Hot spots, hot moments, or hot mess: determining the patterns and drivers of greenhouse gas emissions using continuous automated measurements

Continuous automated measurements are essential for quantifying the spatial and temporal variability in soil greenhouse gas fluxes and for resolving “hot spot” and “hot moments” that contribute disproportionately to ecosystem-scale emissions. This is especially important for methane (CH₄) and nitrous oxide (N₂O), where short-lived emission pulses can account for a substantial fraction of the annual mean and are readily missed by low-frequency sampling.

We found that hot moments accounted for <5% of the measurements but contributed ~71% of the annual N₂O flux from a wheat cropland soil in a high-emission year, and CH₄ hot moments ranged from ~100% to >700% of seasonal means. Similarly, in peatland maize system, hot spots and hot moments accounted for <2% of the measurements but were 45 ± 1% of mean annual N₂O fluxes and up to 140 ± 9% of mean annual CH₄ fluxes. Automated chamber systems can provide multiple flux estimates per hour and dense concentration time series within each enclosure, increasing the likelihood of capturing short-lived pulses, diurnal dynamics, and event-driven responses.

Quantifying extreme flux events is not without challenges, however. Deriving fluxes from automated chamber time series requires careful and transparent processing. Non-linear concentration trajectories may arise from diffusion and certain chamber-soil geometries, in which case linear regressions can underestimate fluxes. Conversely, apparent non-linearity can result from artifacts (e.g., inadequate mixing, leaks, or collar effects), and thus non-linear models may yield good statistical fits but biased flux estimates. High flux events can cause carryover of gases between consecutive chamber measurements and generate substantial artifacts, often appearing as inflated concentrations at the start of the next measurement and, in some cases, as subsequent apparent negative fluxes when the system “recovers.

Relating fluxes to drivers presents additional challenges. Continuous measurements of key environmental variables, such as soil oxygen, moisture, and temperature, are required at the same temporal and spatial scale as greenhouse gas fluxes to accurately capture relationships. Data on soil pH and mineral nitrogen also significantly improve model prediction, although few studies sample with sufficient intensity to provide strong inference.

In this work, we provide a roadmap for improving the utility of automated measurements at ecosystem scales, assessing transparent, physics-based selection of linear vs. nonlinear models, the adoption of standardized, community-aligned flux processing workflows, quality control diagnostics, and recommended sensor suites to improve comparability across studies and strengthen inference about hot moments and hot spots in soil greenhouse emissions.