A review of hydrological modelling of basin-scale climate change and urban development impacts
Bibliographic record
Abstract
Hydrological modelling is a valuable tool for researchers in geography and other disciplines for studying the processes governing impacts of climate change and urban development on water resources and for projecting potential ranges of impacts from scenarios of future change. Modelling is an inherently probabilistic exercise, with uncertainty amplified at each stage of the process, from scenario generation to issues of scale, to simulation of hydrological processes, to management impacts. At the basin scale, significant factors affecting hydrological impacts of climate change include latitude, topography, geology, and land use. Under scenarios of future climate change, many basins are likely to experience changes not only in their mean hydrology, but also in the frequency and magnitude of extreme hydrological events. Impacts of climate change on water quality are largely determined by hydrological changes and by the nature of pollutants as flushingor dilution-controlled. The most significant impact of urban development on water resources is an increase in overall surface runoff and the flashiness of the storm hydrograph. The increase in impervious surface area associated with urban development also contributes to degradation of water quality as a result of non-point source pollution. Modelling studies on the combined impacts of climate change and urban development have found that either change may be more significant, depending on scenario assumptions and basin characteristics, and that each type of change may amplify or ameliorate the effects of the other. Hydrological impacts of climate change and urban development are likely to significantly affect future water resource management.
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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 | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".