Thanh Ngo-Duc, Jun Matsumoto, Hideyuki Kamimera, Hoang-Hai Bui
Released: November 08, 2013

Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia–ThuBon River Basin in Central Vietnam using an artificial neural network

Thanh Ngo-Duc1), Jun Matsumoto2) 3), Hideyuki Kamimera4), Hoang-Hai Bui1)

1) Department of Meteorology, Hanoi College of Science, Vietnam National University, Vietnam
2) Department of Geography, Tokyo Metropolitan University
3) Research Institute for Global Change, JAMSTEC
4) International Centre for Water Hazard and Risk Management (ICHARM)

The performance of Global Satellite Mapping of Precipitation data (GSMaP_MVK, version 5.222.1) over the VuGia–ThuBon River basin and surrounding areas in central Vietnam was examined on a monthly basis in comparison with rainfall gauged at eight meteorological stations and a gridded rainfall product of the Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources project (APHRODITE, V1003R1). APHRODITE represented in situ observations well, whereas GSMaP had very low performance over the study area for the period 2001–2007. Particularly, GSMaP exhibited large negative rainfall biases for the winter monsoon period from October to December and the biases tended to increase as the elevation decreased. A correction method using an artificial neural network (ANN) was implemented for the GSMaP rainfall over the VuGia–ThuBon River basin. Validation showed that the ANN correction method significantly improved the GSMaP quality in terms of spatial correlation, rainfall amplitude, and Nash–Sutcliffe efficiency coefficient for both the dependent period 2001–2005 and the independent period 2006–2007.

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