Attribution of climate change, vegetation restoration, and engineering measures to the reduction of suspended sediment in the Kejie catchment, southwest China
Bibliographic record
Abstract
Abstract. Suspended sediment transport in rivers is controlled by terrain, climate, and human activities. These variables affect hillslope and riverbank erosion at the source, transport velocities and sedimentation opportunities in the river channel, and trapping in reservoirs. The relative importance of those factors varies by context, but the specific attribution to sediment transfer is important for policymaking, and has wide implications on watershed management. In our research, we analyzed data from the Kejie watershed in the upper Salween River (Yunnan Province, China), where a combination of land cover change (reforestation, as well as soil and water conservation measures) and river channel engineering (sand mining and check dam construction) interact with a changing climate. Records (1971–2010) of river flow and suspended sediment loads were combined with five land-use maps from 1974, 1991, 2001, 2006 and 2009. Average annual sediment yield decreased from 13.7 t ha−1 yr−1 to 8.3 t ha−1 yr−1 between the period 1971–1985 and the period 1986–2010. A distributed hydrological model (Soil and Water Assessment Tools, SWAT) was set up to simulate the sediment sourcing and transport process. By recombining land-use and climate data for the two periods in model scenarios, the contribution of these two factors could be assessed with engineering effects derived from residual measured minus modeled transport. Overall, we found that 47.8% of the decrease was due to land-use and land cover change, 19.8% to climate change, resulting in a milder rainfall regime, 26.1% to watershed engineering measures, and the remaining 6.3% was due to the simulation percent bias. Moreover, mean annual suspended sediment yield decreased drastically with the increase of forest cover, making diverse forest cover one of the most effective ecosystems to control erosion. For consideration of stakeholders and policymakers, we also discuss at length the modeling uncertainty and implications for future soil and water conservation initiatives in China.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".