Establishment of channel networks in a digital elevation model of the prairie region through hydrological correction and geomorphological assessment
Bibliographic record
Abstract
Building channel networks in flat regions of digital elevation models (DEMs) is important for watershed delineation and hydrological modeling, particularly for areas with gently-sloped terrain, such as the Canadian Prairies. Existing drainage analysis methods cannot effectively address the spatial correlations of elevation across the flat regions, and the quality of the DEM-derived channel network is mainly evaluated through visual inspection. In this study a hydrological correction method is developed that integrates elevation information from both the existing digital channel network and the original DEM. A set of geomorphological indices is then introduced for quantitative evaluation of DEM-derived channel networks from the perspectives of flow direction and drainage pattern. Both the DEM correction and network assessment methods are implemented in a GIS environment. Their performance is demonstrated through a case study in southern Saskatchewan, Canada. The generated channel network is consistent with the known hydrological information and contains fewer parallel channels compared with existing methods. The developed methods could be valuable for a wide range of applications using the Canadian Digital Elevation Data (CDED) within the context of the Canadian Prairie region. La construction des réseaux de canaux dans les régions plates des modèles numériques d’élévation (MNEs) est importante par rapport à la délimitation des bassins versants et la modélisation hydrologique, en particulier pour les zones terrestres qui ont majoritairement une pente douce, comme les Prairies canadiennes. Les méthodes d’analyse de drainage existantes ne peuvent pas traiter efficacement les corrélations spatiales d’altitude dans les régions plates, et la qualité du MNE dérivé d’un réseau de canaux est principalement évaluée par des inspections visuelles. Cette étude a développé une méthode de correction hydrologique qui a intégré l’information d’altitude du réseau de canaux numériques existants et l’original MNE. Un ensemble d’indices géomorphologiques ont ensuite été introduits pour l’évaluation quantitative des réseaux de canaux dérivés des MNEs du point de vue du sens d’écoulement et des réseaux de drainage. Les deux, la correction à l’aide du DEM et les méthodes d’évaluation du réseau, ont été mises en œuvre dans un environnement de SIG. Leur performance a été démontrée par une étude de cas au sud de la Saskatchewan, Canada. Le réseau de canaux générés par la méthode proposée a été compatible avec les informations hydrologiques connues et contenait moins de canaux parallèles comparés à d’autres méthodes utilisées présentement. Les méthodes développées pourraient être utiles pour améliorer une large gamme d’utilisations des données numériques d’élévation canadiennes (DNEC) dans le contexte de la région des Prairies canadiennes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".