Andrews, Ryan S (2016). The Temporal Variation of Vertical Micrometeorological Profiles in a Lower Montane Tropical Forest. Master’s thesis, Texas A & M University. http://hdl.handle.net/1969.1/157148
Earth system models recently began to implement a multilevel canopy modeling approach to represent vertical variation in biophysical and microclimate parameters. To inform such modeling efforts, this study seeks to characterize the variability and causal relationships between vertical sub-canopy profiles of meteorological variables in a tropical montane forest. Variability of CO2 and H2O concentrations, photosynthetically active radiation (PAR), leaf wetness percentage, temperature, and vapor pressure deficit was analyzed over a range of time scales using one year of continuously collected data. Seasonal, monthly, diurnal, and individual precipitation event time scales were all used to determine how patterns in vertical profile variability change with time scale. Additionally, consideration was given to trace gas transport mechanisms as eddy flux, vertical advection, and storage fluxes were used to determine diurnal average net transfers. Variations in CO2 concentration (382 and 372 μmol mol^-1) profiles between two months with similar PAR (64.0±1.5 and 60.9±1.7 μmol m^-2 s^-1) suggest that plant stomata may limit water loss in the dry season despite continuous water availability. The maximum diurnal PAR value of 263 μmol m^-2 s^-1, occurring at 10:00, indicates that slope aspect strongly influences the light regime in a montane forest sub-canopy. Rainfall and subsequent leaf wetness were also shown to affect most micrometeorological processes at the site, such as causing an increase in CO2 concentration (3.35 μmol mol^-1 mean increase) with canopy wetting. These results indicate that significant error in modeled precipitation, even at sub-daily time steps, could change magnitude and direction of trace gas movements. The data presented should be a valuable tool for validation of existing multilevel canopy models and aid in development of future model improvements.