Distributed modelling of future changes in hydrological processes of Spencer Creek watershed
Bibliographic record
Abstract
Abstract The hydrologic impact of climate change has been largely assessed using mostly conceptual hydrologic models. This study investigates the use of distributed hydrologic model for the assessment of the climate change impact for the Spencer Creek watershed in Southern Ontario (Canada). A coupled MIKE SHE/MIKE 11 hydrologic model is developed to represent the complex hydrologic conditions in the Spencer Creek watershed, and later to simulate climate change impact using Canadian global climate model (CGCM 3·1) simulations. Owing to the coarse resolution of GCM data (daily GCM outputs), statistical downscaling techniques are used to generate higher resolution data (daily precipitation and temperature series). The modelling results show that the coupled model captured the snow storage well and also provided good simulation of evapotranspiration (ET) and groundwater recharge. The simulated streamflows are consistent with the observed flows at different sites within the catchment. Using a conservative climate change scenario, the downscaled GCM scenarios predicted an approximately 14–17% increase in the annual mean precipitation and 2–3 °C increase in annual mean maximum and minimum temperatures for the 2050s (i.e., 2046–2065). When the downscaled GCM scenarios were used in the coupled model, the model predicted a 1–5% annual decrease in snow storage for 2050s, approximately 1–10% increase in annual ET, and a 0·5–6% decrease in the annual groundwater recharge. These results are consistent with the downscaled temperature results. For future streamflows, the coupled model indicated an approximately 10–25% increase in annual streamflows for all sites, which is consistent with the predicted changes in precipitation. Overall, it is shown that distributed hydrologic modelling can provide useful information not only about future changes in streamflow but also changes in other key hydrologic processes such as snow storage, ET, and groundwater recharge, which can be particularly important depending on the climatic region of concern. The study results indicate that the coupled MIKE SHE/MIKE 11 hydrologic model could be a particularly useful tool for understanding the integrated effect of climate change in complex catchment scale hydrology. Copyright © 2010 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".