Scenarios and implications of land use and climate change on water quality in mesoscale agricultural watersheds
Bibliographic record
Abstract
A comparative study in two mesoscale, agricultural watersheds located in mid-latitude, developed regions (Altmühl River, Germany and in Pike River, Canada) investigated potential future land use change and climate change impacts on surface water quality. The two watersheds provided a unique opportunity to compare potential impacts of change in similar physical and climatological regions, yet under different political settings related to agricultural policies as well as water quality management and protection. The objectives of the research were to develop agricultural land use scenarios to apply to a hydrological model simultaneously with climate change simulations. This modelling framework allowed quantifying these combined impacts on streamflow, sediment loads, nitrate-nitrogen loads and concentrations, as well as total phosphorus loads and concentrations to the 2050 time horizon. The impacts of climate change were evaluated alone and then with land use change. Overall, the quality of surface water simulated in both watersheds will be deteriorated according to environmental standards set by the ministries by 2050 due to higher mean annual nutrient loads transported into the rivers. Climate change impacts were greater than land use change impacts; however land use change can have an important influence on water quality, depending on the magnitude of crop changes taking place. Field-level adaptation strategies in the Pike River were simulated to determine the extent of reducing the combined impacts of land use and climate change. The strategies were able to mitigate the combined impacts, and also to improve the quality of surface water compared to the in-stream nutrient concentrations in the reference simulation.In both watersheds, it was determined that the combined interaction between climate change and land use change in the hydrological model are non-linear. Examining the combined impacts are necessary to determine potential alterations in water quality in a basin since the direction and the magnitude are not predictable from the individual changes alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".