Memory effects of depressional storage in Northern Prairie hydrology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it