Implementation of a hysteretic depression model to assess future water availability in the St. Mary and Milk River transboundary basin
Bibliographic record
Abstract
The St. Mary and Milk River (SMM) basin is an international transboundary watershed flowing between Canada and the United States. The basin is composed of 2 distinct headwater basins that flow into the Saskatchewan Nelson and Mississippi basin, respectively. A diversion constructed in 1909 conveys water from the higher-yielding St. Mary River into the lower-yielding Milk River. The 1909 Boundary Waters Treaty between the USA and Canada allowed for specific entitlements for each country, allowing for sharing of the combined basin resource between both countries. Lack of storage, conveyance and changing hydrological conditions in the basin have resulted in both countries receiving less than the treaty entitlement, prompting the International Joint Commission (IJC) to study the situation. This research addresses an important part of the IJC study which required implementing hydrological models to simulate natural flow in the SMM basin and understand the reliability of any solution under future climate. The HYPE hydrological model with the HDS module was implemented to model natural flow in the basin. HDS allowed for the explicit representation of the contributing area dynamics of prairie potholes which significantly impact the hydrology of the Milk River. The model was then used to run an ensemble of statistically downscaled future climate scenarios based on the CMIP6 models. Explicitly representing prairie potholes under future climate provided an opportunity to examine how non-contributing area might change in the future. We present an evaluation of historical model performance, a future climate analysis of streamflow in the basin, and the implications of the future climate conditions on apportionment practices in the basin. Results from this research will inform IJC decisions on future practices and infrastructure in this important transboundary basin and may add a new dimension to future practices as the effects of prairie potholes have never been explicitly considered.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".