Delineating extent and magnitude of river flooding to lakes across a northern delta using water isotope tracers
Bibliographic record
Abstract
Abstract Hydrological monitoring in complex, dynamic northern floodplain landscapes is challenging, but increasingly important as a consequence of multiple stressors. The Peace‐Athabasca Delta in northern Alberta, Canada, is a Ramsar Wetland of International Importance reliant on episodic river ice‐jam flood events to recharge abundant perched lakes and wetlands. Improved and systematic monitoring of landscape‐scale hydrological connectivity among freshwater ecosystems (rivers, channels, wetlands, and lakes) is needed to guide stewardship decisions in the face of climate change and upstream industrial development. Here, we use water isotope compositions, supplemented by measurements of specific conductivity and field observations, from 68 lakes and 9 river sites in May 2018 to delineate the extent and magnitude of spring ice‐jam induced flooding along the Peace and Athabasca rivers. Lake‐specific estimates of input water isotope composition (δI) were modelled after accounting for influence of evaporative isotopic enrichment. Then, using the distinct isotopic signature of input water sources, we develop a set of binary mixing models and estimate the proportion of input to flooded lakes attributable to river floodwater and precipitation (snow or rain). This approach allowed identification of areas and magnitude of flooding that were not captured by other methods, including direct observations from flyovers, and to demarcate flow pathways in the delta. We demonstrate water isotope tracers as an efficient and effective monitoring tool for delineating spatial extent and magnitude of an important hydrological process and elucidating connectivity in the Peace‐Athabasca Delta, an approach that can be readily adopted at other floodplain landscapes.
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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 | 0.001 | 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) | 0.000 | 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".