Land cover change mitigated 20 % of the total increased evapotranspiration in tropical Lancang-Mekong River Basin during 2001–2019
Bibliographic record
Abstract
The tropical Lancang-Mekong River Basin (TLMRB), Southeast Asia. Accurately assessing evapotranspiration (ET) change derived from land cover changes (LCCs) is challenging in tropical regions due to the large bias in existing ET products and the difficulty in improving the ET al.gorithm for tropical ecosystems. This study quantified the LCCs-driven ET in the TLMRB from 2001 to 2019 by using scenario simulations based on an improved MOD16 algorithm. A soil moisture index was introduced to improve the soil evaporation of the original MOD16 model. The improved MOD16 algorithm showed better performance with the average Root Mean Square Error decreasing by 0.12 mm/day compared with the original MOD16 ET. The improved ET of the wet climate conditions was more significant than that under dry climate conditions. LCCs led to a 3.45 mm/yr decrease in average ET, and mitigated 20 % of increased ET. LCCs caused a net reduction of 7.44 billion m³ in ET water consumption. This decrease in ET, may intensify the surface runoff and raise the risk of extreme floods in the rain season. These findings enhance our understanding of the relationship between LCCs and ET in tropical regions, and provide valuable insights for trans-boundary collaborations and water resource management in the TLMRB. • Land cover changes derived evapotranspiration was explored in Mekong River basin. • Evapotranspiration was improved by using a soil moisture index in the MOD16 model. • Evapotranspiration decreased by 7.44 billion m³due to dramatic land cover changes. • Land cover changes mitigated 20 % of increased evapotranspiration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".