TY - JOUR A1 - Blume, Theresa A1 - Schneider, Lisa A1 - Güntner, Andreas T1 - Comparative analysis of throughfall observations in six different forest stands BT - Influence of seasons, rainfall- and stand characteristics JF - Hydrological processes N2 - Throughfall, that is, the fraction of rainfall that passes through the forest canopy, is strongly influenced by rainfall and forest stand characteristics which are in turn both subject to seasonal dynamics. Disentangling the complex interplay of these controls is challenging, and only possible with long-term monitoring and a large number of throughfall events measured in parallel at different forest stands. We therefore based our analysis on 346 rainfall events across six different forest stands at the long-term terrestrial environmental observatory TERENO Northeast Germany. These forest stands included pure stands of beech, pine and young pine, and mixed stands of oak-beech, pine-beech and pine-oak-beech. Throughfall was overall relatively low, with 54-68% of incident rainfall in summer. Based on the large number of events it was possible to not only investigate mean or cumulative throughfall but also its statistical distribution. The distributions of throughfall fractions show distinct differences between the three types of forest stands (deciduous, mixed and pine). The distributions of the deciduous stands have a pronounced peak at low throughfall fractions and a secondary peak at high fractions in summer, as well as a pronounced peak at higher throughfall fractions in winter. Interestingly, the mixed stands behave like deciduous stands in summer and like pine stands in winter: their summer distributions are similar to the deciduous stands but the winter peak at high throughfall fractions is much less pronounced. The seasonal comparison further revealed that the wooden components and the leaves behaved differently in their throughfall response to incident rainfall, especially at higher rainfall intensities. These results are of interest for estimating forest water budgets and in the context of hydrological and land surface modelling where poor simulation of throughfall would adversely impact estimates of evaporative recycling and water availability for vegetation and runoff. KW - forest hydrology KW - forest stand characteristics KW - interception KW - leaf area KW - index KW - rainfall characteristics KW - seasonal effects KW - stratified event KW - analysis KW - throughfall KW - tree species effects Y1 - 2021 U6 - https://doi.org/10.1002/hyp.14461 SN - 0885-6087 SN - 1099-1085 VL - 36 IS - 3 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Arvidsson, Samuel Janne A1 - Perez-Rodriguez, Paulino A1 - Müller-Röber, Bernd T1 - A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects JF - New phytologist : international journal of plant science N2 - To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes. The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times. Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9. We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions. KW - development KW - growth KW - leaf area KW - modeling KW - phenotyping Y1 - 2011 U6 - https://doi.org/10.1111/j.1469-8137.2011.03756.x SN - 0028-646X VL - 191 IS - 3 SP - 895 EP - 907 PB - Wiley-Blackwell CY - Malden ER -