Assessing the impact of meteorological drivers and within-footprint spatial heterogeneity on fluxes at a forest ecosystem site
Year: 2025
Type: conference-paper
Abstract: Subalpine coniferous forests are critical in absorbing atmospheric carbon dioxide ( CO2 ) and in contributing to global terrestrial evapotranspiration. Eddy Covariance (EC) towers are usually used to capture temporal but not spatial variations of energy and matter fluxes within an ecosystem, although it would be possible when a heterogeneous footprint is present. In this study, we assessed the effect of within-footprint heterogeneity on Latent Heat (LE) and CO2 Net Ecosystem Exchange (NEE) and we exploited this information to quantify the relative contribution of forest structure and meteorological drivers on observed fluxes. We retrieved half-hourly daytime LE and NEE flux data together with air temperature (airT), relative humidity (RH) and shortwave incoming radiation (SW_IN) for two summer months (Jul-Aug 2022) at a strongly heterogeneous subalpine forest ecosystem located in the Italian Alps at 1735 m a.s.l. (ICOS code: IT-Ren). Based on wind directions the data were grouped into sectors of equal angle (45°) and outer radius (200m) around the flux tower starting from the geographical north. We then calculated tree height and Leaf Area Index (LAI) distributions from Aerial Laser Scanning (flight year: 2020) for each sector. The effects of the meteorological and sector categorical variables were calculated for daytime LE and NEE. Both LE and NEE were mostly explained by SW_IN, which exhibited strong positive effects with LE and strong negative effects with NEE. Increasing airT above 16 °C significantly reduced the CO2 sink capacity, while it showed positive linear effects with LE up to 18° C exhibiting a stabilized effect upon that threshold. RH was not significant for neither LE nor NEE. Both LE and NEE significantly varied based on the sector attribution, showing median differences up to 14 µmol m-2 s-1 of NEE and up to 200 W m-2 of LE between the most and the least productive sector. These results underscore the importance of an EC footprint heterogeneity characterization and the suitability of LiDAR data for describing the spatial dependence of ecosystemic fluxes. Our approach contributes to quantifying the effects of both meteorological and vegetation structure on such fluxes which is mandatory for a correct parameterization of land surface models to predict the exchange of carbon dioxide and energy between the Earth´s surface and the atmosphere.
Source: ARPHA Conference Abstracts
Institution: Free University of Bozen-Bolzano
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Life on land
Open Access status: diamond
OpenAlex ID: W4410826546