Participatory model building for exploring water management and climate change futures in the Okanagan Basin, British Columbia, Canada
Bibliographic record
Abstract
Studies of climate change impacts on water resources show that some regions may experience negative impacts and additional strain on the ability to meet future demand. However, few practitioners have incorporated climate change into their water planning initiatives. To do so practitioners must first recognize climate change as a concern, acquire climate impacts information specific to their issues and scales, and then assess the potential impacts and adaptation options within the context of the system. Participatory modeling, in which stakeholders are actively involved in the construction of a computer model, is an effective method for accomplishing these tasks. The collaborative process fosters a shared learning experience and the model helps assess future conditions and policies. A year-long participatory modeling exercise was conducted in the Okanagan Basin in south-central British Columbia, Canada. The region's arid, snowmelt-dominated hydrology combined with recent rapid development make its water resources susceptible to climate change impacts. Participants, including water-related professionals, researchers, and representatives of non-governmental organizations, assisted in all stages of model development, from goal setting and issues identification to model calibration and testing. The completed model, constructed in STELLA™, conducts thirty-year monthly simulations of water supply and agricultural, residential, and conservation flow demands for a historic period and for the 2020's and the 2050's, using statistically downscaled climate information from the Hadley, CGCM2, and CSIRO general circulation models. The model suggests that climatic changes could impact the system more severely than population growth. Current projections show reduced ability of the system to meet demand, particularly during the dry month of August, when demand peaks. Adaptation strategies could play a role in maintaining system reliability. Participants found both the process and the resulting model valuable. They found the model to be a relevant and legitimate tool for exploring long-term water management in the Okanagan when used with the appropriate audience and with minor refinements. The model could support further dialogue with the Okanagan community to determine appropriate management options. This methodology is not limited to this case study, but is well-suited for other applications of resource management, policy development, collaborative learning and negotiation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".