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.

Climate change currently affects the resilience and aquatic ecosystem. Climate change alters rainfall patterns which have a great impact on river flow. Annual flooding is an important hydrological characteristic of the Mekong River Basin (MRB) and it drives the high productivity of the ecosystem and biodiversity in the Tonle Sap floodplain and the Mekong Delta. This study aims to assess the impacts of climate change on river flow in the MRB and flood inundation in the Lower Mekong Basin (LMB). The changing impacts were assessed by a two-dimensional rainfall-runoff and inundation model (RRI model). The present climate (1979–2003) and future projected climate (2075–2099) datasets from MRI-AGCM3.2H and MRI-AGCM3.2S models were applied with a linear scaling bias correction method before input into the RRI model. The results of climate change suggested that flood magnitude in the LMB will be severer than the present climate by the end of the twenty-first century. The increment of precipitation between 6.6 and 14.2% could lead to increase extreme flow (Q₅) 13–30%, peak inundation area 19–43%, and peak inundation volume 24–55% in the LMB for ranging of Representative Concentration Pathways (RCP) and sea surface temperature (SST) scenarios while there is no significant change on peak flood timing.

Flood impacts threaten the socioeconomic conditions of peoples’ lives in the Mekong River Basin. In this study, the rainfall-runoff-inundation (RRI) model, capable of simulating rainfall runoff and flood inundation simultaneously, was used to enhance understanding of flooding characteristics in this region by using grid-satellite-based rainfall. Input data for the simulation included HydroSHEDS topographic data, APHRODITE precipitation data, MODIS land use data, and river cross sections. Moreover, the shuffled complex evolution developed at The University of Arizona (SCE-UA) global optimization method was integrated with the RRI model to calibrate sensitive parameters. In this study, the flood event in 2000 has been selected in the Mekong River Basin. The simulation results were compared with observed discharges at monitoring stations along the river and an inundation map from Landsat 7 satellite imagery and the Mekong River Commission (MRC) data. The results indicated good agreement between the observed and simulated discharges, for example, with Nash-Sutcliffe efficiency $(\mathrm{NSE})=0.86$ at the Stung Treng Station. The model predicted inundation extent with a success rate $(\mathrm{SR})=67.50\%$ and modified success rate $(\mathrm{MSR})=74.53\%$ compared with satellite Landsat 7, and $\mathrm{SR}=68.27\%$ and $\mathrm{MSR}=75.11\%$ compared with MRC data. Therefore, the RRI model was successfully used to simulate a large-scale inundation flood event in 2000 using a grid precipitation data set in the Mekong River Basin. However, the underestimation might be due to the uncertainties of input data, river geometry, the large scale of the basin, coarse resolution of topographic data, and error in remote sensing image in detecting the flood extent.