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

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....

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Main Authors: Sagar Ratna Bajracharya, Wahid Palash, Mandira Singh Shrestha, Vijay Ratan Khadgi, Chu Duo, Partha Jyoti Das, Chhimi Dorji
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2015/398687
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author Sagar Ratna Bajracharya
Wahid Palash
Mandira Singh Shrestha
Vijay Ratan Khadgi
Chu Duo
Partha Jyoti Das
Chhimi Dorji
author_facet Sagar Ratna Bajracharya
Wahid Palash
Mandira Singh Shrestha
Vijay Ratan Khadgi
Chu Duo
Partha Jyoti Das
Chhimi Dorji
author_sort Sagar Ratna Bajracharya
collection DOAJ
description 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.
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spelling doaj-art-dbc8f1c5c033457c89c830f4f9e06eda2025-02-03T05:59:17ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/398687398687Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological ApplicationsSagar Ratna Bajracharya0Wahid Palash1Mandira Singh Shrestha2Vijay Ratan Khadgi3Chu Duo4Partha Jyoti Das5Chhimi Dorji6International Centre for Integrated Mountain Development (ICIMOD), P.O. Box 3226, Kathmandu, NepalDepartment of Civil and Environmental Engineering, Tufts University, Medford, Boston, MA 02155, USAInternational Centre for Integrated Mountain Development (ICIMOD), P.O. Box 3226, Kathmandu, NepalInternational Centre for Integrated Mountain Development (ICIMOD), P.O. Box 3226, Kathmandu, NepalInstitute of Tibetan Plateau Atmospheric and Environmental Sciences, Lhasa, Tibet Autonomous Region 850000, ChinaAaranyak, Guwahati, Assam 781028, IndiaDepartment of Hydro-Met Services, Ministry of Economic Affairs, Thimphu, BhutanEstimation 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.http://dx.doi.org/10.1155/2015/398687
spellingShingle Sagar Ratna Bajracharya
Wahid Palash
Mandira Singh Shrestha
Vijay Ratan Khadgi
Chu Duo
Partha Jyoti Das
Chhimi Dorji
Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
Advances in Meteorology
title Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
title_full Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
title_fullStr Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
title_full_unstemmed Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
title_short Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications
title_sort systematic evaluation of satellite based rainfall products over the brahmaputra basin for hydrological applications
url http://dx.doi.org/10.1155/2015/398687
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