Abstract. The errors and uncertainties associated with gap-filling algorithms of water, carbon and energy fluxes data, have always been one of the prominent challenges of the global network of microclimatological tower sites that use eddy covariance (EC) technique. To address this concern, and find more efficient gap-filling algorithms, we reviewed eight algorithms to estimate missing values of environmental drivers, and separately three major fluxes in EC time series. We then examined the performance of mentioned algorithms for different gap-filling scenarios utilising data from five OzFlux Network towers during 2013. The objectives of this research were (a) to evaluate the impact of training and testing window lengths on the performance of each algorithm; (b) to compare the performance of traditional and new gap-filling techniques for the EC data, for fluxes and their corresponding meteorological drivers. The performance of algorithms was evaluated by generating nine different training-testing window lengths, ranging from a day to 365 days. In each scenario, the gaps covered the data for the entirety of 2013 by consecutively repeating them, where, in each step, values were modelled by using earlier window data. After running each scenario, a variety of statistical metrics was used to evaluate the performance of the algorithms. The algorithms showed different levels of sensitivity to training-testing windows; The Prophet Forecast Model (FBP) revealed the most sensitivity, whilst the performance of artificial neural networks (ANNs), for instance, did not vary considerably by changing the window length. The performance of the algorithms generally decreased with increasing training-testing window length, yet the differences were not considerable for the windows smaller than 60 days. Gap-filling of the environmental drivers showed there was not a significant difference amongst the algorithms, the linear algorithms showed slight superiority over those of machine learning (ML), except the random forest algorithm estimating the ground heat flux (RMSEs of 30.17 and 34.93 for RF and CLR respectively). For the major fluxes, though, ML algorithms showed superiority (9 % less RMSE on average), except the Support Vector Regression (SVR), which provided significant bias in its estimations. Even though ANNs, random forest (RF) and extreme gradient boost (XGB) showed close performance in gap-filling of the major fluxes, RF provided more consistent results with less bias, relatively. The results indicated that there is no single algorithm which outperforms in all situations and therefore, but RF is a potential alternative for the ANNs as regards flux gap-filling.