Memory effects of depressional storage in Northern Prairie hydrology
Bibliographic record
Abstract
Abstract The hydrography of the Prairies of western Canada and the north‐central United States is characterized by drainage into small depressions, forming wetlands rather than being connected to a large‐scale drainage system. In droughts, many of these water bodies completely dry up, while in wet periods, their expansion can cause infrastructure damage. As wetlands expand and contract with changing water levels, connections among them are formed and broken. The change in hydrographic connectivity dynamically changes the hydrological response of basins by controlling the area of the basin which contributes discharge to local streams.The objective of this research was to determine the behaviour of prairie basins dominated by wetlands through two sets of simulations. The first consisted of application and removal of water (simulating runoff and evaporation) from a LiDAR digital elevation model (DEM) of a small basin in the south‐east of the Canadian Province of Saskatchewan. Plots of water surface area and of contributing area against depressional storage showed evidence of hysteresis, in that filling and emptying curves followed differing paths, indicating the existence of memory of prior conditions. It was demonstrated that the processes of filling and emptying produced differing changes in the frequency distributions of wetland areas, resulting in the observed hysteresis.Because the first model was computationally intensive, a second model was built to test the use of simpler wetland representations. The second model used a set of interconnected wetlands, whose frequency distribution and connectivity were derived from the original LiDAR DEM. When subjected to simple applications and removal of simulated water, the second model displayed hysteresis loops similar to those of the first model. The implications for modelling prairie basins are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".