Integrated modelling for river basin management: the influence of temporal and spatial scale in economic models of water allocation
Bibliographic record
Abstract
The increasing use of integrated optimization or simulation models to guide river basin management has placed greater attention on the roles that the temporal and spatial scale of each model play in determining a model's suitability and effectiveness. This is especially the case in "economic" models that incorporate monetary incentives and the optimizing behaviour of economic agents to address decisions about the sources and levels of consumptive and non-consumptive water usage within the basin. With respect to spatial scale, models that aggregate behaviour over entire river basins may prove useful for examining inter-sectoral allocations of water, but are unlikely to provide useful information about how these water allocations influence-and are influenced by-choices of crops or of technologies in irrigation, for example. With respect to temporal scale, very short-run models can illustrate options for water management within an irrigation season should unforeseen water surpluses or deficits arise. Conversely, long-run models can allow adjustment time for investments in machinery, infrastructure and changes in land uses and cropping patterns. The basin management alternatives and choices generated by models on each scale are likely to vary considerably. The paper provides specific illustrative examples from recent models of Alberta's Bow River Basin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".