Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China

Water

Alexander Y. Sun¹  Guanghua Xu²  Meixian Liu²  Xianli Xu²  Kelin Wang² 

[1] Bureau of Economic Geology, The University of Texas at Austin, University Station, Box X, Austin, TX 78713-8924, USA;Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China;

关键词: random cascade model;    self-similar;    rainfall;    heterogeneity;    ecohydrology;   

DOI  :  10.3390/w7063083

来源: DOAJ

【 摘 要 】

The lack of high spatial resolution precipitation data, which are crucial for the modeling and managing of hydrological systems, has triggered many attempts at spatial downscaling. The essence of downscaling lies in extracting extra information from a dataset through some scale-invariant characteristics related to the process of interest. While most studies utilize only one source of information, here we propose an approach thatintegrates two independent information sources, which are characterized by self-similar and relationship with other geo-referenced factors, respectively. This approach is applied to 16 years (1998–2013) of TRMM 3B43 monthly precipitation data in an orographic and monsoon influenced region in South China. Elevation, latitude, and longitude are used as predictive variables in the regression model, while self-similarity is characterized by multifractals and modeled by a log-normal multiplicative random cascade. The original 0.25° precipitation field was downscaled to the 0.01° scale. The result was validated with rain gauge data. Good consistency was achieved on coefficient of determination, bias, and root mean square error. This study contributes to the current precipitation downscaling methodology and is helpful for hydrology and water resources management, especially in areas with insufficient ground gauges.

【 授权许可】

Unknown