Regionalizing time of concentration using landscape structural patterns of catchments
Bibliographic record
Abstract
Abstract There is an emerging challenge within water resources on how, and to what extent, borrowing concepts from landscape ecology might help re-define traditional concepts in hydrology in a more tangible manner. A stepwise regression model was adopted in this study to assess whether the time of concentration of catchments could be explained by five landscape structure-representing metrics for land use/land cover, soil and geological patches, using spatial data from 39 catchments. The models suggested that the times of concentration of the catchments could be predicted using the measures of four landscape structure-representing metrics, which include contiguity index ( r 2 = 0.46, p ≤0.05), fractal dimension index ( r 2 = 0.51, p ≤0.05), related circumscribing circle ( r 2 = 0.52, p ≤0.05), and shape index ( r 2 = 0.47, p ≤0.05). The models indicated that the regularity or irregularity in land use/land cover patch shape played a key role in affecting catchment hydrological response. Our findings revealed that regularity and irregularity in the shape of a given patch ( e.g ., urban and semi-urban, rangeland and agricultural patches) can affect patch functions in retarding and/or increasing flow accumulation at the catchment scale, which can, in turn, decrease or increase the times of concentration in the catchments.
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 | 0.000 | 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 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".