Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Model for strain-induced precipitation kinetics in microalloyed steels

Based on Dutta and Sellars's expression for the start of strain-induced precipitation in microalloyed steels, a new model has been constructed which takes into account the influence of variables such as microalloying element percentages, strain, temperature, strain rate, and grain size. Although the equation given by these authors reproduces the typical >C> shape of the precipitation start time (P s) curve well, the expression is not reliable for all cases. Recrystallization-precipitation-time-temperature diagrams have been plotted thanks to a new experimental study carried out by means of hot torsion tests on approximately twenty microalloyed steels with different Nb, V, and Ti contents. Mathematical analysis of the results recommends the modification of some parameters such as the supersaturation ratio (K s) and constant B, which is no longer a constant, but a function of K s when the latter is calculated at the nose temperature (T N) of the P s curve. The value of parameter B is deduced from the minimum point or nose of the P s curve, where ∂t 0.05/∂T is equal to zero, and it can be demonstrated that B cannot be a constant. The new expressions for these parameters are derived from the latest studies undertaken by the authors and this work represents an attempt to improve the model. The expressions are now more consistent and predict the precipitation-time-temperature curves with remarkable accuracy. The model for strain-induced precipitation kinetics is completed by means of Avrami's equation. © 2013 The Minerals, Metals & Materials Society and ASM International.Peer Reviewe