Investigation of Lake and Wetlands Influence on Streamflow in Mesoscale Precambrian Shield Watersheds Using IsoWATFLOOD, A Tracer-Aided Hydrologic Model 
Bibliographic record
Abstract
Hydrologists continue to be challenged in accurately predicting spatial variation in storage, runoff, and other hydrological processes in both natural and disturbed landscapes. Lakes and wetlands are important hydrologic stores in Precambrian shield watersheds. Identifying how they affect streamflow, independently and/or collectively is a challenge. Tracer-aided hydrologic modeling coupled with field-based stable isotope surveys offer a potentially powerful approach to investigation of mesoscale streamflow generation processes because the influence of evaporative enrichment generates a distinct signature of the surface water endmember, and continuous and distributed simulated streamflow can be tested against field observations under a range of flow conditions. The main objectives of this research are to investigate the influence of lakes and wetlands on streamflow generation by developing application of the tracer-aided hydrologic model isoWATFLOOD for the ~ 15275 km2 Sturgeon - Lake Nipissing - French River (SNF) basin located on the Precambrian Shield in Northeastern Ontario, Canada. Monthly surveys of δ18O and δ2H in river flow were collected between 2013 to 2019 (weekly to monthly) across eight sub-catchments, with supporting observations of volumes and stable isotopes in snowcores, snowmelt, precipitation and groundwater. Application of the hydrologic model isoWATFLOOD to the SNF Basin is developed for the first time, allowing for simulation of discharge and stable isotopes in streamflow and soil moisture across multiple sub-catchments. In model building, consideration of differences in quaternary geology, landcover, and sub catchment locations are considered. Landcover ranges from the boreal forests to impervious urban areas, while dominated by temperate forest, with some coverage of agriculture/disturbed impacted systems; several major sub-catchments having hydropower regulations. Previous statistical analysis has highlighted the importance of wetlands, lakes, and quaternary geology as influential on differences in hydrologic and isotope response in SNF watershed, as a result, model building is considering different landcover types as lakes and wetlands. Six different Landover are considered for generating Group Response Units (GRUs). The model is calibrated using discharge and stable water isotope. IsoWATFLOOD can represent variation in streamflow generation across the study area. Identifying the different impacts of lakes and wetlands on streamflow generation processes in study area by applying isoWATFLOOD for the SNF watershed will be the main achievement of this study.
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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 | 0.000 | 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 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".