Photo credit: www.sciencedaily.com
As global sea levels continue to rise due to climate change, cities located along coastlines are often required to adopt a range of protective measures to ensure the safety and sustainability of their communities. Typically, this involves the establishment of protective infrastructures, such as seawalls, which are engineered based on current climate forecasts, leading city planners to assess their cost-effectiveness before proceeding with construction.
However, a significant challenge lies in the unpredictability of future climate conditions, as highlighted by Ashmita Bhattacharya, a doctoral student in civil engineering at Penn State. Bhattacharya is the lead author of a study published in Nature Communications, which emphasizes the risks communities face when relying on potentially outdated climate models. These risks can result in either the overbuilding of infrastructure—burdening cities with costly maintenance and increased carbon emissions—or the development of inadequate defenses, leading to heightened flood-related damages and subsequent repair costs.
“The current framework for climate adaptation often grapples with substantial uncertainty regarding future climate trends,” noted Chris Forest, a professor of climate dynamics at Penn State and co-researcher on the study. Recent years have demonstrated unprecedented global temperature spikes, further complicating the situation.
To aid in informed decision-making and to avoid financially burdensome investments that may not align with actual future conditions, the interdisciplinary research team has developed a model designed to evolve alongside the availability of new climate data. Gordon Warn, a professor at Penn State and a co-author, explained that their model facilitates a dynamic approach to adaptation over time.
“Our model encourages timely and responsive actions in relation to real-time climate developments,” Warn stated, advocating for decisions that align with varying future scenarios in a cost-effective manner.
Adopting a strategic, long-term vision, the model proposes gradual adaptation actions instead of requiring cities to allocate substantial resources upfront. This philosophy could lead to significant savings for local governments. The team assessed their model using scenarios inspired by the coastal regions of Manhattan and Staten Island, revealing that recommendations for adaptation based on ongoing observations and long-term goals yielded lower costs compared to traditional static approaches reliant on fixed cost-benefit analyses.
The model is built upon sophisticated mathematical and computational strategies, such as the “(Partially Observable) Markov Decision Processes,” which operationalize the uncertainty inherent in nature by representing it as “beliefs” that quantify various future scenarios. As new data emerges, the model continuously refines its understanding of potential sea level rise, akin to a chess player analyzing the board after each move, evaluating possible future actions based on previous decisions.
Just as a chess player might choose to withhold an aggressive play for potential future gains, the model may advocate for preliminary, smaller-scale interventions or recommend delaying decisions until conditions warrant action. The recommendations stem from a technique known as “dynamic programming,” which assesses the optimal course of action given the latest available information.
“Our model relies on both direct and indirect observations of crucial physical processes, such as measuring sea-level rise and storm surges,” explained Bhattacharya. “This adaptability results in minimized costs tied to implementation, upkeep, and environmental damage compared to conventional static cost-benefit assessments.”
The environmental implications of construction and maintenance are also factored into the model’s calculations. As Bhattacharya elaborated, the researchers connected the costs associated with building seawalls—including carbon emissions from cement production and other processes—to the U.S. Environmental Protection Agency’s social cost of carbon, which emphasizes the environmental harm associated with carbon dioxide emissions.
In their exploration of coastal resilience, the researchers also examined natural solutions that could complement traditional infrastructure. Options such as smaller seawalls combined with oyster reefs or salt marshes were identified as strategies that could mitigate wave impacts while maintaining a lower carbon footprint.
Simulations revealed that when factoring in the social cost of carbon, communities acted more proactively in implementing adaptation strategies, with more frequent adaptation measures being observed. “This finding suggests that neglecting carbon emissions leads to an underestimation of the comprehensive costs linked to flood damages,” Warn noted.
Looking ahead, the research team aims to further refine and expand the model, testing it against increasingly intricate scenarios and diverse coastal environments. According to the U.N. Atlas of the Oceans, eight of the ten largest cities worldwide are situated along coastlines.
“While the core framework remains unchanged, the model will need to adapt local data, including geographical nuances and property values,” explained Kostas Papakonstantinou, another co-author of the study. He proposed that government entities or insurance companies might eventually leverage this model to incentivize adaptive measures, drawing parallels to past adjustments in auto insurance based on new safety technologies.
“In a similar vein, the costs associated with the National Flood Insurance Program could see reductions if timely actions are justified,” Papakonstantinou suggested. The implications of insurance coverage following flood events could also be integrated into this adaptive decision-making framework, particularly when it involves entities responsible for those decisions, such as the federal government.
The study’s interdisciplinary efforts included contributions from co-investigators at both Penn State and the University of Pittsburgh, underscoring the collaborative nature of this essential research in addressing climate resilience.
This project received funding from the U.S. National Science Foundation.
Source
www.sciencedaily.com