Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use

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 halfhourly 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).Peer reviewe

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

APPLICABILITY OF JUST-IN-TIME TECHNIQUES IN THE ADMINISTRATIVE AREA

In the United States the philosophy of Just-in-Time (JIT) has been chiefly applied to manufacturing. Other non-manufacturing organizations such as hospitals and insurance companies have adopted various aspects of JIT but in a limited fashion. Organizations have not yet taken advantage of the principles of JIT in administrative functions. It is inappropriate to adopt one philosophy for manufacturing, one for marketing, one for finance, etc. An organization\u27s underlying philosophy should be applicable to all aspects of the company and not separated according to functional areas. Organizations today tend to be overly segmented such that a great deal of duplication and conflicting efforts occur. JIT can arrest the progressive segregation of organizational tasks and functions of stimulating and maintaining a high level of wholeness. This study explores the hypothesis that the embodiment of JIT principles can be utilized effectively in the administrative area to improve operational effectiveness and efficiency. To achieve this an examination of JIT manufacturing principles was undertaken in order to identify those principles that can be utilized in an administration setting. A conceptual model was then developed and implemented within two divisions of a Fortune 500 company. The implementation phase consisted of training all employees involved, as well as monitoring their progress. Results of their efforts were then measured, examined, and analyzed. The results indicate that the principles of JIT can be successfully applied to administrative functions. Approximately 95% of all JIT projects were successful. (*p $\u3c$.001) Eighty eight percent of all participants in this research viewed this type of approach as an effective means of increasing productivity. (*p $\u3c$.001) Total labor hours saved on a monthly basis was 272 hours for one division and 167 for another. As a result of the high number of JIT project successes and the significant increase in productivity, it can be concluded that the underlying philosophy of JIT can be applied successfully in different companies with different administrative functions

Team-Based Professional Development: An Emphasis on Collaboration and Cooperation

Rumblings of discontent frequent campuses of higher education when the mention of faculty evaluation occurs. In fact, a 1997 study published by the Richardson Foundation found that only 30% to 35% of provosts and deans are happy with the university evaluation system and less than 20% of the faculty are supportive of these systems. This article chronicles the journey taken by four departments at a Midwestern university in an attempt to shift focus from a rank and sorting system of evaluation, so prevalent in colleges and universities today, to a professional development model

Interrelationships among lean bundles and their effects on operational performance

The aim of our research is to disentangle the complex relations among lean bundles and operational performance. In particular, we focus on the direct and mediating effects on operational performance of three of the main lean manufacturing bundles, namely Just in Time (JIT), Total Quality Management (TQM) and Human Resource Management (HRM). We run statistical analysis on the High Performance Manufacturing round III database, a survey involving 266 plants in nine countries across three different industries. Our results show that JIT and TQM have a direct and positive effect on operational performance while HRM has a mediated effect on it. Theoretical and managerial implications of our findings are then drawn and discussed