Since sediment and nutrient concentrations vary with landuses in different climatic conditions, it is critical in understanding the connection between different landuses activities and water quality, and developing appropriate management strategies for a catchment. The objective of this paper is to assess the effects of climate and landuse activities on nutrient and sediment loads at 5 selected water quality monitoring stations in the Yarra River catchment of Victoria, Australia for 1994-2008 periods. A data-based technique was applied to achieve the above objective using long-term in-stream water quality data and other readily available tools. The methodology addressed the issues of selecting water quality stations, catchment disaggregation, identification of major landuse types, analysis of pollutant concentrations and loads in different climatic conditions, and suitable data-based method (regression model LOADEST) to estimate pollutant loadings. Climatic data were collected from the SILO climate database and the Bureau of Meteorology. Precipitation data from 16 stations and temperature data from 4 stations located in the Middle Yarra segment were collected for the period of 1980-2008. Daily streamflow and monthly water quality grab sample data of Total Suspended Solid (TSS), Total Nitrogen (TN) and Total Phosphorus (TP) were available for the 5 stations from Melbourne Water. ArcGIS 9.3 tool was used for catchment disaggregation and major landuse type identification using ASTER 30m global digital elevation model and landuse map (50m grid raster data collected from Australian Bureau of Agricultural and Resource Economics and Sciences). The water quality monitoring stations were selected based on data availability and dominant major landuse types (urban, agriculture and forest). The dominant landuse type in the tributary stations was either agriculture or urban where as in the main Yarra River stations; it was forest-agriculture mix type. There was an abrupt drop in rainfall after 1996 known as millennium drought in the catchment, and the most extreme rainfall event occurs in that drought period. The study period was categorised into wet, dry and average years based on rainfall for water quality analysis purposes. Since the correlations between the concentrations of TSS, TN, TP, and streamflow (TSS: 0.57-0.72; TN: 0.50-0.57 and TP: 0.50-0.57 except station 5) were high and statistically significant (p<0.01), a regression method based model LOADEST was used to estimate constituent loads from the grab sample data. The LOADEST model is well documented, and is accepted as a valid means of calculating constituent load from a limited number of water quality data. The LOADEST model performed well in estimating TSS, TN and TP loads. Coefficients of determination (R2) for the regression models in LOADEST were greater than 0.84, 0.94 and 0.88 for TSS, TN and TP respectively at all stations. In general, TSS, TN and TP mean concentrations were higher in wet years than in the dry and average years, except at stations 2 and 3 where TN mean concentrations were higher in the average years. Also, TSS and TP mean concentrations were higher in the dry years than in the average years. This is due to the direct correlation of TSS and TP, and high runoff events. In addition, TSS, TN and TP mean concentrations were higher in the urban areas, and then in the agricultural areas. The four wet years (1995, 1996, 2000 and 2004) carried out on average 60% of TSS, 51% of TN and 53% of TP loadings in the monitoring stations. During the study period (1994-2008), the highest export rates of TSS, TN and TP were from urban areas, and the lowest export rates of TSS and TP were from forest areas, and TN from agricultural areas. Overall, water quality and constituent concentrations were influenced by rainfall events and landuse types.