A new model developed by Penn State and University of Pittsburgh researchers offers a dynamic, cost-effective approach to coastal management, promising significant savings and reduced carbon emissions while protecting communities from the dangers of rising sea levels.
Coastal cities have long struggled to defend against the advancing threats of rising sea levels and climate change, often betting on costly infrastructure like seawalls. However, a new study by researchers from Penn State University and the University of Pittsburgh offers a dynamic and adaptive strategy to coastal management that could revolutionize the way cities prepare for an uncertain climate future.
The team of researchers, led by Ashmita Bhattacharya, a civil engineering doctoral student at Penn State, have introduced a new coastal management model detailed in their study published in Nature Communications.
“The issue with the current state of practice in climate adaptation is the large uncertainty associated with how the climate demands us to evolve in the future,” co-investigator Chris Forest, a professor of climate dynamics in the Department of Meteorology and Atmospheric Science at Penn State, said in a news release.
The model aims to address the shortcomings of static cost-benefit analyses that risk either overbuilding expensive infrastructure or under-preparing for climate impacts. By incorporating advanced mathematical techniques and dynamic programming, the model provides decision support in real time, adjusting to new data as it becomes available.
Co-corresponding author Gordon Warn, a professor in the Department of Civil and Environmental Engineering at Penn State, highlighted the model’s agility.
“Our approach suggests dynamic actions in time, responding to the actual evolving climate, while also considering possible future scenarios in an optimal sense,” he said in the news release.
The model’s flexibility was tested using scenarios based on New York City’s coastal regions. By examining sites like Manhattan and Staten Island, the researchers found that the adaptive model offered significant cost savings over traditional methods.
“This dynamic adaptation leads to lower costs in implementation, maintenance, damages, and environmental impacts in comparison to the static cost-benefit actions,” added Bhattacharya.
Central to this innovative approach is the use of Partially Observable Markov Decision Processes, which help the model ‘learn’ from real-time data, updating its recommendations.
The model’s decision-making process is likened to a chess game, where each move is carefully calculated based on the state of the board. It can recommend smaller interim measures or delayed actions based on the evolving situation, reducing unnecessary costs and minimizing environmental impact.
Additionally, the model assesses the carbon footprint of infrastructure projects, factoring in the U.S. Environmental Protection Agency’s social cost of carbon. By integrating these environmental considerations, the model encourages earlier and more frequent adaptation actions, preempting higher long-term costs.
“What this result means is that by ignoring carbon emissions we are underestimating the overall cost of flood-related damages,” Warn added.
Interestingly, the model also champions the combination of “gray” infrastructure, like seawalls, with nature-based solutions, such as oyster reefs and salt marshes. These biologically sustainable approaches not only reduce wave impacts but also offset the carbon footprint from construction activities.
Future iterations of the model will be tested in diverse coastal regions, tailoring the framework to local geography, property values, and specific community needs. The ultimate goal is to scale up the model for broader application, potentially serving as a valuable tool for government and insurance companies to incentivize timely adaptation measures.
“Similarly, National Flood Insurance Program costs could reduce rates when protective measures are justifiably taken in time,” added co-corresponding author Kostas Papakonstantinou, an associate professor of civil and environmental engineering at Penn State.
This approach could see insurance policies adapt favorably for proactive climate resilience actions, reducing the financial burden on at-risk communities.
The study included contributions from Penn State’s Lauren McPhillips and Digant Chavda, along with Melissa Bilec and Rahaf Hasan from the University of Pittsburgh.
With eight of the world’s 10 largest cities located along coastlines, according to the U.N. Atlas of the Oceans, the significance of this model cannot be understated. It offers a promising path forward, combining economic prudence with environmental stewardship to safeguard coastal cities from the escalating effects of climate change.
Source: Pennsylvania State University