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Permafrost thaw exhibits an array of spatially heterogenous patterns. As the Arctic continues to warm, those spatial patterns of permafrost thaw, or degradation, are becoming increasingly intricate and dynamic. In particular, ice-wedge permafrost degradation contains a high degree of spatial heterogeneity as ice wedges transition through undegraded, degraded, and stabilized stages. Developing accurate remote sensing methods for characterizing degradation will better allow us to monitor and forecast Arctic landscape evolution and associated land-atmosphere carbon-climate interactions. In this study, we (i) characterized ice-wedge degradation stages across a regional scale using a novel hydrogeomorphic approach. Then, we (ii) assessed the heterogeneity of degradation from meter- to kilometer-scales, and (iii) identified landscape properties associated with degradation patterns. We leveraged the unique spectral and geometric properties of ice-wedge degradation stages to map those stages across 366 km2 of the Arctic Coastal Plain near Prudhoe Bay, Alaska in sub-meter resolution Worldview-2 satellite imagery. Then, we validated the maps with in-situ observations, airborne LIDAR, and drone multispectral surveys. We evaluated spatial heterogeneity in ice-wedge degradation through a clustering approach. Specifically, we grouped regions into hydrogeomorphic clusters defined by similarities in trough widths and flooding, which reflect distinct degradation stages. This analysis revealed that ice-wedge degradation is heterogeneous across both meter and kilometer scales. At the meter scale, a single ice-wedge polygon is generally bounded by varied degradation stages. In addition, the most advanced stages of degradation occur in areas of low trough relative elevation and at the junctions between troughs. At the kilometer-scale, distinct clustering of degradation stages was identified across the region and linked to spatial patterns in topography: regional clusters of advanced degradation occurred in higher elevation areas. The millennial-scale evolution of the landscape has resulted in heterogeneous topographic, hydrologic, and cryogenic characteristics; these varied features exhibit diverse responses to warming events, which reflect the dynamic interplay that occurs between permafrost landscapes and climate change. 

Abstract Structurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)‐derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem‐scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure‐function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water‐sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies. 

Abstract. This study investigates and compares soil moisture andhydrology projections of broadly used land models with permafrost processesand highlights the causes and impacts of permafrost zone soil moistureprojections. Climate models project warmer temperatures and increases inprecipitation (P) which will intensify evapotranspiration (ET) and runoff inland models. However, this study shows that most models project a long-termdrying of the surface soil (0–20 cm) for the permafrost region despiteincreases in the net air–surface water flux (P-ET). Drying is generallyexplained by infiltration of moisture to deeper soil layers as the activelayer deepens or permafrost thaws completely. Although most models agree ondrying, the projections vary strongly in magnitude and spatial pattern.Land models tend to agree with decadal runoff trends but underestimaterunoff volume when compared to gauge data across the major Arctic riverbasins, potentially indicating model structural limitations. Coordinatedefforts to address the ongoing challenges presented in this study will helpreduce uncertainty in our capability to predict the future Arctichydrological state and associated land–atmosphere biogeochemical processesacross spatial and temporal scales. 

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.