Hot or Not? An Evaluation of Methods for Identifying Hot Moments of Nitrous Oxide Emissions From Soils
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- Institute for Sustainability, Energy, and Environment University of Illinois at Urbana‐Champaign Urbana IL USA, Agroecosystems Sustainability Center University of Illinois at Urbana‐Champaign Urbana IL USA, Currently at Renewable and Sustainable Energy Institute University of Colorado Boulder CO USA
- Department of Statistics University of Kentucky Lexington KY USA
- Institute for Sustainability, Energy, and Environment University of Illinois at Urbana‐Champaign Urbana IL USA, Agroecosystems Sustainability Center University of Illinois at Urbana‐Champaign Urbana IL USA, Department of Plant Biology University of Illinois at Urbana‐Champaign Urbana IL USA
Abstract Effectively quantifying hot moments of nitrous oxide (N 2 O) emissions from agricultural soils is critical for managing this potent greenhouse gas. However, we are challenged by a lack of standard approaches for identifying hot moments, including (a) determining thresholds above which emissions are considered hot moments, and (b) considering seasonal variation in the magnitude and frequency distribution of net N 2 O fluxes. We used one year of hourly N 2 O flux measurements from 16 autochambers that varied in flux magnitude and frequency distribution in a conventionally tilled maize field in central Illinois, USA, to compare three approaches to identify hot moment thresholds: standard deviations (SD) above the mean, 1.5x the interquartile range (IQR), and isolation forest (IF) identification of anomalous values. We also compared these approaches on seasonally subdivided data (early, late, and non‐growing seasons) versus the whole year. Our analyses revealed that 1.5x IQR method best identified N 2 O hot moments. In contrast, using 2 or 4 SD both yielded hot moment threshold values too high, and IF yielded threshold values too low, leading to missed N 2 O hot moments or low net N 2 O fluxes mischaracterized as hot moments, respectively. Furthermore, seasonally subdividing the data set not only facilitated identification of smaller hot moments in the late‐ and non‐growing seasons when N 2 O hot moments were generally smaller but it also increased hot moment threshold values in the early growing season when N 2 O hot moments were larger. Consequently, of the methods evaluated here, we recommend using the 1.5x IQR method on whole year data sets to identify N 2 O hot moments.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AR0001382
- OSTI ID:
- 2496733
- Journal Information:
- Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Journal Issue: 1 Vol. 130; ISSN 2169-8953
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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