Using optimality principles to couple terrestrial carbon and water cycles in hydrological models
Bibliographic record
Abstract
Hydrology has been guided by establishing empirical relationships between the movement of water through landscapes and the application of the conservation of mass law in catchments. This has resulted in models with complex calibration frameworks that often overlook the physical and biochemical water-related processes linking plants to hydrological cycles. Studies have revealed that some of the empirical relationships in catchments might also reflect a potential ecosystem's coevolution with climate, driving catchments to optimise their supply and demand limits. This agrees with the eco-evolutionary optimality principles used in vegetation modelling that are based on the hypothesis that canopy conductance acclimates to environmental variations by balancing the costs of carbon assimilation and maintenance of transpiration rates. Here, we developed meaningful interfaces between simple models and approaches based on the use of optimality principles in vegetation modelling and hydrology. Our work is based on the application of the P-model to estimate to quantify gross primary productivity and transpiration and the use of a mass-balance approach to quantify the root zone storage. These integrations not only provide a more nuanced understanding of hydrological processes but also pave the way for more accurate and physically-informed models in hydrology. Our findings underscore the potential of using eco-evolutionary principles as a unifying framework in hydrological research, offering new insights for understanding and predicting water movement in catchments under varying climatic and ecological conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".