Bibliographic record
Abstract
Two primary purposes of this research were to assess the hydrologic response and to detect the hydrologic similarity of a 9.5 km² needle-leaf forested watershed in the Canadian Prairie province of Alberta known the Marmot Creek basin. In order to achieve these two objectives, three hydrologic models and several analysis methods were applied in this study. -- Topographic index, In(α/tanβ), was calculated by different flow routing algorithms ( single flow and biflow direction algorithms) with 1- and 90-meter resolution digital elevation models (DEM) in this research. A series of maps and tabular outputs showed that in high resolution DEM, the distinction of the frequency distribution of In(α/tanβ) was pronounced between different algorithms. However, in low resolution DEM, the difference was not obvious. -- Evaporative resistances (stomatal and aerodynamic), which was used to calculate potential evapotranspiration (PET), was estimated by Canadian Land Surface Scheme CLASS). Aerodynamic resistance was also produced by the Monteith method (1965) under neutral conditions. The maximum stomatal resistance in the Marmot Creek basin could reach 2000 m s⁻¹ : however, the minimum value was only 0 s⁻¹. A series of comparisons showed that aerodynamic resistance computed by CLASS and Monteith method (1965) were fairly close. -- PET, as one of the input data sets to drive TOPMODEL in this research, was estimated by Penman-Monteith formulation. The peak of PET over the Marmot Creek basin occurred in July, and October had the lowest rate, which was equal to half the peak value in July. The effects of evaporative resistance on PET were also discussed. From the results, it could be concluded that compared to aerodynamic resistance, stomatal resistance had main control of PET. -- TOPMODEL, a topographically-based hydrologic model, was used to assess the hydrologic response in the Marmot Creek basin. This hydrologic model was combined with genetic algorithm (GA) to do calibration and, subsequently, validation with historical streamflow datasets retrieved from the Environment Canada hydrometric database. According to streamflow simulation with TOPMODEL, subsurface flow accounted for 84.9% of the total simulated streamflow during the calibration periods for the Marmot Creek basin. The simulations yielded a Nash-Sutcliffe efficiency of about 0.611, which was acceptable given the limitations of climate data. However, during the validation phase of the model assessment, there were some discrepancies between the simulated streamflow response and the observed values. Moreover, different In(α/tanβ) distributions were observed using different grid sizes in different flow direction algorithms, but these did not lead to significant departures in all the cases of the simulated streamflow. -- Four-meter resolution IKONOS images were used to perform land cover classification in the Marmot Creek basin through a Decision Tree classifier using Normalized Difference Vegetation index (NDVI) input. The variation of In(α/tanβ) between different land covers was investigated using one-way analysis of variance (ANOVA). The results of NOVA showed that no obvious relationship between In(α/tanβ) and the land cover classes could be determined.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".