Simulated and observed timings of leaf senescence for Fagus sylvatica (L.) at selected European sites
Data files
Apr 10, 2025 version files 39.65 MB
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Data_ModelSimulations_AccToOptimalAlgorithmControls.csv
39.64 MB
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README.md
8.02 KB
Abstract
Together with my co-authors, I have formulated a process-oriented model of leaf senescence according to the leaf development process, the DP3 model. This model clearly stands out from previous models and allows new hypotheses to be made, e.g. about ageing or stress-induced senescence. The DP3 model was as accurate as the previously published models. A comparison with the constant simulation of the average timing of leaf senescence also showed that the models are forced to fall back on this average due to the noisy leaf senescence data, which complicates model evaluation. All these analyses were based on the simulated and observed leaf senescence data, which are made available here.
Dataset DOI: 10.5061/dryad.tht76hf97
Description of the data and file structure
The date of leaf senescence was simulated for beech at selected European sites. In particular, the day of year (Doy) when 50% and 100% of the leaves have changed color or have fallen (referred to as the phenological stages LS50 and LS100, respectively) were simulated for the years with corresponding observations between 1950 and 2022. The simulation were based on the DP3 model (Meier et al. 2025) as well as on the previously published models CDD (Dufrêne et al., 2005), DM2 (Delpierre et al., 2009), and PIA (Zani et al., 2020). All models werte driven with climate variables obtained from the E-OBS dataset (Cornes et al., 2018), with CO2 concentration data from Cheng et al. (2022) and Copernicus Climate Change Service (2018), and with leaf area indices from the GIMMS-LAI3g dataset (version 2; Mao and Yan, 2019). For more detailed information consult the original publication Meier et al. (2025).
Please cite as
Meier, M., Bigler, C., and Chuine, I. (2025). An example of how data quality hinders progress: translating the latest findings on the regulation of leaf senescence timing in trees into the DP3 model (v1.0), In review at Geoscientific Model Development.
Files and variables
File: Data_ModelSimulations_AccToOptimalAlgorithmControls.csv
Description: The simulated Doy of the phenological stages LS50 and LS100 paired and the corresponding obseverved Doy at European sites and according to the DP3 model and previous models.
Variables
- Model: The process-oriented model that produced the simulated Doy (i.e., ‘CDD’ by Dufrêne et al., 2005; ‘DM2’ by Delpierre et al., 2009; ‘DP3’ by Meier et al., 2025, and ‘PIA’ by Zani et al., 2020).
- nPar: The number of model parameters that were calibrated.
- Species: The species for which the timing of the phenological stages were observed and simulated, i.e., Fagus sylvatica, (L.; ‘Beech’).
- SamplingProcedure: The applied sampling procedure, which considered either only the stage LS50 or both the stages LS50 and LS100 (sampling procedures ‘LS50’ and ‘LS50-LS100’, respectively; see Meier et al., 2025, for a detailed description of these procedures).
- Sample: The calibration or validation sample (‘Cal’ and ‘Val’, respectively).
- SampleDraw: The draw of the sample. Each pair of calibration and validation sample was drawn twice.
- CalRun: The calibration run. Each model was calibrated five times with each drawn sample.
- Algorithm: The optimization algorithm used to calibrate the model parameters by minimzing the root mean squared error. Here, generalized simulated annealing was used (‘GenSA’; Xiang et al., 1997, 2017).
- MaxIterations: The maximum iterations set in the controls of the GenSA algorithm.
- MaxCalls: The maximum calls set in the controls of the GenSA algorithm.
- Temperature: The temperature set in the controls of the GenSA algorithm.
- Site: The identification of the site for which the Doy of the phenological stage LS50 or LS100 was simulated and at which it was observed.
- Country: The country of the site (i.e. ‘AUT’ = Austria, ‘GBR’ = United Kingdom, ‘GER’ = Germany, ‘SUI’ = Switzerland).
- Latitude: The latitude of the site [°].
- Longitude: The longitude of the site [°].
- Elevation: The elevation of the site [meters above sea level].
- Year: The year of the respective simulated and observed Doy.
- PhenoStage: The respective phenological stage the timing was simulated and observed (i.e., ‘LS50’ = 50% of the leaves have changed color or have fallen, ‘LS100’ = 100% of the leaves have changed color or have fallen).
- ObservedDoy: The day of year at which the respective phenological stage was observed.
- SimulatedDoy: The day of year at which the respective phenological stage was simulated.
- PhenoSource: The source of the visually observed timings of LS50 and LS100 (i.e., the European and Swiss databases ‘PEP725’ and ‘SPN’, respectively; Swiss phenology network, 2025; Templ et al., 2018).
Code/software
Among others, the CSV file may be viewed with Excel (https://www.microsoft.com/), LibreOffice Calc (https://de.libreoffice.org/), and R (https://www.r-project.org/).
References
Cheng, W., Dan, L., Deng, X., Feng, J., Wang, Y., Peng, J., Tian, J., Qi, W., Liu, Z., Zheng, X., Zhou, D., Jiang, S., Zhao, H., and Wang, X. (2022). Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios, Sci. Data, 9, 83, https://doi.org/10.1038/s41597-022-01196-7.
Copernicus Climate Change Service, Climate Data Store (2018). Carbon dioxide data from 2002 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), last access: 30 April 2024, https://doi.org/10.24381/cds.f74805c8.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D. (2018). An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res. Atmospheres, 123, 9391–9409, https://doi.org/10.1029/2017JD028200.
Delpierre, N., Dufrene, E., Soudani, K., Ulrich, E., Cecchini, S., Boe, J., and Francois, C. (2009). Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agric. For. Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014.
Dufrêne, E., Davi, H., Francois, C., le Maire, G., Le Dantec, V., and Granier, A. (2005). Modelling carbon and water cycles in a beech forest: Part I: Model description and uncertainty analysis on modelled NEE, Ecol. Model., 185, 407–436, https://doi.org/10.1016/j.ecolmodel.2005.01.004.
Mao, J. and Yan, B.: Global monthly mean leaf area index climatology, 1981-2015 (1), https://doi.org/10.3334/ORNLDAAC/1653, 2019.
Meier, M., Bigler, C., and Chuine, I. (2025). An example of how data quality hinders progress: translating the latest findings on the regulation of leaf senescence timing in trees into the DP3 model (v1.0), In review at Geoscientific Model Development.
Swiss phenology network (2025). https://www.meteoswiss.admin.ch/weather/measurement-systems/land-based-stations/swiss-phenology-network.html, last access: 29 January 2025.
Templ, B., Koch, E., Bolmgren, K., Ungersbock, M., Paul, A., Scheifinger, H., Rutishauser, T., Busto, M., Chmielewski, F. M., Hajkova, L., Hodzic, S., Kaspar, F., Pietragalla, B., Romero-Fresneda, R., Tolvanen, A., Vucetic, V., Zimmermann, K., and Zust, A. (2018). Pan European Phenological database (PEP725): A single point of access for European data, Int J Biometeorol, 62, 1109–1113, https://doi.org/10.1007/s00484-018-1512-8.
Xiang, Y., Sun, D. Y., Fan, W., and Gong, X. G. (1997). Generalized Simulated Annealing algorithm and its application to the Thomson model, Phys. Lett. A, 233, 216–220, https://doi.org/10.1016/s0375-9601(97)00474-x.
Xiang, Y., Gubian, S., and Martin, F. (2017). Generalized Simulated Annealing, in: Computational Optimization in Engineering - Paradigms and Applications, 25–46.
Zani, D., Crowther, T. W., Mo, L., Renner, S. S., and Zohner, C. M. (2020). Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees, Science, 370, 1066–1071, https://doi.org/10.1126/science.abd8911.