Predicting spring green-up across diverse North American grasslands

Journal Article · Thu Oct 27 00:00:00 EDT 2022 · Agricultural and Forest Meteorology

DOI:https://doi.org/10.1016/j.agrformet.2022.109204· OSTI ID:2420449

Post, Alison K. ^[1]; Hufkens, Koen ^[2]; Richardson, Andrew D. ^[3]

---

1. Northern Arizona University, Flagstaff, AZ (United States); OSTI
2. BlueGreen Labs, Melsele (Belgium)
3. Northern Arizona University, Flagstaff, AZ (United States)

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

• Research Organization: Princeton University, NJ (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: SC0016011
• OSTI ID: 2420449
• Alternate ID(s): OSTI ID: 1895543
• Journal Information: Agricultural and Forest Meteorology, Journal Name: Agricultural and Forest Meteorology Journal Issue: C Vol. 327; ISSN 0168-1923
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Agriculture
Climate change
Forestry
Grasslands
Meteorology & Atmospheric Sciences
Model
Phenology
Precipitation
Spring