Gap-filling of net ecosystem exchange (NEE) in eddy covariance time series is necessary for the accurate estimation of the terrestrial ecosystems’ carbon balance. Available gap-filling techniques are mostly site-specific and require meteorological data (non-linear regression, artificial neural network, semi-parametric models) and historical NEE information (mean diurnal variation method) as inputs. These limitations require the development of a model that is robust in space and time and needs minimum parameter set. Application of scaling can lead to such generalized modeling approach for the gap-filling of different diurnal hours of NEE. In this connection, a scaling based empirical model was developed for spatiotemporally robust predictions of the diurnal cycle of terrestrial growing season NEE from a corresponding single reference observation by using an extended stochastic harmonic algorithm (ESHA). A reference-time observation from each diurnal cycle was utilized as the scaling parameter to normalize the corresponding hourly observations of NEE for different days into a single, dimensionless diurnal curve. The scaled harmonic model was evaluated by predicting the hourly observed growing season NEE time-series of June to September during 2005-2013 for five AmeriFlux sites, representing distinct deciduous forest ecosystems as the case studies. The developed model incorporated a parsimonious set of five parameters, which exhibited spatiotemporal robustness by collapsing into narrow ranges with no apparent trends or groupings among the different days and sites. Model calibrations demonstrated good modeling efficiency (coefficient of determination, R2 = 0.68 to 0.88) and accuracy (ratio of root-mean-square error to the standard deviation of observations, RSR = 0.35 to 0.57) for different sites. Model validations with the years of growing season observations outside the calibration period also showed good performance (R2 = 0.66 to

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Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems

Scaling for Robust Empirical Modeling and Predictions of Net Ecosystem Exchange (NEE) from Diverse Wetland Ecosystems

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Scaling for Robust Empirical Modeling and Predictions of Net Ecosystem Exchange (NEE) from Diverse Wetland Ecosystems

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Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems

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Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems

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