Comparative Evaluation of Gridded Precipitation Datasets in Capturing Hydrological Extremes in a Mesoscale Heterogeneous Catchment in Austria
Bibliographic record
Abstract
ABSTRACT Accurate representation of extreme precipitation is crucial for assessing flood hazards and developing risk mitigation strategies. For such applications, gridded Precipitation Products (PPs) can be a promising alternative to traditional point measurements, especially in regions where such measurements are sparse or non‐existent. However, the accuracy of PPs in representing extreme precipitation should be evaluated before use. In this study, we evaluate the performance of four PPs (SPARTACUS v2.1, IMERG‐F v07, CHIRPS v2.0, and ERA5‐Land) against 33 precipitation gauges at a daily time scale over the Kamp catchment in Austria for the period 1998–2020. The hydrological response in the catchment is influenced not only by the intensity of extreme precipitation events but also by antecedent soil moisture and seasonal conditions. Continuous and categorical performance metrics are used to evaluate the performance of the PPs at gauge locations. Additionally, the Soil and Water Assessment Tool Plus (SWAT+) model is used to assess the reliability of PPs when used as forcings for hydrological modelling. The results reveal that while most evaluated products can detect no‐rain events, their ability to capture extreme precipitation events varies notably. SPARTACUS v2.1 exhibited the best ability to detect extremes at gauge locations, resulting in streamflow simulation that closely matched the observed data. IMERG‐F v07 demonstrated moderate performance in both extreme precipitation detection and corresponding peak flow generation. In contrast, CHIRPS v2.0 and ERA5‐Land showed poor performance in representing extreme precipitation, resulting in underestimated high flows and lower reliability in simulating flood‐related hydrological processes. These findings highlight the importance of evaluating the ability of PPs in capturing extreme precipitation to ensure reliable simulation of flood peaks and hydrological extremes. We conclude that catchment‐specific validation linking precipitation extremes to hydrological responses is essential for selecting appropriate precipitation forcings for hydrological applications.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".