Topographic parameterization in continental hydrology: a study in scale
Bibliographic record
Abstract
Abstract Digital elevation models (DEMs) are useful and popular tools from which topographic parameters can be quickly and efficiently extracted for various hydrologic applications. DEMs coupled with automated methods for extracting topographic information provide a powerful means of parameterizing hydrologic models over a wide range of scales. However, choosing appropriate DEM scales for particular hydrologic modelling applications is limited by a lack of understanding of the effects of scale and grid resolution on land‐surface representation. The scale effects of aggregation on square‐grid DEMs of two continental‐scale basins are examined. Base DEMs of the Mackenzie and Missouri River basins are extracted from the HYDRO1k DEM of North America. Successively coarser grids of 2, 4, 8, … 64 km were generated from the ‘base’ DEMs using simple linear averaging. TOPAZ (Topographic Parameterization) was applied to the base and aggregated DEMs using constant critical source area and minimum source channel length values to extract topographic variables at varying scales or resolutions. The effects of changing DEM resolution are examined by considering changes in the spatial distribution and statistical properties of selected topographic variables of hydrological importance. The effects of increasing grid size on basin and drainage network delineation, and derived topographic variables, tends to be non‐linear. In particular, changes in overall basin extent and drainage network configuration make it impractical to apply a simple scaling function to estimate variable values for fine‐resolution DEMs from those derived from coarse‐resolution DEMs. Results also suggest the resolution to which a DEM can be reduced by aggregation and still provide useful topographic information for continental‐scale hydrologic modelling is that at which the mean hydraulic slope falls to approximately 1%. In this study, that generally occurred at a resolution of about 10 km. Copyright © 2003 John Wiley & Sons, Ltd.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".