Flux-data-qaqc: A Python Package for Energy Balance Closure and Post-Processing of Eddy Flux Data

John Volk,Richard Allen,Ayse Kilic,Forrest Melton,Martha Anderson,Justin Huntington

doi:10.21105/joss.03418

John Volk, Richard Allen + Show 4 more

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https://doi.org/10.21105/joss.03418

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Abstract

Volk et al., (2021). flux-data-qaqc: A Python Package for Energy Balance Closure and Post-Processing of Eddy Flux Data. Journal of Open Source Software, 6(66), 3418, https://doi.org/10.21105/joss.03418

Highlights

Eddy Covariance (EC) systems provide continuous direct measurements of turbulent and radiative heat and energy fluxes between the atmosphere and land surface (Baldocchi et al, 1988)
Error can result from the well-documented energy balance closure problem resulting from inherent limitations in the technique and from data processing errors, instrumentation errors, and other sources (Foken, 2008; Stoy et al, 2013)
Several approaches for post-processing and correcting EC-based ET estimates exist, notably those implemented by major flux measurement networks and datasets like FLUXNET2015 (Pastorello et al, 2020)

Summary

Introduction

Eddy Covariance (EC) systems provide continuous direct measurements of turbulent and radiative heat and energy fluxes between the atmosphere and land surface (Baldocchi et al, 1988). Evapotranspiration (ET) estimates from EC systems are highly valuable for many scientific disciplines, water policy decisions, and operational applications such as irrigation management. The EC technique is considered accurate under certain atmospheric and spatial conditions, inherent theoretical and practical limitations of the technique are magnified by data uncertainty from post-processing methods. Error can result from the well-documented energy balance closure problem resulting from inherent limitations in the technique and from data processing errors, instrumentation errors, and other sources (Foken, 2008; Stoy et al, 2013). Several approaches for post-processing and correcting EC-based ET estimates exist, notably those implemented by major flux measurement networks and datasets like FLUXNET2015 (Pastorello et al, 2020). Post-processing routines often differ between individual site teams, data networks, and end users, and in some cases post-processing decisions are difficult to reproduce

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