Statistical characterization of the spatial variability of soil moisture in a cutover peatland
Bibliographic record
Abstract
Abstract Soil moisture is a significant variable in its importance to the validation of hydrological models, but it is also the one defining variable that ties in all components of the surface energy balance and as such is of major importance to climate models and their surface schemes. Changing the scale of representation (e.g. from the observation to modelling scale) can further complicate the description of the spatial variability in any hydrological system. We examine this issue using soil moisture and vegetation cover data collected at two contrasting spatial scales and at three different times in the snow‐free season from a cutover peat bog in Cacouna, Québec. Soil moisture was measured using Time Domain Reflectometry (TDR) over 90 000 m 2 and 1200 m 2 grids, at intervals of 30 and 2 m respectively. Analyses of statistical structure, variance and spatial autocorrelation were conducted on the soil moisture data at different sampling resolutions and over different grid sizes to determine the optimal spatial scale and sampling density at which these data should be represented. Increasing the scale of interest without adequate resolution in the measurement can lead to significant inconsistency in the representation of these variables. Furthermore, a lack of understanding of the nature of the variability of soil moisture at different scales may produce spurious representation in a modelling context. The analysis suggests that in terms of the distribution of soil moisture, the extent of sampling within a grid is not as significant as the density, or spacing, of the measurements. Both the scale and resolution of the sampling scheme have an impact on the mean of the distribution. Only approximately 60% of the spatial pattern in soil moisture of both the large and small grid is persistent over time, suggesting that the pattern of moisture differs for wetting and drying cycles. Copyright © 2003 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".