Using the Maximum Entropy Production approach to integrateenergy budget modeling in a hydrological model
Bibliographic record
Abstract
Abstract. Total terrestrial evaporation is a key process to understand the hydrological impacts of climate change given that warmer surface temperatures translate into an increase in the atmospheric evaporative demand. To simulate this flux, many hydrological models rely on the concept of potential evaporation (PET) although large differences have been observed in the response of PET models to climate change. The Maximum Entropy Production (MEP) model of land surface fluxes offers an alternative approach to simulate terrestrial evaporation in a simple and parsimonious way while fulfilling the physical constraint of energy budget closure and providing a distinct estimation of evaporation and transpiration. The objective of this work is to use the MEP model to integrate energy budget modeling within a hydrological model. We coupled the MEP model with HydroGeoSphere, an integrated surface and subsurface hydrologic model. As a proof-of-concept, we performed one-dimensional soil column simulations at three sites of the AmeriFlux network. The coupled HGS-MEP model produced realistic simulations of soil water content (RMSE between 0.03 and 0.05 m3 m−3, NSE between 0.30 and 0.92) and terrestrial evaporation (RMSE between 0.31 and 0.71 mm day−1, NSE between 0.65 and 0.88) under semiarid, Mediterranean and temperate climates. HGS-MEP outperformed the standalone HGS model where total terrestrial evaporation is derived from potential evaporation which we computed using the Penman-Monteith equation. This research demonstrated the potential of the MEP model to improve the simulation of total terrestrial evaporation in hydrological models, including for hydrological projections under climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".