Deep soil water <sup>18</sup>O and <sup>2</sup>H measurements preserve long term evaporation rates on China's Loess Plateau
Bibliographic record
Abstract
Abstract. Knowledge about the long-term average soil evaporation, especially the ratio of evaporation to precipitation (f), is important for assessing the total available water resources. However, determining the long-term f remains technically challenging because soil evaporation is highly dynamic. Here we hypothesize that the stable isotopes (2H and 18O) of deep soil water preserve the long-term evaporation effects on precipitation and can be used to estimate long-term f. Our results showed that the deep soil water (2–10 m) had a mean line-conditioned excess (lc-excess) less than zero (−13.1 ‰ to −3.8 ‰) at the 15 sites across China's Loess Plateau, suggesting that evaporation effects are preserved in the isotopic compositions of the deep soil water. We then estimated f by the new lc-excess method that combines lc-excess and the Rayleigh fractionation theory, because it does not require the initial source isotopic values of soil water, which has a distinct advantage over traditional isotope-based methods (e.g. Craig-Gordon model) that require such information a priori. The estimated f of the 15 sites varied from 11 % to 30 %, and over 60 % of the variability of f was explained by the well-known Budyko dryness index. These data are also comparable with available annual estimates under similar climate regions of the world. Furthermore, these data represent a long-term average value because soil water tritium profile shows that deep soil water has a long residence time on the order of years to decades. Our work suggests that isotopic compositions of deep soil water can be used to calculate long-term average f where water flow within the unsaturated zone is piston-like flow predominantly, and the new lc-excess method provides an effective tool to estimate f.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".