ABSTRACT Matter and energy flux dynamics of wetlands are important to understand environmental processes that govern biosphere-atmosphere interactions across ecosystems. This study presents analyses about hourly interaction between wind speed and energy fluxes in Brazilian Wetlands - Mato Grosso - Brazil. This study was conducted in Private Reserve of Natural Heritage (PRNH SESC, 16º39'50''S; 56º47'50''W) in Brazilian Wetland. According to Curado et al. (2012), the wet season occurs between the months of January and April, while the June to September time period is the dry season. Results presented same patterns in energies fluxes in all period studied. Wind speed and air temperature presented same patterns, while LE was relative humidity presented inverse patterns of the air temperature. LE was predominant in all seasons and the sum of LE and H was above 90% of net radiation. Analyses of linear regression presented positive interactions between wind speed and LE, and wind speed and H in all seasons, except in dry season of 2010. Confidence coefficient regression analyses present statistical significance in all wet and dry seasons, except dry season of 2010, suggest that LE and H had interaction with other micrometeorological variables.

ABSTRACT The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that rule the biosphere-atmosphere interactions of all ecosystems. This study provides an analysis of the interaction of energy in the form of photosynthetically active radiation (PAR) in the Pantanal, a Brazilian wetland forest, by studying the variation of PAR reflectance and its interaction with local rainfall. The study site is located in Private Reserve of Natural Heritage, Mato Grosso State, Brazil, where the vegetation is a monodominant forest of Vochysia divergens Phol. The results showed a high correlation between the reflection of visible radiation and rainfall; however, the behavior was not the same at the three heights studied. An analysis of the hourly variation of the reflected waves also showed the seasonality of these phenomena in relation to the dry and rainy seasons. A predictive model for PAR was developed with a neural network that has a hidden layer, and it showed a determination coefficient of 0.938. This model showed that the Julian day and time of measurements had an inverse association with the wind profile and a direct association with the relative humidity profile.