European GeoSciences Union General Assembly 2011
Vienna | Austria | 03 – 08 April 2011
Fabio Zottele
1, Amelia Caffarra
1, Emily Gleeson
2, and Alison Donnelly
3(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 Ireland
Mapping future phenology of birch in Ireland
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(2)
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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
