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Ecosystem connectivity tends to increase the resilience and function of ecosystems responding to stressors. Coastal ecosystems sequester disproportionately large amounts of carbon, but rapid exchange of water, nutrients, and sediment makes them vulnerable to sea level rise and coastal erosion. Individual components of the coastal landscape (i.e., marsh, forest, bay) have contrasting responses to sea level rise, making it difficult to forecast the response of the integrated coastal carbon sink. Here we couple a spatially-explicit geomorphic model with a point-based carbon accumulation model, and show that landscape connectivity, in-situ carbon accumulation rates, and the size of the landscape-scale coastal carbon stock all peak at intermediate sea level rise rates despite divergent responses of individual components. Progressive loss of forest biomass under increasing sea level rise leads to a shift from a system dominated by forest biomass carbon towards one dominated by marsh soil carbon that is maintained by substantial recycling of organic carbon between marshes and bays. These results suggest that climate change strengthens connectivity between adjacent coastal ecosystems, but with tradeoffs that include a shift towards more labile carbon, smaller marsh and forest extents, and the accumulation of carbon in portions of the landscape more vulnerable to seamore »level rise and erosion.

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Coastal ecosystems represent a disproportionately large but vulnerable global carbon sink. Sea-level-driven tidal wetland degradation and upland forest mortality threaten coastal carbon pools, but responses of the broader coastal landscape to interacting facets of climate change remain poorly understood. Here, we use 36 years of satellite observations across the mid-Atlantic sea-level rise hotspot to show that climate change has actually increased the amount of carbon stored in the biomass of coastal ecosystems despite substantial areal loss. We find that sea-level-driven reductions in wetland and low-lying forest biomass were largely confined to areas less than 2 m above sea level, whereas the otherwise warmer and wetter climate led to an increase in the biomass of adjacent upland forests. Integrated across the entire coastal landscape, climate-driven upland greening offset sea-level-driven biomass losses, such that the net impact of climate change was to increase the amount of carbon stored in coastal vegetation. These results point to a fundamental decoupling between upland and wetland carbon trends that can only be understood by integrating observations across traditional ecosystem boundaries. This holistic approach may provide a template for quantifying carbon–climate feedbacks and other aspects of coastal change that extend beyond sea-level rise alone.

Lakes represent as much as ∼25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e. 1975–2019) across 32 690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning 388 968 km². Drivers of lake drainage were determined with boosted regression tree models, using both static (e.g. lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g. temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10% and 16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ∼37% decade⁻¹from 1975–1979 (102–273 lakes draining year⁻¹) to 2010–2014 (3–8 lakes drainingmore »year⁻¹). However this 40 year negative trend was reversed during the most recent time-period (2015–2019), with observations of catastrophic drainage among the highest on record (i.e. 100–250 lakes draining year⁻¹), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R²_adj= 0.42, CV = 0.35,p< 0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R²_adj= 0.75, CV = 0.67,p< 0.0001). Models forecast a continued decline in lake area across northern Alaska by 15%–21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt ‘catastrophic’ drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

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Abstract

Forty-five years (i.e. 1975-2019) of Landsat observations were used to map the spatiotemporal patterns of lake drainage in northern Alaska. All Landsat data was pre-processed by the United States Geological Survey and downloaded by google earth engine in a radiometrically, atmospherically, and geometrically terrain-corrected state. We used Landsat surface reflectance products acquired from the Multispectral Scanner (MSS), Terrestrial Mapper (TM), Enhanced Terrestrial Mapper Plus (ETM&#43;), and Operational Land Imager (OLI) sensors to compute eight image mosaics for the ice-free period (June 15 to September 1) at five-year time-periods. This data product represents the change in lake area between time-periods or epochs. All data used to spatially identify patterns of lake change are presented in a map (LakeDrainChg.tif), where the associated morphometric controls on drainage are summarized for each of the ~33,000 lake boundaries (Lake_Drainage.shp). All data can also be viewed within Google Earth Engine (https://code.earthengine.google.com/?accept_repo&#61;users/mjlara71/LakeDrainage; accessed on 13 September 2021) or clone Git repository (git clone https://earthengine.googlesource.com/users/mjlara71/LakeDrainage ; accessed on 13 September 2021).