Bedrock Controls on Water and Energy Partitioning
Bibliographic record
Abstract
Abstract Across diverse biomes and climate types, plants use water stored in bedrock to sustain plant transpiration. Bedrock water storage ( S bedrock ), in addition to soil moisture, thus plays an important role in water cycling and should be accounted for in the context of surface energy balances and streamflow generation. Yet, the extent to which bedrock water storage impacts hydrologic partitioning and influences latent heat fluxes has yet to be quantified at large scales. This is particularly important in Mediterranean climates, where the majority of precipitation is offset from energy delivery and plants must rely on water retained from the wet season to support summer growth. Here we present a simple and modified water balance approach to quantify the role of S bedrock on controlling hydrologic and energy partitioning. Specifically, we tracked evapotranspiration in excess of precipitation and mapped soil water storage capacity ( S soil , mm) across the western US in the context of Budyko's water partitioning framework. Our findings indicate that S bedrock is necessary to sustain plant transpiration across forests in the Sierra Nevada—some of the most productive forests on Earth—as early as April every year, which is counter to the current conventional thought that bedrock is exclusively used late in the dry season under extremely dry conditions. We found that the proportion of water that returns to the atmosphere would decrease dramatically without access to S bedrock . When converted to latent heat energy, the median monthly flux associated with evapotranspiration of S bedrock can exceed 100 W/m 2 during the dry season.
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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 | 0.001 | 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".