Technical note: Including non-evaporative fluxes enhances the accuracy of isotope-based soil evaporation estimates
Bibliographic record
Abstract
Abstract. Accurately estimating soil water evaporation is essential for quantifying terrestrial water and energy. Isotope-based methods are useful but often rely on steady-state (SS) soil water storage assumptions or non-steady-state (NSS) models that ignore non-evaporative fluxes (such as infiltration and transpiration), leading to mass balance errors. Here, we introduce a new framework, named ISONEVA (ISOtope based soil water evaporation estimation considers dynamic soil water storage and Non-EVAporative fluxes), adapted from lake evaporation models to account for both evaporative and non-evaporative fluxes in soils under dynamic soil water storage. Validation under virtual and field scenarios demonstrated that ISONEVA improved evaporation estimates by 54.1%–83.6% (virtual) and 54.5%–92.4% (field) compared to traditional SS and NSS models. Furthermore, ISONEVA estimated a plausible upper limit of the E/ET ratio (0.139), encompassing the observed value (0.126), whereas SS and NSS methods severely underestimated (0.037) or were unable to produce a limit under field validation. These results highlight the critical role of dynamic soil water storage and non-evaporative fluxes in isotope-based soil water evaporation estimates, offering a robust framework for long-term assessments and informing future coupled land surface modeling efforts.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".