The degradation of river water quality in Victorian agricultural catchments is of concern. Physics-based models are useful analysis tools to understand diffuse pollution and find solutions through best management practices. However, because of high data requirements and processing, use of these models is limited in many data-poor catchments; for example the Australian catchments where water quality and land use management data are very sparse. Recently, with the advent of computationally efficient computers and GIS software, physics-based models are increasingly being called upon in data-poor regions. SWAT is a promising model for long-term continuous simulations in predominantly agricultural catchments. Limited application of SWAT has been found in Australia for modeling hydrology only. Adoption of SWAT as a tool for predicting land use change impacts on water quality in the Yarra River catchment, Victoria (Australia) is currently being considered. The objective of this paper is to evaluate hydrological behaviour of SWAT model in the agricultural part of the Yarra River catchment for 1990-2008 periods. The SWAT model requires the following data: digital elevation model (DEM), land use, soil, land use management and daily climate data for driving the model, and streamflow and water quality data for calibrating the model. All these data were collected from local organizations except DEM. Water quality and land use management data were most sparse. All input files for the model were organized and assembled following the guidelines of ArcSWAT interface of the SWAT 2005 version. The study area was delineated into 51 sub-catchments and 431 hydrological response units (HRU), which are unique combinations of land use, soil type and slope. The main methods used in modeling the hydrologic processes were curve number method for runoff estimating, Penman-Monteith method for PET and Muskingum method for channel routing. SWAT embedded sensitivity and auto-calibration tool was used for sensitivity analysis and calibration. The LH-OAT (Latin-Hypercube and One-factor-At-a-Time) sensitivity analysis method was implemented for all 26 SWAT streamflow parameters. Then the ParaSol (SCE-UA) auto-calibration was performed on 14 most sensitive streamflow parameters. The calibration period 1990-2002 includes both wet and dry period, but the validation period 2003-2008 includes only dry period. Since, a bad representation of baseflow and surface runoff can cause wrong estimates of diffuse pollution loads to the river, baseflow (Qbf) and runoff (Qr) were also calibrated along with the total flow (Qt). For runoff and baseflow calibration, manual tuning was done to the baseflow and runoff related parameters. The SWAT model calibration and validation results were evaluated based on the standard guidelines and using the evaluation statistics of Coefficient of Determination (R2), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS) and RMSE-observations standard deviation ratio (RSR). In the calibration period, the respective daily, monthly and annual values of the evaluation statistics were for; • Qt → R2: 0.78, 0.93, 0.96; NSE: 0.77, 0.89, 0.87; PBIAS: 10, 10, 10 and RSR: 0.48, 0.34, 0.36 • Qbf → R2: 0.90, 0.93, 0.95; NSE: 0.87, 0.89, 0.88; PBIAS: 6, 6, 6 and RSR: 0.36, 0.33, 0.35 • Qr → R2: 0.50, 0.84, 0.97; NSE: 0.42, 0.80, 0.76; PBIAS: 23, 23, 23 and RSR: 0.76, 0.45, 0.49 In the validation period, the respective daily, monthly and annual values of the evaluation statistics were for; • Qt → R2: 0.74, 0.82, 0.87; NSE: 0.72, 0.82, 0.81; PBIAS: -3, -3, -3 and RSR: 0.53, 0.43, 0.43 • Qbf → R2: 0.79, 0.81, 0.84; NSE: 0.77, 0.79, 0.71; PBIAS: -11, -11, -11 and RSR: 0.48, 0.46, 0.54 • Qr → R2: 0.67, 0.82, 0.87; NSE: 0.53, 0.79, 0.70; PBIAS: 19, 19, 19 and RSR: 0.69, 0.46, 0.55 The model performance statistics showed that the SWAT model performed very well for Qt, well for Qbf and satisfactory for Qr. This implies that the SWAT model sufficiently replicated the hydrology of the study area, and can be applied in Australian conditions. In general, the SWAT model overestimated flows in dry years, and underestimated in wet years.