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Using gridded rainfall products in simulating streamflow in a tropical catchment – A case study of the Srepok River Catchment, Vietnam Cover

Using gridded rainfall products in simulating streamflow in a tropical catchment – A case study of the Srepok River Catchment, Vietnam

Open Access
|Dec 2016

Abstract

The precise rainfall estimate with appropriate spatial and temporal resolutions is a key input to distributed hydrological models. However, networks of rain gauges are often sparsely distributed in developing countries. To overcome such limitations, this study used some of the existing gridded rainfall products to simulate streamflow. Four gridded rainfall products, including APHRODITE, CFSR, PERSIANN, and TRMM, were used as input to the SWAT distributed hydrological model in order to simulate streamflow over the Srepok River Catchment in Vietnam. Besides that, the available rain gauges data were also used for comparison. Amongst the four different datasets, the TRMM and APHRODITE data show their best match to rain gauges data in simulating the daily and monthly streamflow with satisfactory precision in the 2000-2006 period. The result indicates that the TRMM and APHRODITE data have potential applications in driving hydrological model and water resources management in data-scarce and ungauged areas in Vietnam.

DOI: https://doi.org/10.1515/johh-2016-0047 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
Language: English
Page range: 18 - 25
Submitted on: Sep 4, 2015
Accepted on: Jun 13, 2016
Published on: Dec 8, 2016
Published by: Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Vu Thi Thom, Dao Nguyen Khoi, Do Quang Linh, published by Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.