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Lookup NU author(s): Xiaobin QiuORCiD, Dr Amy GreenORCiD, Dr Stephen BlenkinsopORCiD, Professor Hayley Fowler
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/Blended rainfall datasets take advantage of the strengths of multiple sources of rainfall data, producing higher accuracy and broader coverage. However, current blending methods either suffer from low accuracy or are overly complex. Some focus solely on improving statistical performance, but sacrifice the spatial structure of rainfall. Here we develop a Gauss Blending Method (GBM), based on an adaptation of the Gauss-Seidel method, to merge radar and gauge rainfall (970 gauges) in Great Britain to produce a high-resolution (1 km) hourly blended precipitation product. A comparison to 194 independent gauges (2006–2018) demonstrates that the blended product reduces the RMSE, MAE and MRE, compared with the only radar data, by 14.5%, 14.5% and 22.1% respectively, while improving its CC and NSE by 7.8% and 23.2%. GBM also enhances rainfall detectability without introducing new systematic bias.GBM is compared with five standard approaches. The Multiplicative Adjustment Method and Mean Field Bias Adjustment Methods perform poorly and fail to improve radar rainfall. The Additive and Mixed Adjustment Methods show slightly worse performance and less stable rainfall detection skill, producing rainfall fields with artificial discontinuities and wedge-shaped artefacts. The only comparable method across most metrics is Kriging with External Drift. However, the GBM generates more complete data coverage, shows a better performance for extreme rainfall of ≥ 10 mm h−1, better preserves rainfall structure and local variability, and is easy to apply. This is shown for several example extreme rainfall events. The resulting radar-gauge blended dataset will facilitate the analysis of spatial rainfall variability and extreme rainfall over GB, with significant advantages for extremes analysis, hydrological modeling, and flood risk assessment.
Author(s): Qiu X, Green AC, Blenkinsop S, Fowler HJ
Publication type: Article
Publication status: Published
Journal: Journal of Hydrology
Year: 2026
Volume: 668
Print publication date: 01/04/2026
Online publication date: 19/01/2026
Acceptance date: 11/01/2026
Date deposited: 16/02/2026
ISSN (print): 0022-1694
ISSN (electronic): 1879-2707
Publisher: Elsevier
URL: https://doi.org/10.1016/j.jhydrol.2026.134954
DOI: 10.1016/j.jhydrol.2026.134954
Data Access Statement: Radar rainfall data without the additional QC can be downloaded from CEDA Archive (http://catalogue.ceda.ac.uk/uuid/82ade c1f896af6169112d09cc1174499/). Radar rainfall after the additional QC and the GRaD-GB (1H1K) dataset could be requested by email after we get the relevant license. We acknowledge that the dataset created in this study was independently created by the authors. It has not been reviewed or approved by the Environment Agency.
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