Water demand management and adaptations for mountain resort communities in the Canadian Columbia Basin
Bibliographic record
Abstract
Mountain resort communities need to consider how they will adapt to the increasing demand for domestic water from a growing, often seasonal, population; and to the prospects of climate change. This investigation evaluated domestic water use in Rossland and Invermere: Two resort communities in the Columbia Basin that both face water supply concerns resulting from excessive use and increased climatic variability. The study examined historic and current water use, then developed scenarios of future domestic water demands that take into consideration possible growth and water conservation, or demand management (DM) options. Indoor and outdoor conservation strategies evaluated for domestic and tourism-related use included metering with an increasing block rate, a DM “package” of fixtures/appliances, and rainwater collection. The study does not involve an economic analysis, nor does it examine the values driving water use and consumption. It focuses on DM as it relates to potential water savings through conservation. The primary methods used involved collecting and correlating current water use and climate data, and estimating potential savings from various conservation strategies, both now and in the future. The results confirmed that when domestic consumption is isolated from other sectors, domestic per capita water consumption in both communities remains very high: The average annual consumption for Rossland was 483 litres/capita/day (lcd), while Invermere used 353 lcd. Outdoor water use during the summer resulted in an increase of 50% for Rossland and 40% for Invermere. Future water demands were modelled using scenarios for no conservation versus conservation strategies. The results showed that conservation could accommodate an extra 5,000 people in Rossland and 2,500 people in Invermere, without increasing supplies. This study reinforces the argument that, rather than searching for new water sources or expanding water storage capacity, immediately reducing water consumption is an effective option for sustainable resource use, and will lessen the effects of climate change. The research also highlights the need for better record keeping and/or data collection in four main areas: water consumption (demand) and river flows/ aquifer recharge rates (supply) at the municipal and watershed levels; tourist activity in towns that are tourism-dependent; and high altitude climate information.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".