Synoptic measurement of stream and atmospheric indicators to improve the monitoring and prediction of climate-induced permafrost degradation across Alaska
Abstract
Climate warming accelerates degradation of permafrost, with broad- and local-scale consequences for the functions of high-latitude lands. The Department of Defense trains across and manages a variety of permafrost landscapes. Current and projected future climate warming leads to changes in soil strength and terrain geomorphologic and geophysical characteristics. However, limited spatial and temporal observations of permafrost thaw leads to uncertainty in predicting rates and consequences of permafrost degradation. We propose coordinated stream, soil, and atmospheric measurements of solutes, gases, and microbes to identify key indicators of the rates, modes, and effects of thawing permafrost. First, we will create a database of ground-truthed permafrost thaw locations across Alaska, including early and later stages of thaw. We will use the database to quantify chemical and microbial indicators of permafrost degradation across several hundred sites representing a range of surface and subsurface conditions. We will apply machine learning approaches to the synoptic measurements along with auxiliary datasets (e.g., remotely sensed elevation, soil maps) to validate these indicators of permafrost thaw. Six observatory sites will be used for detailed laboratory and field monitoring focused on rapid coordinated detection of organic and inorganic transformations involving soil and water across a range of ground ice and ecohydrologic conditions. Geochemical detection will be paired with high throughput multi-omics. Spatiallyand temporally-intensive observations of microbial metabolic activity in soils and water in concert with solute and atmospheric chemistry will build process-level understanding of the indicators of thaw that can be upscaled. These intensive observations will allow us to extract multi-scale temporal patterns indicative of thaw, assessing scaling relationships and signal persistence in stream networks and the atmosphere. Finally, we will integrate data and new knowledge into biophysical models to improve the representation of heterogeneous permafrost terrain and thaw patterns. Together, coordinated machine learning, intensive site monitoring, regionally distributed sampling, and development of novel modeling approaches will allow us to improve understanding and prediction of different modes of permafrost thaw across Alaska. This project will support responsible decision-making for transportation and mobility, land management, operations, and training range sustainability by contributing new foundational and applied understanding of permafrost degradation and decision-aids for detecting and predicting thaw. Unprecedented spatial extent and temporal resolution in observations will provide representative thaw, hydrologic, and ecosystem conditions for building predictive understanding and scaling relationships applicable to all permafrost terrains and novel sites. To facilitate broad application of findings, we will produce automated and configurable workflows and share results and technical approaches with broad audiences via peer reviewed papers, public talks, project social media accounts, and plain-language newsletters. An interdisciplinary team of permafrost experts will lead the proposed research. Together the team has decades of experience working in a range of permafrost-influenced environments, with particular expertise in Alaska, Greenland and Antarctica. Collaborative sub-groups of the team have previously characterized microbial communities and function in permafrost; linked hydrology with biogeochemical reactions in thawing permafrost; applied machine learning and numerical modeling to estimate and scale up rates of thaw-induced erosion; and applied statistical techniques to characterize spatial and temporal patterns resulting from gradual thaw. Thus the proposed research will leverage existing datasets and methodologies previously vetted by the team in changing permaf
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 01, 2023
- Source ID
- W911NF2310311
Entities
People
- Merritt Turetseky
Organizations
- Army Contracting Command
- United States Army
- University of Colorado Boulder