Reducing utility risk during heat waves

Climate change will bring more frequent heat waves, stressing water networks over multiple days.

To understand these impacts, deterministic hydraulic models are simulated under Peak Week demand conditions. Peak Week demands are linked to large pipelines for bulk water transfers to storage tanks.

In Australia, Peak Week modeling profiles are often standardised. Though, utility big data sets can show quite different Peak Week behaviours.

Figure 1

Figure 1 shows a standardised hydraulic model Peak Week profile - shown as the grey dashed line. Demand is normalised between 0 and 1, i.e., if demand equals 1, it’s the Peak Day. The red lines are big data Peak Week consumption profiles from four systems. These are the number 1 ranked 7-day events.

You will notice that only System 3 has the same timing of the Peak Day between model and data.

As System 1 doesn’t experience the Peak Day in its Peak Week, let’s look at more events from these systems.

Figure 2

There’s plenty of variability in the plots above! Suggesting there’s variable drivers for these events.

It’s interesting that similar behaviour of System 1’s No. 1 event, shows up in System 2’s No. 3 event. But these events are not related i.e., they occurred four years apart.

Is temperature driving these events?

The table below summarises daily max temperature during the No.1 ranked events.

You’ll notice that Systems 2 and 3 have consistently high daily max temperatures, likely driving their high demands.

Where Systems 1 and 4, both have 7-day mean temperatures of 26 degrees Celsius. This suggests temperature isn’t driving these particular events, rather other factors are at play.

Table 1

There was no rainfall recorded during the No. 1 ranked Peak Weeks, and no large rainfall events in the 30 day lead up.

The No.2 and 3 ranked events were generally driven by high temperatures.

How is this useful?

Operations - Probabilistic models for Peak Week forecasting can be developed. This can provide valuable decision-support when operating water networks during heat waves.

Planning – Simulating hydraulic models using big data profiles may reveal network blind spots – that don’t appear with standardised profiles. This can reduce the risk of water disruption.

Engineers who use SensorClean can easily see their Peak Weeks.

What’s next?

Climate change will bring more frequent, intense heat waves stressing water networks.  

Considerable variability exists in Peak Week demand events due to multiple demand drivers.

Organising Peak Weeks into probabilistic frameworks can provide valuable decision-support to reduce utility risk during heat waves.

Please contact us for more info.