Portfolio
Stephen John Huysman
Wildfire Danger Forecasting System
This project developed a robust, data-intensive pipeline for forecasting wildfire danger. It integrates large-scale meteorological data (gridMET), historical fire records (MTBS), and land cover information (LANDFIRE) to accurately model and map fire ignition risk. Specifically, the system answers the question: Are conditions dry enough to burn? The system employs R for advanced statistical and geospatial analysis, alongside shell scripts for automated data processing and forecasting. Automated forecasts, including lightning strike warnings based on ignition danger, are specifically implemented for the Yellowstone National Park region. The entire system is containerized with Docker, ensuring reproducibility and ease of deployment.
High-Resolution Water Balance Model
This project uses a high-resolution (1m) water balance model to assess planting sites for whitebark pine in Yellowstone National Park. The model incorporates fine-scale topoedaphic features such as slope, aspect, and soil water holding capacity to identify potential microrefugia for seedlings. By calculating Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD), the model assesses growing conditions and drought stress to inform planting decisions.
White Pine Blister Rust Model
White pine blister rust (WPBR) is the primary driver of the range-wide decline of whitebark pine (WBP), a federally threatened species. As a keystone and foundational species of high-elevation ecosystems, WBP's decline has potentially widespread consequences for forest composition and ecological functions. An understanding of the climatic drivers of WPBR infection is necessary to manage impacts of the pathogen during ongoing climate change. We assembled a long-term dataset indicating WPBR presence or absence in WBP from monitoring programs across WBP's range in the contiguous United States. We identified a spatially explicit model that included August and September temperature and precipitation as the best climatic predictors of WPBR infection in WBP during the basidiospore transmission season, with larger trees more likely to be infected than smaller trees. At high levels of precipitation (around and above 100 mm total August and September precipitation), the relationship between mean August and September temperature and probability of WPBR infection is parabolic, with highest infection rates around 11 °C. This parabolic relationship inverts at lower totals of August and September precipitation (0 to around 100 mm) and minimum infection rates occur around 11 °C and maximum infection rates at low (around 7 °C) or high (around 13 °C) temperatures. Projections of WPBR disease hazard (defined as probability of WPBR infection) through the end of the century show wide variability in geography and magnitude of disease impacts depending on plausible changes in temperature and precipitation across WBP's range.
Landcover Change Model
Climate projections almost universally show increase in temperature and changes in precipitation due to increased radiative forcing from anthropogenic emissions. Climate is a force that fundamentally shapes the distributions of plants. Understanding how plant distributions may shift in response to climate change is an important question with implications across human society. Accurate predictions of changes in plant cover due to climate change are relevant to policy makers, land managers, recreationists, and the general public as shifts in plant distributions could affect food security, industry, and land availability. This analysis identified the climatic drivers of the current vegetation cover types across the Contiguous United States (CONUS). Then, using projections of climate to the end of the century, projections of cover change across CONUS were developed.
