TY  - JOUR
A1  - Ziegler, Katrin
A1  - Pollinger, Felix
A1  - Böll, Susanne
A1  - Paeth, Heiko
T1  - Statistical modeling of phenology in Bavaria based on past and future meteorological information
T2  - Theoretical and Applied Climatology
N2  - Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961–1990).
KW  - statistical modeling
KW  - phenology
KW  - Bavaria
Y1  - 2020
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/23271
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-232717
SN  - 0177-798X
VL  - 140
ER  -