Estudo sobre os padrões do balanço de energia e carbono em uma área de caatinga regenerada.

Abstract

The Caatinga is a vegetation that is very susceptible to the degradation process, which could evolve into desertification, which is why understanding and simulating the dynamics in a given area that has the presence of this biome and which has undergone a degradation process, causing a loss in productivity, but which has managed to recover is of great importance. In this context, this work seeks to study the meteorological variables, the energy and carbon balance components in a regenerated Caatinga environment, using the turbulent vortex covariance technique and the biophysical soil-vegetation-atmosphere transfer model (SVAT) as an alternative tool to simulate the CO2 components (gross primary production, GPP; ecosystem respiration, Reco; and net ecosystem CO2 exchange, NEE) and energy balance, for which the years 2014 and 2016 were used. Initially, a study of meteorological variables was carried out: air temperature, relative humidity and rainfall, using INMET automatic station.Data from a micrometeorological tower located in the municipality of Campina Grande, Paraíba, was used to study the energy and carbon balance. The results showed that the dynamics of the CO2 flux components varied depending on the magnitude and distribution of rainfall and, consequently, the variability of vegetation cover. Even during the dry season, NEE was in balance and the Caatinga acted as an atmospheric carbon sink during the years studied. The GPP in 2016 was lower than in 2014, as it ranged from 0.1 to 0.3 gCm²h-1. H in 2014 had a maximum value of around 40 W/m² and a minimum value of 30 W/m² (July), while in 2016 the highest values were around 35 W/m². Both years showed higher H values in the dry season. Meanwhile, LE showed that the highest values ranged from 60 to 100 W/m² in 2014 and from 40 to 60 W/m² in 2016. In both years, these values were found in the period with the highest rainfall, due to the greater availability of water. For the modeling, the year 2014 was used and, with this, the model was able to satisfactorily simulate H, LE, GPP and NEE. In addition, in general, it managed to capture the seasonality of LE and the values, especially in the dry season. The same occurred with H, where it managed to capture the partitioning of available energy after calibration. The model was also able to capture GPP and NEE values after calibration. It is therefore important to take vegetation changes into account.