Project Funding: 2013 - 2015

NRA: 2012 NASA: Ecological Forecasting   

Funded by NASA

Abstract:
The timing of key life events like reproduction (i.e. phenology) is tightly linked to climate. For example, alpine wildflowers emerge and flower within a few weeks of snow disappearance, and complete their life-cycles before first frost in early autumn. Because annual variability in snow disappearance is large, the timing of seasonal wildflower displays also varies annually, influencing visitation and staffing needs within parks. Additionally, as climate change causes earlier snow disappearance, wildflowers that cannot shift their phenology to match this altered climate window may decline. Thus, resource managers and conservation biologists need the ability to seasonally forecast snow disappearance and wildflower phenology as well as monitor their long-term annual trends to better conserve and manage high mountain wildflower meadows. To address these issues, we propose to combine MODIS-based images of snow covered area (SCA), citizen science observations and models to develop decision-making tools at Mt. Rainier National Park (Washington). Specifically, we will develop and validate snow models driven by MODIS SCA and daily observations of temperature, precipitation and snow (from SNOTEL climate stations) that generate spatially explicit forecasts of snow disappearance date. Next, we will use date-stamped photos of wildflowers from photo- sharing websites to develop phenological forecasts driven by snow disappearance date. Phenological models will be validated with data from an existing citizen science program, which will be expanded in scope to meet monitoring goals. Finally, we will partner with the National Park Service to create a long-term monitoring program of snow dynamics and wildflower phenology. Operationally, these estimates will 1) help managers plan where and when conservation/restoration activities, monitoring, and trail maintenance can occur, 2) allow visitors to better plan trips to view and photograph wildflowers, and eventually, 3) help resource managers identify the climatic and biological signs of climate change.

Publications:

Breckheimer, I. K., Theobald, E. J., Cristea, N. C., Wilson, A. K., Lundquist, J. D., Rochefort, R. M., HilleRisLambers, J. 2019. Crowd-sourced data reveal social-ecological mismatches in phenology driven by climate. Frontiers in Ecology and the Environment. 18(2), 76-82. DOI: 10.1002/fee.2142

Cristea, N. C., Breckheimer, I., Raleigh, M. S., HilleRisLambers, J., Lundquist, J. D. 2017. An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery. Water Resources Research. 53(8), 6802-6820. DOI: 10.1002/2017wr020799

More details may be found in the following project profile(s):

• NASA Biological Diversity & Ecological Conservation (BDEC) Project Profile