Spatial relationships between soil moisture patterns and topographic variables at multiple scales in a humid temperate forested catchment
Bibliographic record
Abstract
New tools are needed in hydrology to improve our understanding of process heterogeneity and its relationship to catchment topography. We tested the distance‐based Moran's eigenvector maps (DBMEM) method, which models patterns using a combination of positively and negatively autocorrelated structures, searching for soil moisture characteristic scales in a temperate humid forested system. We focused on three questions: (1) What are the characteristic spatial scales of shallow soil moisture? (2) Is there a strong relationship between soil moisture patterns and topographic variables at these scales? and (3) Which hydro‐meteorological variables influence soil moisture scales and topographic controls in a significant way? Data consisted of 16 surveys of soil moisture at depths of 5, 15, 30, and 45 cm in the 5.1 ha Hermine catchment (Laurentians, Canada). The global DBMEM model explained 21 to 96% (adjusted R square) of the spatial variations in soil moisture apportioned into decreasing fractions over six spatially nested, additive submodels: very large (0.85–1.4 ha), large (0.54–0.85 ha), meso (0.50–0.54 ha), fine positive (0.22–0.50 ha), fine negative (0.10–0.22 ha), and very fine (0.02–0.10 ha). The effects of catchment topography (e.g., slope and contributing area) on soil moisture were significant at large and very large scales. Moisture patterns at these scales were dependent on previous storm properties and were good predictors of catchment response. The DBMEM approach provided insightful quantitative evidence regarding the temporal dependency of the relationships between dynamic soil moisture content and static topographic variables across scales.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".