An Exploration of Water Resources Futures under Climate Change Using System Dynamics Modeling
Bibliographic record
Abstract
Results from an integrated assessment of water resources in the Okanagan Basin in south-central British Columbia, Canada, show that climate change will both reduce water supply and increase water demand, leading to more frequent and more severe water shortages than in the recent historic record. Competing uses of water are primarily agricultural irrigation (orchards, cropland, pasture, and vineyards), residential, and ecological (aquatic ecosystem supports salmonids). The region is semi-arid and the agriculturally-based economy is particularly sensitive to the effects of climate change. The model characterizes a region that is 7500 km2 and simulates using a monthly timestep. Scenarios are derived through 2069 using downscaled climate model results coupled with watershed modeling studies, as well as studies that linked crop water and urban demands to climate. The model enables users to explore plausible future supply and demand scenarios (agricultural, residential and instream flow demands) while evaluating strategies for adapting to future climate change. In the simulated worst case scenario, the combined effect of future climate change and population growth could cause annual water deficits (historically experienced once every 10 years) to become increasingly frequent by the 2050’s time period – perhaps every 2 out of 3 years annually. During the dry month of August, when demand is high, shortages could occur every 1 out of 2 years. An adaptation scenario with moderate levels of conservation is tested and shows minor improvements from the no adaptation scenario. Further study is required to explore the potential of adaptation on reducing future water deficit.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".