Engineering Innovations
Engineering Innovation

Adapting to Climate Change Impacts Using a Data-Driven Resiliency Approach

Recent extreme weather events and natural disasters, as well as long-term climate trends, have exposed vulnerabilities and shown that communities need to seriously and proactively consider the threat of these events.

An innovative, data-driven and risk-based approach is providing new opportunities to plan and design more resilient infrastructure that is better prepared to withstand the impact of hurricanes and other natural events.

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Allan Estivalet

“This is emerging as an approach to flooding and storm mitigation,” said Allan Estivalet, water resources group leader and Northeast resiliency lead for WSP USA. “Our firm is a leader in the creation of data-driven solutions”, which looks at how data can be better used to effectively design projects and engage people with what climate change will mean for their communities.”

Estivalet was part of a panel discussing “Data-Driven Resiliency” at the Data for Good Exchange 2017 conference sponsored by Bloomberg on Sept. 24 in New York City, an annual conference on the application of data science to solve problems for the social good and enhance the quality of life. Data scientists from academia, industry and the public sector build relationships, share insights and progress, and work together on applying modern machine learning and data science methods to challenges in the public and non-profit sectors.

“It’s a new way of planning; looking at future risks and project context rather than designing structures based solely on regulatory criteria, which is the traditional approach but doesn’t necessarily reflect current and future risks,” Estivalet said. “As we’ve come to realize, much of our infrastructure was not designed and built to withstand the risks we face today.”

Estivalet was joined on the panel by Pippa Brashear of SCAPE and Paul Tschirky of ARCADIS. Brashear and Tschirky focused on flooding issues related to the New York City coastal area, while Estivalet discussed prevention measures for inland areas.

Estivalet explained how his team collects, evaluates and selects data used in advanced models to accurately determine flooding risks and develop appropriate solutions for future growth, development and reconstruction.

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©2017 THE BLOOMBERG DATA FOR GOOD EXCHANGE

Pippa Brashear of SCAPE, Paul Tschirky of ARCADIS and Allan Estivalet of WSP (from left to right discussed how data-driven resiliency can lead to improved infrastructure.

“The important role of the engineer is to figure out the best data to use,” he said. “It’s a complex selection process, but even the best data in a project context is limited by what regulatory agiencies accept for project to be constructed. We are there to find that optimum balance between the need of our projects, what is permitted to be constructed, and where regulations may need to be updated.”

Building Resiliency

As an example of WSP’s use of data-driven planning, Estivalet discussed his role in the development of a large drainage master plan in Orange County, New York, which was seriously impacted by Hurricane Irene in 2011. Several Orange County communities were selected to participate to in the NY Rising Community Reconstruction Program announced by Gov. Andrew M. Cuomo in April 2013.

“We expanded our view to develop a comprehensive, integrated hydrologic and hydraulic analysis for the 200-square-mile watershed and create flood mitigation strategies based on future flooding risks,” he said.

WSP performed the inspection and survey of more than 300 assets including dams, levees, floodwalls, bridges, culverts and buildings located in the floodplain.

“A lot of flooding studies focus on the town scale, but to really understand how the water behaves in this area, it was essential to expand our view to the watershed scale and look at all of the potential flooding risks over the next 50-100 years as conditions change … not just areas that have previously experienced floods,” Estivalet said.

The study evaluated the most critical needs and impacts of recent major storms, and identified flood mitigation solutions to reduce flooding, increase safety and quality of life and create incentives for economic growth.

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©2017 WSP/ALLAN ESTIVALET

Data scientists gathered in New York City to share experiences on how machine learning and data science methods can address challenges in the public and non-profit sectors.

“We took a financial approach to prioritize our proposed projects and strategies to make sure that the communities spend their money wisely by monetizing the prevented losses that municipalities could expect under existing conditions,” Estivalet said. “Our pragmatic data-driven resiliency approach creates a better understanding that really helps us design effective projects for the communities.”

Captive Audience

Estivalet’s panel received high marks from the audience and fielded several questions following the presentation.

“With this event coming so soon after Hurricane Harvey devastated Texas, a lot of people were very aware and concerned about the impact of these severe events, so we did have a captive audience,” Estivalet said.

He said the audience was responsive to the idea that a pragmatic use of climate data can help bring government up to speed with modern techniques.

“The U.S. is experiencing a rising number of catastrophic events that cause significant amounts of damage coupled with an increase in population and wealth, which increases our vulnerability,” he said.

“Adapting to climate risks requires a multidisciplinary approach and a comprehensive understanding of our client’s assets,” Estivalet continued. “Understanding future risk is paramount for our sector. WSP’s experts work collaboratively across disciplines to develop solutions that are effective and provide short- and long-term benefits. This is a great asset to best serve our clients.”


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