Model-based flood attribution over Poland: the roles of precipitation, snowmelt and soil moisture excess
Bibliographic record
Abstract
Poland is characterized by hydrometeorological variability, where conditions such as snowmelt, extreme precipitation, or soil moisture excess could be the main natural mechanisms causing fluvial flooding. The interplay of these factors may be additionally modified by climate change. Therefore, it is of high interest to attribute the occurrence of floods over Poland to single or multiple drivers as well as to analyse how this attribution evolved over time.To meet this objective in the present study, we used the dataset covering components of the water balance with a daily time step at the sub-basin level over Poland for the period 1951-2020. The data set was derived from the previously calibrated and validated Soil & Water Assessment Tool (SWAT) model for over 4,000 sub-basins. The high spatial and temporal resolution of the dataset as well as its temporal continuity allowed us to comprehensively analyse the flood drivers over the country and their evolution over time. We used a method based on the circular statistics approach, using dates of occurrence of annual maximum floods and flood-generating mechanisms to estimate the relative importance of each flood driver. In addition, two sub-periods (1952-1985 and 1986-2020) were considered in order to detect the climate change signal.The analysis of the relative importance of flood drivers showed that snowmelt is the most important cause of flooding throughout the country, followed by soil moisture excess and precipitation. The latter appeared to be the dominant driver only in a small, mountain-dominated region in the south. Soil moisture excess gained importance mainly in the northern part, although not in a uniform way, suggesting that the spatial pattern of flood generation mechanisms is also governed by other features. We also found a strong signal of climate change in large parts of northern Poland, where snowmelt is losing importance in the second sub-period in favor of soil moisture excess, which can be explained by the temperature warming and the diminishing role of snow processes. This study for the first time quantified the importance of different flood generating mechanisms over Poland, suggesting that more attention should be paid to soil moisture excess. This work also shows the potential of using high-resolution simulated water balance data sets in flood attribution studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".