A comparison of wetness indices for the prediction of observed connected saturated areas under contrasting conditions
Bibliographic record
Abstract
ABSTRACT For lack of other widely available spatial information, topography is often used to predict water fluxes and water quality in mesoscale watersheds. Such data have however proven to be misleading in many environments where large and flat valley bottoms and/or highly conducive soil covers determine water storage and water transport mechanisms. Also, the focus is generally on the prediction of saturation areas regardless of whether they are connected to the catchment hydrographic network or rather present in isolated topographic depressions. Here soil information was coupled with terrain data towards the targeted prediction of connected saturated areas. The focus was on the 30 km2 Girnock catchment (Cairngorm Mountains, northeast Scotland) and its 3 km2 sub‐catchment, Bruntland Burn in which seven field surveys were done to capture actual maps of connected saturated areas in both dry and humid conditions. The 1 km2 resolution UK Hydrology of Soil Types (HOST) classification was used to extract relevant, spatially variable, soil parameters. Results show that connected saturated areas were fairly well predicted by wetness indices but only in wet conditions when they covered more than 30% of the whole catchment area. Geomorphic indices including information on terrain shape, steepness, aspect, soil texture and soil depth showed potential but generally performed poorly. Indices based on soil and topographic data did not have more predictive power than those based on topographic information only: this was attributed to the coarse resolution of the HOST classification. Nevertheless, analyses provided interesting insights into the scale‐dependent water storage and transport mechanisms in both study catchments. Copyright © 2013 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".