Abstract. Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Autocalibration are performed for the SWAT model. The NashSutcliffe Efficiency (NSE) and Coefficient of Determination (R 2 ) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R 2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R 2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R 2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

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This study focuses on the Hydro-Meteorological Drought assessments by Ensemble Climate Projections from a regional climate model (Weather Research and Forecasting, WRF) that downscaled 3 Global Climate Models under a baseline period and under a future scenario A2 for 2071-2100. The Meteorological Drought is assessed using the Standardized Precipitation Index (SPI) while the Hydrological Drought is analyzed by using both the semi-distributed hydrology model SWAT and Standardized Runoff Index (SRI). The catchment under study is a small river basin lying on the Central Highland area of Vietnam. This area is the source for perennial plantation which produces most of the coffee for Vietnam making it the world's second most exporter of coffee next to Brazil. Additionally, this region is also one of the important sources for hydropower of Vietnam and one of the main tributaries for the Mekong river at the downstream. This region has been known prone to drought, especially during dry seasons of March and April. Therefore, simulating drought for this area is significant to study the water supply and water balance for the region for future planning and adaptation.