Vulnerability of Transboundary River Basins in a Changing Climate: A Case Study of the Saskatchewan River Basin
Bibliographic record
Abstract
About half of the Earth’s land surface is covered by transboundary water resources. Approximately 40 percent of the world’s population relies on water resources crossing political borders. Within transboundary river basins, allocating these limited and often depleting resources to states is challenging due to various, and often conflicting interests of stakeholders. Treaties and River Basin Organizations (RBOs) provide the primary means of cooperation between states, building institutional capacity, and lowering the likelihood of hydropolitical tensions. A resilient transboundary river system should be able to tolerate the pressures from different stressors to provide a reliable source of water. However, geopolitical, socio-economic, and biophysical stressors threaten the governance of these basins. Climate change is one of the biophysical stressors which is likely to increasingly challenge transboundary river systems. A thorough understanding of climate-change-induced vulnerabilities of a transboundary system, therefore, can help decision and policy makers to plan for adaptive measures to avoid hydropolitical tensions. The Saskatchewan River Basin, located in western Canada and shared amongst the three Canadian provinces of Alberta, Saskatchewan, and Manitoba and also the American state of Montana, is used as a case study. In particular, this thesis assesses the viability of the 1969 Master Agreement on Apportionment that provides the basis for water allocation of eastward flowing interprovincial streams in face of deep uncertainty around future climate change. To this end, a vulnerability assessment methodology consisting of three main components is proposed. First a large set of plausible weather scenarios is generated by perturbing important features of climate including winter precipitation, summer precipitation, annual temperature, and the annual number of dry days. Second, the weather scenarios are fed into a conceptual hydrological model calibrated to historical record to generate a wide range of plausible future streamflow scenarios. Third, the streamflow scenarios are used as input to a water resources management model that distributes the water throughout the transboundary river system. Results show a moderate risk of failure in the southern part of the basin in meeting the criteria established in the apportionment agreement under certain possible changes in climate regime of the region. The risk of not meeting the minimum flow is accompanied by major deficits to irrigation and non-irrigation demands as well as minimum environmental flows. A lower risk is observed in other parts of the basin, mainly due to lower water usage and abstraction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".