Runoff modifications due to the conversion of natural grasslands to forests in a large basin in Uruguay
Bibliographic record
Abstract
Abstract Uruguay has encouraged the development of the forestry sector since 1989. As a member of the Montreal Process, the country has followed a set of criteria and indicators for the Sustainable Forest Management. The aim of this paper is to describe the studies carried out in a large basin of 2097 km2, located in an area of humid subtropical climate and 1300 mm of long‐term mean annual rainfall, where the conversion of natural grasslands to forests increased up to 540 km2 during the last 15 years. Using data from daily rainfall and streamflow, the study analyses the effects of afforestation on the runoff and water loss. The analysis comprises hydrographs resulting from comparable rainfall events and annual and seasonal streamflow and water loss behaviour, both before afforestation (1975–1993) and during the afforestation period (1994–2008). A statistically significant reduction of runoff volumes (33–43%) and peak flows (59–65%) were identified on storm hydrographs. The annual and seasonal streamflow also showed diminishing tendencies due to the forestry development, whereas the water loss increases. The annual streamflow decreased between 8·2 and 36·5% depending on the annual rainfall totals. The streamflow reduction was higher during spring and summer (25·2–38·4%) and smaller during autumn and winter (15–20·3%). The water loss is expected to increase by 98 mm for the long‐term mean annual rainfall. The resulting information is a valuable input for the Integrated Water Resources Management of the Negro river basin located downstream, where hydroelectric power, rice irrigation and forestry development are supported. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".