NCAmapper: A GIS Model for Accurate Quantification of the Spatiotemporal Changes in Noncontributing Areas and Depressional Storage
Bibliographic record
Abstract
The North American prairie region is characterized by numerous land depressions, resulting in spatiotemporally variable noncontributing areas (NCAs) that impact runoff translation into streamflow. Current hydrological models address temporal changes in NCA but neglect spatial distribution and geolocation. The only spatial NCA maps available for the prairies were derived by the Prairie Farm and Rehabilitation Association (PFRA) from paper-based contour maps using subjective interpretation of a two-year rainfall event. PFRA maps are therefore static and inadequately represent the dynamic nature of NCAs across different return periods. To address this gap, this study introduces NCAmapper, a GIS-based model relying on digital elevation models to map NCAs dynamically for different runoff events in prairie and arctic regions. Evaluation of NCAmapper demonstrates its capabilities in dynamically representing the spatiotemporal variability in NCAs corresponding to different rainfall events over multiple prairie basins. NCAmapper additionally enhances hydrological model parameterization, aiding practitioners in quantifying effective drainage areas and evaluation of flood vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".