Assessing the Impact of Climate Change on Global Wetland Extent using CMIP6 multi-model analysis.
Bibliographic record
Abstract
Wetlands play a crucial role in the Earth's system, interacting with various processes such as the hydrological cycle, energy and water exchange with the atmosphere, and global nitrogen and carbon cycles. However, the historical extent of wetlands has suffered significant losses, primarily driven by human activities, particularly in Europe, North America, China, and Southeast Asia. Because of their remote locations, northern Canada and Siberia remain relatively untouched, while South America and Central Africa face current threats. The future trajectory of wetlands is anticipated to be influenced not only by direct human actions but also by climate change. Here we present our assessment of climate-driven global change in wetland extend, focusing on the main wetland complexes. We used an approach based on the Topographic Hydrological model (TOPMODEL), and soil liquid water content projections from 14 models of the Coupled Model Intercomparison Project phase 6 (CMIP6). Our analysis reveals a consistent decrease in wetland extent in the Mediterranean, Central America, and Northern South America, with a substantial long-term loss of 28% in the western Amazon Basin under high radiative forcing (SSP370). Conversely, Central and Western Africa exhibit an increase in wetland extent, excluding the Congo Basin. Nevertheless, most of the area studied (80%) presents uncertain results, due to conflicting projection of changes between the models. Notably, we show that there is significant uncertainty among CMIP6 models regarding liquid soil water content in high latitudes, due to permafrost representation and its thawing. By narrowing our focus to 10 models that seem to best represent the thawing of permafrost, we find modest decline in the overall global area (< 5%), yet significant spatial diversity, with better model agreement. Beyond 50°N, long-term losses of 13% are noted globally, with specific areas like the Hudson Bay Lowlands experiencing a 21% decrease and the Western Siberian Lowlands a 15% decrease under high radiative forcing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".