Practically all records of eddy-covariance flux measurements are affected by gaps, caused by several reasons. In this work, we propose analog period (AP) methods for gap-filling, and test them for filling gaps of latent heat flux at five AmeriFlux sites.The essence of the methods is to look for periods in the record that bear a strong resemblance, in the variable to be filled, to the periods immediately before and after the gap. Similarity between periods is gauged by the coefficient of determination, and the search for similar periods and their ranking is straightforward. The methods are developed in a univariate version (that uses only the latent heat flux data series itself) and several multivariate ones, that incorporate sensible heat flux, ground heat flux and net radiation data. For each set of independent variables used for gap-filling, the methods are tested against an existing gap-filling procedure with similar data requirements. Thus, the univariate version is tested against the mean diurnal variation method, and the multivariate versions are tested against corresponding simple and multiple linear regression techniques that use energy-budget data, and in one case the evaporative fraction as well. In our tests, the proposed univariate version performs better than the mean diurnal variation method, and the multivariate versions perform better than simple/multiple linear regression methods. An often used available computer package, REddyProc, was also tested as a basis of comparison. In general, the proposed methods (in univariate and multivariate versions) and simple/ multiple linear regressions performed better than REddyProc. For the datasets analysed, gap filling via the evaporative fraction method performed poorly. K E Y W O R D S eddy-covariance method, gap-filling, latent heat flux 1 | INTRODUCTION Continuous time series of measured surface fluxes are important in many applications of the geophysical sciences including spatial flux

Natural and artificial lakes are a common part of the landscape, and essential for human life, in their multiple uses for recreation, water supply for industry, irrigation and domestic use, energy generation, and so on; they also act as "sentinels" and integrators of terrestrial and atmospheric processes (Williamson et al., 2008), and play an important role in the emission of greenhouse gases to the atmosphere (DelSontro et al., 2018). The latent and sensible heat fluxes (and attendant water vapor mass flux) between the water surface of lakes and the atmosphere are needed as boundary conditions for atmospheric models and to quantify water losses. They are also used as boundary conditions in models for the evolution of the water temperature (see Hostetler & Bartlein, 1990), which plays a fundamental control on all biochemical processes occurring in the lake's body.For well-known hydrological and environmental reasons, therefore, reliable lake evaporation estimates remain at the center stage of water resources management, and even more so in the face of increased water demand and scarcity, and climate change (Veldkamp et al., 2017;Wang et al., 2018). Consequently, the need persists for reliable operational estimates of lake evaporation, that is, estimates than can use readily available environmental data and can be applied as widely as possible, at timescales ranging from daily to yearly.It is in the nature of the underlying physical processes, however, that the best flux measurements or model-based estimates are derived from data collected directly above the water surface: the physical basis for this fact is modernly provided by Monin-Obukhov Similarity Theory (MOST) (

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Estimativa de fluxo de calor latente em reservatórios através de uma rede neural artificialEstimating latent heat flux over reservoirs using an artificial neural network

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