One of the most visible signs of climate change is less mountain snow. In the Western U.S., deep snow has historically been a cornerstone of life for many plants and animals. For example, snow can provide denning shelter for certain species like the wolverine, and snowmelt provides dependable water to mountain streams that are home to fish like the bull trout. Yet snow losses driven by warming temperatures are already causing land and water managers to rethink whether certain species can thrive in the future. A recently completed study by this research team helped the U.S. Fish and Wildlife Service investigate whether wolverines will have enough snow to survive in two areas of the Rocky Mountains.   In June 2020, the project team gathered a large group of regional land managers at a “Snow Collider” workshop event to learn about the wide range of needs for future snow information. Managers identified needs focused on how much snow will be around in the future, as well as how that snow will melt to support streams. This input will guide the direction of the future snow modeling in this study. The main goal of this project is to build better models of future snow conditions for the key areas of the Rocky Mountains identified previously.   The team seeks to understand whether changes in future snowpacks will be sufficient for key species to thrive. This research will zoom in to model future snow conditions at much higher resolutions compared to earlier studies, to allow for an improved method to understand how snow will accumulate and melt across landscapes. This project aims to help managers make more informed decisions about future snow dependent species and choose the most effective ways to allocate resources towards recovery plans and monitoring.

This report explains scenario planning as a climate change adaptation tool in general, then describes how it was applied to Wind Cave National Park as the second part of a pilot project to dovetail climate change scenario planning with National Park Service (NPS) Resource Stewardship Strategy development. In the orientation phase, Park and regional NPS staff, other subject-matter experts, natural and cultural resource planners, and the climate change core team who led the scenario planning project identified priority resource management topics and associated climate sensitivities. Next, the climate change core team used this information to create a set of four divergent climate futures—summaries of relevant climate data from individual climate projections—to encompass the range of ways climate could change in coming decades in the park. Participants in the scenario planning workshop then developed climate futures into robust climate-resource scenarios that considered expert-elicited resource impacts and identified potential management responses. Finally, the scenario-based resource responses identified by park staff and subject matter experts were used to integrate climate-informed adaptations into resource stewardship goals and activities for the park's Resource Stewardship Strategy. This process of engaging resource managers in climate change scenario planning ensures that their management and planning decisions are informed by assessments of critical future climate uncertainties.

Near-surface remote sensing has been used to document seasonal growth patterns (i.e. phenology) for plant communities in diverse habitats. Phenology from this source may only apply to the area within the images. Meanwhile ecosystem models can accommodate variable weather and landscape differences to plant growth, but accuracy is improved by adding ground-truthed inputs. The objective of this study was to use PhenoCam data, image analysis, and Beer’s law with established extinction coefficients to compare leaf area index (LAI) development in the ALMANAC model for diverse plant types and environments. Results indicate that PhenoCam time series imagery can be used to improve leaf area development in ALMANAC by adjusting parameter values to better match LAI derived values in new diverse environments. Soybeans, mesquite, and maize produced the most successful match between the model simulations and PhenoCam data out of the eight species simulated. This study represents, to our knowledge, the first independent evaluation of the ALMANAC process-based plant growth model with imagery in agroecosystems available from the PhenoCam network. The results show how PhenoCam data can make a valuable contribution to validate process-based models, making these models much more realistic and allows for expansion of PhenoCam influence.