Statistical modeling of phenology in Bavaria based on past and future meteorological information

Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases are commonly considered some of the most obvious effects of climate change. However, current climate models lack a representation of vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modeling approach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data for analyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of several meteorological variables were used as predictors and phenological anomalies of the flowering date of the test plant Forsythia suspensa as predictand. Several cross-validated prediction models using various numbers and differently constructed predictors were developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observations per phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust and successful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its average coefficient of determination and root mean square error (RMSE) per station are 60% and +/- 8.6 days, respectively. However, the prediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparable level of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants and regions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century compared to the phenology of the reference period (1961-1990).