The Soil and Water Assessment Tool (SWAT) model is recognized as one of the top hydrological models applied for addressing hydrologic and environmental issues. This is the first review on the SWAT model studies in Southeast Asia, with an emphasis on its applications, current challenges and future research directions. A total of 126 articles were identified since 2006; roughly 50% of these studies were conducted in Vietnam or Thailand. About 16% of the studies were performed at a transnational scale, which included Cambodia, Lao PDR, Thailand, and Vietnam. Model capability assessment, land use, and climate change assessment are the main SWAT applications that have been reported for the region. Most of the SWAT calibration and validation results for these studies were classified as satisfactory to very good results based on widely recognized performance indicators. However, the parameterization, calibration and validation procedures are not well reported in some articles. Availability of reliable data is one of the main problems that SWAT users are confronted with, as these data are either not freely available or restricted from public access in some countries. Hence, future studies should be considered on identification and development of reliable input data for SWAT modeling. SWAT model modification based on the SEA climate, geographical and land use conditions is another research direction to be considered in the future. Moreover, application of SWAT for extreme events simulation requires more attention in this region.

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.