Abstract. This paper presents simulations of daily hydrological process of the Mun River, the largest tributary of the Mekong, with a geomorphology-based hydrological model (GBHM) driven by two forcing sets: traditional station data and grid data derived from remote sensing and GLDAS products. Driven by the station data, the Mun-GBHM model is successfully calibrated against the discharge observed in 1991, but the model accuracy decreases with the increase of simulation time during the validation period of 1992–1999. Driven by the TRMM rainfall and other meteorological data from GLDAS, using the same parameters as above, the model performs reliably at both the monthly and daily scale. Moreover, when the model is calibrated with one year of gridded data, its performance can be further improved. Our results demonstrate that TRMM and GLDAS are able to drive the GBHM so providing reliable hydrologic predictions in such data-poor or ungauged basins.