Effects of reduced land cover detail on hydrological model response
Bibliographic record
Abstract
Abstract Problems related to scale continue to be at the forefront of research in hydrology. Past research into issues of scale has focused mainly on digital elevation model grid size, the appropriate number and size of sub‐areas for subdividing a watershed, parameter transferability between watersheds and appropriate scales for linking hydrological and general circulation models. Much less attention has been given to the effects of scale on the representation of land cover and hydrological model response. Recent studies with respect to changes in land cover and hydrologic response have tended to focus on the issue of land cover maturity and the conversion of land through agricultural and forestry practices. The focus of this study is to examine the impact of the level of detail at which land cover is represented in modelling the hydrological response of Wolf Creek Basin in northwest Canada. A grid‐based land cover map with a spatial resolution of 30 m is coarsened or smoothed using several common grid‐based methods of aggregating categorical data, including: pixel thinning, modal smoothing and modal aggregation. A majority rule method based on polygons is also applied to the 30 m base cover. The SLURP hydrologic model is calibrated for the base cover and used as a reference for comparing simulations for the coarsened or ‘generalized’ land cover maps. Results of the simulations are compared to examine the sensitivity of hydrologic response to generalized land cover information. Comparisons of the SLURP model runs for Wolf Creek suggest that reducing the level of detail of land cover information generally has a limited effect on hydrologic response at the outlet. However, results for averages of water balance components across the basin suggest that the local variability of hydrologic response is affected in general. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| 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".