How Landscape Characteristics Influence Spatial Patterns of Transpiration

Abstract:

Quantifying transpiration in landscapes remains a challenging task. Especially bridging the gap between tree- or plot-scale measurements and information on the landscape scale which could be gathered from remote sensing, digital elevation models or forest inventories still poses considerable problems. These problems reach from errors associated with the measurements to the reliability of representing transpiration amounts by large-scale data.

In this study we analyse spatial patterns of sap velocity to identify the importance of tree- or site-specific characteristics for transpiration at the landscape scale. We set up multiple linear regression models for a dataset of daily sap velocities for 61 trees at 24 locations in mixed beech and oak forests in a catchment in Luxemburg, recorded during the growing season of 2014. As predictors we use the tree-specific characteristics species, diameter and height and the site-specific characteristics basal area and number of stems for the respective stands as well as landscape attributes such as aspect, slope position and geology. Analysing the importance of these predictors could be useful for upscaling tree-based measurements to the landscape-scale based on data from digital elevation models, forest inventories or remote sensing. We also assess the temporal dynamics of the importance of tree- vs. site-specific predictors and link them to typical controls for sap flow such as atmospheric demand and soil moisture.

First results indicate that site-specific predictors contribute considerably to the explained variance of the linear models. However, remotely sensed information explained very little of the variation in daily sap velocity patterns. Further analyses will quantify to which extent we can use the landscape-scale information from digital elevation models, geology and forest inventories to upscale tree-based transpiration estimates.