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 [email protected]
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.
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