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Tropical hydroelectric reservoirs generally constitute an appreciable source of CH4 (methane), a potent greenhouse gas. In this letter, we investigate the statistical characteristics of methane ebullition fluxes in hydroelectric reservoirs. To this end, we use CH4 flux measurements obtained in Manso (wet season, 2004) and Corumbá (dry and wet seasons, 2005) reservoirs, located respectively in Mato Grosso and Goiás, Brazil. Methane ebullition fluxes were measured using open dynamic chambers, connected to an infrared photo‐acoustic trace gas analyzer (TGA). Our main result indicates that when properly rescaled, all methane ebullition data collapse into a single statistic well described by a Generalized Pareto distribution, with shape parameter well above zero. The approach presented here, which combines high‐frequency CH4 ebullition data and Extreme Value theory analytical tools, shows that, although bubbling patterns appear to be highly complex and unpredictable, they may still be described by a rather simple (but non trivial) dynamics.

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In this work, two new entropic regularization techniques are introduced. They represent a generalization of the standard MaxEnt regularization method, and allow for a greater flexibility for introducing any prior information about the expected structure of the true physical model, or its derivatives, into the inversion procedure. The first technique is based on the minimization of the entropy of the vector of first-differences of unknown parameters. Adopting standard terminology, it is known as the minimum first-order entropy method (MinEnt-1). To illustrate the essential feature of the method, MinEnt-1 is applied to the reconstruction of two-dimensional geoelectric conductivity distributions from magnetotelluric data. The second technique is based on the maximization of the entropy of the vector of second-differences of the unknown parameters, and is denoted as the MaxEnt-2 method. The MaxEnt-2 method is applied to the retrieval of vertical profiles of temperature in the atmosphere from remote sensing data.