Next Article in Journal
Spatial and Temporal Variation of Wind Erosion Climatic Erosivity and Its Response to ENSO in the Otindag Desert, China
Previous Article in Journal
Employing the Method of Characteristics to Obtain the Solution of Spectral Evolution of Turbulent Kinetic Energy Density Equation in an Isotropic Flow
Open AccessArticle

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

1
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2
Research Center for East-West Cooperation in China, East China Normal University, Shanghai 200241, China
3
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4
School of Social Sciences, Shanghai University of Engineering Science, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(10), 613; https://doi.org/10.3390/atmos10100613
Received: 24 August 2019 / Revised: 7 October 2019 / Accepted: 8 October 2019 / Published: 10 October 2019
Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend.
Keywords: precipitation; downscaling simulation; polynomial regression; machine learning; batch gradient descent; data-scarce river basin precipitation; downscaling simulation; polynomial regression; machine learning; batch gradient descent; data-scarce river basin
MDPI and ACS Style

Zuo, J.; Xu, J.; Chen, Y.; Wang, C. Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products. Atmosphere 2019, 10, 613.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
  • Supplementary File 1:

    PDF-Document (PDF, 77 KB)

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3376353
    Description: the supplementary spreadsheet: the parameters of BGD-based polynomial regression
Back to TopTop