RESUMEN
Soil respiration (CO2 emission to the atmosphere from soils) is an important component of the global carbon cycle. In highly seasonal ecosystems the magnitudes and the underlying mechanisms that control soil respiration (RS) are still poorly understood and measurements are underrepresented in the global flux community. In this dataset, systematic and monthly measurements of RS were conducted with an infrared gas analyzer coupled to a static chamber during 2015, 2016, 2017 and 2019 in a tropical dry forest with a land use history from Northwestern México. These data is useful to assess the intra-annual and seasonal variations of RS at a highly seasonal dry forests and serves as a base line to benchmark soil carbon models in regional and global contexts. The data presented supports the research manuscript: "Soil respiration is influenced by seasonality, forest succession and contrasting biophysical controls in a tropical dry forest in Northwestern Mexico" from Vargas-Terminel et al. [1].
RESUMEN
Mobile monitoring platforms (MMP) are popular in air quality studies. One application of MMP is in estimating pollutant emissions from area sources. The MMP is used to measure concentrations of the relevant species at several locations around the area source, while the associated meteorological information is measured at the same time. Emissions from the area source are inferred by fitting the measured concentrations to estimates from dispersion models. These models require meteorological inputs, such as the kinematic heat flux and the surface friction velocity, that are best computed with measurements of time resolved velocity and temperature made with 3-D sonic anemometers. Because the setting up and dismantling of a 3-D sonic anemometer is not compatible with the necessary mobility of the MMP, it is useful to use alternative instrumentation and methods that provide accurate estimates of these inputs. In this study, we demonstrate such a method based on measurements of horizontal wind speed and temperature fluctuations at a single height. The method was evaluated by comparing methane emissions from a dairy manure lagoon inferred from a dispersion model that uses modeled meteorological inputs to those inferred from measurements with 3-D sonic anemometers. The emission estimates from the modeled meteorological inputs were close to those based on measurements made with 3-D sonic anemometers. We then demonstrate how this approach can be adapted for mobile platform applications by showing that winds measured using a 2-D sonic anemometer and temperature fluctuations measured with a bead thermistor, which can all be carried or mounted on a MMP, yields results that are close to those from a 3-D sonic anemometer.