Statistical identification of nitrous oxide hot moments and their significance across global ecosystems

Nitrous oxide (N2O) emissions from agricultural soils contribute 4% of total anthropogenic greenhouse gas (GHG) emissions globally. Events known as ‘hot moments’ can occur following environmental changes that favor N2O production, which contribute disproportionately to annual cumulative emissions. Despite their significance, hot moments and their impact have not been statistically well defined, particularly on a global scale. We collected 13,787 soil N2O flux measurements from 42 publications and evaluated 14 methods of statistical anomaly detection for their ability to identify hot moments within datasets. Two methods achieved highest overall performance by Matthews correlation coefficient (MCC): median absolute deviation (MCC: 0.80) and minimum covariance determinant (MCC: 0.80), the latter which also performed evenly across highly dissimilar datasets and identified more contextually important minor hot moments (39%) that other methodologies may misidentify. Interquartile range, which..., , , # Hot Moment Identification

This work uses several methods of statistical outlier detection for the detection of hot moments of nitrous oxide emissions using a dataset of daily average emissions collected from publications across the globe. Three files are included: first is a CSV file containing all data collected from publications (HotMomentTreatments.csv). Second, “Supplemental_Material.pdf” contains further description of statistical concepts and the final optimized model parameters used. The third file “Hot_Moment_Identification_Code-_Actuals.ipynb” is a Jupyter notebook containing all code used to perform data analysis and figures.

## Sharing/Access information

The source of each data point is cited within HotMomentTreatments.csv.

## Code/Software

All code for data analysis is contained in the file “Hot_Moment_Identification_Code-_Actuals.ipynb”, which is a Jupyter notebook file.

Analysis was performed using Python 3.8, Pyod 1.0.9, Fitter 1.5.2, Pandas 1.4.3, Numpy 1.21.2, S...,