Incorporation of the equilibrium temperature approach in a Soil and Water Assessment Tool hydroclimatological stream temperature model
Bibliographic record
Abstract
Abstract. Stream temperature is an important indicator for biodiversity and sustainability in aquatic ecosystems. The stream temperature model currently in the Soil and Water Assessment Tool (SWAT) only considers the impact of air temperature on stream temperature, while the hydroclimatological stream temperature model developed within SWAT model considers hydrology and the impact of air temperature in simulating the water-air heat transfer process. In this study we propose using the equilibrium temperature approach to model complex heat transfer processes at the water-air interface, which reflects the influences of air temperature, solar radiation, wind speed and stream water depth on the heat transfer process. The thermal capacity of the streamflow is modelled by the variation of the stream water depth. An advantage of this equilibrium temperature model is the simple parameterization, with only two added parameters to model the heat transfer processes. The equilibrium temperature model is applied and tested in the Athabasca River Basin (ARB) in Alberta, Canada. The model is calibrated and validated at five stations throughout different parts of the ARB for which high-frequency observed stream temperature data are available. The results indicate that the equilibrium temperature model provided better and more consistent performances for the different regions of the ARB with the values of Nash-Sutcliffe Efficiency (> 0.67) greater than those of the original SWAT model and the hydroclimatological model. Overall, the equilibrium temperature model uses existing SWAT meteorological data as input, can be calibrated using fewer parameters and less effort, and has an overall better performance for the simulation of daily stream temperatures. Thus, it can be used as an effective tool for predicting the change in stream temperature regimes under varying hydrological and meteorological conditions. In addition, the impact of the stream temperature simulations on chemical reaction rates and concentrations was tested. The results indicate that the improved performance of the stream temperature simulation could significantly affect chemical reaction rates and the simulated concentrations and the equilibrium temperature model could be a potential tool to model stream temperature for water quality simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".