CL2.5

Olive (Olea europaea L.) trees are traditionally cultivated in the Mediterranean basin, providing both healthy food and ecosystem services, such as climate change mitigation and soil erosion control, particularly in arid areas. Despite its importance, olive phenology, as impacted by climate change, is under-studied. To tackle this gap, we assessed the potential of feed-forward artificial neural network models to predict five main olive phenophases (apex budbreak, inflorescence, flowering, pit hardening and olive maturation index 1) at their onset for cultivars ‘Picholine’, ‘Carolea’ and ‘Coratina'. The dataset was collected from seven sites across Italy during the years 1997-2000.  Due to gaps in the dataset, the models were initialized by supervised training with the subset of full phenological observations, followed by semisupervised training based on the full dataset and iterative estimations of the missing observations. The softmax activation function was used in the output layer by interpreting the incremental phenological transitions as proportional to probabilities. The networks with at least four hidden layers activated by the sigmoid function and trained with the momentum method and linearly-decreasing parameters were best performing (validation RMSE of 15.5 d and 17.1 d for ‘Picholine’ and ‘Carolea’, respectively). Daily insolation consistently improved budbreak prediction with respect to daily mean temperature, suggesting the operation of photoreceptor activation mechanisms. Inflorescence was better predicted when daily minimum temperature was added, consistent with a chilling-warm requirement mechanism. Flowering was less consistent, but mean temperature was a primary controlling cue. Therefore, each phenophase is likely controlled by different climate cues. When tested on two independent flowering dates in 2017 and 2018 from one of the sites , the best performing models for each cultivar gave median errors of 4.3 d, 12.1 d, 7.4 d and 3.7 d for the ‘Picholine’, ‘Carolea’, ‘Coratina’ and the combinaed ‘Picholine+Carolea+Coratina’, respectively. The worse predictions for 'Carolea' is likely due to the hypothesized sensitivity of this cultivar to climate change, that occurred in the years between the training and the testing observations. Therefore, the olive sensitivity to climate change could be strongly cultivar-dependent, which calls for more in-depth investigation in the future. The calibrated models can be used both as operational and hypothesis-testing tools to study climate change effects on olive phenology. 

How to cite: Cagnarini, C., Gnecco, G., Salimonti, A., Zaffina, F., Samad, N., and Chiriacò, M. V.: Artificial neural network models applied to olive tree phenology in Italy reveal daily insolation control of budbreak, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2500, https://doi.org/10.5194/egusphere-egu22-2500, 2022.

Understanding how leaf autumn phenology varies at different spatio-temporal scales is key to accurately predicting phenological changes under future climate. Recent projections and observations of autumn phenology in deciduous temperate and boreal forests appear conflicting. At the interannual scale, autumn senescence correlates positively with spring leaf-out and negatively with growing season total photosynthesis. These links have been interpreted as the effect of premature carbon sink saturation with potentially far-reaching consequences for carbon cycle projections in a high-CO₂ world. In this study, we use multi-decadal ground and remote-sensing observations to show that these relationships are opposite at the interannual and the decadal time scales. We found a decadal trend towards later autumn senescence in parallel with a trend towards increasing photosynthesis, despite their negative relationship at the interannual scale. Our findings reveal that the leaf longevity constraint has not remained stationary over longer time scales. These shifting relationships suggest a gradual acclimation of phenology, leading to a relief of effects that dominate year-to-year variations. This apparent acclimation implies that in the long run, trees may benefit from increased photosynthesis under rising CO₂ and evade a direct effect by which increased photosynthesis induces an earlier leaf senescence. This apparent plasticity in phenology appears to have driven plants towards optimal functioning in a changing climate.

How to cite: Marqués, L., Hufkens, K., Bigler, C., Crowther, T. W., Zohner, C. M., and Stocker, B. D.: Long-term trends towards delayed autumn senescence prevail over short-term effects by high early-season CO2 assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10165, https://doi.org/10.5194/egusphere-egu22-10165, 2022.

Mountain grasslands are strongly exposed to multiple global changes, including elevated CO₂, climate warming and the increased occurrence of drought events. While the individual effects of these global change drivers on the phenology of grasslands have been comparatively well studied, there is a lack of understanding of their interactive effects. In a multifactor global change experiment on a managed montane grassland typical for many parts of the Alps, we tested the individual and combined effects of elevated CO₂ (eCO₂; +300 ppm), warming (eT; +3°C) and severe summer drought on phenology. We derived the canopy-level phenological transition dates from Green Chromatic Coordinates (GCC) time series calculated from phenocam images. Preliminary results reveal that warming, individually and when combined with elevated CO₂, led to an early spring advancement and that drought accelerated senescence, more strongly under future (eCO₂ + eT) compared to ambient conditions. Our preliminary findings suggest non-additive effects of interacting global change drivers on the phenology of mountain grassland, with cascading consequences for grassland productivity.

How to cite: Joseph, L. S. K., Cremonese, E., Migliavacca, M., Schaumberger, A., and Bahn, M.: Individual and Interactive Effects of Elevated CO2, Warming and Drought on the Phenology of Mountain Grassland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8446, https://doi.org/10.5194/egusphere-egu22-8446, 2022.

The leaves of evergreen coniferous species in the temperate region sometimes were observed to change from green to red before or during winter and persist until next spring. This phenomenon is also called 'winter leaf reddening', which might be a photoprotection strategy for plant leaves to deal with excess light stress during winter and early spring. In gymnosperms, the xanthophyll cycle (VAZ cycle) and accumulation of red pigment (e. g. rhodoxanthin) can prevent excess light damage to the photosynthetic apparatus. However, the joint role of these two processes in corresponding with canopy photosynthesis under winter excess light stress has not been further studied. This study aimed to clarify: (1) whether the low temperature is the dominant factor affecting the winter leaf reddening phenomenon and ascertain the air temperature conditions when this phenomenon occurs; (2) whether rhodoxanthin and the VAZ cycle play a collaborating role in regulating low light-use efficiency (LUE) under low air temperature conditions during the winter season; (3) the difference between two leaf redness indicators obtained from phenological method and remote sensing method.

Canopy leaf redness indicators were obtained in two ways. The automated system with a digital camera was used to monitor the canopy phenological changes. The RGB channels of image data were extracted to calculate the Red-Green vegetation index (RGVI). Red index (RI) is obtained by spectral reflectance analysis to track rhodoxanthin variation patterns. The photochemical reflectance index (PRI) was utilized as a tool to reflect the VAZ cycle. The canopy CO₂ flux was measured with the eddy covariance method, which can be used to calculate LUE. Micrometeorological data were also monitored.

Our results suggest that low air temperatures in winter play a domain role in the occurrence of winter leaf reddening. The onset of winter leaf reddening was accompanied by a decrease in LUE and PRI and a corresponding increase in RGVI. This suggests that the accumulation of rhodopsin and the VAZ cycle may play a collaborative role in regulating LUE under the combined effect of chilling temperatures and high solar radiation conditions. There were temporal differences in the peak occurrence of RI and RGVI, but the change characteristics were largely consistent, which may indicate that RI can more sensitively monitor the timing of red appearance in the vegetation canopy.

Keywords: winter leaf reddening, Japanese cypress, photochemical reflectance index (PRI), Red index (RI), Red-Green vegetation index (RGVI), phenological analysis, digital camera, light-use efficiency (LUE)

How to cite: Chen, S., Kosugi, Y., Jiao, L., Nakaji, T., Noda, H., Hikosaka, K., and Nasahara, K.: Why does the leaf of Japanese Cypress in temperate region experience transient leaf reddening under winter excessive light stress, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11863, https://doi.org/10.5194/egusphere-egu22-11863, 2022.

The Phenological network of Catalonia (Fenocat) was established by the Meteorological Service of Catalonia (SMC) in 2013, due to the lack of phenological records in Catalonia and with the aim to preserve the former phenological information available to study the impact of climate change on natural ecosystems.

Based on citizen science, today the network consists of 60 volunteers around Catalonia that observe 25 different spices of wild and crop plants, 14 birds and 6 butterflies. The observation is done two or three times per week in order to record both the onset and the duration of the different phenophases. BBCH code is used for vegetal species, and the data is sent regularly to PEP725 database. 

Until 2021, observers submitted their observations by email on a monthly base, which had to be manually inserted into the database. This slowed down the quality control and exploitation of the database, which could never be done in real time. 

Since January 2022, an object-relational database is created with the open-source system PostgreSQL, permitting the storage and management of all the information from the Fenocat network, and conducting real-time inquiries to generate, visualize and download reports. In addition, observers are able to enter the information directly into the database via a web application. Fenocat web application makes data entry more flexible -the data can be introduced in situ at the same moment that is observed by any device with internet connection (mobile phone, tablet or PC computer)- and allows the observer to compare their observations with those produced by other observers.

Both, the new database and the web application, mean a significant step forward in the management of the Phenological Network of Catalonia, strengthening our relationship with the observers, enhancing the data analysis and opening the door to a network expansion. 

How to cite: Busto, M., de Yzaguirre, X., and Cunillera, J.: The new web application of the Phenological Network of Catalonia (Fenocat), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4336, https://doi.org/10.5194/egusphere-egu22-4336, 2022.

Hemiboreal forest encompasses the shifting optimal distribution limits of both boreal and temperate forest types, providing an opportunity to develop insights for the potential effects of global change on each forest type. Leaf phenology, the timing of leaf life cycle events, serves as a dynamic indicator of biological response to climate change and signifies the potential robustness or susceptibility of particular species to future change. In order to better understand how environmental context influences the leaf phenology of hemiboreal tree species we installed a network of 34 leaf phenocam stations across Maritime Canada encompassing a range of 3° latitude and 2 °C in annual average temperatures. The most common broadleaf species observed were red maple (Acer rubrum) and paper birch (Betula papyrifera), while the most common needleleaf species we observed were red spruce (Picea rubens) and balsam fir (Abies balsamea). Our phenocam stations consist of a solar-powered consumer grade cellular time-lapse camera and colour reference panel, and were installed prior to and throughout the 2019, 2020, and 2021 growing seasons. We dissected image field of views into regions of interest corresponding to discernable individuals and used green chromatic coordinate curve fitting and threshold extraction approaches. We found that most species had a high degree of plasticity in phenological response to varying site conditions, though some had a conserved response to varying site conditions relative to other species. We also observed an unusually early fall green-down for paper birch at one site in July of 2021. This suggests that climate change may have differential effects on hemiboreal tree species due to phenology triggers being distinct among species. This work demonstrates the complexity of environmental influence upon leaf phenology, as well as the utility of phenocams in monitoring leaf phenology in remote regions of Maritime Canada.

How to cite: Spafford, L. and MacDougall, A. H.: How does environmental context influence the leaf phenology of tree species in Maritime Canada?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2589, https://doi.org/10.5194/egusphere-egu22-2589, 2022.

Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. Therefore, we established a measurement network that is distributed across varying plant communities in the high arctic valley of Adventdalen on the Svalbard archipelago, with the aim to monitor vegetation phenology. The network consists of ten racks equipped with sensors that measure NDVI (Normalized Difference Vegetation Index), soil temperature and moisture, as well as time-lapse RGB cameras. Three additional time-lapse cameras are placed on nearby mountain tops to provide an overview of the valley. From these RGB photos we derived the vegetation index GCC (Green Chromatic Channel), which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust timeseries for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. We show how this data can be used to monitor different vegetation communities in the landscape and that this can form the basis for a direct comparison to space-borne observations and further upscaling.

How to cite: Parmentier, F.-J. W., Nilsen, L., Tømmervik, H., and Cooper, E. J.: A distributed time-lapse camera network on high-arctic Svalbard to track vegetation phenology with high temporal detail and at varying scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3827, https://doi.org/10.5194/egusphere-egu22-3827, 2022.

Airborne pollen may have a substantial contribution to respiratory allergies affecting the public health badly, especially in combination with long-term exposure by other air pollutants. In some European countries the prevalence of people with pollinosis is up to 40%. In Belgium, ~10% is sensitive to birch pollen and ~15% to grass pollen. In the future, even more people might be affected since climate change and land-use change elicit an increased amount of allergenic airborne pollen and prolonged pollen seasons.

In this study we have used the pollen transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) for attributing the long-term changes in the releases of pollen by birches and grasses to meteorology and vegetation dynamics. The pollen transport model is applied for Belgium and is driven by ECMWF ERA5 meteorological data in a bottom-up emission approach for the period 1982-2019. The corresponding maps with grass and birch pollen emissions sources, i.e. the dynamic vegetation component, are based on merging multi-decadal datasets of spaceborne NDVI with forest inventory data and grass distribution maps for 1982-2019. For each model gridcell we compute temporal trends based on the Theil Sen slopes and the Area Under the Curve (AUC) of the seasonal birch and grass pollen cycles based on daily pollen levels, and of the daily meteorological model input for the period 1982-2019. The gridcell based association between trends in pollen and meteorology are derived based on the Kendall correlation coefficient.

Our findings indicate that the increasing radiation, the decreasing precipitation and the decreasing horizontal wind speed are associated with a strong increase in birch pollen levels for the period 1982-2019. The decreasing grass pollen levels in the air over the same period are associated with decreasing precipitation. This is, however, induced by the decreasing trend in grass pollen sources. The associations between meteorology and airborne birch pollen levels are much stronger compared to grass pollen. The specific contribution of birch pollen production dynamics to the levels of birch pollen in the air is highly associated with wind speed and precipitation. By introducing the inter-seasonal variation in birch pollen production the overall increase rate is dampened with ~7%. In contrast, the grass pollen production dynamics resulted into 3.5 times less grass pollen in the air over the studied period.

How to cite: Verstraeten, W., Bruffaerts, N., Kouznetsov, R., Sofiev, M., and Delcloo, A.: On the sources of long-term trends of airborne birch and grass pollen, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2472, https://doi.org/10.5194/egusphere-egu22-2472, 2022.

One of the most visible signs of climate change in nature are shifting growing cycles. In northern areas, mostly energy limited environments, the growing seasonality is usually defined in 3 different ways:
- frost free period: time period between last spring frost and first autumn frost;
- thermal growing season: in the Baltic region traditionally is defined as the number of days with mean temperatures above 5°C. 
- phenological growing season: the period between leaf unfolding (BBCH11) and senescence (coloring (BBCH92) or leaf fall (BBCH93)).

In this study we examine recent trends in growing season length in the Baltic region according to these 3 definitions from 1991-2020. The duration of frost-free period and thermal growing season was calculated using the gridded daily temperature from the E-OBS data set version v24.0e (Cornes et al., 2018). The phenological growing season was examined using a recently published open source phenological observations data set (Kalvāne et al, 2021) and remote sensing leaf area index (LAI) obtained from Copernicus data store.
Changes in the growing season vary within species, for example, for Acer platanoides, the prolongation of the growth trend is highly pronounced due to earlier flowering and later onset of autumn phases. For Populus tremula on the other hand, the changes in flowering are not as significant as the changes in the autumn phases (occurring on average later, the trend of which is positive), and the trend in the duration of the growing season is neutral.  The results indicate a statistically significant trend toward pioneer species such as Alnus incana and Corylus avellana.  The data regarding the end of the growing season is less conclusive. Autumn senescence in many cases does not show a significant trend.
However rising temperatures contribute to the increasing length of the thermal growing season. These thermal shifts have already influenced the bird migratory patterns, for example later migration of  Anser anser, as well as agricultural practices - sowing of winter cereals and soil cultivation - are taking place later in the autumn than it used to (Kalvāne and Kalvāns, 2021).
This study has shown that significant seasonal changes have taken place across the Baltics landscape due to climate change.

This study was carried out within the framework of the Climate change and sustainable use of natural resources institutional research grant of the University of Latvia (No. AAP2016/B041//ZD2016/AZ03) and the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change” (No. lzp-2019/1-0165).
 
References: Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res. Atmos., 123(17), 9391–9409, doi:10.1029/2017JD028200, 2018.
Kalvāne, G., Kalvāns, A., Ģērmanis, A.: Long term phenological data set of multi-taxonomic groups and agrarian activities, abiotical parameters from Northern Europa, Latvia, Earth Syst. Sci. Data, 13, 4621–4633.,https://doi.org/10.5194/essd-13-4621-2021, 2021
Kalvāne, G. and Kalvāns, A.: Phenological trends of multi-taxonomic groups in Latvia, 1970-2018, Int. J. Biometeorol., 65, 895–904, doi:https://doi.org/10.1007/s00484-020-02068-8, 2021.

How to cite: Kalvane, G., Kalvans, A., and Briede, A.: Phenological and thermal growing season changes in the Baltics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6845, https://doi.org/10.5194/egusphere-egu22-6845, 2022.