An analog period method for gap‐filling of latent heat flux measurements

Abstract

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

Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems

“…With the exception of two models, all other existing gap-filling approaches are dependent on relationships between λE flux or ET a and other driving variables, such as additional flux and meteorological variables. The two exceptions are the mean diurnal variation (MDV) [10] and the analogue period (AP) [16] methods in which gap-filling relies solely on the variable to infill itself. Thus, the applicability and performances of most of these gap-filling methods are highly dependent on the quality and availability of those additional variables.…”

“…Thus, the applicability and performances of most of these gap-filling methods are highly dependent on the quality and availability of those additional variables. For example, the feasibility of such methods is limited when the required meteorological/flux data are also missing [7,16]. Further, the infilling may be affected by spurious relationships produced from changing meteorological conditions and/or outliers and low-quality records within the meteorological observations [4].…”

“…The MaxCor model (Daily temporal pattern matching for ET a ) fills gaps in a day based on another day with complete records that is selected as having the most similar diurnal pattern with the day to infill. This model is conceptually similar to the analogue period (AP) model which fills a gap by searching the full dataset for an 'analogue period' that that has similar temporal patterns with the data surrounding the gap [16]. However, the implementation of MaxCor is much simplified as the search is based on a daily time-step, rather than the more flexible, user-defined time step as implemented in AP; specifically, MaxCor searches for an 'analogue day', while AP allows any length of analogue period and recommends case-specific investigation to determine the optimal length.…”