Assessment of hydrological extremes and water resources availability under climate change in the Main river basin, Germany
Bibliographic record
Abstract
Human-induced climate change is already impacting hydrometeorological extremes in every region across the globe (IPCC, 2021). In fact, changes in the climate system are projected to become larger with increasing global warming. This includes regional increase of frequency and intensity of heavy precipitation, hydrological extremes, agricultural and ecological droughts. Recent studies indicate that this problematic seems to be particularly relevant also in Central Europe, a region usually perceived as an area of comparatively low vulnerability to climate change due to its high adaptive capacity. The presented study focuses on the Main river basin, a tributary to the Rhine river in Germany: the watershed, covering an area of 21.519 km² (at Kleinheubach gauging station) with over four million inhabitants, is characterized by intense gradients of topography and climate, and diversified land use. The region already suffers from water scarcity and consequently water use conflicts are becoming more relevant recently, especially during summer months. This study presents results from a single hydrological model initial condition large ensemble (i.e. the spatially explicit process-based hydrological model WaSiM (Willkofer et al., 2020)) being driven by 50 members of the Canadian Regional Climate Model Vers.5 (CRCM5) over Europe (Leduc et al., 2019) for the time interval 1950-2099. A remarkable decline of mean annual runoff in the Main river basin is projected, while both frequency and intensity of extreme floods show strong increasing trends. This work is meant to tackle this challenge as a first step to achieve co-designing systemic solutions and science-driven technical and cross-sectoral innovations to build new climate-resilient development pathways for efficient water resources management. The presented study is supported by results from the project ClimEx (www.climex-project.org), funded by the Bavarian State Ministry for the Environment and Consumer Protection, and the project ARSINOE (GA: 101037424), funded under EU’s Horizon 2020 research and innovation programme.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".