A Preliminary Assessment of Water Partitioning and Ecohydrological Coupling in Northern Headwaters Using Stable Isotopes and Conceptual Runoff Models
Bibliographic record
Abstract
We combined a conceptual rainfall-runoff model and input–output relationships of stable isotopes to understand ecohydrological influences on hydrological partitioning in snow-influenced northern catchments. Six sites in Sweden (Krycklan), Canada (Wolf Creek; Baker Creek; Dorset), Scotland (Girnock) and the USA (Dry Creek) span moisture and energy gradients found at high latitudes. A meta-analysis was carried out using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model to estimate the main storage changes characterizing annual water balances. Annual snowpack storage importance was ranked as Wolf Creek > Krycklan > Dorset > Baker Creek > Dry Creek > Girnock. The subsequent rate and longevity of melt were reflected in calibrated parameters that determine partitioning of waters between more rapid and slower flowpaths and associated variations in soil and groundwater storage. Variability of stream water isotopic composition depends on the following: (i) rate and duration of spring snowmelt; (ii) significance of summer/autumn rainfall; and (iii) relative importance of near-surface and deeper flowpaths in routing water to the stream. Flowpath partitioning also regulates influences of summer evaporation on drainage waters. Deviations of isotope data from the Global Meteoric Water Line showed subtle effects of internal catchment processes on isotopic fractionation most likely through evaporation. Such effects are highly variable among sites and with seasonal differences at some sites. After accounting for climate, evaporative fractionation is strongest at sites where lakes and near-surface runoff processes in wet riparian soils can mobilize isotopically enriched water during summer and autumn. Given close soil–vegetation coupling, this may result in spatial variability in soil water isotope pools available for plant uptake. We argue that stable isotope studies are crucial in addressing the many open questions on hydrological functioning of northern environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".