Key message Leaf CA measurement should take into account angle variation during measurement time. Leaf wettability of common deciduous forest plants is characterized by wetting contact angles ranging from 60° to 140° with a significant variation between species of the same family. Abstract Leaf wettability is an important phenomenon that has an influence on several processes such as the hydrological cycle, plant pathogen growth, or pollutant and pesticide absorption/deposition. The main objective of this research was to investigate the leaf wettability differences of 19 species (16 trees and 3 shrubs) of deciduous plants commonly occurring in Polish forests (temperate climate). The measurements were gathered as follows: 20 undamaged leaves were selected for each species and the wettability was determined by contact angle measurements with an optical goniometer CAM 100 using the sessile drop method. The contact angle was measured with 1-s intervals during 2 min from droplet deposition on adaxial and abaxial leaf surface. Laboratory analyses were completed during the summer of 2016 during full vegetation growth. A general CA decrease with time was observed on both leaf sides. The contact angle values ranged from 60° to 140° depending on species and leaf side. Differences between contact angle values at the beginning and the end of measurement reached 23.6° and engendered changes of wetting classes for some species. In many cases, no wettability class change was observed despite a CA lowering of 20°. The abaxial side was found to be the more repellent for 14 out of 19 species. Altogether, the leaves were classified from highly wettable to highly non-wettable, probably depending on the plant-survival strategy.

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.

Spatial and temporal variability of the interception in the natural wetland valley, the lower Biebrza basin case study. The paper presents the research carried out in the lower basin of Biebrza River valley in order to identify interception for natural wetland plant communities. Maximum interception, i.e. the largest amount of water, expressed in millimeters, which can be captured and retained by plant canopy from rainfall is one of the key parameters of the water cycle modeling. Maximum interception was determined based on the difference of the masses of wet and dry fresh plant samples. Collection of plant material samples took place during the five measurement sessions, which began immediately after the flood recedes, and then lasted until the end of the growing season. Interception spatial variability was analyzed on the basis of the results of maximum interception measured for selected plant aggregations in the different sampling points. The obtained values were extrapolated to the area of the lower basin of Biebrza River using vegetation map of the Biebrza National Park. By conducting a test sessions in the five coming months, the maps of the spatial variability also show changes over time. Methodology used in the described tests allowed for obtaining of satisfactory results. They present, in a correct way, variation occurring between the plant aggregations due to their morphology. In most cases the results are consistent with data from the literature. As results of the analysis of spatial variability of the maximum interception, the highest values were found for the plant communities located in the immediate vicinity of the river channel. With the increase of the distance from river towards the valley edges the maximum interception values decrease. These changes can be seen in the form of strips parallel to the river channel, which corresponds to the plant zones. Obtained map of spatial variability of the maximum interception, which is the results of extrapolation of the values assigned to plant communities, has a high correlation with the map resulting from the analysis of satellite images

In this study we develop a spatial model for interception capacity of vegetation based on LiDAR data. The study is conducted in the natural wetland river valley dominated meadows, reeds and small bushes. The multiple regression model was chosen to relate the field measurements of interception capacity and LiDAR statistics at 2m grid. The optimal model was chosen by stepwise selection and further manual variables selection resulting in the r 2 of 0.52 and the residual standard error of 0.27 mm. The model preserved the vegetation pattern spatially and showed reasonable estimates for both vegetation covered and not covered by field sampling. The model was, however, affected by LiDAR measurements corrupted by river inundation. The results show good perspective for using LiDAR data for interception capacity estimation.

Modelling groundwater depths in floodplains and peatlands remains a basic approach to assessing hydrological conditions of habitats. Groundwater flow models used to compute groundwater heads are known for their uncertainties, and the calibration of these models and the uncertainty assessments of parameters remain fundamental steps in providing reliable data. However, the elevation data used to determine the geometry of model domains are frequently considered deterministic and hence are seldom considered a source of uncertainty in model-based groundwater level estimations. Knowing that even the cutting-edge laser-scanning-based digital elevation models have errors due to vegetation effects and scanning procedure failures, we provide an assessment of uncertainty of water level estimations that remain basic data for wetland ecosystem assessment and management. We found that the uncertainty of the digital elevation model (DEM) significantly influenced the results of the assessment of the habitat's hydrological conditions expressed as groundwater depths. In extreme cases, although the average habitat suitability index (HSI) assessed in a deterministic manner was defined as 'unsuitable', in a probabilistic approach (grid-cell-scale estimation), it reached a value of 40% probability, signifying 'optimum' or 'tolerant'. For the 24 habitats analysed, we revealed vast differences between HSI scores calculated for individual grid cells of the model and HSI scores computed as average values from the set of grid cells located within the habitat patches. We conclude that groundwater-modelling-based decision support approaches to wetland assessment can result in incorrect management if the quality of DEM has not been addressed in studies referring to groundwater depths.