure our models by that one criteria George Box proposed to us – how useful they are.

Games for Geoscience #EGU18 @EuroGeosciences

I am super-excited to be Convening a session on Games for Geoscience at the 2018 General Assembly of the European Geoscience Union (EGU). In fact, I am so super-excited, I am prepared to use the phrase ‘super-excited’. I am also super-excited to be co-convening alongside two of my favourite people, Sam Illingworth and Rolf Hut.

I like playing games. Personally, I’m not a fan of board games, I prefer games with a narrative – I like tabletop strategy games, having been addicted to Games Workshop games since the age of 10. I like computer games, but having slow reactions and no hand-to-eye co-ordination, I have to stick to games like Football Manager (which my wife describes as ‘just answering emails’).

It’s probably not surprising then my research revolves around numerical modelling. There is great potential for game-like application for numerical modelling – I once got a group of 40+ 9-year olds running CAESAR-Lisflood by describing it as ‘Minecraft with worse graphics’ – and those who work with them often have a playful curiosity. We like to ask questions like ‘I wonder what happens if I do this?’, and this playful curiosity can lead to the discovery of some of the most fundamental knowledge about how our planet works.

From the original hacked version of CAESAR-Lisflood, through to TideBox and the Defend the City workshop, I’ve found that the numerical model has lent itself to a gaming environment extraordinary well for the use in teaching and public engagement.

Games are pervasive throughout Geosciences, finding use in research, in teaching, and in wider communication. They are powerful training tools. I bet you have used or played games in your work, maybe without even realising it. If you have, then this is the session for you! We are not going to be strict about definitions for what is considered a game or not, just as long as it is playful, interesting, and most importantly, fun.

Abstract submission is open from 13th October 2017, and closes 10th January 2018.

If you’ve never submitted to an Educational and Outreach Symposia (EOS) session before, I would encourage you to do so – they are very enjoyable, and as they don’t prohibit you submitting another Oral abstract for another session they are great way to maximise the exposure of your research.

You find more details here.

Alongside the session we are hoping to host a related gaming session, giving us all the opportunity to try each other’s games – have something you want to bring along? Let us know.

EGU Blood Bowl Cup – I’m also interested in running the first ever EGU Blood Bowl Cup. I only need at least one opponent to make this happen, so let me know if you want in. I might even make a special pitch for the occasion.

Solved! – The Great Blade Conspiracy #Hull2017 #CityofCulture

Back in January I wrote about how the turbine blade, which was in Hull City Centre briefly, looked photoshopped in every picture I saw of it. It even looked photoshopped in real life, which was quite odd.

Read my original post here.

Now, researchers at the University of Lincoln have found that it’s all a trick of the light, with the blade lit from the side whilst our brains are thinking that it surely must be lit from above by the sun.

Read the research article here. Or, read the BBC article here.