Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model
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Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model. / Boke-Olen, Niklas; Lehsten, Veiko; Ardo, Jonas; Beringer, Jason; Eklundh, Lars; Holst, Thomas; Veenendaal, Elmar; Tagesson, Håkan Torbern.
I: PLoS ONE, Bind 11, Nr. 4, e0154615, 29.04.2016.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model
AU - Boke-Olen, Niklas
AU - Lehsten, Veiko
AU - Ardo, Jonas
AU - Beringer, Jason
AU - Eklundh, Lars
AU - Holst, Thomas
AU - Veenendaal, Elmar
AU - Tagesson, Håkan Torbern
PY - 2016/4/29
Y1 - 2016/4/29
N2 - Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.Figures
AB - Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.Figures
U2 - 10.1371/journal.pone.0154615
DO - 10.1371/journal.pone.0154615
M3 - Journal article
C2 - 27128678
VL - 11
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 4
M1 - e0154615
ER -
ID: 167886466