Evaluation of methods for estimating the effects of vegetation change and climate variability on streamflow
Bibliographic record
Abstract
Changes in vegetation cover can significantly affect streamflow. Two common methods for estimating vegetation effects on streamflow are the paired catchment method and the time trend analysis technique. In this study, the performance of these methods is evaluated using data from paired catchments in Australia, New Zealand, and South Africa. Results show that these methods generally yield consistent estimates of the vegetation effect, and most of the observed streamflow changes are attributable to vegetation change. These estimates are realistic and are supported by the vegetation history. The accuracy of the estimates, however, largely depends on the length of calibration periods or pretreatment periods. For catchments with short or no pretreatment periods, we find that statistically identified prechange periods can be used as calibration periods. Because streamflow also responds to climate variability, in assessing streamflow changes it is necessary to consider the effect of climate in addition to the effect of vegetation. Here, the climate effect on streamflow was estimated using a sensitivity‐based method that calculates changes in rainfall and potential evaporation. A unifying conceptual framework, based on the assumption that climate and vegetation are the only drivers for streamflow changes, enables comparison of all three methods. It is shown that these methods provide consistent estimates of vegetation and climate effects on streamflow for the catchments considered. An advantage of the time trend analysis and sensitivity‐based methods is that they are applicable to nonpaired catchments, making them potentially useful in large catchments undergoing vegetation change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".