Modelling stream temperature with multiple hydroclimatological temperature models
Bibliographic record
Abstract
Stream temperature is an important parameter to evaluate the water quality and biodiversity in aquatic ecosystems. Climate change and human activities (e.g. land use change) are affecting stream temperature, potentially leading to negative impacts on the habitats of native species and sustainability of aquatic ecosystems. Therefore, it is important to monitor and understand stream temperature under different conditions to better protect the aquatic ecosystems. The conventional in-situ measurements from gauge stations provide the most accurate stream temperature data, but they are often sparse and limited in terms of data length (temporal) and spatial coverages (many regions have no measurements). Stream temperature modelling is an effective way to extrapolate from limited measurements in both space and time, and it is the only way to predict the future to assess the climate change impacts. The stream temperature is influenced by meteorological and hydrological factors, and the relationship between the stream temperature and physical conditions is complex and can vary spatially and temporally. Different statistical and process/physically-based stream temperature models have been developed with the latter generally performing better. The Soil and Water Assessment Tool (SWAT) is a semi-distributed process-based hydrological model built with a simple statistical model to simulate stream temperature using only air temperature. Two hydroclimatological stream temperature models were recently developed to improve the capability of the SWAT model for simulation of stream temperature by considering influences of hydrological conditions and more detailed water-air heat transfer processes. The two recently developed models were tested mainly in a few river basins in U.S. and Canada. This study aims to compare and evaluate -for the first time- the performance of three models in simulating stream temperature in the Vils Basin located in Bavaria, Germany. The SWAT model is first calibrated and validated against the measured streamflow at the basin outlet on a daily timescale to ensure satisfactory streamflow simulation. Then the three different stream temperature models are run and evaluated with measured stream temperature at both daily and monthly time scales. The parameters and simulation results from the three different stream temperature models are analyzed. This study complements existing studies to improve our understanding of the performance of different stream temperature models in different river basins.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".