Streamflow Alteration Index (SAI): Mapping Dam and Reservoir Impacts on Streamflow for Improved Hydrological Modeling
Bibliographic record
Abstract
Dams and reservoirs are integral to regional water management, providing critical services such as flood and drought control, water supply, hydropower generation, and recreation. However, their streamflow regulation often disrupts hydrological connectivity, sediment transport, and biodiversity, leading to significant ecological consequences. These alterations modify flow regimes across various time scales (hourly to annual), complicating the accuracy of hydrological models in affected regions. Thus, understanding how dam-induced streamflow alterations propagate through river networks is essential for informed water resource management.Current flow regulation indicators, such as those official flags from Water Survey Canada (WSC), are point-scale binary values that often under- or over-estimate regulation effects and lack spatial continuity. To address this limitation, we propose a spatially continuous metric, the Streamflow Alteration Index (SAI), which incorporates point-based alteration signals from dams, reservoirs, lakes, hydropower facilities, and hydrometric gauges into a subbasin-scale river and routing network. The SAI allows hydrologists to quantify cumulative upstream streamflow alterations at any point in a vector-based routing network. Using Ontario, Canada, as a case study, we applied the SAI to a network encompassing 245,576 subbasins, 82,928 lakes, and over 3,000 alteration sources identified from provincial and global datasets. This approach produced a seamless, high-resolution map of streamflow alteration signals across Ontario (total area: 1.07 million km2) at the subbasin scale, importantly covering both gauged points (including 1,320 flow and level gauges) and ungauged locations within the routing network. The SAI was validated against nearly 500 hydrometric gauges with WSC regulation flags.Results demonstrate that the SAI effectively identifies near-natural gauges with over 95% accuracy while revealing that more than 40% of gauges that are flagged as regulated by WSC could instead be reconsidered as model calibration targets, as many of them show little signs of significant regulation. By offering a less restrictive yet more reliable alternative, the SAI enables hydrologists to retain a larger pool of near-natural gauges for calibration, thereby enhancing streamflow predictions, particularly in data-sparse or ungauged regions. Furthermore, the SAI approach can be generalized to other routing networks in Canada and globally.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".