Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5667-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-25-5667-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy
Chuanfa Chen
College of Geodesy and Geomatics, Shandong University of Science
and Technology, Qingdao 266590, China
Key Laboratory of Geomatics and Digital Technology of Shandong
Province, Shandong University of Science and Technology, Qingdao 266590, China
Baojian Hu
College of Geodesy and Geomatics, Shandong University of Science
and Technology, Qingdao 266590, China
Key Laboratory of Geomatics and Digital Technology of Shandong
Province, Shandong University of Science and Technology, Qingdao 266590, China
Yanyan Li
CORRESPONDING AUTHOR
College of Geodesy and Geomatics, Shandong University of Science
and Technology, Qingdao 266590, China
Key Laboratory of Geomatics and Digital Technology of Shandong
Province, Shandong University of Science and Technology, Qingdao 266590, China
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Latest update: 07 Oct 2024
Short summary
This study proposes an easy-to-use downscaling-calibration method based on a spatial random forest with the incorporation of high-resolution variables. The proposed method is general, robust, accurate and easy to use as it shows more accurate results than the classical methods in the study area with heterogeneous terrain morphology and precipitation. It can be easily applied to other regions where precipitation data with high resolution and high accuracy are urgently required.
This study proposes an easy-to-use downscaling-calibration method based on a spatial random...
Special issue