Testing for the effects of pre-season temperature and winter-chilling on land-surface phenology of coniferous and broadleaved forests in Central Europe

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Authors

Cornelius Senf , Tobias Krueger

Abstract

Phenology is an important indicator of climate change impacts on vegetated ecosystems. Changes in leaf unfolding dates in response to changing temperatures have been well documented from in-situ phenological measurements across Central Europe. However, it is unclear whether those processes can be scaled to the landscape scale, which is important to accurately represent phenology in (global) vegetation models. Moderate resolution remote sensing time series, which measure land surface phenology instead of species specific phenophases, can help answering this question. We here test for the effect of pre-season temperature and winter-chilling on the inter-annual variation in start of season derived from Landsat time series for a forest landscape in southern Germany. The landscape is comprised of broadleaved and coniferous forests and thus typical for montane forest landscapes in Central Europe. We find strong evidence for average pre-season mean daily temperature driving inter-annual variation in start of season, with a -3.74 d °C-1 earlier start of season for broadleaved forests and a -2.68 d °C-1 earlier start of season for coniferous forests over the time period 1985 to 2015. This relationship, however, was modulated by the number of chilling days during winter, with a decreasing effect of pre-season temperature with decreasing number of chilling days. The inter-annual variation in start of season predicted from our model – i.e., calibrated solely from Landsat satellite time series – showed good agreement with in-situ observations of bud-break (Pearson’s r = 0.79/RMSE = 4.88 d for broadleaved forests and r = 0.62/RMSE = 6.57 d for coniferous forests). We conclude that in-situ based processes are also detectable at the landscape-scale and that considering winter-chilling is important for accurately predicting phenology, which should be recognized in (global) vegetation models.

DOI

https://doi.org/10.31223/osf.io/sx6w2

Subjects

Ecology and Evolutionary Biology, Life Sciences, Terrestrial and Aquatic Ecology

Keywords

remote sensing, Landsat, climate change, Bayesian Hierarchical Modelling, phenology

Dates

Published: 2018-07-10 00:27

Last Updated: 2019-01-11 11:42

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License

CC BY Attribution 4.0 International

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