Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale
Bibliographic record
Abstract
Vegetation and the water cycles are inherently coupled across a wide range of spatial and temporal scales. Water availability interacts with plant ecophysiology and controls vegetation functioning. Concurrently, vegetation has direct and indirect effects on energy, water, carbon, and nutrient cycles. To better understand and model plant–water interactions, highly interdisciplinary approaches are required. We present an overview of the main processes and relevant interactions between water and plants across a range of spatial scales, from the cell level of leaves, where stomatal controls occur, to drought stress at the level of a single tree, to the integrating scales of a watershed, region, and the globe. A review of process representations in models at different scales is presented. More specifically, three main model families are identified: (1) models of plant hydraulics that mechanistically simulate stomatal controls and/or water transport at the tree level; (2) ecohydrological models that simulate plot‐ to catchment‐scale water, energy, and carbon fluxes; and (3) terrestrial biosphere models that simulate carbon, water, and nutrient dynamics at the regional and global scales and address feedback between Earth's vegetation and the climate system. We identify special features and similarities across the model families. Examples of where plant–water interactions are especially important and have led to key scientific findings are also highlighted. Finally, we discuss the various data sources that are currently available to force and validate existing models, and we present perspectives on the evolution of the field. WIREs Water 2016, 3:327–368. doi: 10.1002/wat2.1125 This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Science of Water > Hydrological Processes
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".