Evident cooling effects of surface wetlands to mitigate climate change - a study of the Prairie Pothole Region
Bibliographic record
Abstract
Wetlands are an important land type – they provide vital ecosystem services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extent and hydrological processes is important for valuing wetlands’ function. The purpose of this study is to dynamically simulate wetlands’ hydrological processes and their feedback to the regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP Land Surface Model with two major modifications: (1) modifying the sub-grid saturation fraction for spatial wetland extent; (2) incorporating a dynamic water storage to simulate hydrological processes. This scheme was tested at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while decreasing sensible heat and runoff. Finally, the dynamic wetland scheme was tested using the coupled WRF model, showing an evident cooling effect of 1~3℃ in summer where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for more than 10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling, as well as wetland conservation, for valuing wetlands in providing a moisture source and mitigating extreme heatwaves, especially under climate change.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".