Hydrology and water quality evaluation for potential HABs under future climate scenarios
Bibliographic record
Abstract
Harmful algal blooms (HABs) are increasingly a global concern and the issue of all fifty states in the U.S as it poses a threat to human health and aquatic ecosystem. This study aimed to investigate the relationship of HABs with streamflow and water quality parameters and assess the hydrology-based potential future HABs in the Ohio River Basin at Ironton (ORBI) using the Soil and Water Assessment Tool (SWAT). SWAT was calibrated and validated against potential HABs indicators including streamflow, total suspended solids (TSS), and dissolved oxygen (DO) with acceptable accuracies. Twenty-one years (2002–2022) of model simulated data were analyzed to relate the 2015 HABs occurrence in the Ohio River with unique temperature, streamflow, TSS, and DO conditions. Additionally, a future climate model was developed to project these variables for the next two decades (2023–2043) using data from the Canadian Regional Climate Model (CRCM5) for Representative Concentration Pathway (RCP) 4.5. The 2015 HABs formation was found to be associated with a series of high flows contributing to high nutrient transport followed by an extended period of low flows balancing nutrients flushing rate. The projections of average temperature, streamflow, and TSS concentration showed increments of 5%, 15%, and 28%; whereas DO concentration showed a decrement of 8%. Flood frequency analysis was conducted to better understand the HABs probability related to peak flow conditions. For the base condition, results showed 3% probability of peak flow (4550 m 3 /s) associated with 2015 HABs formation at the Ironton gauge station and 20% probability of the same flow in the next two decades demonstrating an increased risk of HABs and highlighting the necessity of mitigation measure implementations. • Investigated the relationship between hydrology, water quality and HABs parameters. • Simulated future climate scenarios to gain insights into potential HABs formation. • Flood frequency analysis and projected indicators suggested increased risk of HABs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".