An event-based typology of hydrological droughts in Bavaria: Type definition, uncertainties and future probability changes at decadal scales
Bibliographic record
Abstract
<!--!introduction!--><b></b> Southern Germany was subject to considerably adverse effects on water supply during recent hot and dry (compound) summers like 2022, 2018 or 2015. They included, but were not limited to drinking water scarcity, transport shortages, forest damages, and crop yield failures. Major rivers experienced extreme low flows which in parts caused or aggravated these effects. In order to categorize these (hydrological) drought events, we investigate the occurrence and extremeness of alike events in observational data and hydrological simulations in 98 southern German, Austrian and Swiss river catchments under current and climate change conditions. Low flow events correspond to days below a variable percentile threshold with respect to 1991-2020. Recently observed events serve as analogue for event type definition. Event type (e.g., 2018-type) specific characteristics include volume deficit, event duration, and spatial coverage. With this, we also address limitations and uncertainties related to event type definition. Since we aim at robustly quantifying these event types, their characteristics and implications, the sample size of observational records alone is insufficient. Therefore, we employ a large single model initial condition ensemble of a regional climate model (the Canadian Regional Climate Model, version 5; CRCM5-LE) to produce 50 instances of hydrological simulations (1960-2099) in the Water balance Simulation Model (WaSiM). This allows to also investigate currently extremely unlikely or newly emerging events. We further use the comprehensive sample of typified events in the ensemble to assess preceding meteorological and hydrological conditions to create a basis of potential drought predictors on catchment scale.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".