Temperature Modeling, a Key to Assessing Impact on Rivers Due to Urbanization and Climate Change
Bibliographic record
Abstract
With widespread ongoing urbanization and as climate change continues, the importance of protecting the water quality of streams and lakes is intensifying. However, while many water quality constituents in lakes and rivers are of overall interest, water temperature is a ‘key’ variable as temperature influences mixing within a waterbody, influences the acceptability of the habitat for flora and fauna, and serves as a guide to the general health of a stream. To enable the assessment, a physics-based, deterministic hydraulic and heat-balance modeling procedure using the combination of MIKE SHE, MIKE HYDRO and ECO Lab is described to assess heat transfer magnitudes in portions of the Credit River, Ontario. Changes in instream temperature regimes are examined, including both frequency and spatial extent, providing insights into the impacts of urbanization in terms of seasonal temperature shifts arising from land use changes. For flow and temperature regimes, Nash–Sutcliffe model efficiency coefficient (NSE) values of 0.49 and 0.955 were achieved, respectively, for current threshold conditions. Durations of temperature increases from threshold levels indicate that land use changes from current agriculture conditions to urbanization may change stream water temperatures for 9% of the time by 1 °C, and 2% of the time by 2 °C for distances of 1000 m downstream, because of land use change from agriculture to low-density urbanization, and for 20% of the time by 1 °C, and 4% of the time by 2 °C at distances of 1000 m downstream with land use change to high-density urbanization. With climate change RCP 4.5 Scenario in 2050 (Base, for a Wet Year—2017), the continuous amount of time the stream water temperature remains at elevated temperatures of more than 3 °C (from 5000 m to 25,607 m from the most upstream point of Fletchers Creek) for a distance of 20,000 m is more than 13 h. These elevations in temperature may have serious implications for flora and fauna in the creek, particularly impacting the cold-water and mixed-water fish species.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".