The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States

Abstract

<p>Spring <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/phenology" title="Learn more about phenology from ScienceDirect's AI-generated Topic Pages">phenology</a> of <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/temperate-ecosystem" title="Learn more about temperate ecosystems from ScienceDirect's AI-generated Topic Pages">temperate ecosystems</a> is highly sensitive to climate change, generating various impacts on many important terrestrial surface biophysical processes. Although various prognostic models relying on environmental variables of temperature and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/photoperiod" title="Learn more about photoperiod from ScienceDirect's AI-generated Topic Pages">photoperiod</a> have been developed for spring phenology, comprehensive ecosystem-scale evaluations over large landscapes and long-time periods remain lacking. Further, environmental variables other than temperature and photoperiod might also importantly constrain spring phenology modelling but remain under-investigation. To address these issues, we leveraged around 20-years datasets of environmental variables (from Daymet and GLDAS products) and the spring phenology metric (i.e., the greenup date) respectively derived from <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis" title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages">MODIS</a> and PhenoCams across 108 sites in the Northern and Eastern United States. We firstly cross-compared MODIS-derived greenup date with official PhenoCams product with high accuracy (<em>R</em>² = 0.70). Then, we evaluated the three prognostic models (i.e., Growing Degree Date (GDD), Sequential (SEQ) and optimality-based (OPT)) with MODIS-derived spring phenology, assessed the model residuals and their associations with soil moisture, rainfall, and solar radiation, and revised the two photoperiod-relevant models (SEQ, OPT) by replacing the daylength variable with solar radiation, which was found to contribute the most to model residuals. We found that 1) all models demonstrated good capability in characterizing spring phenology, with OPT performing the best (RMSE = 8.04 ± 5.05 days), followed by SEQ (RMSE = 10.57 ± 7.77 days) and GDD (RMSE = 10.84 ± 8.42 days), 2) all models displayed high model residuals showing tight correlation with solar radiation (<em>r</em> = 0.45–0.75), and 3) the revised models that included solar radiation significantly performed better with an RMSE reduction by 22.08%. Such results are likely because solar radiation better constrains early growing season plant photosynthesis than photoperiod, supporting the hypothesis of spring phenology as an adaptive strategy to maximize photosynthetic carbon gain (approximated by solar radiation) while minimizing <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/frost-damage" title="Learn more about frost damage from ScienceDirect's AI-generated Topic Pages">frost damage</a> risk (captured by temperature). Collectively, our study reveals the underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems, and suggests ways to improve spring phenology modelling and other phenology-related ecological processes.</p>