Connecticut - SLAMM V2 Coastal Inundation Frequency 4 Medium High 2025 - 2100 - Department of Energy & Environmental protection

Coastal marshes are dynamic ecosystems that provide significant ecological and economic value. Located at the margin between land and water, they are also among the most susceptible ecosystems to climate change, especially accelerated rates of sea-level rise (SLR). Similarly, public infrastructure near coastal waters are increasingly susceptible to flooding in a rising sea. To help identify the potential responses of coastal marshes and infrastructure to sea level rise, Warren Pinnacle Consulting, Inc. (WPC), the Northeast Regional Ocean Council (NROC), and the Connecticut Department of Energy and Environmental Protection (CT DEEP) applied the Sea Level Affecting Marshes Model (SLAMM version 6) to Connecticut’s coast. This 2016 application of SLAMM updates a previous 2014 effort that includes new SLR scenarios ranging from 0.38 meters to 1.9 meters from the base year (2002) to year 2100. Also included in the current model run are new environmental factors such as tidal muting of select areas due to flow constrictions, marsh collapse, enhanced hydraulic connectivity and more current elevation data. This application of SLAMM also provides new data on the effects of tidal and storm surge flooding on roads and other infrastructure.

The SLAMM project data can help support environmental planning efforts by identifying potential future locations of new coastal marsh currently occupied by undeveloped dry upland, how current coastal high marsh/low marsh composition ratios are expected to change and how changes in the frequency of tidal and storm surge road flooding can inform infrastructure resilience planning. For the purposes of the data presented here, the coastal marsh is defined as low marsh (regularly-flooded marsh), high marsh (irregularly flooded marsh), undeveloped dry land recently converted to marsh (transitional or scrub-shrub marsh), and tidal-fresh marsh.

Model output is presented as either deterministic or probabilistic (likelihood) results. Deterministic results predict future land cover type (e.g., coastal marsh, intertidal flat, open water, flooded developed land, etc.) and flooding under specific SLR scenarios for each modeled time step (Year 2025, 2040, 2055, 2070 2085, 2100) using discrete data (e.g., marsh accretion rate, tide range, marsh surface elevation, road centerline elevation, etc.)