Analysis of Lakewater Isotopes in the Northern Great Plains: Insights From Long-Term Monitoring and Spatial Surveys
Bibliographic record
Abstract
General circulation models predict that the precipitation deficit of the Northern Great Plains (NGP) will become more pronounced in the future, and that human adaptation to future climate change will require a greater understanding prairie hydrology in order to maintain freshwater ecosystems. One method for disentangling hydrological complexities is to use stable isotopes of hydrogen and oxygen in water to measure the hydrological properties of lakes, including their water balance. In this thesis, I present a long-term (2003-2016), bi-weekly record of water isotopes during summer to assess the differences between instrumental and isotopic measures of hydrology, and determine the variability in lakewater isotopes at the sub-annual and interannual scales. Insights from this long-term record were then applied to a spatial survey of over 100 lakes to quantify the spatial diversity of hydrologic parameters in lakes of the Canadian NGP and investigate the relationship between catchment characteristics and water balance of individual basins. Overall, lake-specific ratios of evaporation to inflow (E/I, %) from direct measurements (mean (μ) = 15.0%, standard deviation (σ) = 20.7) agreed well with isotopic estimates using headwater basin models (μ = 15.3, σ =7.8). Isotope-instrument agreement was improved (μ =12.6 vs. 6.2%) when basin-specific isotope models that considered local connectivity to upstream water bodies were used in calculations. In my assessment of isotope variability, seasonal variation in water isotope values routinely exceeded that at annual scales and demonstrated rapid changes in water sources within a single season, particularly in systems with water residence times <1 year. Inter-annual variability was regulated mainly by large hydrological events that caused isotopic source waters to become more similar across the Qu’Appelle River watershed. A spatial survey of 100+ prairie lakes revealed that isotopic values of inflow to basins was more similar to that of rainfall (δI, μ = -13.5‰, σ = 2.0) than to snow, and further showed that the flow regimes of most lakes (71%) were categorized as open despite the scarcity of channelized surface inflows. Mean water yield 100.8 mm yr-1 (σ= 181.0) and runoff coefficients 22.1% (σ= 45.9) were elevated relative to average conditions in the region, suggesting that lakes were an important feature capturing water on the prairie landscape. Together, these three studies provided a comprehensive assessment of the hydrological status of lakes in the Canadian NGP, and can be used to inform future research and management projects.
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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) | 0.000 | 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 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".