Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-2931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regionalization of IDF curves for mainland China: a comparative evaluation of machine learning versus spatial interpolation techniques
Yuantian Jiang
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
Wenting Wang
CORRESPONDING AUTHOR
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
Andrew T. Fullhart
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA
Bofu Yu
Australian Rivers Institute, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
Bo Chen
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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A gridded input dataset at a 10 km resolution of a weather generator, CLIGEN, was established for mainland China. Based on this, CLIGEN can generate a series of daily temperature, solar radiation, precipitation data, and rainfall intensity information. In each grid, the input file contains 13 groups of parameters. All parameters were first calculated based on long-term observations and then interpolated by universal kriging. The accuracy of the gridded input dataset has been fully assessed.
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Short summary
Intensity-Duration-Frequency (IDF) curves is important for designing infrastructure that can withstand floods. We compared traditional interpolation methods with machine learning to map these curves across mainland China. ML using widely available daily gridded data can estimate sub-daily intensity as accurately as methods needing rarer hourly site data. This study provides a valuable understanding for IDF in data-limited regions and generates a new IDF dataset for mainland China.
Intensity-Duration-Frequency (IDF) curves is important for designing infrastructure that can...