Natural Lakes Are a Minor Global Source of N<sub>2</sub>O to the Atmosphere
Bibliographic record
Abstract
Abstract Natural lakes and reservoirs are important yet not well‐constrained sources of greenhouse gasses to the atmosphere. In particular for N 2 O emissions, a huge variability is observed in the few, observation‐driven flux estimates that have been published so far. Recently, a process‐based, spatially explicit model has been used to estimate global N 2 O emissions from more than 6,000 reservoirs based on nitrogen (N) and phosphorous inflows and water residence time. Here we extend the model to a data set of 1.4 million standing water bodies comprising natural lakes and reservoirs. For validation, we normalized the simulated N 2 O emissions by the surface area of each water body and compared them against regional averages of N 2 O emission rates taken from the literature or estimated based on observed N 2 O concentrations. We estimate that natural lakes and reservoirs together emit 4.5 ± 2.9 Gmol N 2 O‐N year −1 globally. Our global‐scale estimate falls in the far lower end of existing, observation‐driven estimates. Natural lakes contribute only about half of this flux, although they contribute 91% of the total surface area of standing water bodies. Hence, the mean N 2 O emission rates per surface area are substantially lower for natural lakes than for reservoirs with 0.8 ± 0.5 versus 9.6 ± 6.0 mmol N·m −2 ·year −1 , respectively. This finding can be explained by on average lower external N inputs to natural lakes. We conclude that upscaling‐based estimates, which do not distinguish natural lakes from reservoirs, are prone to important biases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".