The FAO-56 Penman-Monteith (PMF56) model is accepted as the standard method for estimating reference crop (grass) evapotranspiration. However, poor data availability limits the use of this method in many regions. This study aimed to overcome this limitation (1) by evaluating the goodness-of-fit of selected simple evapotranspiration methods in relation to the PMF56 model and (2) by adjusting four of these models and developing a multiple regression equation for conditions in Poland using the PMF56 model as a reference. Using daily meteorological data, four simple models were calibrated by the generalised reduced gradient (GRG) method; moreover, a multiple regression equation was developed on the basis of Cochrane-Orcutt estimation because the error term of the ordinary least squares model was autocorrelated. When radiation data were lacking and sunshine duration data were available, the PMF56 method with a calibrated Ångström-based estimation of solar radiation was the most accurate method (relative error, RE = 0.096, for Sulejów). When neither radiation nor sunshine duration data were included in the dataset, the calibrated Penman method performed best (RE = 0.254). Moreover, when only temperature or pan evaporation data were available, the calibrated Hargreaves method provided the most accurate results (RE = 0.275). Much poorer results were obtained using the calibrated pan coefficient method (RE = 0.435). Ultimately, we developed a multiple regression method in this study that exhibited good performance (RE = 0.170). Taking into account the data limitations, the methods calibrated in this study are recommended for estimating evapotranspiration in Central Europe. This approach would substantially decrease the errors produced by the recommended non-calibrated equations.

A significant increase in summer temperatures has been observed for the period 1996–2011 in Poznań, Poland. The phenological response of four weed taxa, widely represented by anemophilous species (Artemisia spp., Rumex spp. and Poaceae and Urticaceae species) to this recent climate warming has been analysed in Poznań by examining the variations in the course of airborne pollen seasons. Pollen data were collected by 7-day Hirst-type volumetric trap. Trends in pollen seasons were determined using Mann–Kendall test and Sen’s slope estimator, whereas the relationships between meteorological and aerobiological data were established by Spearman’s rank correlation coefficient. Significant trends in pollen data were detected. The duration of pollen seasons of all analysed taxa increased (from +2.0 days/year for Urticaceae to +3.8 days/year for Rumex), which can be attributed to a delay in pollen season end dates rather than earlier start dates. In addition, the intensity of Artemisia pollen seasons significantly decreased and correlates with mean July–September daily minimum temperatures (r = −0.644, p < 0.01). In contrast, no significant correlations were found between temperature and characteristics of Rumex pollen seasons. The results of this study show that observed shifts in weed pollen seasons in Poznań, i.e. longer duration and later end dates, might be caused by the recorded increase in summer temperature. This influence was the strongest in relation to Artemisia, which is the taxon that flowers latest in the year. The general lack of significant correlations between Rumex and Urticaceae pollen seasons and spring and/or summer temperature suggests that other factors, e.g. land use practices, could also be partially responsible for the observed shifts in pollen seasons.

Forest undergrowth plants are tightly connected with the shady and humid conditions that occur under the canopy of tropical forests. However, projected climatic changes, such as decreasing precipitation and increasing temperature, negatively affect understory environments by promoting light‐demanding and drought‐tolerant species. Therefore, we aimed to quantify the influence of climate change on the spatial distribution of three selected forest undergrowth plants, Dracaena Vand. ex L. species, D. afromontana Mildbr., D. camerooniana Baker, and D. surculosa Lindl., simultaneously creating the most comprehensive location database for these species to date.A total of 1,223 herbarium records originating from tropical Africa and derived from 93 herbarium collections worldwide have been gathered, validated, and entered into a database. Species‐specific Maxent species distribution models (SDMs) based on 11 bioclimatic variables from the WorldClim database were developed for the species. HadGEM2‐ES projections of bioclimatic variables in two contrasting representative concentration pathways (RCPs), RCP2.6 and RCP8.5, were used to quantify the changes in future potential species distribution.D. afromontana is mostly sensitive to temperature in the wettest month, and its potential geographical range is predicted to decrease (up to −63.7% at RCP8.5). Optimum conditions for D. camerooniana are low diurnal temperature range (6–8°C) and precipitation in the wettest season exceeding 750 mm. The extent of this species will also decrease, but not as drastically as that of D. afromontana. D. surculosa prefers high precipitation in the coldest months. Its potential habitat area is predicted to increase in the future and to expand toward the east.This study developed SDMs and estimated current and future (year 2050) potential distributions of the forest undergrowth Dracaena species. D. afromontana, naturally associated with mountainous plant communities, was the most sensitive to predicted climate warming. In contrast, D. surculosa was predicted to extend its geographical range, regardless of the climate change scenario.

Birch trees are abundant in central and northern Europe and are dominant trees in broadleaved forests. Birches are pioneer trees that produce large quantities of allergenic pollen efficiently dispersed by wind. The pollen load level depends on the sizes and locations of pollen sources, which are important for pollen forecasting models; however, very limited work has been done on this topic in comparison to research on anthropogenic air pollutants. Therefore, we used highly accurate aerial laser scanning (Light Detection and Ranging—LiDAR) data to estimate the size and location of birch pollen sources in 3-dimensional space and to determine their influence on the pollen concentration in Poznań, Poland. LiDAR data were acquired in May 2012. LiDAR point clouds were clipped to birch individuals (mapped in 2012–2014 and in 2019), normalised, filtered, and individual tree crowns higher than 5 m were delineated. Then, the crown surface and volume were calculated and aggregated according to wind direction up to 2 km from the pollen trap. Consistent with LIDAR data, hourly airborne pollen measurements (performed using a Hirst-type, 7-day volumetric trap), wind speed and direction data were obtained in April 2012. We delineated 18,740 birch trees, with an average density of 14.9/0.01 km², in the study area. The total birch crown surface in the 500–1500 m buffer from the pollen trap was significantly correlated with the pollen concentration aggregated by the wind direction (r = 0.728, p = 0.04). The individual tree crown delineation performed well (r² ≥ 0.89), but overestimations were observed at high birch densities (> 30 trees/plot). We showed that trees outside forests substantially contribute to the total pollen pool. We suggest that including the vertical dimension and the trees outside the forest in pollen source maps have the potential to improve the quality of pollen forecasting models.