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 and Dawei Han

PLOS ONE, 2020, vol. 15, issue 1, 1-13

Abstract: Precipitation, as a primary hydrological variable in the water cycle plays an important role 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 of the whole basin. Firstly, 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. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to 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 the annual maximum 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0226814

DOI: 10.1371/journal.pone.0226814

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