Socio-hydrological modeling of the tradeoff between flood control and hydropower provided by the Columbia River Treaty
Bibliographic record
Abstract
Abstract. The Columbia River Treaty (CRT) signed between the United States and Canada in 1961 is known as one of the most successful transboundary water treaties. Under continued cooperation, both countries equitably share collective responsibilities of reservoir operations and flood control and hydropower benefits from treaty dams. As the balance of benefits is the key factor of cooperation, future cooperation could be challenged by external social and environmental factors which were not originally anticipated or change in the social preferences of the two actors. To understand the robustness of cooperation dynamics, we address two research questions. (i) How does social and environmental change influence cooperation dynamics? (ii) How do social preferences influence the probability of cooperation for both actors? We analyzed infrastructural, hydrological, economic, social, and environmental data to inform the development of a socio-hydrological system dynamics model. The model simulates the dynamics of flood control and hydropower benefit sharing as a function of the probability to cooperate, which in turn is affected by the share of benefits. The model is used to evaluate scenarios that represent environmental and institutional change and changes in political characteristics based on social preferences. Our findings show that stronger institutional capacity ensures equitable sharing of benefits over the long term. Under the current CRT, the utility of cooperation is always higher for Canada than non-cooperation, which is in contrast to the United States. The probability to cooperate for each country is lowest when they are self-interested but fluctuates in other social preference scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".