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The 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi‐instantaneous ice‐wedge melt, much larger areas were characterized by subtle, spatially variable post‐flood changes. Surface deformation from 2015 to 2019 estimated from ALOS‐2 and Sentinel‐1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice‐rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice‐rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice‐rich locations showed increases in observed greenness and wetness. Conversely, many ice‐poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost‐affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets.

 

Environmental impact assessments for new Arctic infrastructure do not adequately consider the likely long-term cumulative effects of climate change and infrastructure to landforms and vegetation in areas with ice-rich permafrost, due in part to lack of long-term environmental studies that monitor changes after the infrastructure is built. This case study examines long-term (1949–2020) climate- and road-related changes in a network of ice-wedge polygons, Prudhoe Bay Oilfield, Alaska. We studied four trajectories of change along a heavily traveled road and a relatively remote site. During 20 years prior to the oilfield development, the climate and landscapes changed very little. During 50 years after development, climate-related changes included increased numbers of thermokarst ponds, changes to ice-wedge-polygon morphology, snow distribution, thaw depths, dominant vegetation types, and shrub abundance. Road dust strongly affected plant-community structure and composition, particularly small forbs, mosses, and lichens. Flooding increased permafrost degradation, polygon center-trough elevation contrasts, and vegetation productivity. It was not possible to isolate infrastructure impacts from climate impacts, but the combined datasets provide unique insights into the rate and extent of ecological disturbances associated with infrastructure-affected landscapes under decades of climate warming. We conclude with recommendations for future cumulative impact assessments in areas with ice-rich permafrost. 

Abstract. Infrastructure built on perennially frozen ice-richground relies heavily on thermally stable subsurface conditions. Climate-warming-induced deepening of ground thaw puts such infrastructure at risk offailure. For better assessing the risk of large-scale future damage to Arcticinfrastructure, improved strategies for model-based approaches are urgentlyneeded. We used the laterally coupled 1D heat conduction model CryoGrid3to simulate permafrost degradation affected by linear infrastructure. Wepresent a case study of a gravel road built on continuous permafrost (Daltonhighway, Alaska) and forced our model under historical and strong futurewarming conditions (following the RCP8.5 scenario). As expected, the presenceof a gravel road in the model leads to higher net heat flux entering theground compared to a reference run without infrastructure and thus a higherrate of thaw. Further, our results suggest that road failure is likely aconsequence of lateral destabilisation due to talik formation in the groundbeside the road rather than a direct consequence of a top-down thawing anddeepening of the active layer below the road centre. In line with previousstudies, we identify enhanced snow accumulation and ponding (both aconsequence of infrastructure presence) as key factors for increased soiltemperatures and road degradation. Using differing horizontal modelresolutions we show that it is possible to capture these key factors and theirimpact on thawing dynamics with a low number of lateral model units,underlining the potential of our model approach for use in pan-Arctic riskassessments. Our results suggest a general two-phase behaviour of permafrost degradation:an initial phase of slow and gradual thaw, followed by a strong increase inthawing rates after the exceedance of a critical ground warming. The timing ofthis transition and the magnitude of thaw rate acceleration differ stronglybetween undisturbed tundra and infrastructure-affected permafrost ground. Ourmodel results suggest that current model-based approaches which do notexplicitly take into account infrastructure in their designs are likely tostrongly underestimate the timing of future Arctic infrastructure failure. By using a laterally coupled 1D model to simulate linearinfrastructure, we infer results in line with outcomes from more complex 2Dand 3D models, but our model's computational efficiency allows us to accountfor long-term climate change impacts on infrastructure from permafrostdegradation. Our model simulations underline that it is crucial to considerclimate warming when planning and constructing infrastructure on permafrost asa transition from a stable to a highly unstable state can well occur withinthe service lifetime (about 30 years) of such a construction. Such atransition can even be triggered in the coming decade by climate change forinfrastructure built on high northern latitude continuous permafrost thatdisplays cold and relatively stable conditions today. 

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.