“…The linear regression models of LAT19 3 and SC20 2 did not take into consideration that total flood area, flooded wetland area, and total at risk property value have interactions. For example, LAT19 3 suggested that the slope of the wetland width is the wetland value, while SC20 2 developed a formula to calculate the wetland value. However, the assumptions made by them were too simplistic because they calculated the wetland value by replacing the flooded wetland areas with zero but used the same total at risk property value and total flood area corresponding to "with wetlands" results.…”
Section: Resultsmentioning
confidence: 99%
“…During Superstorm Sandy, coastal wetlands along New Jersey (NJ), New York (NY), and Connecticut (CT) coasts (Fig. 1a) provided a modest reduction of structural loss in coastal communities 1,3,6 due to the relatively sparse and low Spartina marsh and the high storm tide. On the other hand, Sheng et al 7 found that the tall and dense Phragmites-dominated Piermont Marsh buffered the Village of Piermont, located 40 km north (upstream) of New York City (NYC) on the Hudson River, from massive structural loss during Sandy.…”
Wetlands such as tidal marshes and mangroves are known to buffer coastal communities from wave, flood, and structural loss during storms. Coastal communities and resource managers seek to understand the ecosystem service value of coastal wetlands for reducing storm-induced flood loss in a changing climate. A recent modeling study found that a tall and dense Phragmites-dominated Piermont Marsh reduced the flood loss in the Village of Piermont, New York, U.S.A. during Superstorm Sandy and the 1% annual chance flood and wave event by 8% and 11%, respectively. Here we used the same modeling approach to examine the marsh’s buffering capacity in a changing climate (from 2020 to 2100), considering a potential marsh restoration plan (from 2020 to 2025) and potential marsh loss due to sea-level rise. Results showed that from 2020 to 2100, the 1% annual chance flood, wave, and structural loss would increase due to sea-level rise, storms, and marsh loss. However, the marsh will buffer ~ 11–12% of structural loss until 2050. Under the extreme SLR scenario of 2.89 m and a low accretion rate, Piermont Marsh is expected to lose its buffering capacity by 2080–2100 but will retain some buffering capacity with a high accretion rate of 10 mm/year and marsh growth. The marsh’s buffering capacity will remain during extra-tropical storms during winter and spring unless the wind has a significant northerly component. Lessons learned from this study can be used by coastal communities and marsh managers to develop coastal resiliency and marsh restoration plan.
“…The linear regression models of LAT19 3 and SC20 2 did not take into consideration that total flood area, flooded wetland area, and total at risk property value have interactions. For example, LAT19 3 suggested that the slope of the wetland width is the wetland value, while SC20 2 developed a formula to calculate the wetland value. However, the assumptions made by them were too simplistic because they calculated the wetland value by replacing the flooded wetland areas with zero but used the same total at risk property value and total flood area corresponding to "with wetlands" results.…”
Section: Resultsmentioning
confidence: 99%
“…During Superstorm Sandy, coastal wetlands along New Jersey (NJ), New York (NY), and Connecticut (CT) coasts (Fig. 1a) provided a modest reduction of structural loss in coastal communities 1,3,6 due to the relatively sparse and low Spartina marsh and the high storm tide. On the other hand, Sheng et al 7 found that the tall and dense Phragmites-dominated Piermont Marsh buffered the Village of Piermont, located 40 km north (upstream) of New York City (NYC) on the Hudson River, from massive structural loss during Sandy.…”
Wetlands such as tidal marshes and mangroves are known to buffer coastal communities from wave, flood, and structural loss during storms. Coastal communities and resource managers seek to understand the ecosystem service value of coastal wetlands for reducing storm-induced flood loss in a changing climate. A recent modeling study found that a tall and dense Phragmites-dominated Piermont Marsh reduced the flood loss in the Village of Piermont, New York, U.S.A. during Superstorm Sandy and the 1% annual chance flood and wave event by 8% and 11%, respectively. Here we used the same modeling approach to examine the marsh’s buffering capacity in a changing climate (from 2020 to 2100), considering a potential marsh restoration plan (from 2020 to 2025) and potential marsh loss due to sea-level rise. Results showed that from 2020 to 2100, the 1% annual chance flood, wave, and structural loss would increase due to sea-level rise, storms, and marsh loss. However, the marsh will buffer ~ 11–12% of structural loss until 2050. Under the extreme SLR scenario of 2.89 m and a low accretion rate, Piermont Marsh is expected to lose its buffering capacity by 2080–2100 but will retain some buffering capacity with a high accretion rate of 10 mm/year and marsh growth. The marsh’s buffering capacity will remain during extra-tropical storms during winter and spring unless the wind has a significant northerly component. Lessons learned from this study can be used by coastal communities and marsh managers to develop coastal resiliency and marsh restoration plan.
Coastal communities in New Jersey (NJ), New York (NY), and Connecticut (CT) sustained huge structural loss during Sandy in 2012. We present a comprehensive science-based study to assess the role of coastal wetlands in buffering surge and wave in the tri-state by considering Sandy, a hypothetical Black Swan (BS) storm, and the 1% annual chance flood and wave event. Model simulations were conducted with and without existing coastal wetlands, using a dynamically coupled surge-wave model with two types of coastal wetlands. Simulated surge and wave for Sandy were verified with data at numerous stations. Structural loss estimated using real property data and latest damage functions agreed well with loss payout data. Results show that, on zip-code scale, the relative structural loss varies significantly with the percent wetland cover, the at-risk structural value, and the average wave crest height. Reduction in structural loss by coastal wetlands was low in Sandy, modest in the BS storm, and significant in the 1% annual chance flood and wave event. NJ wetlands helped to avoid 8%, 26%, 52% loss during Sandy, BS storm, and 1% event, respectively. This regression model can be used for wetland restoration planning to further reduce structural loss in coastal communities.
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