Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin
Bibliographic record
Abstract
Both climate and land use changes can influence water quality and quantity in different ways. Thus, for predicting future water quality and quantity trends, simulations should ideally account for both projected climate and land use changes. In this paper, land use projections and climate change scenarios were integrated with a hydrological model to estimate the relative impact of climate and land use projections on a suite of water quality and quantity endpoints for a Canadian watershed. Climatic time series representing SRES change scenario A2 were generated by downscaling the outputs of the Canadian Regional Climate Model (version 4.1.1) using a combination of quantile-quantile transformation and nearest neighbor search. The SWAT (Soil and Water Assessment Tool) model was used to simulate streamflow, nitrogen and phosphorus loading under different climate and land use scenarios. Results showed that a) climate change will drive up maximum monthly streamflow, nitrate loads, and organic phosphorus loads, while decreasing organic nitrogen and nitrite loads; and b) land use changes were found to drive the same water quality/quantity variables in the same direction as climate change, except for organic nitrogen loads, for which the effects of the two stressors had a reverse impact on loading.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".