Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming
Bibliographic record
Abstract
Abstract River flooding is among the costliest natural disasters with severe economic, societal, and environmental consequences. However, substantial uncertainties remain in global and regional projections of future flood conditions simulated by global climate models (GCMs) and/or global hydrological models (GHMs). Using physical models coupled with machine learning (ML), for the first time, we project changes in flood magnitudes of 2062 global river basins by constraining physical‐based streamflow simulations with observations under 1.5°C and 2°C warming scenarios identified for the Representative Concentration Pathway 8.5. We found that, during the validation period, the GHMs‐simulated flood magnitudes would improve with reduced uncertainty over the selected river basins after ML with a Long Short‐Term Memory network. Our estimation suggested that flood magnitudes would increase in many Northern Hemisphere mid‐ and high‐latitude rivers (e.g., Lena River, Amur River and Volga River) but decrease in some river basins in southern Finland and Eastern Europe in future periods (i.e., 1.5°C and 2°C warming levels). In 1.5°C and 2°C warmer worlds, the decreasing flood magnitudes in most South American rivers are associated with decreased soil moisture and increased evapotranspiration induced by warmer temperatures. Although the geographical pattern of changes in flood magnitudes for the +2°C experiment is close to that of the +1.5°C experiment, a 1.5°C warming target is more likely to reduce flood magnitudes of many river basins worldwide (e.g., in central and eastern Siberia, Alaska/Northwest Canada and South America).
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".