Scenario Analysis of Cooperation Dynamics on the Columbia River under Changing Conditions using Socio-Hydrological Modelling
Bibliographic record
Abstract
The Columbia River Treaty, signed in 1961, solidifies cooperation between the United States and Canada to manage the operation of the Columbia River’s extensive dam network jointly to optimize benefits for the whole system. Under the treaty, Canada operates dams to provide flood protection and maximize hydropower potential downstream. In exchange, the U.S. compensates Canada with half of the estimated benefits of the treaty, which provides an economic incentive to cooperate not seen in many other transboundary basins. However, since the treaty was established, this highly-managed system has responded to unanticipated external social and environmental factors. For example, mounting social pressure in the 1990s to protect the aquatic environment resulted in operational changes to U.S. dams to accommodate flows for fish migration, which ultimately resulted in financial losses for hydropower producers. These changes affected the relative benefits each country receives from cooperation. Utilizing a range of hydrological, economic, social, and environmental datasets, a socio-hydrological model was developed that simulates system operations using historical data to mimic operational changes, shifts in flood control and hydropower production, and cooperation dynamics. Renegotiations of the Columbia River Treaty started in 2018, and the new treaty in 2024 must include provisions for environmental protection that were, originally, not considered. The purpose of this study is to use the established model to envision how changing conditions such as climate change, spring fish flows, and First Nation rights would affect each country’s willingness to cooperate. For example, how would changes in snowpack upstream or seasonal changes in precipitation alter the hydrology of the basin and, in turn, the benefits each country receives from cooperation. This scenario analysis provides insight into how a revised treaty that takes future uncertainties into account would affect the balance of benefits to maintain or disrupt cooperation on the Columbia River.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".