Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 11
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.
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.
Articles | Volume 25, issue 11
Research article
 | 
03 Nov 2021
Research article |  | 03 Nov 2021

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Chuanfa Chen, Baojian Hu, and Yanyan Li

Viewed

Total article views: 5,001 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,205 1,690 106 5,001 87 149
  • HTML: 3,205
  • PDF: 1,690
  • XML: 106
  • Total: 5,001
  • BibTeX: 87
  • EndNote: 149
Views and downloads (calculated since 01 Jul 2021)
Cumulative views and downloads (calculated since 01 Jul 2021)

Viewed (geographical distribution)

Total article views: 5,001 (including HTML, PDF, and XML) Thereof 4,781 with geography defined and 220 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2025
Download
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.
Read more
Share
Special issue
Experiments in Hydrology and Hydraulics