Apart from being a very British conversation topic, the weather is extremely important as is our ability to predict it. Even if these predictions are off from time to time, the fact that we can leave the house with an indication of what’s going to happen is something that most of us take for granted.
If you start to think about the number of operations that actually rely upon weather forecasts: gritting roads before a cold spell; take-off and landing patterns of jumbo-jets, food harvesting, cargo shipping advisories, etc, the true reach of this predictive capability becomes apparent. Being able to predict the weather isn’t a convenience, but a cornerstone of many efficiencies and services we rely on.
Waterways are very different.
Once it reaches the ground, water travels downhill to reach the sea. The area of land that collects rainfall and drains it to a common point, such as a lake or river is known as a catchment. As local geography determines the size of a catchment and manmade and natural features dictate how the water drains, different catchments can behave in dramatically different ways.
Like the weather, catchments are hard to predict, but unlike the weather, forecasts aren’t available. Local councils, airline companies, farmers, shipping companies, etc can use weather forcasts to provide safer, more effective and efficient services. A shipping company doesn’t need to take measurements of the bay and surrounding sea in order to determine if it’s safe to ship cargo, it simply looks at a weather forecast. The current equivalent for catchments, however, doesn’t exist.
This means that utility companies, government agencies, research groups, farmers, etc, all have to create their own systems to quantify the state of catchments to determine how best to perform their core activities. This is bad for a few reasons.
It’s hugely inefficient. Organisations are likely collecting mutually beneficial or duplicate data without realising or sharing it, increasing costs and complexity.
Specialism helps. Measuring and quantifying catchments aren’t the core activities of these organisations, they simply want to use the information to do what they do better.
The power of scale isn’t available. Due to the two points above, the scope of data collected is normally to allow organisations to respond to changes in catchments, not to predict them. Making predictions requires a large scale, systematic, collection of data, which in turn is much more complex. Without pooled resources, this complexity cannot be offset. For example, weather forecasts are required for commercial airline travel. It’s unlikely, however, that if weather forecasting didn’t exist any one airline would create and maintain the entire infrastructure required to enable weather predictions to then enable their own operations.
This is why I think a ‘weather forecast for waterways’ service would be incredibly useful and efficient. Not only to those who already use water, but for services and optimisations that require it before they can be built.
I have a roadmap of how this can be achieved and FreeUP is currently undertaking the work required for phase one. If you are interested in knowing more or being involved, just let me know.
This is a significant undertaking, but one that I believe will move everyone who deals with water forwards. Given the importance of water to everyone, it also seems like a very good use of effort.