Petra Dížková

Phenological shifts represent one of the most evident biological responses to climate change. This study used the two-band Enhanced Vegetation Index derived from Moderate Resolution Imaging Spectroradiometer satellite data to analyze changes in the growing seasons of arable land, broad-leaved forest, coniferous forest, and grassland across the central European region from 2000 to 2022. Phenological metrics included the start, end, and length of the growing season. Nonparametric Theil–Sen regression indicated an earlier start of the season (median of trends for all land cover classes ranging from -9.3 to -17.5 days per decade), a later end of the season (ranging from +8.1 to +11.4 days per decade), and consequently longer length of the season (ranging from +13.3 to +25.0 days per decade), with advancing start of the season identified as the primary driver of length of the season prolongation. The most pronounced changes across all phenological metrics were associated with arable land. Elevation zone-based analysis showed that the highest median of trends of start and length of the season was found for arable land and coniferous forest in elevations below 600 m a.s.l., with values decreasing progressively with increasing elevation. The analysis of environmental zones revealed a higher median of trends of the start and length of the season in cool-moist and warm-mesic zones, while end-of-season-related changes were more pronounced in cold-wet zones. This study demonstrates coherent long-term trends in vegetation phenology across different land cover classes, with stronger acceleration over arable land, highlighting potential implications for agricultural systems.

Spring phenology in temperate trees is strongly controlled by air temperature, yet the role of short-term thermal anomalies preceding phenological events remains insufficiently understood. In our previous analyses of silver birch (Betula pendula) phenology in Poland, we showed that the timing of both ground-observed leaf unfolding (LU) and satellite-derived start of season (SOS) was closely related to temperature anomalies in March and April, and that not only the magnitude but also the within-season timing of warming played an important role in shaping spring onset. Preliminary anomaly-based analyses further suggested that the period of approximately 20 days preceding leaf-out may be particularly important. However, contrasting early-spring seasons indicated that similar phenological outcomes may arise from different thermal trajectories, including both sustained warming and short but intense late-season warm pulses.In this study, we revisit these relationships using an extended dataset comprising LU observations for 2007–2026 and SOS estimates for 2018–2026. LU data were obtained from the Institute of Meteorology and Water Management – National Research Institute, while SOS was derived from Sentinel-2 imagery using the Enhanced Vegetation Index. Temperature conditions were assessed from in-situ measurements and a high-resolution station-based gridded dataset. We focus specifically on pre-event temperature anomalies, examining their magnitude, persistence, and timing in relation to the observed dates of spring onset. Rather than relying solely on calendar-month means, we analyse temperature anomalies in moving windows preceding LU and SOS to identify the period of greatest phenological sensitivity and to test whether exceptionally early spring onset is more strongly associated with sustained positive anomalies or with brief but intense warm episodes occurring shortly before the event.By combining long-term ground observations with satellite-based estimates, this study provides a more process-oriented perspective on spring phenology and its thermal controls. The results are expected to improve our understanding of how silver birch responds to anomalous thermal conditions and to help refine interpretations of phenological sensitivity under ongoing climate warming.

Ecosystem models provide a valuable tool to quantitatively describe the complex interlinked processes at the soil-plant-atmosphere interface. When combined with measurements, the well-structured process-based models allow to integrate the observed data in a framework where the individual variables gain higher interpretability and can better contribute to understanding of the complex ecosystem responses to a wide range of environmental conditions. Moreover, the models can be then applied to upscale the observed data to larger spatial scales, can be used for simulation under different (e.g. future) climatic conditions and atmospheric composition, or can be used for testing the impacts of changes in management, land use change, disturbances, etc. Biome-BGCMuSo is a widely used, popular biogeochemical model that simulates the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems. In this contribution, Biome-BGCMuSo will be evaluated at several sites representing agricultural and forest ecosystems in the Czech Republic and Austria. These sites are equipped with eddy-covariance measurements of CO2, water and energy fluxes including other ancillary measurements. The main emphasis will be put on evaluation of gross and net primary productivity, net ecosystem exchange, transpiration, evaporation and soil water content dynamics. The robustness of the phenological submodule parameterization will be further examined using remotely sensed leaf area index data along the altitudinal gradient. The evaluation and model parameterization provides a first step in the wider effort in which the Biome-BGCMuSo will be integrated together with measurements of CO2 and other greenhouse gases concentrations within an atmospheric inversion system aiming to understand the spatial distribution of the greenhouse gases fluxes (i.e., sources and sinks) and their temporal dynamics. This knowledge is crucial for enhancing climate change mitigation strategies. Acknowledgment: This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic (grant AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635).

Using remote sensing data to assess plant phenology is a useful method for monitoring areas at larger spatial scales. One of the most widely used methods for analyzing changes in the timing of the growing season is the employment of vegetation indices and phenological metrics (phenometrics) derived from them. Using phenometrics, it is possible to evaluate changes in the growing season for different land covers and different environmental conditions not only at the local level but also in larger areas. At the larger scales, the meteorological conditions and especially their influence on plant phenology can be more diverse. For the determination of basic phenometrics (the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LGS)), we used Enhanced Vegetation Index2 (EVI2) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Phenometrics and their trends were calculated for four different land covers (arable land, broad-leaved forest, coniferous forest, and grassland) in the time period from 2000 to 2022 in the central European region. Furthermore, the changes in the timing of the growing season were calculated in different altitude and environmental zones and their trends were compared with meteorological variables (e.g., minimum and maximum air temperature, length of day, global radiation, precipitation, soil moisture, etc.). Based on this analysis, the main factors influencing phenological changes for different land covers were evaluated in different environmental conditions.Acknowledgment: This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic (grant AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635).

The most reliable method for monitoring plant phenology and changes in vegetation development is ground observation (in-situ). However, this method is difficult to implement on a larger spatial scale. For that reason, remote sensing data have begun to be used to quantify vegetation phenology in recent decades. Information about phenology is derived from remote sensing data using ie. phenological metrics. In this study, we used 3 base metrics - the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LGS) for evaluating changes in the phenology of four land covers (coniferous forest, broadleaf forest, field, and grassland) in the European region (approx. 30 countries). For their determination, Enhanced Vegetation Index2 (EVI2) was used based on images from The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Satellite images were obtained at a spatial resolution of 5 x 5 km in the time period from 2000 to 2022. For four land covers within the 22-year period, it was evaluated that the SOS significantly shifted to an earlier date in about half of the evaluated states. The EOS significantly shifted to a later date in almost all evaluated states. Based on these results, we evaluated the whole length of the growing season and found a significant shift to a later date also in almost all evaluated states. Acknowledgement: This work was supported by an individual IGA project - Determination of vegetation time and its spatiotemporal variability using remote sensing of the Earth (AF-IGA2022-IP-067) and by SustES project – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797), which provided essential data and software access.

Climate change affects the agroecological conditions and persistence of cherry tree flowering. Detailed evaluation of minimum air temperature and occurrence of synoptic events occurrence during spring frosts within the cherry tree flowering in the Czech Republic (Central Europe) is missing. The main objective of this study was to evaluate the above-mentioned variables during the cherry tree flowering in different parts of the country from 1924 to 2012. Our question was how the frequency of frost days occurrence changed during the cherry tree flowering. A trend analysis was conducted with the Mann-Kendall test. The onset of the beginning of flowering and end of flowering shifted to an earlier date per the whole examined period (up to −13.9 and −8.1 days) and the period of flowering extended (up to 4.1 days). The shifts were more pronounced at higher elevations. During the period of the cherry tree flowering, the trend in change of the number of frost days was negative at the lowland station (−0.3 day) and positive at the highland station (+1.2 day). At all stations, “Ap3” synoptic event (anticyclone) occurrence during cherry tree flowering on days with the highest spring frost risk (T_min at 2 m < −1.1 °C) prevailed. The positive trend of frost-day occurrence and the negative trend of minimum air temperature in cherry tree flowering indicate that blossoms are more endangered at higher elevations.

The phenological phases of field crops have shifted to earlier times in the Czech Republic in recent decades; additionally, they have shown correlations with temperatures from previous spring months. Using a thermal time model called PhenoClim, the correlations between temperatures and phenophases allow us to evaluate the strongest predictors (i.e., maximum temperature) and indicators of base temperatures and growing degree days for the selected phenophases of winter wheat (Triticum aestivum L.). With the help of this model, it is possible to explain 0.6–0.82% of the phase variability and to estimate the onset of phenophases for the selected time period and stations (with the RMSE values of 9.4 days for jointing, 4.3 days for heading, and 5.3 days for full ripeness). To further refine the modeled onsets of phenophases, we used satellite data, specifically the normalized difference vegetation index and the enhanced vegetation index 2 from MODIS; based on these vegetation indices, the start of the growing season (SOS) was determined. After including SOS to model PhenoClim, we modeled the onsets of phenophases, with average accuracies ranging from 6.2 to 15.2. By combining the thermal time model and remote sensing data, specifically the data concerning the determination of SOS, we can refine the modeling of the onset of full ripeness in some locations.

The present study analyses 61 years of phenological observations (1961–2021) of five herb, five shrub, four tree, and one bird species representing the prevalent spring species of floodplain forest ecosystems in the Czech Republic, central Europe. The in situ observations were conducted at the Vranovice site (48°48′ N, 16°46′ E, 170 m above mean sea level) representing the Plaček’ forest National Reserve. The observed plants and bird species showed statistically significant (p < 0.05) shifts in phenological terms to an earlier date of the year, but the rate of the shift among the observed species differed. The most progressive shifts were detected for the herbs (14 days), followed by the shrubs (13 days), trees (9 days), and finally by the bird species (8 days). All the phenophases were significantly correlated with the daily maximum temperature (r = 0.72–0.91). The results also showed a decline in the correlation for species among the phenophases of the herbs and trees. The phenophases that were highly correlated in the past were less correlated and had higher variability in the last decades. We conclude that the phenological response of the ecosystem to warming in the spring resulted in higher variability and a lower correlation among the observed phenophases mainly caused by the most expressive phenological shifts of the early herbs.

Winter oilseed rape (Brassica napus) is one of the most cultivated oilseeds in the Czech Republic and belongs among major pollen allergens. Pollen allergies have an extensive clinical impact worldwide, and as well as in the Czech Republic. In this paper, meteorological variables such as mean air temperature, maximum and minimum air temperature, precipitation total and number of rainy days in the period 1991–2012 were studied using the PhenoClim phenological model to find the best predictor of the beginning of flowering of the Brassica napus in the Czech Republic. In addition, temporal and spatial evaluations of the beginning of flowering of the Brassica napus were examined at individual stations in different climatic zones within the same period. In total, three phenological stations at altitudes from 270 m asl to 533 m asl located in warm (W2), medium warm (MW7) or cold (C7) climatic zones were used for detailed evaluation. Based on the observation results at selected stations, the beginning of flowering of the Brassica napus advanced progressively in timing (nearly −15 days) in the 1991–2012 period. The base temperature and temperature sums were calculated for the beginning of flowering of the winter oilseed rape using the PhenoClim computer tool. As the most accurate predictor for the beginning of flowering of the Brassica napus, the mean air temperature was determined. The optimal start day for calculation was 30th January, the threshold (base temperature) was 6.0 °C and the temperature sum was 157.0 °C. The RMSE value was 4.77 and the MBE value was −3.00. The simulated data had a good correlation with the real observed data (the correlation coefficients were within the range from 0.56 to 0.76), and the PhenoClim model results indicate using them in the forecast modeling of the beginning of flowering of the Brassica napus in the Czech Republic.