Water resilience starts with seeing risk sooner

By Kohtari

This summer has brought the challenge of water resilience into sharp focus, and a need for a proactive approach to protect supply now and in the future.

Let’s look at the facts:

• By late July, almost 40% of England’s population was facing water-use restrictions. (Environment Agency)

• England had received just 3% of its average July rainfall by 21 July. (Environment Agency)

• Reservoir storage had fallen to 75.3%, around 7.4% below the seasonal average. (Environment Agency)

• By 15 July, the UK had already recorded more days above 30°C than during the whole of 1976. (The Met Office)

Hotter weather increases demand at the same time as rainfall, river flows and reservoir levels decline. It can also change raw water conditions, contributing to risks such as algal blooms that place further pressure on treatment and operational teams.

When several pressures develop at once, relying on information about what has already happened is no longer enough.

The direction of travel is clear

In recent weeks, the Drinking Water Inspectorate (DWI) and Ofwat have both pointed to the same direction of travel: a more proactive, future-ready water sector.

The DWI’s Chief Inspector’s Report calls for earlier identification and management of risk, rather than waiting for water quality or supply issues to emerge. Ofwat’s AI adoption plan sets out how responsible AI could be adopted at scale across the future regulatory system.

One focuses on risk. The other on technology. Together, they raise a bigger question:

Are water companies building the strategies, data foundations and practical solutions needed for tomorrow’s risks, or still relying on traditional tools and reactive approaches?

From records to foresight

The data needed to predict water risks already exists. The problem is that this information often sits across separate systems, teams and formats. This means that important patterns remain hidden, while operational decisions continue to depend on snapshots of current conditions or retrospective reports.

AI and machine learning change how that data is used.

By analysing multiple sources together, from historic water quality data, to weather, satellite and catchment data – predictive models can identify changing conditions and relationships that would go undetected manually. Instead of simply recording what has happened, AI and machine learning can help teams understand what is likely to happen next.

And this does not mean handing decisions over to an algorithm. The real value comes when trusted predictions are combined with operational experience, transparent governance and human judgement. A forecast only creates value when it gives someone enough time and confidence to make a better decision.

Turning foresight into better decisions

That shift becomes most valuable when applied to complex, fast-changing risks.

Algal blooms are a clear example. They are influenced by a combination of weather, water quality, catchment conditions and reservoir behaviour. Looking at any one of these factors in isolation provides only part of the picture.

Seeing the reservoir as one connected system can reveal how bloom risk develops, distinguish between events that may require different responses and identify where intervention could have the greatest effect.

The cost of standing still

This is not just a technology gap. It is an operational and investment risk.

When warning comes late, teams have fewer choices. Monitoring becomes more intensive, treatment changes become reactive and planned work gives way to firefighting. Overtime, energy costs and incident fatigue continue to add up.  

In severe cases, the consequences can extend much further. Algal blooms can restrict treatment capacity, affect abstraction decisions and increase the risk of disruption to drinking water supply.  

At the same time, water companies may be making major capital decisions without a complete understanding of what is driving the problem or which intervention will have the greatest impact.

Earlier visibility changes the decision-making window.

It gives teams more time to plan abstraction, prepare treatment, target monitoring and deploy resources before conditions escalate. It also creates stronger evidence for investment, helping leaders challenge assumptions, optimise scope, phase expenditure and build confidence that funding is being directed towards the right solution.

The real divide will not be between water companies that use AI and those that do not. It will be between those that can turn data into earlier action and those still forced to respond after risk has escalated.

Moving from ambition to action

The DWI is calling for a more proactive approach to drinking water risk. Ofwat is laying the groundwork for AI adoption that can scale. Climate and supply pressures are already increasing.

The next step is to put practical solutions in place. At Kohtari, we work with water companies to enable that shift so that we can better protect our waters.

Our algal bloom prediction solution, BloomIQ, connects environmental, weather, satellite, historical and operational data to forecast risk. Embedded into existing workflows, it gives teams the evidence they need to make earlier, data-led decisions that protect water quality and strengthen resilience.

To find out how this works in practice, get in touch with our team today.

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