Classification of Floods in Europe and North America with Focus on Compound Events
Bibliographic record
Abstract
Compound events occur when multiple drivers or hazards occur in the same region or on the same time scale, hence amplifying their impacts. Compound events can cause large economic damage or endanger human lives. Thus, a better understanding of the characteristics of these events is needed in order to protect human lives. This study investigates the drivers and characteristics of floods in Europe and North America from the compound event perspective. More than 100 catchments across Europe and North America were selected as case study examples in order to investigate characteristics of floods during a 1979–2019 period. Air temperature, precipitation, snow thickness, snow liquid water equivalent, wind speed, vapour pressure, and soil moisture content were used as potential drivers. Annual maximum floods were classified into several flood types. Predefined flood types were snowmelt floods, rain-on-snow floods, short precipitation floods and long precipitation floods that were further classified into two sub-categories (i.e., wet and dry initial conditions). The results of this study show that snowmelt floods were often the dominant flood type in the selected catchments, especially at higher latitudes. Moreover, snow-related floods were slightly less frequent for high altitude catchments compared to low- and medium-elevation catchments. These high-altitude areas often experience intense summer rainstorms that generate the highest annual discharges. On the other hand, snowmelt-driven floods were the predominant flood type for the lower elevation catchments. Moreover, wet initial conditions were more frequent than the dry initial conditions, indicating the importance of the soil moisture for flood generation. Hence, these findings can be used for flood risk management and modelling.
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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 | 0.000 | 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".