Measuring and Modeling Stable Isotopes of Mobile and Bulk Soil Water
Bibliographic record
Abstract
Core Ideas Bulk soil water isotopes have an evaporation signal, but mobile water isotopes do not. These differences are time variant and linked to the volume and age of the mobile water. Two pore domains (fast and slow) improve simulations of soil water isotope dynamics. A new model accounts for isotopic exchange via water vapor between two pore domains. This exchange is relevant for proper simulation of the evaporation signal in bulk soil water. Recent findings from stable isotope studies have opened up new questions about differences in the isotopic composition (δ 2 H and δ 18 O) of mobile (MW) and bulk water (BW) in soils. We sampled the isotopic compositions of MW using suction lysimeters and BW with the direct‐equilibration method. The study was conducted at two landscape units in each of three catchments: the Bruntland Burn (Scotland), Dorset (Canada), and Krycklan (Sweden). We further used the numerical one‐dimensional flow model SWIS (Soil Water Isotope Simulator) to simulate the hydrometric and isotopic dynamics. The model included evaporation fractionation, allowed differentiation between a fast and a slow flow domain, and included isotopic exchange via water vapor. Our measurements showed that MW plots along the local meteoric water lines, whereas BW plots below, which is indicative of evaporation fractionation. We suggest that the relative volume of MW to BW is relevant for explaining these isotopic differences because MW volumes are usually relatively low during periods of high evaporation. Under this condition, differences between MW and plant water isotopes are not paradoxical but rather related to the water that cannot be sampled with suction lysimeters but is still available for plant water uptake. The simulations accounting for fast and slow flow supported the conceptualization of the two soil pore domains with isotopic exchange via vapor exchange because this model setup resulted in the best model performance. Overall, these findings are of high relevance for current understanding related to the source and isotopic composition of water taken up by plants.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".