Using a plant hydraulic model to design more resilient rehabilitated landscapes in arid ecosystems
Bibliographic record
Abstract
Abstract Mining is a major driver of dryland disturbance and degradation, and there is a growing need for effective and resilient methods for restoration of former mine sites. An important restoration goal is preventing water from accessing mine waste, thus avoiding mobilization and transport of contaminants. Evapotranspiration (ET) covers are soil covers where vegetation manages the water balance to minimize leakage into underlying waste, with potential co‐benefits of restoring ecological function and fixing carbon. However, cover designs often overlook potentially complex interactions between plant physiology and physical design parameters (cover depth, soil properties, etc.) that affect plant water fluxes, particularly in water‐limited environments. To better understand how physiologically mediated dynamics impact cover performance, we develop an ET cover model that mechanistically describes plant‐environment interactions through a plant hydraulics framework. We use the model to determine how soil cover depth, a fundamental design parameter, interacts with physiology to impact leakage, plant stress/mortality, and carbon sequestration. The model is parameterized using data from a prior study of plant water relations in engineered cover systems of varying depths. When run under historical rainfall trajectories, the model shows that significant plant water stress was ubiquitous across cover depths and was most frequent in shallower covers, where it was accompanied by higher leakage and lower net carbon assimilation. Precipitation variation had an important role in driving outcomes, and hydraulic impairment of vegetation played a role in higher leakage and lower net carbon assimilation. Design approaches that account for plant physiological processes have the potential to yield more effective and resilient systems, and we present a framework for incorporating these critical feedbacks into the design process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".