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Water 2015, 7(6), 3083-3102; doi:10.3390/w7063083

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

1
Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
2
Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Huanjiang 547100, China
3
Bureau of Economic Geology, The University of Texas at Austin, University Station, Box X, Austin, TX 78713-8924, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Ataur Rahman
Received: 20 April 2015 / Revised: 29 May 2015 / Accepted: 12 June 2015 / Published: 19 June 2015
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Abstract

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 that integrates 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. View Full-Text
Keywords: random cascade model; self-similar; rainfall; heterogeneity; ecohydrology random cascade model; self-similar; rainfall; heterogeneity; ecohydrology
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xu, G.; Xu, X.; Liu, M.; Sun, A.Y.; Wang, K. Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China. Water 2015, 7, 3083-3102.

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