Speaking: Neal Pastick, US Geological Survey
The Permafrost Discovery Gateway hosts a monthly webinar series on a Thursday at 09:00 Alaska time. The webinar aims to 1) connect the international science community interested in big data remote sensing of permafrost landscapes, and 2) provide the Permafrost Discovery Gateway development team with end-user stories (by the presenter and webinar participants), such as exploring tools the community needs to create and explore big data.
Areas along the Arctic coast are changing the fastest among all of Earth’s habitats due to climate change. Accelerated warming and changing precipitation regimes has led to extensive permafrost thaw that can substantially impact land cover distributions and the regional carbon balance. Moreover, there is growing interest in exploring for oil and gas resources in these areas which provide habitat for migratory birds, fish, caribou, and other species that are endangered or critical for local subsistence living. The coastal plain (1002 area) of the Arctic National Wildlife Refuge has seen renewed interest for oil and gas extraction recently, but past investigations suggest that the area has ongoing and extensive natural ecosystem changes. It is therefore urgent to improve the understanding of this area and its vulnerability to change. Here we describe results from a 3-year project that leverages: (1) field surveys to assess local vegetation, snow, topography soil conditions, and river/stream discharge; (2) remote sensing data (e.g. LiDAR, CubeSat, Maxar, Landsat, IceSAT-2) and analysis to document regional variations in surface and near-surface conditions through time, and; (3) advanced empirical and mechanistic modeling to simulate historical (1950 – 2022) and future (2023 – 2100) land cover, soil conditions, snow depth and runoff, and carbon dynamics in response to changes in climate and disturbances. We will provide an overview of study design and initial results from our field campaigns, remote sensing analysis (e.g., snow depth, topography), and modeling and data assimilation efforts. We will highlight efforts to characterize changing hydrologic conditions, thermokarst disturbances, and land cover using in situ observations, data acquired from passive and active sensors, deep neural networks, and a cryohydrologic (SUTRA-ICE) and state-and-transition model (the Alaska Disturbance Model). We are also exploring the use of a process-guided deep learning system that couples an ecosystem model (the Integrated Ecosystem Model) and an ensemble of deep neural networks for improving estimates of soil conditions (i.e., temperature, moisture) and carbon dynamics. The resulting data is to be used to identify areas vulnerable to change and will allow managers to better understand risks and guide oil and gas development if it occurs in the region.