Inferring soil water movement and streamflow response in Canadian Prairie riparian areas using hydrologic state variables
Bibliographic record
Abstract
Abstract Flat terrain and soils with variable permeability make it difficult to assess the relative importance of surface and subsurface runoff in the Canadian Prairies, especially in riparian areas that are critical for water transmission and solute transport. The main objective of this study is therefore to determine whether patterns of hydrologic state variables, namely, near‐surface soil moisture (SM), soil electrical conductivity (SEC), and soil temperature (ST), can help infer riparian‐to‐stream soil water movement in Prairie landscapes. Focus is on the near‐level Catfish Creek Watershed (south‐eastern Manitoba, Canada) where 3 riparian sites were monitored: a naturally vegetated grassland site, a headwater forested site, and a highly impacted grassed site adjacent to an engineered drainage dyke and a man‐made drainage channel. Data from 9 to 12 SM, SEC, and ST surveys completed at each site in 2015 using a 75‐point grid are matched with riparian water table data, surface water level data from adjacent drainage channels, and indicators of antecedent moisture conditions. Pattern characteristics, in the form of descriptive statistics and variogram parameters, are estimated for each state variable and then correlated to indicators of antecedent moisture conditions, stream, and subsurface water level data to infer soil water movement. Results show that potential evapotranspiration, depth to water table, and antecedent precipitation have a significant yet variable impact on SM, SEC, and ST patterns. A switching behaviour, between dry and wet conditions, is present in riparian areas characterized by grassland vegetation and well‐drained soils. The occurrence of shallow subsurface flow is inferred during the wettest conditions. Although riparian SM conditions are useful for predicting streamflow response in adjacent channels, such is not the case for riparian SEC and ST. Further investigations are however necessary to confirm the usefulness of SM spatial patterns for predicting streamflow response in other landscapes across the Canadian Prairies.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".