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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 13-30, 2018
https://doi.org/10.5194/hess-22-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 22, 13-30, 2018
https://doi.org/10.5194/hess-22-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Jan 2018

Research article | 04 Jan 2018

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Sibylle Kathrin Hassler1,2, Markus Weiler3, and Theresa Blume2 Sibylle Kathrin Hassler et al.
  • 1Karlsruhe Institute of Technology (KIT), Institute of Water and River Basin Management, Chair of Hydrology, Karlsruhe, Germany
  • 2Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany
  • 3Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany

Abstract. Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil–vegetation–atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls.

We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls.

Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal dynamics of the explanatory power of the tree-specific characteristics, especially species, are correlated to the temporal dynamics of potential evaporation. We conclude that transpiration estimates on the landscape scale would benefit from not only consideration of hydro-meteorological drivers, but also tree, stand and site characteristics in order to improve the spatial and temporal representation of transpiration for hydrological and soil–vegetation–atmosphere transfer models.

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We use sap velocity measurements from 61 trees on 132 days to gain knowledge about the controls of landscape-scale transpiration, distinguishing tree-, stand- and site-specific controls on sap velocity and sap flow patterns and examining their dynamics during the vegetation period. Our results show that these patterns are not exclusively determined by tree characteristics. Thus, including site characteristics such as geology and aspect could be beneficial for modelling or management purposes.
We use sap velocity measurements from 61 trees on 132 days to gain knowledge about the controls...
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