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Advances in Meteorology
Volume 2015, Article ID 398687, 17 pages
Research Article

Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications

1International Centre for Integrated Mountain Development (ICIMOD), P.O. Box 3226, Kathmandu, Nepal
2Department of Civil and Environmental Engineering, Tufts University, Medford, Boston, MA 02155, USA
3Institute of Tibetan Plateau Atmospheric and Environmental Sciences, Lhasa, Tibet Autonomous Region 850000, China
4Aaranyak, Guwahati, Assam 781028, India
5Department of Hydro-Met Services, Ministry of Economic Affairs, Thimphu, Bhutan

Received 30 June 2014; Revised 25 September 2014; Accepted 8 October 2014

Academic Editor: Dimitrios Katsanos

Copyright © 2015 Sagar Ratna Bajracharya et al. 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.


Estimation of the flow generated in the Brahmaputra river basin is important for establishing an effective flood prediction and warning services as well as for water resources assessment and management. But this is a data scarce region with few and unevenly distributed hydrometeorological stations. Five high-resolution satellite rainfall products (CPC RFE2.0, RFE2.0-Modified, CMORPH, GSMaP, and TRMM 3B42) were evaluated at different spatial and temporal resolutions (daily, dekadal, monthly, and seasonal) with observed rain gauge data from 2004 to 2006 to determine their ability to fill the data gap and suitability for use in hydrological and water resources management applications. Grid-to-grid (G-G) and catchment-to-catchment (C-C) comparisons were performed using the verification methods developed by the International Precipitation Working Group (IPWG). Comparing different products, RFE2.0-Modified, TRMM 3B42, and CMORPH performed best; they all detected heavy, moderate, and low rainfall but still significantly underestimated magnitude of rainfall, particularly in orographically influenced areas. Overall, RFE2.0-Modified performed best showing a high correlation coefficient with observed data and low mean absolute error, root mean square error, and multiple bias and is reasonably good at detecting the occurrence of rainfall. TRMM 3B42 showed the second best performance. The study demonstrates that there is a potential use of satellite rainfall in a data scarce region.