Distributed Plant Hydraulic and Hydrological Modeling to Understand the Susceptibility of Riparian Woodland Trees to Drought‐Induced Mortality
Bibliographic record
Abstract
Abstract The mechanistic understanding of drought‐induced forest mortality hinges on improved models that incorporate the interactions between plant physiological responses and the spatiotemporal dynamics of water availability. We present a new framework integrating a three‐dimensional groundwater model, Parallel Flow, with a physiologically sophisticated plant model, Terrestrial Regional Ecosystem Exchange Simulator. The integrated model, Parallel Flow‐Terrestrial Regional Ecosystem Exchange Simulator, was demonstrated to quantify the susceptibility of riparian cottonwoods (Populus angustifolia, Populus deltoides, and native hybrids) in southwestern Canada to sustained atmospheric drought and variability in stream flow. The model reasonably captured the dynamics of soil moisture and evapotranspiration in both wet and dry years, including the resilience of cottonwoods despite their high vulnerability to xylem cavitation. Unrealistic predictions of mortality could be generated when ignoring lateral groundwater flow. Our results also illustrated a mechanistic linkage between streamflow and cottonwood health. In the absence of precipitation, normal streamflow could sustain 94% of cottonwoods, and higher streamflows would be required to sustain all of the floodplain cottonwoods. Further, the risk of mortality was mediated by plant hydraulic properties. These results underpin the importance of integrating groundwater processes and plant hydraulics in order to analyze the forest response to sustained severe drought, which could increase in the future due to climate change combined with increasing river water withdrawals.
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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".