Global Inland Water Greenhouse Gas Emissions: Patterns, Trends, and Anthropogenic Drivers
Bibliographic record
Abstract
Inland waters (streams, rivers, lakes, and reservoirs) are important sources of greenhouse gases (GHGs), including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), to the atmosphere. Their importance has been acknowledged in the IPCC assessment reports and in the regional and global greenhouse budgets coordinated by the Global Carbon Project (GCP). In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative of the GCP, a comprehensive synthesis of existing estimates of regional to global inland water GHG emissions was conducted (Lauerwald et al., 2023a, 2023b) to support the inclusion of these emissions in (sub-)continental GHG budgets. Although that synthesis was published only two years ago, a number of new global estimates have been published since. Here, we present an updated synthesis of recent, global inland water GHG emissions estimates. Moreover, we go beyond the scope of the RECCAP2 synthesis by analyzing regional patterns in more detail, and summarizing the state of knowledge about long-term trends of inland water GHG emissions in response to changes in climate, land use, wastewater management and river damming. Based on that, we discuss how contemporary inland water GHG emissions are impacted by anthropogenic activities and how they may evolve over the 21st century.We estimate that global inland water GHG emissions have a combined warming potential of 8 (5–13) Pg CO2-eq. yr⁻¹ for a 100-year time horizon (GWP100). CO2 emissions, primarily from tropical river systems, contribute approximately three-quarters of this total, while CH4, largely from lakes and reservoirs, accounts for most of the remainder. Notably, boreal and Arctic lakes are important emitters due to their large total area, while nutrient-rich lakes and reservoirs with warmer temperatures in the mid to low latitudes exhibit the highest per-area CH4 emission rates. Contributions from N2O emissions are relatively minor.About one third of CH4 emissions and about three quarters of N2O emissions from global inland waters can be attributed to anthropogenic perturbations, primarily through eutrophication. For inland water CO2 emissions, quantification of the anthropogenic component is more complex. Empirical and modelling studies suggest that global greening also increases terrestrial carbon deliveries to inland waters, and through that, emissions of CO2 from inland waters. Moreover, changes in streamflow are an uncertain, but very important driver. Most dramatic increases are expected for inland water CH4 and N2O emissions, which are projected to strongly increase in response to global warming, while changes in nutrient loads from agricultural runoff may offset or enhance that trend.ReferencesLauerwald et al. 2023a, GBC, https://doi.org/10.1029/2022GB007657Lauerwald et al. 2023b, GBC, https://doi.org/10.1029/2022GB007658
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".