Global Water Monitor 2023, Summary Report
Bibliographic record
Abstract
Our global water systems are under mounting pressure as climate change drives more extreme weather events and disrupts the water cycle. The year 2024 was a year of extremes but not an isolated occurrence. It fits with a worsening trend of more intense floods, prolonged droughts, and record-breaking extremes. These changes impact water availability and increase the risks to lives, infrastructure and ecosystems from water-related disasters. Reliable and timely information about water resources and hazards is more crucial than ever, yet traditional groundbased measurement networks continue to decline. Satellite observations now play a vital role, offering rapid and consistent global data on the atmosphere and Earth's surface, but they should not replace networks on the ground. The Global Water Monitor Consortium unites public and private organisations to deliver open, actionable climate and water data. By integrating satellite and ground observations, we aim to provide timely updates on critical aspects of the water cycle. Our Global Water Monitor platform (www.globalwater.online) allows anyone to explore a wealth of climate and water data free of charge. This third annual report builds on the work of previous years, summarising the state of the global water cycle in 2024, identifying key trends, and analysing major hydrological events. It includes updated metrics on rainfall, temperature, air humidity, river flows and water stored in lakes, soil and underground. It also provides insights into extreme rainfall and temperatures. This report reinforces a clear message: as the planet warms, water challenges are escalating, year after year. By trying to provide information on changes and events, we hope to support informed decision-making to protect communities, infrastructure, and ecosystems in an increasingly volatile future
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.033 |
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".