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Does it need to be there? Remembering exposure in risk

In the engineering world we are often met with questions around risk mitigation, especially when it relates to the rising risk of flooding and coastal inundation. This is often met with a large concrete structure or building hardening, all designed to withstand the elements. But we can take a more holistic approach to risk management and reduction.


Hazard, Exposure & Vulnerability

Risk is composed of three distinct elements: Hazard, exposure, and vulnerability. They allows us to understand and reduce risk in different and complementary ways. Each is a critical element, and each demands a specific management approach.

To manage the hazard, we try to hold back the water using levees and sea walls.

To decrease our vulnerability we develop higher buildings, with stronger walls and flexible connections.

But to remove the need for either of these we can shift our exposure. In other words, leave nothing valuable to be flooded.

A holistic approach considers all three elements and finds the appropriate balance of measures dependent on level of risk, cost to mitigate, and socioeconomic benefits of the asset in question.

New Questions are Needed in Flood-Prone Areas

Recent flood events around the world highlight the importance of asking, does it really need to be there?

If we look at the Mississippi River, it has been engineered to such an extreme level that today it barely resembles the natural and changing flow channel it once was. This, coupled with residential and commercial development in its floodplains, left only the inevitable to happen. Three days of rain starting on the 26 December 2015 caused 25 deaths, caused thousands to be evacuated, and resulted in huge rebuild costs (The Economist, 2016). So should we have continued to develop in its floodplains?

Floods over Christmas 2015 in the UK similarly highlighted the need to consider development approvals in flood-prone areas. It’s expected that 20,000 homes will be built in flood-prone areas across the UK (The Telegraph, 2015).

Although these new developments may be behind existing protection measures, the ever-changing nature of the hazard, driven by climate change, means the water is only getting higher. The story is no different in Australia with the Productivity Commission last year calling land use planning perhaps the ‘most potent policy lever’ for influencing the level of future natural disaster risk.

Understanding Exposure

The emphasis on land use planning and consideration of exposure in disaster risk reduction often focuses on restricting new development. But it can (and should) be more subtle than that.

When managing the exposure to any natural hazard, considering supply chains, critical infrastructure, essential services and network redundancies are all equally important. When we broaden our thinking and delve into the factors that allow society to evolve, our management approaches equally broaden. This provides decisions makers with many more approaches with which to deal with the hazards societies face, apart from a yes or no development approval.

Modelling Exposure

As we broaden our thinking in terms of risk, to manage and reduce it we need to model all of its components. The modelling of exposure is particularly challenging. It’s a challenge that relates to some of this group’s (iWade’s) work.

Modelling exposure into the future requires an understanding of demographic and economic drivers for new investments and developments. The uncertainty involved in this can also be staggering. Methods need to be developed to ensure risk reduction options are robust or can adapt to future hazards and societal needs.

An approach this research group is taking is to model land use change. This is driven by the need to meet the State’s population and economic projections. We are also overlaying flood modelling (along with other disasters) to understand the changing risk due to climate change, economic development and population changes. These, coupled with developing scenarios for the future of cities, allows the capture of uncertainties and the testing of policies to assess their future effectiveness.

Research Report on Modelling, Understanding Reducing Exposure

Members of this research group are currently developing decision support systems which include the land use change and hazard models for government departments in South Australia, Victoria and Tasmania.

The research includes developing software packages and running workshops to ensure the models are designed to be as relevant as possible to assist decision makers to make better long-term decisions for risk reduction.

Optimise pump controls automatically to save time and money

This article looks at how to optimise pump operations using rule-based controls using such things as the EPANET2 Toolkit. Until now, this has not been possible. We modified the toolkit so that it is.


Every time we open a tap, water comes out. I had never asked myself why, before starting my civil engineering degree. It was only after I did that I realised that there is a lot of work behind water distribution systems (a water distribution system is the system of pipes, valves, tanks and pumps that deliver us water). Before that, I thought it was kind of magic.

Now, about 15 years later, I think it is still a kind of magic. Not only because the design of these systems is complex, but also because their operation needs to take into account a lot of constraints. In particular, the pump operation.

How do you operate a pump?

The easy answer is to switch it on or off depending on whether the tank is empty or full, respectively. But, the small example in Figure 1 will show you that it is not that easy after all.

The pump in Figure 1 fills a tank that is used to provide water to the users. We can use the tank level to decide when to switch on or off a pump.

Figure 2 shows the tank level and the pump operation if we decide to switch the pump on when the tank level reaches 7.9 m (this is the lower trigger level) and if we decide to switch the pump off when the tank level reaches 9.7 m (this is the upper trigger level).

This figure shows a pumping system. On the far right are some houses, labelled “users”. Above them is a tank, labelled “tank”. To the left and below the tank is an item labelled “pump” and next to it is an item labelled “water source”. It shows that the pump moves water from the water source to the tank, and then the tank serves the users.

Figure 1: simple example of pumping system

The pump controls in Figure 2 are not bad after all:

  • the tank is never empty, so users can have water the whole time
  • the tank is refilled after 24 hours
  • the number of pump switches is not excessive.

    This diagram shows a comparison between pump flow and tank levels. Between 1 pm and 1 am, at around 7 pm, a label says “some of this pumping could have been delayed to the off-peak tariff period!”. That off-peak tariff period is shown above it. The label says this because the pump flow is above 100 Litres per second, and yet the tank level is low at that time.

    Figure 2: example of pump operation with one set of tank trigger levels

However, we could have done better and saved a bit of money if we had pumped more in the off-peak tariff period, where energy is cheaper!

Who cares?

Maybe, at this point, you are already wondering ‘who cares?’ Well, the water utility, and all the people involved in the pump operations do.  Research has also cared for a relatively long time (e.g. Lingireddy and Wood, 1998; van Zyl et al. 2004; López-Ibáñez et al. 2008).

On some level, you should care too. Here’s why: Pumps use energy, which costs money. No matter where you are, you pay for water (and the electricity used to move it) directly or indirectly (e.g. through taxes).

It makes sense to switch the pumps on when the energy is cheaper (i.e. in the off-peak tariff period), but that is not easy. We could define the pump operation based on the time of the day (i.e. using scheduling), so that we are sure that we pump as much as we can when energy is cheaper. Figure 3 shows an example where we decide to switch off the pump at 8 am and to switch it on again at 4 pm. Now we exploit the off-peak tariff period as much as we can!  Perfect! Or, at least, it seems perfect. But what if the demands were bigger than expected and the tank runs empty before the off-peak tariff period starts? You cannot let this happen.

The problem is that we don’t know the water demands ahead of time. It makes predicting when we need to switch on or off a pump difficult.

Figure 3: the pump is switched on or off according to the time of the day

Rule-based controls help deal with uncertainty

One way to take into account the uncertainty in water demands is to control the pumps based on multiple conditions.

For example, if we define a different set of tank trigger levels (when to switch on or off a pump) for peak and off-peak tariff periods, we can reach the pump operations shown in Figure 4. The figure shows that we don’t pump more than necessary in the peak-tariff period, but we can  also make sure that the pump will be switched on before our tank runs empty.

Figure 4: example of pump operation with two sets of tank trigger levels (one for the peak and one for the off-peak tariff period) using rule-based controls

We can implement this type of pump controls in the hydraulic simulator EPANET2 (Rossman, 2000).

A rule-based control in EPANET2 looks like this:

AND TANK t6 LEVEL < 8.5000

You can see that the status of the pump depends both on the time of the day and on the tank level.

What happens is that usually the tank trigger levels in the peak tariff period are lower than the tank trigger levels in the off-peak tariff period. This way, the tank will not be completely refilled during the expensive period of the day; and it will be maintained as full as possible in the off-peak tariff period.

Optimising rule-based controls

The hydraulic simulator EPANET2 can be easily linked to optimisation algorithms using the EPANET2 toolkit. By doing this, the optimisation algorithm can find the best solution (or solutions) for you. For example, optimisation can find the optimal set of tank trigger levels to minimise costs and/or minimise energy consumption etc, Using trial and error would take a lot more time.

EPANET2 toolkit modification allows automation

Until  now, the toolkit did not allow the automatic modification of rule-based controls during optimisation. Now, we have adjusted the EPANET2 toolkit (see Marchi et al. (2016) and the ETTAR toolkit), so that we can optimise rule-based controls automatically.

This means that we can have an optimisation algorithm that optimises the tank trigger levels taking into account peak and off-peak tariff periods.

Automatic optimisation saves you time and money

But what if you want to operate a pump based on the level of multiple tanks? Real systems usually have more than one pump and one tank! Now we can optimise multiple conditions at the same time.

In Marchi et al. (2016) we also tried to let the algorithm optimise the entire set of rules. That is, the algorithm is deciding every word and value in a rule (for example RULE 1 above).

What we showed was that the algorithm was able to find less expensive solutions for the 24 hours tested!

There are many other possibilities

Maybe having the algorithm decide everything seems a bit too futuristic (even for me!). There is still a long way to go. There are a lot of considerations to take into account before the algorithm can really decide everything. But, this opens up a lot of interesting possibilities.

My hope is that the ETTAR toolkit can be used to find more cost-effective and reliable pump controls.

I hope you enjoyed this blog! If you are interested in this topic too, please leave a comment here or contact me.



Lingireddy, S. and Wood, D. (1998). “Improved Operation of Water Distribution Systems Using Variable-Speed Pumps.” J. Energy Eng., 10.1061/(ASCE)0733-9402(1998)124:3(90), 90-103.

López-Ibáñez, M., Prasad, T., and Paechter, B. (2008). “Ant Colony Optimization for Optimal Control of Pumps in Water Distribution Networks.” J. Water Resour. Plann. Manage., 10.1061/(ASCE)0733-9496(2008)134:4(337), 337-346.

Marchi, A.Simpson, A., and Lambert, M. (2016). “Optimization of Pump Operation Using Rule-Based Controls in EPANET2: New ETTAR Toolkit and Correction of Energy Computation.” J. Water Resour. Plann. Manage. , 10.1061/(ASCE)WR.1943-5452.0000637 , 04016012.

Rossman L.A., “EPANET2 user’s manual”, National Risk Management Research Laboratory, United States Environmental Protection Agency, Cincinnati, OH, 2000.

van Zyl, J. E. , Savic, D. A. , and Walters, G. A. (2004). “Operational optimization of water distribution systems using a hybrid genetic algorithm.” J. Water Resour. Plann. Manage. 130 (), 160–170.

The importance of multi-disciplinary research for alternative water sources

As researchers, we need a range of expertise to fully understand complex water supply systems. In this article I demonstrate what this means in the real world. Read on to find out how multi-disciplinary teams can be so important.


Water supply systems are complex. As we begin using more alternative sources of water, such as harvested stormwater and groundwater, these systems only become more complex. In a traditional water supply system, we rely on surface water from less developed or natural catchments. Climate change, population growth and overuse of these sources have put stress on our water resources. To ease the pressure, water utilities, local councils, developers and other water system managers have turned to alternative water supplies.

Analysing systems that use alternative sources from a hydraulic engineering background does not provide all the answers. We need different expertise to fully understand these systems.

How the hydraulic analysis approach works, in a traditional water supply system

A hydraulic analysis of a water supply/distribution system typically considers the system all the way from a supply reservoir to the consumers. It includes the pumps, tanks, pipes and valves along the way (Fig. 1). When we design and analyse such a system from a hydraulic perspective, our main considerations are:

  • Sizing of tanks: Taking into account the amount of water required by consumers and supplying water in emergencies (such as fires or pump outages).
  • Sizing of gravity pipelines:  To provide adequate pressure for consumers, but also avoid high velocities that may damage pipelines.
  • Sizing of pumps and pressure pipelines together: Considering pressure and velocity constraints on the pipe and energy losses due to friction
  • Adding valves where required: To sustain pressure, reduce pressure, or isolate sections of the network.
  • Determining operating rules for the system that ensure tanks always have enough water to supply demands, and, where possible, defer pumping to off-peak (cheaper) electricity tariff periods.

This approach assumes that water is always available in the water supply reservoir at the start of the system. We may need some assistance from hydrologists to ensure that this is a reasonable assumption.

This diagram shows the path of supply and distribution. The model runs left to right, as follows: Reservoir, pump, through a pressure pipeline and up to a tank, then through a gravity pipeline downwards to consumers.










This diagram shows the path of supply and distribution. The model runs left to right, as follows: Reservoir, pump, through a pressure pipeline and up to a tank, then through a gravity pipeline downwards to consumers.

Figure 1: A simple example of a traditional water supply system


Harvested stormwater systems need additional expertise

Harvested stormwater is run-off collected from urban areas. It is often used for non-potable supplies such as irrigation of open green spaces.

The Ridge Park Managed Aquifer Recharge Project in the City of Unley, South Australia, which is at the edge of Adelaide (Fig. 2) is a harvested stormwater system. In winter, this system:

  • collects water from Glen Osmond Creek (run-off comes from urbanised areas around the bottom of the South Eastern Freeway)
  • treats the water through biofiltration and a small treatment plant
  • injects the water into an aquifer for storage.

In summer, water is extracted from the aquifer and used to irrigate parks and reserves in the City of Unley area (Fig. 3).

This image shows a map of South Eastern metropolitan Adelaide in South Australia. Highlighted is the Glen Osmond Creek. Circled is the approximate catchment area upstream of the harvest point.

Ridge Park Managed Aquifer Recharge Project in the City of Unley, South Australia

This is a diagram of the Ridge Park Stormwater Harvesting and Aquifer Recharge System.

This is a diagram of the Ridge Park Stormwater Harvesting and Aquifer Recharge System. It shows why additional expertise is needed. This diagram has a harvest pond that Glen Osmond Creek flows into. Water is pumped from the pond into a bioretention basin, and from there up into a storage tank via a treatment plant. Water is also pumped into the tank from the aquifer, but that water does not go via the treatment plant. From the tank water is then distributed.

Figure 3: The Ridge Park Stormwater Harvesting and Aquifer Recharge System

Additional information needed to analyse the system

We need to consider the hydrology of Glen Osmond Creek and its catchment to know how much water is available to be harvested. This is in addition to a typical hydraulic understanding of the pumps, pipes and valves.

When analysing this system in our research, we have come across several problems that require expertise of other disciplines. Of note is the expertise provided by hydrogeologists and electrical engineers.

  • How much pressure is required to pump water into and out of the aquifer?
  • How do the aquifer properties affect the flow rates that can be achieved?
  • How much energy does it take to pump water through the treatment plant?
  • How much water can be held by the biofiltration basin and how long does it take to filter through?

In order to solve these problems, we reached out to people in our networks that have different expertise.

Non-technical expertise can be important

Input from non-technical areas, for example economic and social aspects, may be important.

The economic analysis of a system is particularly important in the concept or proposal phase. It can help to justify the benefits of going ahead with a project.

A social analysis of a system is also important. It helps us consider how alternative water source systems affect people’s use of water and public land. It also helps in considering the amenity of the land used for the system’s infrastructure. For example, building a dam on a creek to harvest stormwater may take land away from public use. But, if the water is used to irrigate other open green spaces, the project may be beneficial overall.

Networks and co-operative research centres improve research

Researchers, particularly PhD students, often work on very specific topics. They have very deep but not necessarily broad knowledge in their respective technical areas. In order to solve the problems identified above, we need to talk to people with different technical backgrounds and learn from them.

PhD students do not often have a broad network of people that they can go to for help on issues outside their fields. Our academic supervisors can be great resources in this respect. They help us to expand our networks, and show us where to start looking for answers.

I have found that being part of a Cooperative Research Centre (CRC) for Water Sensitive Cities has also proved useful. This CRC is a group of researchers from several different universities, from different disciplines. We collaborate with industry and government partners. Our outcomes are urban water management solutions, education and industry engagement. The goal is to make towns and cities more water sensitive. This large group of researchers and industry partners help me to better understand my research. It also improves the final results of my work.


This research is part of the CRC for Water Sensitive Cities Project C5.1 (Intelligent Urban Water Networks). It is supported by funding for post-doctoral research and a PhD top-up scholarship. The support of the Commonwealth of Australia through the Cooperative Research Centre program is acknowledged.

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