The Large-Scale Effect of Forest Cover on Long-Term Streamflow Variations in Mediterranean Catchments of Central Chile
Bibliographic record
Abstract
Forest ecosystems play an important role in hydrological processes as surface and subsurface runoff, as well as the storage of water at the catchment scale. Therefore, it is important to have a greater understanding of the effects of forests in the long-term water balance of Mediterranean catchments. In this sense, this study evaluates the effect of native forests, forest plantations, and the combination of both, on long-term streamflow variations in central Chile, an unusual area of Mediterranean climate characterized by a well-marked annual cycle with dry summers and wet winters. Thus, the temporal pattern of monthly streamflow was evaluated for mean flow (Qmean), maximum flow (Qmax), and minimum flow (Qmin) in 42 large-scale (>200 km2) Mediterranean catchments. Each series of monthly streamflow data was QA/QC, and then evaluated using the Mann–Kendall’s non-parametric statistical test to detect temporal variations between 1994 and 2015. In addition to the previous analysis, the monthly series were grouped into wet seasons (April–September) and dry seasons (October–April), to determine if there were any significant differences within the annual hydrological cycle. The areas covered with native and forest plantations and their relative changes were evaluated for each catchment through streamflow variations and forest cover indicators. Results revealed that streamflow variations are positive and significant when more forest cover exists. The intra-catchment relationships assessed for both species revealed the significant role of native forests and mixed masses as key ecosystems for the long-term conservation of summer streamflow in Mediterranean catchments of central Chile. These findings encourage an urgent need to create highland afforestation programs on degraded areas of central Chile, to maximize water storage in a region that is quickly drying out due to unsustainable water and land use management practices and the effects of global warming.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".