
95% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Hydrosphere
Volume 13 - 2025 | doi: 10.3389/feart.2025.1548557
The final, formatted version of the article will be published soon.
You have multiple emails registered with Frontiers:
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Accurate precipitation data are crucial for many sectors and applications, like managing water resources, for agriculture, or assessing the risks of hydrometeorological extreme events like floods and droughts, which are expected to further increase with climate change. This study compares the spatial and temporal characteristics of ten state-of-the-art, commonly used precipitation datasets, with each other and against reference in-situ precipitation gauge observations from the European Climate Assessment & Dataset (ECA&D) over Germany. The objectives are to evaluate whether bias adjustment is needed for the European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution (HRES) meteorological forecasting dataset, which is used in near real-time water resources modeling with the ParFlow integrated hydrologic model, and if so, to assess whether any of the observation-based comparison datasets might be suitable for this bias adjustment. Results show that HRES and Reanalysis v5 (ERA5) capture spatial patterns well, albeit with deficits in reproducing extremes, and over- and underestimation at low and high altitudes, respectively. COSMO-REAnalysis (COSMO-REA6) captures the spatial precipitation patterns less effectively but outperforms HRES and ERA5 in reproducing extreme events. HYRAS-DE-PRE (HYRAS), Radar Online Adjustment (RADOLAN), and Radarklimatologie (RADKLIM) perform very well, showing strong spatial accuracy and potential for bias adjustment, though their limited spatial coverage potentially restricts their use across all river catchments affecting Germany. The Operational Program of the Exchange of Weather Radar Information (OPERA) tends to underestimate mean precipitation quantities and extreme events. Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) Final shows an improvement over IMERG-Late. EUropean RADar CLIMatology (EURADCLIM) outperforms OPERA due to gauge adjustments. The methodology and findings from this study may also be applicable to similar evaluations in other regions, and may help in the selection of precipitation datasets, e.g., for hydrological model forcing or for bias adjustments.
Keywords: precipitation dataset evaluation1, bias adjustment3, Germany4, daily temporal resolution5, extreme precipitation6, hydrological modeling and forecasting
Received: 19 Dec 2024; Accepted: 19 Mar 2025.
Copyright: © 2025 Hammoudeh, Goergen, Belleflamme, Giles, Troemel and Kollet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Suad Hammoudeh, Agrosphere (IBG-3), Institute of Bio- and Geosciences, Julich Research Center, Helmholtz Association of German Research Centres (HZ), Jülich, Germany
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Research integrity at Frontiers
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.