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-October 2019. The purpose of the study is to examine how the atmospheric boundary layer 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 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 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 sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) 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 in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions 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. SC metrics included in the structure-function relationship as well as the strength of drivers 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 SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) 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 in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions 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. SC metrics included in the structure-function relationship as well as the strength of drivers 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 SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Light detection and ranging (LiDAR) technologies are changing the ways in which scientists research the Arctic. Unmanned aerial vehicle (UAV)-based LiDAR collects detailed structural landscape data by returning high density point clouds. LiDAR systems are improving the quality and accuracy of data collection compared to field surveys and help to remove some of the logistical barriers of research in remote and complicated terrain. Our study mapped thermokarst depressions in a 3 km2 watershed on the Seward Peninsula near Nome, Alaska in 2017 and 2018. The watershed is characterized as tussock permafrost landscape consisting of grasses and mosses interspersed with patches of dense shrubs. By configuring the UAV with a 32 laser swath and flying slowly at low altitude, we collected high density point clouds of about 4,000 points m2, including high density terrain surface points underneath dense shrubby vegetation. We then modeled the sub-vegetation terrain surface at very fine detail to detect thermokarst depressions. Combining these high resolution data with vegetation surveys and topographic properties, we tested the relationship between permafrost subsidence, thermokarst depressions and vegetation type, specifically the relationships in shrub-associated thermokarst features. By coupling our LiDAR data and analysis with hydrologic models, climate variables (e.g., snow depth, soil moisture), and vegetation surveys, we can infer geospatial relationships between thermokarst development, vegetation, and landscape position throughout the watershed. The technologies used in our study have implications for predicting the development of future thermokarst features and permafrost thaw sites across the Arctic.