Climate change impacts on river basin and freshwater ecosystems: some observations on challenges and emerging solutions
Bibliographic record
Abstract
Despite uncertainty pertaining to methods, assumptions and input data of climate change models, most models point towards a trend of an increasing frequency of flooding and drought events. How these changes reflect water management decisions and what can be done to minimize climate change impacts remains unclear. This paper summarizes and extends the workshop outcomes on ‘Climate Change Impacts on Watershed Management: Challenges and Emerging Solutions’ held at the IWA World Water Congress and Exhibition, Montréal, 2010, hosted by the IWA Watershed and River Basin Management Specialist Group. The paper discusses climate change impacts on water management of freshwater ecosystems and river basins, and illustrates these with three case studies. It is demonstrated through the case studies that engagement of relevant stakeholders is needed early in the process of building environmental flows and climate change decision-making tools, to result in greater buy-in to decisions made, create new partnerships, and help build stronger water management institutions. New alliances are then created between water managers, policy makers, community members, and scientists. This has been highlighted by the demonstration of the Pangani integrated environmental flow assessment, through the Okavango River Basin case study, and in the more participatory governance approach proposed for the Delaware River Basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".