This thesis explores the application of Distributed Temperature Sensing (DTS) technology to observe temperature gradients and turbulent eddies within and above a forest vegetation layer. Atmospheric temperature gradients tie directly to heat transfer, a primary mode of which are turbulent eddies. The goal in studying these elements is to enhance the understanding of land-atmosphere coupling, which plays a role in climate and weather modelling. The data collection took place in the first two weeks of November 2022 at a meteorology study site in Loobos, the Veluwe. A fiber optic cable was installed, running from the forest floor to 1.5 times the canopy height along a tower. The DTS technology provided high-resolution temperature measurements at one-second intervals. Fluctuations in these profiles could potentially be associated with turbulent eddies. By analyzing temperature profiles over time and integrating meteorological data, the study provided insights into the temporal evolution of vertical temperature distribution at the test site and its sensitivity to weather conditions. Phenomena such as direct sunlight, rainfall, canopy effects, and inversion layers were identified in the DTS data and validated with other meteorological measurements. Notably, sharp temperature inversions observed on two nights exhibited strong correlations with wind speed and its variation. Increases in wind speed and variability were associated with lower inversion heights.

Evaporation, the transformation of liquid water to vapor, plays a crucial role in forested ecosystems by contributing significantly to the total evaporation through interception evaporation and transpiration. This process is critical in climate models used to forecast both immediate and long-term climatic changes. Yet, accurately measuring and partitioning evaporation in forests presents challenges due to the complex interplay of factors like canopy height and density, vegetation type, and soil characteristics. Properly segmenting total forest evaporation into its key components—interception evaporation, transpiration, and soil evaporation—is vital for enhancing hydrological and climate modeling. Simplifications in current global climate models, such as GLEAM or the EC-Earth3 used by the Royal Netherlands Meteorological Institute, often overlook the essential role of interception evaporation, focusing mainly on transpiration. This research examines a way to partition total evaporation into its fundamental segments. The study’s main objectives were to partition total evaporation into interception evaporation and transpiration, and further into canopy and forest floor interception evaporation. This was accomplished using three methodologies: 1) Eddy Covariance (EC) systems positioned above the canopy to measure total evaporation, with leaf wetness sensors distinguishing between wet and dry canopy states; 2) Analysis of leaf wetness data to quantify canopy interception evaporation; 3) The Bowen Ratio Energy Balance (BR-EB) method to assess overall evaporation and its split into canopy and forest floor components. Selected case days for analysis included scenarios following rain and dew events, with selection criteria based on minimum evaporation thresholds and weather conditions. Results underscored the significant roles of transpiration and interception in total evaporation, affected by environmental dynamics and sensor placement. Notably, sensors at higher canopy levels indicated faster drying and lower interception to transpiration ratios due to increased exposure to environmental factors. Despite employing diverse methodologies, the research did not uncover uniform patterns in evaporation partitioning, pointing to the intricate relationships between environmental conditions and canopy structure. The study pinpointed methodological constraints, such as in the assumptions related to leaf wetness sensor data, which might skew evaporation calculations. Future studies should integrate additional measuring techniques, like sap flow sensors and enhanced BR-EB methods, to improve data accuracy and deepen understanding of forest evaporation dynamics.