Climate adaptation for water infrastructure

STAGE | Strategy and planning, Finance and funding

TECHNOLOGIES | Big Data, Data & Analytics


Leveraging asset level data sets with climate models can quantify the climate change risks to water infrastructure, assess adaptation options, and provide economic modelling for smarter investment decisions. The goal is to increase climate resilience of water systems through new infrastructure, and replace, retrofit or upgrade existing assets. These models are used to identify hazards caused by climate change such as rising sea levels, saltwater intrusion, drought and other extreme events, and the risks it presents to water and wastewater infrastructure such as dams, treatment plants and pipe networks. This assists asset managers and policy developers to adapt infrastructure to be more resilient to the climate.

As computing power increases, the ability to analyse large data sets also increases. Climate science projections and asset failure models comprise extremely large data sets. By linking complicated variables in climate science knowledge, specific asset infrastructure failure and cost models it is now possible to synthesise useful insights and infrastructure recommendations for complex systems.

Climate change is in the forefront of immediate and long terms policy and infrastructure decisions. The World Bank (2010) estimates that the cost of climate adaptation between 2010 and 2050 is in the range of $75 billion to $100 billion a year. While the United Nations Environment Programme estimates the costs of climate adaptation could range from $140 billion to $300 billion by 2030, and between $280 billion and $500 billion by 2050.  Increased incidence of extreme events, such as floods, can cause disturbances and significant damage to all infrastructure, including water treatment plants and distribution networks. Technology and innovative solutions are needed to enable strategic and critical infrastructure to adapt to changing conditions. Water networks are already under stress due to increasing demand and aging infrastructure, and a lack of appropriate investment responses will lead to additional pressures. This can lead to worse health and economical outcomes and increase the potential for conflict where water resources are scarce.

Modelling is an important first step to prevent climate induced water infrastructure failure. Cost benefit analysis of potential planning scenarios and infrastructure decisions will assist asset owners to develop better justification for infrastructure investments. These investments will help increase capacity to mitigate these risks and are key to minimising damage to infrastructure and ensure water services are always available.

Though this use case focuses on water infrastructure, the technology can be applied to all types of infrastructure to measure their climate resilience and optimise capital investments. As modelling and analysis technology develops results will become more robust. Analysing the complicated cost and risk relationships between interdependent infrastructure such as roads, rail, water, electricity, and communications will also be achievable to assist collaboration between government agencies.



Improving efficiency and reducing costs:

  • The use of modelling and risk tools can optimise capital expenditure for new or upgrading infrastructure over the long term so that it can be responsive to climate adaptation risk.

Enhancing economic, social and environmental value:

  • Help to ensure clean water and sanitation services in changing environmental conditions.

Reshaping infrastructure demand and creating new markets

  • There is a potential increased need of new or different types of infrastructure depending on the results of modelling as they apply to climate change adaption.



Legislation and regulation: Inclusion of climate adaptation modelling could be added into legislation if governments wish to make this a necessary inclusion for infrastructure developments. This would require further exploration and policy development as to what level of risk modelling needs to be included in infrastructure planning.

Procurement and contract management: As per legislation above, the inclusion of climate adaptation modelling can be included in procurement processes if governments wish to make this more of a requirement for infrastructure developments.

Funding and financing: Due to the global nature of the problem and the scale of finance needed to combat climate change, global finance and funding will need to be scaled up to ensure successful climate mitigation or climate adaptation of global infrastructure. Especially with assisting developing nations to increase the climate adaptability of their infrastructure.

Effective institutions: Collaboration between all infrastructure owners to work toward a collaborative model for interdependent infrastructure would be an ideal future state. Asset owners could assist in sharing the investment load for single government entities as well as sharing the benefits of more comprehensive climate adaptabilities studies.



Implementation risk

Risk: Forecast of potential climate futures are constantly evolving. This may affect the planning and investment recommendations given by these types of technologies.

Mitigation: Plans should be developed to consider multiple future projections of uncertain variables such as population growth, population density, and climate change.

Risk: As water infrastructure affects and is affected by upstream and downstream infrastructure (such as electricity and transport), data from different government agencies will be needed to obtain results. Data management and format may differ across these entities which can lead to poor results.

Mitigation: A data management plan will need to be developed to ensure the collection of necessary data is standardised with the quality control plans to ensure optimal results.

Safety risk

Risk: Data collection, particularly regarding multiple assets spanning several utilities, government bodies and sometime different countries, can be sensitive particularly when involving risk models of critical infrastructure.

Mitigation: Plans to determine the data ownership model, the data that will be available to public and which data needs to be confidential will have to be considered.



Example: XDI

Implementation: XDI Sydney is a collaboration between NSW Government and Australian climate tech company XDI Cross Dependency Initiative. It’s a large-scale, multi-utility climate risk using world-first cloud computing technology that brings together geospatial hazard maps, climate change impact projections, engineering data and financial analysis.

Cost: It receives about $1 million in joint funding from local governments, water, energy and transport utilities.

Timeframe: The 3 year pilot started in 2017.

Example: RESilience to cope with Climate Change in Urban arEas (RESCCUE)

Implementation: RESilience to cope with Climate Change in Urban arEas a multisectorial approach focusing on water infrastructure in 3 cities: Bristol (UK), Barcelona (Spain) and Lisboa (Portugal). Outputs will include hazard, risk, and vulnerability assessment for urban services operation, resulting in a resilience and adaptation strategy ready for market uptake for each of the cities.

Cost: This project has received more than 6 million funding from European Commission by means of Horizon 2020, the EU Framework Programme for Research and Innovation.

Timeframe: A 48 month project spanning from May 2016 to April 2020.

Example: RESIN

Implementation: The RESIN project investigated climate change adaptation practices in European cities (Paris (France), Manchester (United Kingdom), Bratislava (Slovakia) and Bilbao (Spain)) and developed tools to guide the process of impact and vulnerability analysis for critical infrastructures and built areas.

Cost: This project has received funding from the European Union’s Horizon 2020 research and innovation programme.

Timeframe: The project started in 2015 and was completed in 2018.



Peter Bosch


Scientific Co-ordinator

Rohan Hamden



Marc Velasco


Project Manager

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