Application of dynamic contributing area for modelling the hydrologic response of the Assiniboine River Basin to a changing climate
Bibliographic record
Abstract
The Prairie landscape consists of numerous pothole depressions which produce complex fill-and-spill runoff generation processes that result in intermittent hydrologic connectivity and dynamic contributing areas (DCA). We investigated the effect of including DCA in the modified version of the Soil and Water Assessment Tool (SWAT) model and its implication on future streamflow projection for the pothole dominated Assiniboine River Basin (ARB). The fill-and-spill processes that lead to DCA were captured using a physically-based approach, with a volumetric threshold to reduce the computational demand. Despite the challenges in accurately simulating prairie pothole hydrology, both in terms of timing and volume of runoff, the modified approach improved streamflow modelling performance, and reduced model uncertainty. Further, we evaluated the effects of representing DCA on projecting future streamflow by using eight statistically downscaled CMIP5 GCMs, forced with the RCP4.5 and RCP8.5 scenarios. End of century projections indicate increases in annual precipitation and temperature across the ARB, with decreasing summer precipitation relative to the 1976–2005 baseline period. Compared to the standard SWAT setup that does not allow for DCA, the modified model was found to be more responsive to climatic change with relatively larger projected increases in seasonal and annual flows at the majority of evaluated stations. This advance in DCA modelling will facilitate longer-term large basin-scale simulations that are more representative for the Prairie region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".