Comparison and evaluation of different precipitation products in capturing climate extremes in Kamp Catchment, Austria
Bibliographic record
Abstract
The availability of accurate long-term gap-free precipitation data at high spatiotemporal resolutions is crucial for hydroclimatic extremes assessment, water resources management, infrastructure design, hydrological modeling, and evaluation of climate change impacts. However, many ground precipitation data contain gaps, which can hinder accurate assessments and analyses. Therefore, different gridded precipitation products (PPs) are promising alternatives to overcome this deficiency, especially in heterogeneous regions with different terrains where ground observations are sparse or non-existent. This study evaluates four daily precipitation products, i.e., SPARTACUS, IMERG-V07, CHIRPS-V2.0, and ERA5-land, to determine their performance in representing observed patterns, the intensity, and frequency of extreme precipitation events in Kamp Catchment in Austria for the period 1998-2020 at different temporal scales. The Kamp River is the longest in the “Waldviertel” region and has key ecological, societal, and economic functions, with many popular leisure and excursion destinations for tourism. The catchment also frequently experiences severe floods, causing adverse socioeconomic impacts. Ground-based precipitation data from 33 stations distributed within and around the catchment are used to conduct point-to-pixel evaluation for the four products. To measure the disparity between the products and the ground observations, six performance metrics were used: the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Nash-Sutcliffe Efficiency (NSE), Correlation coefficient (r), and Willmott index of agreement (d). The intensity and frequency of extreme precipitation reflected by the four evaluated PPs are assessed using selected extreme climate indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The PPs were ranked to select the best-performing product in the study area. The ranking results of the performance metrics revealed that SPARTACUS is the top-performing product on a daily and monthly scale and in capturing the frequency and intensity of precipitation extremes, followed by IMERG-V07 and ERA5-land, whereas CHIRPS-V2.0 ranked the lowest. SPARTACUS showed superior performance across the catchment with the highest correlation with the observed data and lowest bias (on daily and monthly scales, mean r values are 0.92 and 0.96 and mean MBE values are -0.02 and -0.81, respectively). Other products exhibit systematic precipitation underestimation. Regarding capturing precipitation extremes, all products show low skills and overestimate the daily extreme precipitation events, with the highest NSE of -0.32 shown in SPARTACUS. CHIRPS-V2.0 and ERA5-land presented different performances for detecting the longest wet and dry spells in the catchment. CHIRPS-V2.0 overestimated the consecutive dry days (CDD) and underestimated the consecutive wet days (CWD), whereas ERA5-land shows the opposite pattern. SPARTACUS shows minor overestimation of CDD and underestimation of CWD (MBE = -0.09 and 0.13 mm, respectively). Accordingly, a simple drought assessment was performed in the catchment using SPARTACUS data and the Standardized Precipitation Index (SPI). Our results highlight the importance of site-specific validation before using any precipitation products.This study is conducted within the frame of the DISTENDER project (EU Horizon-ID 101056836), where climate extremes and climate change impacts upon several European catchments are analyzed and robust adaptation strategies are developed. Keywords: Precipitation extremes, Precipitation products, Point-to-pixel evaluation, SPI, Kamp catchment, Austria
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".