Connectivity and runoff dynamics in heterogeneous basins
Bibliographic record
Abstract
Abstract A drainage basin's runoff response can be determined by the connectivity of generated runoff to the stream network and the connectivity of the downstream drainage network. The connectivity of a drainage basin modulates its ability to produce streamflow and respond to precipitation events and is a function of the complex and variable storage capacities throughout the drainage basin and along the drainage network. An improved means to measure and account for the dynamics of stream network connectivity at the catchment scale is needed to predict basin scale streamflow. At a 150 km2 subarctic Precambrian Shield catchment where the poorly drained heterogeneous mosaic of lakes, exposed bedrock, and soil filled areas creates variable contributing areas, hydrological connectivity was measured in 11 sub‐basins with a particular focus on three representative sub‐basins. The three sub‐basins, although of similar relative size, vary considerably in the dominant typology and topology of their constituent elements. At a 10‐m spatial resolution, saturated areas were mapped using both multispectral satellite imagery and onsite measurements of storage according to land cover. To measure basin‐scale hydrological connectivity, the drainage network was represented using graph theory where stream reaches are ‘edges’ connecting sub‐basin ‘nodes’. The overall hydrological connectivity of the stream network was described as the ratio of actively flowing relative to potentially flowing stream reaches. The hydrological connectivity of the stream network to the outlet was described as the ratio of actively flowing stream reaches that were connected to the outlet to the potentially flowing stream reaches. Hydrological connectivity was then related to daily average streamflow and basin runoff ratio. Improved understanding of causal factors for the variable streamflow response to runoff generation in this environment will serve as a first step towards improved streamflow prediction in formerly glaciated landscapes, especially in small ungauged basins. Copyright © 2011 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 | 0.001 |
| 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 | 0.000 | 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".