Root Water Uptake Resolved by Distributed Moisture Storage Changes Through Soil and Weathered Bedrock
Bibliographic record
Abstract
Abstract Understanding how plants access water is critical to biosphere‐atmosphere interactions. However, it remains challenging to resolve root water uptake in space and time. Here, we introduce (a) a mass balance method that uses depth‐distributed moisture changes in the vadose zone to spatially resolve patterns of evapotranspiration (ET) and (b) an application of this method to a unique data set of continuous moisture dynamics across a deeply weathered root zone in a seasonally dry forest in coastal California. These observations are made possible by a Vadose‐zone Monitoring System on a steep hillslope (“Rivendell”) in the Angelo Coast Range Reserve. The new mass balance method accurately distinguishes between numerically generated vertically distributed ET and drainage fluxes. Synthetic tests across nine climate types show that the new method is broadly applicable in arid and Mediterranean regions. By applying the new mass balance method to the Rivendell data set, we determined spatiotemporal water fluxes in the deep root‐zone at daily temporal resolution. Layers of the subsurface wet up simultaneously in the wet season. In the wet season, plant moisture for root water uptake was derived primarily from the soil. As the dry summer progresses, water uptake spreads to successively deeper depths until it occurs nearly equivalently across all depths. Water uptake at all depths across years is essentially the same, except in soil where water use patterns follow wet season precipitation patterns. Our results demonstrate that dry season unsaturated zone dynamics mediate the timing and magnitude of recharge to groundwater, with potential implications for summer streamflow.
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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 | 0.000 |
| 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 | 0.000 |
| 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".