The relative role of soil type and tree cover on water storage and transmission in northern headwater catchments
Bibliographic record
Abstract
Abstract Soil water storage and stable isotopes dynamics were investigated in dominant soil–vegetation assemblages of a wet northern headwater catchment (3.2 km2) with limited seasonality in precipitation. We determined the relative influence of soil and vegetation cover on storage and transmission processes. Forested and non‐forested sites were compared, on poorly drained histosols in riparian zones and freely draining podzols on steeper hillslopes. Results showed that soil properties exert a much stronger influence than vegetation on water storage dynamics and fluxes, both at the plot and catchment scale. This is mainly linked to the overall energy‐limited climate, restricting evaporation, in conjunction with high soil water storage capacities. Threshold behaviour in runoff responses at the catchment scale was associated with differences in soil water storage and transmission dynamics of different hydropedological units. Linear input–output relationships occurred when runoff was generated predominantly from the permanently wet riparian histosols, which show only small dynamic storage changes. In contrast, nonlinear runoff generation was related to transient periods of high soil wetness on the hillslopes. During drier conditions, more marked differences in soil water dynamics related to vegetation properties emerged, in terms of evaporation and impacts on temporarily increasing dynamic storage potential. Overall, our results suggest that soil type and their influence on runoff generation are dominant over vegetation effects in wet, northern headwater catchments with low seasonality in precipitation. Potential increase of subsurface storage by tree cover (e.g. for flood management) will therefore be spatially distributed throughout the landscape and limited to rare and extreme dry conditions. Copyright © 2014 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".