The 16th International Conference on Diffuse Pollution and Eutrophication, Beijing, China, 18 – 23 August, 2013 Page 1 of 2 Data Processing for Complex Diffuse Pollution Models Sushil K Das 1 , Anne WM Ng 1 and Bodiyabaduge JC Perera 1 , 1 College of Engineering and Science, Victoria University, Melbourne, Victoria, AUSTRALIA Presenting Author: Bodiyabaduge JC Perera Keywords: Data processing; Complex physics-based models; Water quality; SWAT; LOADEST; Australia Introduction 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. In general, Australian catchments are data-rich in terms of hydroclimatic data, but data-poor for water quality (WQ) and land management data. Letcher et al (1999) pointed out that physics-based models are not appropriate across most Australian catchments due to lack of sufficient input data. Commonly used WQ models in Australia such as CatchMODS are either empirical or lumped/semi-distributed conceptual models. The objective of this paper is to discuss the data sources and processing required for complex physics-based models in the context of Australia. Then a SWAT based model was developed to model hydrology of the Yarra River catchment in Victoria (Australia). The information provided in this paper will be beneficial for model developers about data sources and processing for developing physics-based models. Material and Methods Study area The Yarra River catchment, degraded by diffuse pollution, is predominantly an agricultural (57 per cent) catchment in Victoria, Australia. The middle agricultural segment of this catchment which is shown in Figure 1.1 was selected as the case study area. Data collection and processing SWAT model requires DEM, land use, soil, climate and land management data for model development, and streamflow and WQ data for model calibration. The spatial data were processed using ArcSWAT 2.3.4 version (Figure 1.1). The climate data were collected from SILO climate database (http://www.longpaddock.qld.gov.au/silo/). Limited land management data were available from Australian Bureau of Statistics at a larger area scale than the study area. Daily streamflow data (1990-2008) and monthly Total Suspended Solid (TSS), Total Nitrogen (TN) and Total Phosphorus (TP) grab samples were available from Melbourne Water for the period 1998-2008. Regression based LOADEST (Runkel et al, 2004) model was used to estimate monthly and annual TSS, TN and TP loads from the WQ grab samples since correlation between WQ grab samples and streamflow were significant (r> 0.70; p<0.01). SWAT model development and calibration All necessary spatial datasets and database input files for the model were organized and assembled following the guidelines of Winchell et al (2009). The study area was subdivided into 51 sub-catchments, and 431 hydrologic response units. ArcSWAT embedded sensitivity and auto-calibration tool was used for sensitivity analysis and calibration of the model.