Comparison of isotopic mass balance and instrumental techniques as estimates of basin hydrology in seven connected lakes over 12 years
Bibliographic record
Abstract
Mass-balance models using stable isotopes of hydrogen and oxygen provide useful estimates of the water balance of lakes, particularly in the absence of instrumental data. However, isotopic mass balances are rarely compared directly to measured water fluxes. Here we compared instrumental and isotope-based determinations of water fluxes in seven connected lakes over 12 years to quantify how agreement between the two approaches is affected by lake type and its position in the landscape. Overall, lake-specific ratios of evaporation to inflow (E/I) from instrumental measurements (median, x̃ = 0.06, median absolute deviation, MAD = 0.06) agreed well with isotopic estimates using headwater models (x̃ = 0.14, MAD = 0.08), with the exception of one lake with limited channelized inflow of surface waters (x̃instrumental = 0.51 vs. x̃headwater = 0.24). Isotope-instrument agreement improved (x̃ = 0.09 vs. x̃ = 0.03) when basin-specific (‘best-fit’) isotope models also considered local connectivity to upstream water bodies. Comparison among years revealed that mean isotopic E/I values were lowest in 2011 (mean, μ = 0.06, standard deviation, σ = 0.09) during a 1-in-140 year spring flood, and highest during a relatively arid year, 2003 (μ = 0.22, σ = 0.19), while interannual variability in E/I generally increased with distance downstream along the mainstem of the watershed. Similar patterns of agreement between methods were recorded for water-residence time. Isotope models also documented the expected low water yield from lake catchments (μ = 36.2 mm yr−1, σ = 62.3) suggesting that isotope models based on late-summer samples integrate annual inputs from various sources that are difficult to measure with conventional methods. Overall, the strong positive agreement between methods confirms that water isotopes can provide substantial insights into landscape patterns of lake hydrology, even in ungauged systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".