Displays

For several cultivars of Malus domestica (apple) and Pyrus communis (pear), records of seven decades (1950-2019) from the Research Centre for Fruit in north-east Belgium revealed that flowering occurred on average 9.5 (apple) and 11.5 (pear) days earlier following dormancy periods (October to April) that were warmer than the average (Drepper et al., 2020). However, the relationship between winter temperature and flowering date is not linear and relative delays of flowering following the warmest winters suggest that increasing temperatures before and after dormancy break (so-called chilling and forcing periods) have respectively delaying or advancing effects on the time of flowering of fruit trees in temperate regions (Drepper et al., 2020).

Well calibrated phenological models are potentially usable to support decision-making regarding (new) orchard locations, cultivar selection and frost mitigation measures. To this end a dynamic chill model was coupled to a growing degree day forcing model, calibrated and validated to the local cultivars for the Research Centre’s conditions. The combined model was applied for apple and pear on a 5km X 5km grid covering the region of Flanders in Belgium and run based on observed temperatures since 1950 from the Belgian Meteorological Institute on the one hand and regionally downscaled and adjusted temperature projections from the CORDEX project for the near future (up to 2060) on the other hand. This temporal horizon is farm practice driven and covers the lifespan of orchards planted in 2020.

The results (forthcoming) allow to investigate spatial patterns of (i) date of start of flowering, (ii) the occurrence of frost during sensitive stages around the flowering time, (iii) timing of dormancy break as well as (iv) its interaction with forcing completion.    

Drepper, Bianca, Anne Gobin, Serge Remy, and Jos Van Orshoven. “Comparing Apple and Pear Phenology and Model Performance: What Seven Decades of Observations Reveal.” Agronomy 10, no. 1 (January 4, 2020): 73. https://doi.org/10.3390/agronomy10010073.

How to cite: Drepper, B., Gobin, A., Verjans, W., and Van Orshoven, J.: Increasing temperatures and fruit phenology – Comparing spatio-temporal trends for apple and pear in Belgium, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2945, https://doi.org/10.5194/egusphere-egu2020-2945, 2020.

A phenological data set collected by volunteers` observers from the Latvia Phenological Observation Network covering period 1970 to 2018 has digitized from original paper based publications in Nature Calendars and analysed. The data set includes more than 40 thousand observations, 148 phenological phases across five different taxonomic groups: insects, amphibian, birds, fungi and plants as well as agrarian activities like sowing, harvesting date and some meteorological parameters like first and late frost, snow, ice regime.

The phenological changes or trends was analysed in two ways: 1. by combining data rows (station-phase-species) for one phase, such as leafing (BBCH11) for all trees and bushes; 2. by performing regression analyses for each phase and for each observation point separately.

More than 80% of spring data series shows negative tendency as reported in most scientific publications on European phenology. In our data set, overall, autumn phenologies are occurring later over time or the trends are neutral.

Regression analyses of phenology date versus year shows the disparities among species and among locations within a species: spring migrants’ return earlier, while staying longer in the fall with exceptions, for example the white stork in autumn leaves earlier than in the beginning of the period.

The commencement of the agricultural activities in spring such as sowing date have not changed significantly. However, such activities as livestock grazing and sowing of winter cereals takes place latter in the autumn.  These both appear to have affected by both technological changes and changes in meteorological parameters, for example, the trend of first autumn frost and first snow is positive – they have observed latter.

We have analysed trends and cross correlation with phenology in temperature regime, heat waves, precipitation, drought indexes, evapotranspiration, and soil temperature for the last 40 years.

Research is supported by the ERDF Project No. 1.1.1.2/VIAA/2/18/265 at the University of Latvia.

How to cite: Kalvane, G., Kalvans, A., and Briede, A.: Phenological data set of five taxonomic groups and agrarian activities in temperate climate: trends and influencing factors, Latvian case study , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4735, https://doi.org/10.5194/egusphere-egu2020-4735, 2020.

Phenology can contribute to many scientific disciplines from climate change, biodiversity, agriculture and forestry to human health. The knowledge of timing of phenological events and their variability can provide valuable data for agriculture. Accurate and timely information on the dates of specific stages of crop development is needed for various applications including crop yield forecasting. Despite the proven capabilities of Sentinel satellites for crop mapping and estimating phenology, they have not yet been applied effectively for tracking crop development across large areas. 

A methodology is proposed to systematically identify phenology phases from time series generated by the Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) sensors. This is done by linking specific agricultural-parcel temporal S1 and S2 signatures to phenology observations representative for 5-km buffers around the 6573 Deutscher Wetterdienst (DWD) stations spatially distributed across Germany. First, a S1-based 10-m crop type classification was made around each DWD station trained with LUCAS (Land Cover and Land Use Area frame Survey) 2018 data which allowed identifying parcels as well as crop types. Second, the average crop specific S1 (VV and VH) and S2 (NDVI) temporal signal is extracted for each DWD station and the correlation between the DWD BBCH event and characteristic behaviour in the satellite signals such as dips or peaks is systematically assessed for each crop. 

This approach identified the unique and crop-specific temporal signatures of S1 and S2 associated with specific phenology events such as emergence, flowering or ripening. We further discuss the potential and limitations of S1 and S2 to extract this type of information. These temporal S1 and S2 signatures can contribute to a digital reference library that could be used to monitor crop phenology operationally for parcels across the globe. Moreover, it unveils the potential of S1 and S2 to study detailed spatial and temporal gradient of crop phenology in the light of climate change.

How to cite: d'Andrimont, R., Lemoine, G., and van der Velde, M.: Using Sentinel-1 and -2 satellite time series to monitor crop phenology at the parcel level, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15171, https://doi.org/10.5194/egusphere-egu2020-15171, 2020.

Trees in temperate climates show distinct seasonality of leaf photosynthetic function and tree growth, which has strong influence on the annual cycle of terrestrial carbon sequestration. Thus, there are intense efforts to explore phenological pattern of leaf photosynthetic function and tree growth in temperate tree species and understand their internal and external regulation. In this presentation, we summarize our past research in this field, combining results from different experimental studies and field observations on a large number of European tree species. We show not only the well-known dependency of the onset of spring bud burst and leaf development on temperature and photoperiod and their large inter- and intra-specific variability, but also refer to further, fairly unknown, environmental factors. We give examples how varying soil properties and drought stress may interact with temperature on the seasonal timing of bud burst, photosynthesis, shoot growth and autumnal leaf senescence. Finally, we give information on the temporal coordination of bud burst, canopy greening and tree growth, showing strong differences among European tree species. With the collected information, we identify potential sources of uncertainty in approaches predicting the seasonal timing of leaf photosynthetic activity and tree growth with climate warming.

How to cite: Arend, M., Zahnd, C., and Hoch, G.: Exploring the control of phenological patterns of leaf function and tree growth in European tree species, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18012, https://doi.org/10.5194/egusphere-egu2020-18012, 2020.

Annual grasslands are an essential component of Mediterranean oak savannas, the most extensive agroforestry system in Europe, as the primary source of fodder for livestock and wildlife. Monitoring its phenology is key to adequately assess the impacts of global warming on different time scales and identify pre-critical states in the framework of early warning decision making systems. The natural variability of the climatic-hydrological regime in these areas and the usually complex spatial patterns of the vegetation, with sparse distribution and multiple layers, encourage the exploitation of available data from remote sensing sources. This work presents an assessment of vegetation indexes (VI) from Sentinel-2 validated against field data from terrestrial photography in an oak-grass system in southern Spain as a multi-approach method to monitor phenology in grass pastures. The analysis also has provided an insight into the links of the phenology dynamics with hydrological variables under these conditions.

From December 2017 to May 2019 a quantitative value of grassland greenness was computed using the Green Chromatic Coordinate (GCC) index. The phenological parameters of the start of the season (SOS), the peak of the season (POS) and end of the season (EOS) were extracted using the 50% amplitude method and confirmed using field photography. These values were compared with those provided by eight VI's derived from Sentinel-2 (NDVI, GNDVI, SAVI, EVI, EVI2, MTCI, IRECI and S2REP) and the difference in days between the key phenological dates were estimated. The results showed that for annual grasslands NDVI was the index providing estimations closest to those of ground GCC, with differences below 10 days for all phenological dates and the best correlation with GCC values (r = 0.83, p <0.001). None of the VIs using bands in the red-edge region have improved the NDVI results. Two of them, MTCI and S2REP, followed a different trend that the rest of explored indices, presenting a high temporal variability. The high diversity of species, typical of Mediterranean grasslands, might explain the high variability observed in these values. However, the third index using red-edge bands, IRECI, presented a high correlation with GCC. In this case, the index was designed to focus on the chlorophyll content of the canopy instead of the leaf scale addressed by S2REP. The influence of the vegetation ground coverage and foliage density is then higher and more similar to the broad-band indices. GNDVI also provided good general results. Soil moisture (SM) time-series were also used to estimate phenology and have presented a good agreement with GCC in SOS and EOS estimations, with SM reaching threshold values a few days before greenness ones, as measured by GCC. However, SM was not a good indicator of the POS, presenting significant biases with respect to GCC estimations.

How to cite: González-Dugo, M. P., Gómez-Giraldez, P. J., Pérez-Palazón, M. J., and Polo, M. J.: Monitoring Mediterranean grass phenology from digital terrestrial camera and Sentinel-2 vegetation indices in an oak-grass savanna ecosystem, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17923, https://doi.org/10.5194/egusphere-egu2020-17923, 2020.

As a natural ecosystem dominated by grasses, phenological studies of pastures have attracted increased attention for their important roles in global carbon cycling, ecosystem biodiversity, and public health. To better understand pasture phenology from in-situ to regional scales, accurate monitoring of pasture greenness variations across different scales is critical. As an alternative approach to labor-intensive field surveys, digital time-lapse cameras (termed phenocams) can provide diurnal and long-term vegetation greenness observation at in-situ scale with less impact from atmospheric effects. Even so, monitoring of phenology at regional to global scales only can be obtained by satellite remote sensing. The data from satellite sensors whether medium-resolution (i.e. Moderate Resolution Imaging Spectrodiometer, MODIS, 250 m) or fine spatial resolution (i.e. Sentinel-2 mission, 10 m) is widely used for vegetation phenology monitoring. However, achieving accurate pasture greenness dynamics using satellite data remains challenging due to limitations resulting from heterogeneity in Australian pastures.   

Combining phenocam, Sentinel-2 data and MODIS land surface products, this study aimed to (1) compare differences in temporal profiles of pasture greenness derived from ground-based phenocam and satellite sensors with fine- and medium-spatial resolutions, respectively; (2) assess the capacity of Sentinel-2 pixels for representing the phenocam footprint for monitoring greenness dynamics; and (3) evaluate the potential of improving greenness upscaling from phenocam to MODIS by masking non-grass areas via Sentinel-2 data.

A set of RGB phenocams was deployed over sites located over eastern Australian pastures. Green chromatic coordinate (GCC) was calculated from phenocam images. Six spatial footprints centered at phenocam sites were defined (i.e. 10 m, 30 m, 90 m, 250 m, 750 m and 1250 m), in which the Enhanced Vegetation Index (EVI) was calculated from Sentinel-2 and MODIS. The correlations between phenocam GCC and Sentinel-2 EVI were analyzed at single and multiple sites within the phenocam footprint (< 100 m) across all phenophases. Similarly, the correlations between GCC and EVI derived from Sentinel-2 and MODIS were analyzed for larger scales (> 100 m). Finally, we analyzed the relationships between GCC and MODIS EVI derived after applying a Sentinel-2 grass mask.

First, generally consistent temporal patterns of GCC and EVI were found at all spatial scales and phenophases, though there were differences at larger scales. Second, relationships between GCC and Sentinel-2 EVI within the phenocam footprint (< 100 m) kept nearly consistent regression trends and significant correlations whether from single or multiple sites, but decreasing at scales beyond 100 m. Third, correlations between GCC and MODIS EVI were similar to Sentinel-2 EVI at the same scales (< 100 m). However, at > 250 m scale, EVI derived from Sentinel-2 non-grass filtered data improved the correlation with GCC compared with EVI from all Sentinel-2 pixels and MODIS pixels. Our results indicate that Sentinel-2 can enable retrieval of grass pasture phenology in heterogeneous landscapes with higher accuracy compared with MODIS, and demonstrated the potential of Sentinel-2 data as a land cover filter to improve phenocam upscaling to MODIS.  

How to cite: Liu, Y., Huete, A., Xie, Q., and Nguyen, H.: Multi-scale phenology from digital time-lapse camera to Sentinel-2 and MODIS over Australian pastures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7261, https://doi.org/10.5194/egusphere-egu2020-7261, 2020.

The length of the period of vegetation activity is a significant driver of the global carbon cycle. Thus, the observation of plant phenology and seasonal vegetation dynamics has become an essential tool to quantify the impact of climate change on ecosystems. However, the accurate prediction of potential shifts of plant phenology in a warmer future requires a detailed spatio-temporal quantification of phenological patterns observed today. While phenological data derived from satellite-based remote sensing platforms often lack the spatial and/or temporal resolution to resolve the responses on the species level or to even reveal intraspecific patterns across the landscape, accurate visual observations by a human observer for thousands of trees are often not feasible due to time constraints. Therefore, we here present a novel near-surface remote sensing method that allowed the accurate tracking of tree phenology along an elevation- and urbanization gradient using a car-mounted camera. Using deep-learning-based image segmentation, we were able to track distinct patterns in the timing of leaf phenology of tens of thousands of trees along a nearly 100 km transect in New England throughout two growing seasons. The efficient collection of such high-resolution, multi-species, spatiotemporal data provides an excellent opportunity to quantify variation in tree phenology down to the level of individual organisms, across landscape and regional scales and for the fine-tuning of phenological models.

How to cite: Basler, D. and Richardson, A. D.: PhenoCar: Assessment of phenology of thousands of trees along an environmental gradient using car mounted cameras and deep-learning based image segmentation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18521, https://doi.org/10.5194/egusphere-egu2020-18521, 2020.

Direct in situ phenological observations of co-located trees and shrubs help characterize the phenological profile of ecosystems, such as, temperate deciduous forests. Accurate determination of the start and end of the growing season is necessary to define the active carbon uptake period for use in reliable carbon budget calculations. However, due to the resource intensive nature of recording in situ phenology the spatial coverage of sampling is often limited. In recent decades, the use of freely available satellite-derived phenology products to monitor ‘green-up’ at the landscape scale have become commonplace. Although these data sets are widely available they either have (i) high temporal resolution but low spatial resolution, such as, MODIS (daily return time; 250m) or (ii) low temporal resolution but high spatial resolution, such as, Landsat (16-day return time; 30m). However, the recently (2017) launched VENμS (Vegetation and Environment monitoring on a New Micro-Satellite) satellite combines both high temporal (two-day return time) and spatial (5-10m) resolution at a local scale thus providing an opportunity for small scale comparison of a range of phenometrics. The next challenge is to determine what in situ phenophase corresponds to the satellite-derived phenology. Our study site is a temperate deciduous woodlot on the campus of the University of Wisconsin-Milwaukee, USA, where we monitored in situ phenology on a range of (5) native (N) and (3) non-native invasive (NNI) shrub species, and (6) tree species for a 3-year period (2017-2019) to determine the timing and duration of key spring (bud-open, leaf-out, full-leaf unfolded) and autumn (leaf color, leaf fall) phenophases. The monitoring campaign coincided with the 2-day return time of VENμS to enable direct comparison with the satellite data. The shrubs leafed out before the trees and the NNIs, in particular, remained green well into the autumn season when the trees were leafless. The next step will be to determine what exact in situ phenophses correspond to NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived start, peak and end of season from MODIS and VENμS data. In addition, we will determine if VENμS can detect differences in phenological profile between N and NNI shrubs at seasonal extremes. We anticipate that the high resolution VENμS data will increase the accuracy of phenological determination which could help improve carbon budget determination and inform forest management and conservation plans.

How to cite: Donnelly, A. and Yu, R.: Combining in situ observations and high resolution VENμS data to monitor temperate deciduous shrub and tree phenology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3719, https://doi.org/10.5194/egusphere-egu2020-3719, 2020.

Temperate and boreal forests are shaped by late spring freezing events after budburst, which are also known as false springs. Research has generated conflicting results on whether or not these events will change with climate change, potentially because---to date---no study has compared the myriad climatic and geographic factors that contribute to a plant's risk of a false spring. We assessed and compared the strength of the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six temperate, decidious tree species across 11,648 sites in Central Europe and how these predictors shifted with climate change. Across species before recent warming, mean spring temperature and distance from the coast were the strongest predictors, with higher mean spring temperatures associated with decreased risk in false springs (–7.64% per 2°C increase) and sites further from the coast experiencing an increased risk (5.32% per 150km from the coast). Elevation (2.23% per 200m increase in elevation) and NAO index (1.91% per 0.3 increase) also increased false spring risk. 

With climate change, elevation and distance from coast---i.e., the geographic factors---remain relatively stable, while climatic factors shifted in magnitude for mean spring temperature (down to -2.84% in risk per 2°C), and in direction, with positive NAO phases leading to lower risk (-9.15% per 0.3). The residual effects of climate change---unexplained by the climatic and geographic factors already included in the model---magnified the species-level variation in risk, with risk increasing among early-leafout species (i.e., Aesculus hippocastanum, Alnus glutinosa and Betula pendula) but a decline or no change in risk among late-leafout species (i.e., Fagus sylvatica, Fraxinus excelsior and Quercus robur). Our results show that climate change has reshaped the major drivers of false spring risk and highlight how considering multiple factors can yield a better understanding of the complexities of climate change.

How to cite: Chamberlain, C., Cook, B., Morales-Castilla, I., and Wolkovich, E.: Climate change reshapes the drivers of false spring risk across European trees, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5871, https://doi.org/10.5194/egusphere-egu2020-5871, 2020.

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Spatial patterns and mechanisms of this phenomenon were extensively investigated, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We investigated the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. We had two objectives :

(i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types.

Sixty-three percent of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation.

Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts ( > 1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

How to cite: Cremonese, E., Filippa, G., Galvagno, M., Morra di Cella, U., and Migliavacca, M.: Climatic Drivers of Greening Trends in the Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18671, https://doi.org/10.5194/egusphere-egu2020-18671, 2020.

Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.

Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1^st). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.

Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.

How to cite: Izquierdo-Verdiguier, E., Zurita-Milla, R., Moreno-Martinez, Á., Camps-Valls, G., Klisch, A., Atzberger, C., and Running, S. W.: Gross Primary Production and False Spring: a spatio-temporal analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17837, https://doi.org/10.5194/egusphere-egu2020-17837, 2020.

Abstract

Leaf-out timing is crucial for the fitness of deciduous trees inhabiting temperate and higher latitudes. Optimal leaf-out allows minimizing freezing damages and herbivory pressure while maximizing growing season length and resource uptake in order to increase their competitiveness. However only a few attempts have been made to classify species according to their strategy along this trade-off.

Using climate chambers, we artificially provoked 5 different flushing dates that span the maximum possible range of natural occurring flushing dates of 4 tree species (Prunus avium, Carpinus betulus, Fagus sylvatica and Quercus robur). Shortly after each of the five leaf-out timings, 12 saplings per species were exposed to a frost treatment that is expected to either kill all leaves (LT₁₀₀, i.e. lethal temperature killing 100% of the leaves) or to partially damage them. These temperature thresholds have been adapted to each species according to their freezing resistance found in the literature. A subset of 12 indviduals per species served as a control and were not subjected to a frost treatment. Shortly after the frost treatment, all saplings were planted outside in the ground under a shading net (~-60% of light transmission) simulating below canopy conditions at the WSL research facility near Zürich.

Growth parameters (diameter, height) and recovery state (percentage of greenness compared to the control) were regularly measured during the consecutive growing season as well as the leaf coloring in autumn 2019. Preliminary results suggest that cherry and oak have recovered more than 80% by the end of the growing season, whereas beech and hornbeam only recovered about 50%. Oak was the fastest species to recover, already reaching 80% three weeks after the frost treatment. Our results allow to better quantify to what extend damaging spring frost reduces competitiveness for resources (light, nutrients) among species.

How to cite: Baumgarten, F., Vitasse, Y., and Gessler, A.: No risk - no fun: The tradeoff between avoiding frost and maximizing growth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21586, https://doi.org/10.5194/egusphere-egu2020-21586, 2020.

Phenology is the science that studies the timing of periodic plant and animal life cycle events, as well as their causes, interrelations, and variations in space and time. Phenological information has a plethora of use and hence of users. For example, this information is often used to study climate change because phenological timings respond to changes in environmental conditions. Besides this, phenological information helps to model the water, carbon and energy cycles, is necessary to monitor and manage natural and artificial man-made ecosystems and even supports nature lovers and public health practitioners. The well-established EGU session on “Phenology and seasonality in climate change” shows the diversity of phenological research and products and brings together multiple research communities: ecologists, agronomists, foresters, climatologists, geo-information and remote sensing scientists, and of course, citizen science experts. We believe that this diversity deserves attention and propose carrying out a first analysis of users, use and usability of phenological products by interacting with the participants of this EGU session. For this we will use a presentation software that allows posing questions to the audience and collecting their views in real-time. This presentation will then provide a better view of the phenological community, including their most commonly used data sources, tools, and needs. Special attention will be paid to identify major achievements and research and/or operational gaps that can help to define a phenological agenda for this new decade.

How to cite: Zurita-Milla, R., Garcia de Cortazar-Atauri, I., and Izquierdo-Verdiguier, E.: An interactive analysis of users, use and usability of phenological information, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22453, https://doi.org/10.5194/egusphere-egu2020-22453, 2020.

The exceptional warmth of spring and early summer of 2018 caused the earliest beginning of fruit ripening dates in Austria since 1946 of black elder and red currant, the second earliest of apricot, as well as the shortest period between the beginning of flowering and fruit ripening for all three species (same as 1956 for red currant). These phenological extremities of the 2018 spring correspond with the highest Austrian preseason (temperatures before the phenological event) April/May/June average since 1768.

In order to put the spring of 2018 into a long term perspective, the above mentioned phenological time series were extended back to 1768 by the much longer homogenised HISTALP temperature time series. This was achieved by multiple regression driven by preseason mean monthly temperatures. In order to accommodate for the uncertainty of the regression model, the lower (5%) and upper (95%) bounds of the confidence intervals were added to the reconstructed time series. Even when considering the lower bounds, the 2018 entry date of black elder beginning of fruit ripening remains the earliest since 1768. The 2018 entry date of apricot comes fourth (after 1811, 1794, 1797 and same as 1822) and that of red currant third (after 1811 and 1794). In order to evaluate the phenological variability since 1970 a 11 year moving average and a 41 year moving trend were calculated for the combined time series consisting of the modelled (from 1768 to 1945) and observed (from 1946 – 2018) sections. Neither the level of the 11 year averages nor the level of the 41 year trend values since 1970 have occurred during any other period since 1768.

These results contribute to the discussion of the temperature sensitivity of phenological phases. In spite of the unprecedented spring and early summer temperature level our phenological data do not indicate that lower bounds of the time period between flowering and fruit ripening have yet been reached. The fruit ripening phenology of the mid latitudes is still sensitive enough to faithfully record temperature trends and extreme events supplementing the instrumental record.

How to cite: Scheifinger, H.: The extreme warmth of the Central European spring in 2018 and its effects on fruit ripening phenology in Austria – a 251 year perspective, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2968, https://doi.org/10.5194/egusphere-egu2020-2968, 2020.

Spring leaf unfolding is a spectacular recurring event at the mid- and high latitudes that is associated with deciduous vegetation. Several lines of evidence indicate that the timing of spring green-up (i.e. the start of the season, SOS) changed in the past decades resulting in an earlier leaf unfolding - a phenomenon which is considered to be a major indicator of the effects of global warming. Contrary to the timing of the SOS, considerably less attention was paid to studying the dynamics of vegetation green-up, characterized by the leaf unfolding speed or the duration of spring green-up. The importance of studying the spring green-up dynamics lies in the fact that the duration of leaf development and timing of the onset of growth jointly determine the annual cycle of vegetation activity including carbon and energy balance, canopy conductance and evapotranspiration.

The aim of our research was to characterize the dynamics of leaf unfolding of deciduous broadleaf forests in the wider Carpathian Basin, located in Central Europe, using satellite remote sensing. The study was based on the Normalized Difference Vegetation Index (NDVI) time-series derived from the MOD09A1 official MODIS products during 2000–2019, the IGBP land cover classification dataset of the MCD12Q1 products, the CORINE 2012 (CLC2012) land cover dataset, the SRTM elevation dataset, and the FORESEE meteorological database. Our results clearly show that there is considerable interannual variability in the green-up duration of the deciduous broadleaf forest during 2000–2019. The last three years had, on average, the shortest (2018) and the two longest (2017 and 2019) recorded green-up durations in the region. Observed variability was partially attributed to the meteorological conditions, namely the extreme weather events occurring during the spring. We demonstrate that the meteorological conditions during the green-up period have a strong effect on the duration. The relationship between the SOS and the green-up duration reveals that the SOS also played an important role as a driver. Our results also reveal considerable elevation dependency both in the green-up duration and also in its correlation with SOS. Multiple linear regression models based on the SOS and the meteorological variables were also created to explain and predict the green-up duration.

How to cite: Kern, A., Marjanović, H., and Barcza, Z.: Variability of green-up duration of deciduous broadleaf forests in Central Europe during 2000-2019 based on MODIS NDVI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-840, https://doi.org/10.5194/egusphere-egu2020-840, 2020.

Plant phenology focuses on the annual repetitive development phases of the terrestrial vegetation. Since the date of the onset and the cessation of vegetation growth define the possible time period for photosynthesis, plant phenology strongly affects the carbon cycle of the ecosystems. Phenology has a serious impact on the climate system through the carbon-, water- and energy cycle. Observations indicate changes in the phenological cycle of the vegetation worldwide that are clear indicators of climate change. Warming climate can be associated with more intense carbon uptake, but it can also negatively affect production. Current studies clearly indicated that the phenological cycle is not properly represented in the Earth System Models which means that further research is needed.

Meteorological variables affecting the state of the environment, such as temperature and precipitation, also play a key role in the development of vegetation. Phenology models of different complexity were developed to quantify the timing of the onset of vegetation growth based on meteorological data. The sensitivity of the models to the source meteorological datasets is rarely studied. The aim of the present study is to quantify the sensitivity of widely used phenology models to the selection of the driving meteorological dataset.

Two phenology models were used to evaluate the different databases. One is the so-called Growing Degree Day (GDD) method, which calculates the onset date based on the degree day logic. The GDD model is further divided into simple thermal forcing model and thermal model, where the latter includes chilling requirement as well. The second method uses minimum temperature, photoperiod and vapor pressure deficit and calculates a so-called Growing Season Index (GSI) which is used to estimate onset date

Considering the meteorological data, three different datasets were used. The ERA5 is a reanalysis database, which is the product of the European Centre for Medium-Range Weather Forecasts (ECMWF). The CarpatClim and the FORESEE (Open Database FOR ClimatE Change-Related Impact Sudies in CEntral Europe) are observation based, gridded datasets for the larger Carpathian Region (Central Europe).  

In any modelling exercise aiming at simulating the stages of phenology, observations are essential. In the present study the phenological observation data is originating from satellite data and field observations. The first means the third generation Normalized Vegetation Index (NDVI3g) disseminated by GIMMS (Global Inventory Modeling and Mapping Studies), and the latter means the PEP725 phenology dataset and field observations from the botanical garden of Eötvös Loránd University, located in Budapest.

How to cite: Dávid, R. Á., Kern, A., and Barcza, Z.: Sensitivity of phenology models to the selection of driving meteorological datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2551, https://doi.org/10.5194/egusphere-egu2020-2551, 2020.

Vegetation phenology is highly sensitive to climate change. Previous studies focusing on the trends of phenological events have found that temperature and precipitation primarily regulate the dates of spring phenology in temperate grasslands. However, the variation of spring phenology and its controlling factors are still unclear. In this study, we investigated the start of the growing season (SOS) in temperate semi-dry grasslands in China using five methods, and determined the variation of SOS and its primary factor over the study period 1982-2015. We found that, in line with previous studies, the SOS date did not change significantly during the entire study period 1982-2015, but its variation increased significantly from the first subperiod (1982-1998, Std: 8.8±1.1 day) to the second (1999-2015, Std: 10.3±1.1 days), the latter of which coincides with fast warming. The larger variation in SOS may be caused by the different climatic drivers on phenology in different areas. The fluctuation of temperature was significantly increased over the study area and subsequently may result in a larger variation of SOS. Furthermore, precipitation and soil moisture has increased until the mid-1990s, which may lead to the removal of water as a limiting factor and increase the response of semi-dry grassland spring phenology to temperature, and finally result in larger variation in SOS.

How to cite: Zhou, X., Fu, Y., and Zhang, Y.: Climatic change caused larger variation of spring phenology in temperate semi-dry grasslands in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9545, https://doi.org/10.5194/egusphere-egu2020-9545, 2020.

Forests are globally an important environmental and ecological resource since they retrain water through their routes and therefore limit flooding events and soil erosion from moderate rainfall. They also act as carbon sinks, provide food, clean water and natural habitat for humans and other species, including threatened ones. Recent reports stressed the vulnerability of EU forest ecosystem to climate change impacts (EEA, 2012) (IPPC, et al., 2014). Climate change is a significant factor in the increasing forest fires and tree species being unable to adapt to the severity and frequency of drought during the summer period. Consequently, the possibility of increased insect pests and tree diseases is high as trees have been weakened by the extreme weather conditions. In Cyprus, there are two types of pine trees that exists on Troodos mountains, Pinus Nigra and Pinus Brutia, that may have been influenced by the reduced snowfall and extended summer droughts during the last decades.

The overarching aim of this project is to research the impact of Land Surface Temperature on Cypriot forests on Troodos mountains by analysing time-series of radar and thermal satellite data. Impacts may include forest decline that does not relate to fire events, decreased forest density and alternations to timing of forest blooming initiation, duration and termination. Radar systems emitted pulses that can penetrate forest canopy due to the size of its wavelength and, therefore, collect information between tree branches without being affected by clouds. This presentation will focus on radar analysis conducted; testing of various methods, and how the processing pipeline has been automated.

The project ‘ASTARTE’ (EXCELLENCE/0918/0341) is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Innovation Foundation.

How to cite: Prodromou, M., Yfantidou, A., Theocharidis, C., Miltiadou, M., and Danezis, C.: Analysis of radar and thermal satellite data time-series for understanding the long-term impact of land surface temperature changes on forests , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10582, https://doi.org/10.5194/egusphere-egu2020-10582, 2020.

Vegetation phenology is recognized to exert crucial influences on carbon sequestration and the role of vegetation phenology in mediating carbon cycle varies with ecosystem type. However, the relationship between vegetation phenology and productivity has not been fully understood in the alpine ecosystem due to a lack of field observations, poor model performances and their complex mechanisms. In this study, we examined the spatio-temporal variation in beginning of growing season (BGS) and net primary productivity (NPP) for the alpine grassland on the Tibetan Plateau (TP) and the regulation effects of spring phenology on seasonal NPP by integrating field observations, remote sensing monitoring and ecosystem model simulation. The ecosystem model performances were improved by optimizing ecosystem parameters from field observations. The results indicated a significant advance in BGS with a rate of 0.31 days/yr (P < 0.1) in the alpine grassland during 2001-2015 while the annual NPP increased significantly at a rate of 1.25 gC/m²/yr (P < 0.01). With regard to the relationship between BGS and NPP, large spatial heterogeneities were identified. Overall, a negative but non-significant correlation (R = -0.34, P > 0.1) was observed between BGS and annual NPP for the entire grassland ecosystem on the TP. But responses of NPP to BGS varied with seasons. Specifically, BGS showed significant negative correlation with spring NPP (R = -0.73, P < 0.01), and advanced spring led to increased spring NPP. The positive effects of advanced BGS on NPP tended to weaken in summer. Moreover, BGS was significantly and positively correlated with autumn NPP in some relatively arid zones of the southwestern TP, suggesting the suppressing effects of earlier spring on carbon assimilation during the later growing season in water limited areas. This study improved our understanding on the impacts of biotic factors on carbon cycles of the alpine ecosystem and implies that the effects of phenology can’t be concluded simply for an annual sum, and their relationships for each separate season are also critical.

How to cite: Zheng, Z., Zhu, W., Zhang, Y., Huang, K., and Cong, N.: Influence of spring phenology on seasonal net primary productivity in the alpine grassland on the Tibetan Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12393, https://doi.org/10.5194/egusphere-egu2020-12393, 2020.

Using RGB camera data (e.g. webcams, wildlife cameras) has great potential to measure forest phenology over climate gradients, because of its very high temporal resolution, while at the same time being more objective and less time consuming than in situ observations. To make images useful for the purpose of measuring phenological events, such as Start of Season (SOS) and End of Season (EOS), there is need to derive Regions of Interest (ROI) objectively and (semi-)automatically. In order to answer this need, Bothmann et al. (2017) proposed a method which randomly sets a number of pinpricks in the image and calculates how greenness over time from all other pixels correlates to these different pinpricks. Subsequently, ROIs are created by discarding the pixels with low correlation, using multiple thresholds. Despite its advantage of being automated and more objective compared to prevailing expert-based ROIs, and therefore its potential applicability for phenological research using a large amount of cameras, the method has not been reproduced for this purpose so far. Therefore, we assess here how well this method is able to separate foliage of different deciduous species from evergreens and phenologically irrelevant components in time-lapse wildlife camera data and in that way how suitable it is in explaining variation in phenology over a temperature gradient. We used 73 Cuddleback wildlife cameras troughout Bavaria which were installed within nine quadrants of 6*6 kilometers spanning a temperature gradient of 2.5°C. Hourly taken images of deciduous forests in spring, summer and autumn 2019 were analysed. Half of them were facing canopy, and half of them were facing understory. We applied the principles of the method from Bothmann et al. (2017) and assigned the best matching ROI to foliage of Fagus sylvatica or other deciduous species. Within this ROI, mean Green Chromatic Coordinate (GCC), a greenness index, over all pixels within the ROI, was derived per time-stamp. Afterwards, a time-series was calculated on these GCC values and with a suitable combination of curve-fitting techniques, SOS and EOS were derived, expressed in Day of Year (DOY). We compared these SOS and EOS dates with weekly in situ observations of spring and autumn phenology, which were taken in the same quadrants. Despite that Bothmann's method was developed on a single tower-mounted scientific webcam which viewed on canopy from above, while we made use of wildlife cameras at 73 different locations facing either understory perpendicular or canopy from below, it was able to distinguish F. sylvatica and other deciduous foliage from phenologically less relevant information. Time-series derived from these ROIs were able to explain variability in phenology between understory and canopy and over the temperature gradient similarly and supplementary to in situ observations. 

How to cite: Uphus, L. and Menzel, A.: Time-series within automatically generated ROIs from wildlife cameras are well able to explain variability in forest phenology on a temperature gradient, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16006, https://doi.org/10.5194/egusphere-egu2020-16006, 2020.

Citizen science is the scientific research that involves the participation of the public assisting professional scientists. This typically occurs in helping to data collection and/or data analysis, and an increasingly popular use of citizen science is the collection of phenological data, like wildflowers blooming in summer or leaves changing color in fall. Studying the life cycles of plants (phenology) reveals some consequences of climate change.

The PCTO (Percorsi per le Competenze Trasversali e per l'Orientamento) is a school-work alternation program and represent an innovative teaching method, introduced in 2015 by the Italian Ministry of Education, University and Research. This program, through practical experience, helps to consolidate the knowledge acquired at school and to enrich the student training. The school-work alternation is compulsory for all the students of the last three years of high school (13-17 years age). This program is a cultural change that incorporates good European practices, aimed at creating a synergy between school and work in order to encourage students to follow program learning inside of a public/private company.

The National Research Council of Italy is a partner of this program and each year students from high school are involved in technical and research activities. During the years 2015-2019, the Institute for the BioEconomy of Sassari, offered a School-Work learning program dedicated exclusively to Phenological and Pollen monitoring to groups of students of High School. While they employed their skills at work, they learnt to implement the specific protocols of a scientific project. These experiences increased their awareness of the essential role they can play by acquiring new knowledge of the environment and skills through scientific tools of citizen science. In this paper, results of the Phenological and Pollen monitoring program held at IBE-CNR Sassari are illustrated.

In the future, citizen scientists can provide reliable observations when following scientific methods and standardized protocols. Phenological monitoring programs based on volunteers support will become increasingly important in providing open‐access, standardized data sets capable of supporting the process of answering ecological and global change questions.

How to cite: Cesaraccio, C., Canu, A., Pellizzaro, G., Masia, P., and Fadda, M. L.: Students learning phenology for becoming citizen scientists: an example of Italian High School students and CNR researchers teamwork experience, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16521, https://doi.org/10.5194/egusphere-egu2020-16521, 2020.

Recent study highlighted large microclimatic variation occurring within forests, especially concerning light and temperature. In this study we aimed to quantify to what extent variation in light, soil humidity, nutrient availability and bud temperature alter the phenology of four tree species (Fagus sylvatica, Quercus robur, Prunus avium and Fraxinus excelsior). Various treatments were applied to seedlings grown in large wooden boxes in situ conditions near Zurich. The different treatments included shade (~60% of light transmission), reduced precipitation using rain shelters, fertilizer, additional watering during summer, as well as painting buds in black or white to alter bud temperature via albedo change. Budburst timing and leaf coloration were observed twice a week during the spring and autumn 2019.

Preliminary results show that the time of budburst was delayed when seedlings were grown under shade conditions (from +3 to +11 days for Quercus and Fagus respectively) or when buds are painted in white compared to black (from +4 to +11 days from Quercus and Prunus respectively), whereas no significant effect was found under reduced precipitation for any species. For the timing of leaf coloring, a very significant effect of light was found with a delay of +22, +39 and +42 days observed under shade conditions for Fraxinus, Prunus and Fagus. Preliminary results based on the temperature recorded within the buds or close to the plants suggest that bud temperature explain the differences observed in the time of bud burst among the different treatments, though light intensity may have also directly influenced bud development of Fagus in spring. Regarding leaf coloration, our results suggest that light intensity has a strong influence on most of temperate trees whereas soil water and nutrient content has only a minor species-specific effect. Overall, our results underline the importance of microclimatic variation to explain phenological variation among trees within or among nearby sites, especially in topographically complex regions as in mountains or in forests with varying vertical structure.

How to cite: Vitasse, Y., Kaewthongrach, R., and Baumgarten, F.: Microclimatic effects on spring budburst and autumn leaf coloration of four temperate tree species, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16922, https://doi.org/10.5194/egusphere-egu2020-16922, 2020.

The Swiss phenology network operated by MeteoSwiss counts approximately 160 stations where up to 69 phenological events are observed by private persons. Currently, 68% of the observer transmit their data online by a recently developed tool called Phenotool. In order to reduce typing errors during the entry of the data, the values are instantly checked by Phenotool. The observer receives a visual warning if the data exceeds defined limits of an expected time-period giving him the opportunity to verify the date entered. The defined limits need to be as suitable as possible for each station and phenological event as numerous false warnings reduce the sensitivity of the observers and cause them to ignore the warning. 
Until June 2019, limits had been used for five altitudinal layers and for each phenological event resulting from the mean ± 2 SD (standard deviation) rounded to the nearest 10. However, for some stations these limits were not appropriate, therefore, we decided to calculate station specific limits as follows: The median and SD was calculated for each phenological series consisting of at least 10 observations. In a second step, the mean of all SDs < 20 days was calculated and 2.5 times SD added/subtracted from the median. This approach leads to the same range of the limits for each phenological event, while the start of the limits is specific for each stations depending on the previously calculated median. If we would have used a station-specific standard deviation, stations with high variability and often less accurate data, would have been “awarded” with a large range. 
For new stations, data-series consisting of less than 10 observations or deviant data-series, we calculated the limits with the mean standard deviations as described above and a predicted median from a linear regression model showing the relationship between the medians of a specific phenological event and the station heights. Deviant data-series were recognized by a difference larger than 30 days between modelled and calculated median.
The comparison of the old and new limits revealed that the newly calculated limits have an average range which is 8.52 days smaller. 55 out of the 69 phenological events have a smaller range, two has the same, and the remaining 12 have a larger range. Using the previous limits, in average 8.12% of the data from 1985-2019 was outside the defined ranges, however, applying the new limits results in 3.98% of the observations not fitting the limits. Considering the fact that the new limits have in average a smaller range, this improvement becomes even more significant. To conclude, we can say that the new limits produce clearly less warnings and more appropriate warnings in Phenotool enhancing data quality.

How to cite: Pietragalla, B. and Füzér, L.: New station-specific limits in phenology to improve data quality during online-data-entry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18786, https://doi.org/10.5194/egusphere-egu2020-18786, 2020.

Airborne pollen data are an important source of information on flowering phenology, because they record the response of plants surrounding the sampling station, rather than the responses of individual plants, as with direct phenological observation. Plant phenology represents a good indicator of vegetation responses to long-term variation to temperatures. Furthermore, several studies have evidenced that aerobiological data series and pollen season are often strongly correlated to climate change.

This research aims to analyze airborne pollen data of Poaceae and Fagaceae measured from 1986 to 2008 in a urban area of northern Sardinia (Italy) and to investigate the trends in these data and their relationship with meteorological parameters using time series analysis. The aerobiological monitoring station was located in the center of the city very close to a public garden, and it is part of both the Italian and the European - A.I.A. Aeroallergen monitoring Network. Meteorological data were recorded during the same period by an automatic weather station.

The following parameters were calculated for each pollen: start, end and duration of pollen season, date of peak pollen concentration, number of days from the beginning of the season to the peak, annual pollen index (API), percentage distribution of API and maximum daily concentration.

The correlation between meteorological variables and the different characteristics of pollen seasons was analyzed using Spearman’s correlation tests.

A linear regression model was used for the trend analysis of the API of airborne pollen spread of the two family from 1986 to 2008.

How to cite: Canu, A., Vargiu, A., and Pellizzaro, G.: Climate Change and its impact on Poaceae and Fagaceae pollen season in Northern Sardinia, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19787, https://doi.org/10.5194/egusphere-egu2020-19787, 2020.

Spatiotemporal information about crop phenology and physiology during the growing season is critical for estimates and forecasts of productivity and yield. Further, knowledge of phenology during the season provides information for applying efficient irrigation, scheduling fertilization, pest management and harvesting at optimal times.^. Yield loss from climatic stress, like drought or heat, is critically dependent on both phenology and physiology. Yet, current yield forecasting models do not fully use all the potential of phenology and physiology related variables that can be retrieved from satellites. We attempt to address this research gap, focusing on the major winter crops grown in northern France, wheat and rapeseed. The yields of these crops are inaccurately predicted, in this region (and elsewhere) despite their economic importance. We will derive key crop phenological stages and physiological parameters at high spatial resolution with validation at site level, and using those data together with climate fields to develop statistical models of seasonal crop yield forecast. The proposed approach has potential to be applied to other crop types and areas.

How to cite: Zhu, P., Ciais, P., and Makowski, D.: Improving cropping management and yield prediction with satellite derived crop phenology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19894, https://doi.org/10.5194/egusphere-egu2020-19894, 2020.

This study was estimated harvest date of corn in South Korea based on the temperature index called the accumulated temperature. The accumulated temperature was calculated using observed daily mean temperature. We assumed a unified seeding date, 5 April, across the South Korea. The daily mean temperatures from 61 weather stations provided by the Korean Meteorological Administration were obtained for the period 2009-2018 (10 years). We used 1,650℃ as the criterion of the accumulated temperature to identify harvest date of corn for early-cultivated variety. The accumulated temperature over the most areas generally meted the criterion (1,650℃) in early July. In case of 2018, 66% area of Gang-won province, major corn producer, become suitable to harvest corn in July, peaking in the middle July (51%). The harvest date has been accelerating due to increase in daily mean temperature during the recent 10 years. This study infers that changes in farming activities are needed through reflecting the environmental change.        

Acknowledgements

: This study was carried out with the support of “Research Program for Agricultural Science & Technology Development (Project No. PJ014882)”, National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.

How to cite: Hur, J., Shim, K.-M., Kim, Y., and Jo, S.: Estimation of corn harvest date in South Korea based on the accumulated temperature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20804, https://doi.org/10.5194/egusphere-egu2020-20804, 2020.

Optical Earth observation satellites provide spatially-explicit data that are necessary to study trends in vegetation dynamics. However, more of often than not optical data are discontinuous in time, due to persistent cloud cover and instrumental noises. Hence, the operating constraints of these data require several essential pre-processing steps, especially when aiming to reach towards monitoring of vegetation seasonal trends.  To facilitate this task, here we present an end-to-end processing software framework applied to Sentinel-2 images.

To do so, first biophysical retrieval models were generated by means of a trained machine learning regression algorithm (MLRA) using simulated data coming from radiative transfer models. Among various tested MLRAs, the variational heteroscedastic Gaussian process regression (VHGPR) was evaluated as best performing. to train the retrieval model.  The training and retrieval were conducted in the Automated Radiative Transfer Models Operator (ARTMO) software framework.

Subsequently, in view of retrieving the phenological parameters from the obtained vegetation products, a novel times series toolbox as part of the ARTMO framework was used, called:  Decomposition and Analysis of Time Series software (DATimeS). DATimeS provides temporal interpolation among other functionalities with several advanced MLRAs for gap filling, smoothing functions and subsequent calculation of phenology indicators. Various MLRAs were tested for gap filling to reconstruct cloud-free maps of biophysical variables at a step of 10 days.

A demonstration case is presented involving the retrieval of Leaf area index (LAI), fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from sentinel-2 time series.  A large agricultural Algerian site of 143, 75 km² including Oued Rhiou, Ouarizane, Djidioua (1,345,075 pixels) was chosen for this study.  A reference image was excluded from the time series in order to evaluate the reconstruction accuracy over a 40-day artificial gap.

  The reference vs.  Reconstructed maps produced by the gap-filling methods were compared with statistical goodness-of-fit metrics.  Considering both accuracy and processing speed, the fitting algorithms Gaussian process regression (GPR) and Next neighbour interpolation (R²= 0.90 / 0.081 sec per pixel and R²=0.88 / 0.001 sec per pixel respectively) interpolations proved to reconstruct the vegetation products the most efficient, with GPR as more accurate but Next faster by a factor of 70.

Finally, we evaluated of the phenology indicators such as start-of-season and end-of-season based on LAI and FAPAR. The obtained maps provide valid information of the vegetation dynamics.  Altogether, the ARTMO-DATimeS software framework enabled seamless processing of all essential steps:  (1) from L2A sentinel-2 images converted to vegetation products, (2) to cloud-free composite products, and finally (3) converted into vegetation phenology indicators.

How to cite: arezki, D., Fizazi, H., Belda, S., De Grave, C., Pipia, L., and Verrelst, J.: A machine learning software framework for extraction of phenology indicators from multi-temporal sentinel-2 images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21952, https://doi.org/10.5194/egusphere-egu2020-21952, 2020.

Is bud burst of temperate trees promoted by a critical daylength?

Bud burst of temperate trees is mainly controlled by cool temperatures during winter-dormancy (chilling), warm temperatures in spring (forcing) and daylength (photoperiod). Some tree species may rely more on one of these drivers than others (e.g. temperature driven species) but recent studies emphasize complex interactions among them for most species. As one of these factors, photoperiod can act by preventing trees from flushing too early, minimizing the risk of damaging spring frost. Yet it is unclear whether stimulating and/or inhibiting effects of photoperiod on spring phenology act (i) gradually (i.e. increasing daylength progressively accelerates bud development response to temperature) or (ii) whether photoperiod slows down bud development until reaching a critical threshold.

In this study we tested the second hypothesis by exposing twig cuttings of 5 species (Acer pseudoplatanus, Carpinus betulus, Fagus sylvatica, Quercus petraea, Tilia cordata) to different constant photoperiods that occur before leaf-out in the latitudes of Zurich (10h, 11h, 12h, 13h). Two additional photoperiods of 8h and 16h served as a control to simulate shortest and longest natural occurring daylengths. The experiment was repeated on three occasions (from October 2019 to January 2020) to account for different dormancy depths.  Bud development was monitored twice a week.

The experiment is still running. We expect that temperature-sensitive species would leaf-out regardless of the photoperiod, while photoperiod sensitive species such as beech may wait until a critical threshold has passed. Furthermore, longer photoperiods might substitute for insufficient chilling by decreasing the required amount of forcing to bud burst. The results could serve to better implement photoperiod into phenological models.

How to cite: Pohl, N., Baumgarten, F., and Vitasse, Y.: Is bud burst of temperate trees promoted by a critical daylength?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21960, https://doi.org/10.5194/egusphere-egu2020-21960, 2020.

European beech is known to be a masting species, i.e. fruit production does not occur every year. It is thought to be a species which is flowering controlled, i.e. that after successful pollination, fruits and seeds would be produced. In the last two decades, years with high fruit production occurred every two to three years in Middle Europe, which may be indication for an inherent biennial cycle. However, successful fruit production can be hampered by disadvantageous weather conditions, such as frost events, during the pollination season.

In Switzerland, after high beech pollen concentration was measured in spring of 2018, high fruit production was expected. However, during the extremely hot and dry European summer of 2018, beech produced no, or only small amounts of beechnuts in two of three long-term monitoring beech stands in Switzerland, which are part of the Swiss Long-Term Forest Ecosystem Research Programme. We observed that beechnuts were aborted in early summer already. Over the last decades, we found similar examples of mast failure and fruit abortion in years with hot and dry summer conditions. These extreme conditions can thus act as an “environmental veto”, similar to frost events during flowering. In years with fruit abortion, summer mean temperatures were 1.2°C higher, and precipitation sums were 45% lower than the long-term average. Our findings are evidence for a biennial masting cycle in European beech, which can be interrupted by extreme weather conditions such as extreme summer heat and drought or frost during flowering.

How to cite: Nussbaumer, A., Meusburger, K., Schmitt, M., Waldner, P., Gehrig, R., Haeni, M., Rigling, A., Brunner, I., and Thimonier, A.: Extreme summer heat and drought acts as an environmental veto for fruit production in European beech, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7053, https://doi.org/10.5194/egusphere-egu2020-7053, 2020.

Will warmer winters induce more forest and crop pests in Switzerland?

Léonard Schneider*,**

*Institute of Geography, University of Neuchatel, Espace Tilo-Frey 1, 2000 Neuchatel (leonard.schneider@unine.ch)

**Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf

With current global warming, recent winters have often been milder in Switzerland than they were in previous decades and should still become more so in the coming decades. Some insect species sensitive to winter extreme cold events could increase their survival rates during the cold season. Forest pests, such as pine processionary moth (Thaumetopoea pityocampa), green spruce aphid (Elatobium abietinum), and some crop pests, such as southern green stink bug (Nezara viridula), could overwinter more easily in Switzerland. These species are affected by temperatures below -12°C (Thaumetopoea pityocampa, Elatobium abietinum) to below -8°C (Nezara viridula).

This research aims to determine to what extent the evolution of winter minimum temperatures could increase the winter survival rate of some pest species in various places in Switzerland. We examine the trends for winter temperatures, with a special focus on cold events (days with minimal air temperature below -8°C and -12°C). We first analyse daily air temperature between 1980 and 2019 using 67 meteorological stations located all over Switzerland. Then, we use available data from CH2018 climatic scenarios to estimate possible trends along the coming century.

Preliminary results showed that the frequency of cold days has been decreasing in Switzerland over the last 40 years even though winter minimum temperatures have been increasing less than yearly minimum temperatures. By the end of the 21st Century, occurrences of temperatures below -12°C could become irregular up to 1700 m and winters with temperatures below -8°C could become rare at lower elevations in Switzerland. As a consequence, some crop pests such as southern green stin bug could overwinter more easily on the Swiss Plateau, and some forest pests such as green spruce aphid and pine processionary moth could reach higher elevations in mountain areas by the end of the century.

How to cite: Schneider, L.: Will warmer winters induce more forest and crop pests in Switzerland?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10450, https://doi.org/10.5194/egusphere-egu2020-10450, 2020.

A new generation of land surface models (LSMs) have been developed in the framework of the EU-funded CRESCENDO project aiming to improve understanding of the Earth system as part of the community CMIP6 effort. 
These new LSMs explicitly represent key processes in the carbon and nitrogen cycles, enabling more realistic vegetation-climate interactions to be simulated. For instance, vegetation phenology, the seasonality of vegetation, is explicitly represented in all these new LSMs. Intra- and inter-annual variations in vegetation phenology can substantially influence land-atmosphere exchanges of energy, moisture and carbon. Changes in phenological events also provide clear indicators of climate impacts on ecosystems. 
Results are presented on the evaluation of phenological variability from offline runs of this new generation of LSMs. In particular, the timing of growing season onset and offset at global scale, and the Leaf Area Index (LAI) peak timing are investigated using monthly mean outputs. Three satellite-derived LAI datasets are used as benchmark observations for this evaluation.
In general, LSMs exhibit high skill in reproducing the observed phenology cycle in the North hemisphere mid- and high-latitudes, while lower skill is obtained in the South hemisphere. All LSMs simulate an offset in the timing of the active vegetative season characterized by later onset and LAI peak. Offset timings are slightly better captured by the LSMs. For these reasons, further development of the representation of phenology is required in LSMs, especially in the South hemisphere, where more complex vegetation and reduced in-situ observations are available.

How to cite: Hemming, D., Peano, D., Materia, S., Park, T., Warlind, D., Fan, Y., Lee, H., Wiltshire, A., and Jones, C. D.: Plant phenology evaluation of CRESCENDO land surface models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20606, https://doi.org/10.5194/egusphere-egu2020-20606, 2020.

Studying grassland phenology and its relationships to climate would deepen our understanding of vegetation-air interactions under global climate change. To date, however, our knowledge of the responses of grassland phenology to climatic factors is still limited at the continental scale. In this study, we retrieved the start (SOS) and end (EOS) of the growing season for mid-latitude (30°N~55°N) grasslands of the Northern Hemisphere during 1981-2014, and investigated their relations with previous temperature, rainfall, and snowfall (only for SOS) through trends analysis and time window analysis. Results illustrated a predominant significant advancing/delaying trend of SOS/EOS in 23.2%/20.5% of the study region. They jointly resulted in a primarily significant prolongation trend of growing season length in 22.7% of the study region. Next, a dominated negative correlation between air temperature/rainfall and SOS was found in 62.4%/57.6% of areas. Snowfall showed converse effects (positive/negative) among different grasslands. The time window opening date for air temperature to start to affect SOS was identified as the day 1-90 before the multi-year average SOS in 76.1% of areas, while the time window opening date for the effect of rainfall/snowfall on SOS was relatively evenly distributed between the 1st and 180th day before the multi-year average SOS. EOS was found to be significantly negatively/positively correlated with air temperature/precipitation in 74.8%/83.7% of areas. The time window opening date for the effect of air temperature on EOS was identified as the 90-180th day before the multi-year average EOS in 66.9% of areas, while the time window opening date for the effect of precipitation on EOS was mainly concentrated on the 60-120th day before the multi-year average EOS in 51.5% of areas. Overall, this study highlights the distinctly different time windows for the thermal-moisture effects on grassland vegetation phenology and this should be considered when establishing process-based phenological models.

How to cite: Ren, S., Li, Y., and Peichl, M.: Diverse effects of climate at different times on grassland phenology in mid-latitude of the Northern Hemisphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3992, https://doi.org/10.5194/egusphere-egu2020-3992, 2020.

Regions at high latitudes and high altitudes are undergoing a more pronounced winter warming than spring warming, and such asymmetric warming will affect chilling and forcing processes and thus the spring phenology of plants. We analyzed winter chilling and spring forcing accumulation in relation to the spring phenology of three tree species (Ulmus pumila, Populus simonii, and Syringa oblata) growing in a cold region (CR) compared with trees in a warmer reference region (WR, using the Dynamic Model and the Growing Degree Hour (GDH) model. We tested that forcing rather than chilling affects the spring phenology of trees in CR (hypothesis I), and that trees in CR have both lower chilling and lower forcing temperatures and thus longer accumulation periods than trees in WR (hypothesis II). In line with our hypotheses, forcing played a crucial role in spring phenology in CR, but chilling and forcing combined to determine spring phenology in WR. The temperatures during the chilling and forcing periods were lower and the accumulation period started earlier and ended later in CR than in WR. Moreover, the chilling accumulation was broken into two periods by the low deep winter temperature in CR. We conclude that asymmetric warming, with a stronger temperature increase in winter than in spring, could decrease the forcing accumulation effects and increase the chilling effects on the spring phenology of plants in CR. This change in the balance between chilling and forcing will lead to a shift in plant phenology, which will further have major impacts on biogeochemical cycles and on ecosystem functioning and services.

How to cite: Yang, Y., Li, M.-H., Wu, Z., He, H. S., Du, H., and Zong, S.: Effects of winter chilling vs. spring forcing on the spring phenology of trees in a cold region and a warmer reference region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6053, https://doi.org/10.5194/egusphere-egu2020-6053, 2020.

Changes in winter soil freeze-thaw (F-T) phenology not only affect nature, but also affect social-economy in permafrost regions. However, a lack of understanding of its response to global warming is a critical gap in knowledge to preclude adaptation to climate change. Here we explored effects of warming gradient (0, 1, 2 and 4^oC) combined with precipitation addition on it by which further on CO₂ emission on the Tibetan Plateau. We find that only warming delays start and end dates of soil F-T cycle during autumn-winter season, but advances them during winter-spring season, thus shortens the durations of completely freezing (14.9 days ^oC^-1) and total duration of soil F-T period from autumn to spring (11.7 days ^oC^-1). Thus, asynchronic shifts of the soil F-T cycle induced by warming significantly decreased total CO₂ emission by 31-47% relative to T₀ treatment during the whole F-T period from autumn to spring.

How to cite: wang, S., wang, Q., lv, W., zhou, Y., and jiang, L.: Asynchrony of winter soil freeze-thaw phenology induced by warming reduces ecosystem respiration of alpine meadow during the freeze-thaw period, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12031, https://doi.org/10.5194/egusphere-egu2020-12031, 2020.

During 1971-2000 phenological responses of wild species in spring and summer matched the warming pattern in Europe, whereas timing of farming activities as well as autumnal leaf colouring did not mirror climate change to the same extent (Menzel et al. GCB 2006). These findings were a backbone of the corresponding global attribution study of the IPCC AR4 (Rosenzweig et al. 2007, 2008). Two decades of warming later, however, new phenological findings suggest that especially a lack of chilling and / or increasing influence of photoperiod may have lowered the phenological temperature response and that adaptation in agricultural management is taking place. We therefore updated the GCB 2006 study by asking three questions: What drives the inherent variation of trends? Can we now detect a warming signal in “false” agricultural (i.e. those being directly or indirectly determined by farmers’ management) and autumn phases? Is there still an attributable warming signal in phenology?

The complete phenological dataset of Germany, Austria and Switzerland (1951-2018, ~97.000 times series, corresponding to 96.3% of PEP725 data) was analysed. We determined linear trends, studied their variation by plant traits / phenogroups, across season and time, and followed IPCC methodology for attributing phenological changes to warming patterns.

For spring and summer phases of wild plants we found more (significantly) advancing trends (~90% and ~60% sign.) which were stronger in early spring, at higher elevations, but smaller for non-woody insect-pollinated species. Although mean trend strength decreased, changes in spring were strongly attributable to warming in spring and winter. We had similar but less strong findings for agricultural crops in these seasons. In contrast only ~75% of phenological phases set by farmers’ decisions were advancing, however this was the only phenological group for which the mean advance increased, indicating adaptation. Equally trends in farming phases in spring and summer were attributable to warming in winter and summer, respectively. Leaf coloring and fall was now predominantly delayed (57%) which was attributable to winter and spring warming, too.

Thus, this update of the GCB2006 study demonstrates that there is still a significant and attributable phenological change pattern in Europe, in which number of (significant) trends pointing into the direction of warming increased, but mean trend strength mostly decreased, probably due to a lack of chilling and smaller forcing trends. More attention should be paid to the inherent variability of trends with traits / species groups, season and time triggering divers (e.g. ecological) consequences of these phenological shifts. Still existing differences between the generative period of crops and wild species as well as between the farming season and the general growing season call for more research in this area.

How to cite: Menzel, A., Yuan, Y., Matiu, M., Sparks, T. H., Scheifinger, H., Gehrig, R., and Estrella, N.: Phenological changes in Europe are still attributable to climate change induced warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18551, https://doi.org/10.5194/egusphere-egu2020-18551, 2020.