Evaluating and modelling the impacts of climate change and reference datasets on river water temperatures for a hydropower system with two outlets
Bibliographic record
Abstract
Water temperature modelling has become an essential tool in the management of ectotherm species downstream of dams in North American rivers. The main objective of this project is to compare different datasets and their ability to adequately simulate water temperatures in the Nechako River, (B.C., Canada) downstream of a major dam where the flow is not managed for hydroelectric production, but spills are programmed to cool the downstream reaches. This will ultimately lead to a reassessment of water management in the context of climate change to ensure the survival of fish migrating or living in the reaches located downstream of the dam during warm periods. Water in the Nechako River stems from the Nechako reservoir at the Skins lake spillway and flows into river through a series of lakes prior to reaching Finmoore, where federal regulations stipulate that water temperatures must be maintained below 20 °C. The river has multiple tributaries on it’s 250 km journey including the Nautley river. The river flow is simulated using a 1D unsteady flow simulation and lateral inflows using HEC-RAS. Water temperature simulations are then conducted using different datasets. The first is a series of observed meteorological data spanning from 2017 to present day from two different weather stations near the river. The second dataset is ERA5, a reanalysis product that’s gridded every 0.25°. Eleven stations nearest to the river were extracted over the same period as the observations. Both datasets were used to calibrate five parameters (dust coefficient, three wind function parameters and the Richardson number) three times using the mean absolute error (MAE), Nash-Sutcliffe coefficient (NS) and root mean squared error (RMSE) by comparing the observed and simulated temperatures near Finmoore. Individual calibrations were performed over each available summer from early June to late August and then validated over the rest of the data to ensure the robustness of the results. Overall, the reanalysis dataset outperformed the available observations for thermal representation of the river. To further understand the thermal model, a sensitivity analysis was performed on the different inputs (inflow water temperature, air temperature, wind speed, etc.). The model showed very little sensitivity to the characteristics of the inflow (temperature, volume) as the point of interest was so far downstream. In fact, environmental factors such as air temperature had a greater impact on water temperature than upstream conditions at the reservoir spillway. This effect seems to be mostly attributable to Cheslatta Lake with its long water residence time that can reach upwards of three days. The potential effects of climate change on water temperature were then investigated by modifying existing weather data like air temperature with the delta method on a monthly basis using the RCP8.5 emission scenario. Water temperatures increased throughout by roughly 2.5°C downstream, near Finmoore.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".