Comparison of Gridded Precipitation Datasets for Rainfall-Runoff and Inundation Modeling in the Mekong River Basin

Sophal Try, Shigenobu Tanaka, Kenji Tanaka, Takahiro Sayama, Chantha Oeurng, Sovannara Uk, Kaoru Takara, Maochuan Hu, Dawei Han

Research output: Contribution to journalArticle (Academic Journal)peer-review

44 Citations (Scopus)
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Precipitation is a primary hydrological process in the water cycle, and it is an indispensable input in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited; therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling in the whole basin. First, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000-2003) and validation (2004-2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. Then, five available gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were input into the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of annual maximum flood inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin
Original languageEnglish
Number of pages13
JournalPLoS ONE
Issue number1
Publication statusPublished - 8 Jan 2020


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