Scarcity Amidst Plenty: Lower Himalayan Cities Struggling for Water Security
Bibliographic record
Abstract
In recent years, growing water insecurity in the Himalayan region has attracted new scientific research and fresh attention on policy. In this paper, we synthesize field research evidence from a sample of five Himalayan cities—three in Nepal and two in the western Indian Himalayas—on various forms of water insecurity and cities’ responses to such challenges. We gathered evidence from a field research conducted in these cities between 2014 and 2018. We show how different types of Himalayan towns (mainly hilltop, foot hill, river side, touristic, and regional trading hub) are struggling to secure water for their residents and tourists, as well as for the wider urban economy. We found that even though the region receives significant amounts of precipitation in the form of snow and rainfall, it is facing increasing levels of water insecurity. Four of the five towns we studied are struggling to develop well-performing local institutions to manage water supply. Worse still, none of the cities have a robust system of water planning and governance to tackle the water challenges emerging from rapid urbanization and climate change. In the absence of a coordinated water planning agency, a complex mix of government, community, and private systems of water supply has emerged in the Himalayan towns across both Nepal and India. There is clearly a need for strengthening local governance capacity as well as down-scaling climate science to inform water planning at the city level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".