The effect of climate and land use change on flow duration in the Maryland Piedmont region
Bibliographic record
Abstract
Abstract This paper describes the use of a continuous streamflow model to examine the effects of climate and land use change on flow duration in six urbanizing watersheds in the Maryland Piedmont region. The hydrologic model is coupled with an optimization routine to achieve an agreement between observed and simulated streamflow. Future predictions are made for three scenarios: future climate change, land use change, and jointly varying climate and land use. Future climate is modelled using precipitation and temperature predictions for the Canadian Climate Centre (CCC) and Hadley climate models. Results show that a significant increase in temperature under the CCC climate predictions produces a decreasing trend in low flows. A significant increasing trend in precipitation under the Hadley climate predictions produces an increasing trend in peak flows. Land use change by itself, as simulated by an additional 10% increase in imperviousness (from 20·5 to 30·5%), produces no significant changes in the simulated flow durations. However, coupling the effects of land use change with climate change leads to more significant decreasing trends in low flows under the CCC climate predictions and more significant increasing trends in peak flows under Hadley climate predictions than when climate change alone is employed. These findings indicate that combined land use and climate change can result in more significant hydrologic change than either driver acting alone. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".