Impact of lake–river connectivity and interflow on the Canadian RCM simulated regional climate and hydrology for Northeast Canada
Bibliographic record
Abstract
Lakes affect regional climate by modulating surface albedo, surface energy, and moisture budgets. This is especially important for regions such as Northeast Canada with approximately 10 % of the landmass covered by lakes, wetlands and rivers. From the regional hydrology perspective, interactions between lakes and rivers are important as streamflow patterns can be significantly modified by lake storage, and similarly lake levels can be modified by streamflows. In this study, using a suite of experiments performed with the fifth generation Canadian Regional Climate Model (CRCM5) driven by the European Centre for Medium range Weather Forecasting ERA40 reanalysis data at the lateral boundaries for the 1979–2010 period, lake–river–atmosphere interactions and their impact on the regional climate/hydrology of north-east Canada are assessed. In these CRCM5 simulations, a one-dimensional lake model represents lakes, while the rivers are modeled using a distributed routing scheme, and one of the simulations includes interflow, i.e. lateral flow of water in the soil layers. Comparison of CRCM5 simulations with and without lakes suggests significant differences in winter/summer precipitation and winter temperature for the study region. CRCM5 simulations performed with and without lake–river interactions suggest improved representation of streamflows when lake storage and routing are taken into account. Adding the interflow process leads to increased streamflows during summer and fall seasons for the majority of the rivers, causing modest changes to land–atmosphere interactions via modified soil moisture. The impact of interflow on streamflow, obtained in this study, is comparable to the impact of lake–atmosphere interactions on streamflows. This study clearly demonstrates the need for realistic representation of lake–river interactions in regional climate models for realistic simulation of regional hydrology, particularly streamflows.
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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 | 0.001 |
| 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".