The role of hydrological model complexity and uncertainty in climate change impact assessment
Bibliographic record
Abstract
Abstract. Little quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment. This study investigates and compares the varieties of different model response of three hydrological models (PROMET, Hydrotel, HSAMI), each representing a different model complexity in terms of process description, parameter space and spatial and temporal scale. The study is performed in the Ammer watershed, a 709 km2 catchment in the Bavarian alpine forelands, Germany. All models are driven and validated by a 30-year time-series (1971–2000) of observation data. It is expressed by objective functions, that all models, HSAMI and Hydrotel due to calibration, perform almost equally well for runoff simulation over the validation period. Some systematic deviances in the hydrographs and the spatial patterns of hydrologic variables are however quite distinct and thus further discussed. Virtual future climate (2071–2100) is generated by the Canadian Regional Climate Model (vers 3.7.1), driven by the Coupled Global Climate Model (vers. 2) based on an A2 emission scenario (IPCC 2007). The hydrological model performance is evaluated by flow indicators, such as flood frequency, annual 7-day and 30-day low flow and maximum seasonal flows. The modified climatic boundary conditions cause dramatic deviances in hydrologic model response. HSAMI shows tremendous overestimation of evapotranspiration, while Hydrotel and PROMET behave in comparable range. Still, their significant differences, like spatially explicit patterns of summerly water shortage or spring flood intensity, highlight the necessity to extend and quantify the uncertainty discussion in climate change impact analysis towards the remarkable effect of hydrological model complexity. It is obvious that for specific application purposes, water resources managers need to be made aware of this effect and have to take its implications into account for decision making. The paper concludes with an outlook and a proposal for future research necessities.
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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) | 0.000 | 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".