European GeoSciences Union General Assembly 2011Vienna | Austria | 03 – 08 April 2011

Fabio Zottele1, Amelia Caffarra1, Emily Gleeson2, and Alison Donnelly3

(1)Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, ITALY

(2) MET ÉIREANN, Glasnevin Hill, Dublin 9, IRELAND

(3) Trinity College Dublin, College Green, Dublin 2, IRELAND

Corresponding author:

www.fmach.eu fabio.zottele@iasma.it

Motivation

Bibliography

Conclusions

Materials and Methods

Results

Mapping future phenology of birch in IrelandMapping future phenology of birch in Ireland

(1) (2) (3)

Most tree phenological models are based on

temperature , but experimental evidence shows an

important role of photoperiod on phenology.

This is the case of birch (Betula Pubescens). Starting from

an existing phenological model (UNIFIED, Chuine 2000)

Caffarra et al. have integrated photoperiod using both

information from previous studies and experimental data

(DORMPHOT model, Caffarra et al 2011). The aim of this

work is to predict the beginning of the growing season for

birch over Ireland using ENSEMBLE scenarios and

evaluate regional differences in trends of bubdurst.

Chuine I. (2000) A Unified model for budburst of trees. J Theor Biol 207:337-347

Caffarra A. et al. (2011) Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim Res 46: 159-170

GRASS developement Team (2010) Geographic Resource Analysis Support System (GRASS GIS) software, Open Source Geospatial Foundation

R Developement Core Team (2011) R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing

Wilby, R L. et al. (1997) Downscaling general circulation model output: a review of methods and limitations, Progress in Physical Geography, 21: 530–548

Jarvis, A. et al. (2008), Hole filled seameless SRTM data V4, International Centre for Tropical Agriculture (CIAT)

DORMPHOT model (Caffarra, 2011)

The effect of photoperiod was integrated into the

model at two levels. Firstly, photoperiod, in

interaction with temperature, affects the course of

dormancy induction. Secondly, photoperiod

modifies the response to temperature during the

phase of forcing. This model has been validated

using dataset collected in Ireland, Germany,

Switzerland, Norway.

Model Inputs

We coupled both GRASS GIS and R softwares

for map I/O and processing.

Photoperiod maps were calculated using

SOLPOS algorithm natively implemented in GIS.Figure 1: conceptual model of the DORMPHOT model

We used ENSEMBLE daily temperature (C4IRCA3, HadCM3Q16_DM, scenario:

A1B , 3 decades: 1991-2000, 2021-2030, 2051-2060) for training the algorithm.

GCMs dataset come with 0.25 arc degree resolution so downscaling was necessary

for studying local effects. We used regression downscaling (Wilby, 1997) as it is fast

and low in computational resource demands. We performed daily stepwise

regression of daily mean temperature vs. position, elevation and distance from the

sea. When we obtained a significative model (p-value < 0.05) and R2>=0.5 then the

regressive model was retained and applied to irish spatial domain, otherwise the

model was discarded and a bilinear interpolation of GCM data was performed.

Figure 2: from top to bottom, Ireland as seen by GCMs (15min), working resolution (1min) and SRTM resolution (3 sec)

Spatial resolution drives computer’s resources

consumption (CPU and disk storage). As the resolution of

Global Circulation Models (15’ arc degree) was too coarse

to catch morphological variability and the resolution of the

Digital Elevation Models resolution (3‘’ arc degree) (Jarvis,

2008) was too high for quick geoprocessing we reached a

compromise by fixing computations on a 1’ arc degree

grid (Fig.2). This resolution retains sufficient details to

catch morfological variability in the downscaling process,

while not demanding excessive computational resources.

Figure 3: from mean day of budbreak over nine year. The greatest advance rate is attained in the North-East region

Regression downscaling could be applied in the 75.34 % of

cases and bilinear interpolation of daily mean temperatures

was applied in the remaining cases.

After spatially enabling the DORMPHOT model, a set of

control points were extracted from the map to check the

accuracy of the implementation and we obtained no

discrepancies with the original algorithm by Caffarra et al

(2011).

A strong inter-annual variability in budburst timing was

shown over Ireland (Fig. 4).

Means over 9-year periods (Fig. 3) show that:•over the period between the 1990s and 2050s

budbreak advances over Ireland (mean advance ~ 5

days)•the earliest date of budburst advances from day 82 to

80;•The latest date of budburst advances from day 102 to

94

Figure 4: inter annual variability of budburstl from 1992 and 1999.

As pointed out in (Caffarra, 2011) photoperiod and chilling

act to stabilize the timing of budburst and the stabilizing

effect of photoperiod and chilling is well shown in the long

term (Fig 5).

In “early zones” (South-West) the advance is 1 day in

2020/30, and 1.5 days in 2050/2060

In “late zones” (North-East), where there is more room for

change, the advance is 4.4 days in 2020/30, and 8.6 days

in 2050/2060

Thus, according to these simulations early zones will be

the least affected by climate change.

The spatialization of DORMPHOT model is feasible but computational time is

strongly influenced by the choice of the final spatial resolution. These simulations

suggested that the effect of climate change on birch budburst might not be

homogeneous over Ireland. Simulated budburst timing showed a general trend of

advance but more pronounced in North-eastern areas and minimal in the South-west

(co. Kerry). We are extending the result dataset by applying the model to the

remaining ENSEMBLE scenarios to better quantify the stabilizing effect of chilling

and photoperiod.

Our next step will be to calibrate the model on birch flowering to obtain simulations of

future flowering time, which will enable us to assess the length of the pollen season

under climate change scenarios.

We would thank Dr. O’Neill for all the support.

Figure 5: long term stabilizing effect of photoperiod on budbreak advance.