Evaluating the impact of flash flood on the water quality of Alaknanda river using Water Quality Index: A study from the Garhwal Himalaya, India
Bibliographic record
Abstract
The Alaknanda River, a major tributary of the Ganga in the Garhwal Himalayas, poses severe challenges to water quality and ecosystem health. The region has a history of disastrous flash floods (e.g., 1894, 1979, 2013, 2021, and 2023) and is very vulnerable to flooding due to its geographic location. To date, no scientific research has been examined the effects of the flash flood on August 14, 2023, on the water quality dynamics of the Alaknanda River, despite the frequency and intensity of such major flood events. While previous research has analyzed river water quality in the Himalayas in general, it has not particularly used a time-series WQI technique to investigate the short-term effects of flash floods on physico-chemical parameters. This study addresses that gap by highlighting the challenges of increased sedimentation, pollutant transport, and cation variability during monsoon events. Utilizing the Weighted Arithmetic Water Quality Index (WQI), the research analyses trends across six river sites during July (pre-flood), August (during flood), and September (post-flood) 2023. Results reveal that while water quality remains mainly good i.e., within permissible limits, the flood event significantly altered physico-chemical parameters, with WQI values peaking in August at all sites. This study provides critical insights into the vulnerabilities of water systems to extreme weather events and underscores the need for robust flood management strategies to ensure potable water during monsoons. These findings contribute to developing resilient water resource management practices in flood-prone Himalayan regions. • Flash flood on Aug 14, 2023, altered Alaknanda River water chemistry. • WQI peaked during flood month, indicating quality decline at some sites. • Physico-chemical parameters showed seasonal and site-specific variations. • Most sites had good water quality; some approached poor levels in August. • Study emphasizes flood monitoring for Himalayan water management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".