Runoff reduction by forest growth in Hiji River basin, Japan / Diminution de l'écoulement causée par la croissance forestière dans le bassin versant du Fleuve Hiji, Japon
Bibliographic record
Abstract
Forest growth unfavourably reduces low flows and annual runoff in a basin in Japan.Annual precipitation and runoff of the watershed are summarized from observed daily rainfall and discharge, and annual evapotranspiration is estimated from the annual water balance.The water balance analysis shows obvious trends: reduced annual runoff and increased evapotranspiration over a 36-year period when forest growth increased the leaf area index.Between two periods, 1960Between two periods, -1969Between two periods, and 1983Between two periods, -1992, mean annual runoff decreased 11%, from 1258 to 1118 mm, due to a 37% increase in evapotranspiration (precipitation minus runoff) from 464 to 637 mm.This increase in evapotranspiration cannot be attributed to changed evaporative demand, based on climatic variability over the 36-year period of record.Flow duration curves show reduced flows in response to forest growth.In particular, they suggest stronger absolute changes for higher flows but stronger proportional changes for medium and lower flows.A distributed model is applied to simulate the influences of five scenarios based on a 30% change in leaf area index and 5% change in soil storage capacity.From the simulation results, canopy growth appears to contribute much more to flow reduction than changes in soil storage capacity.
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 | 0.000 | 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".