A rigorous evaluation of future hydro-climatic changes is necessary for developing climate adaptation strategies for a catchment. The integration of future climate projections from general circulation models (GCMs) in the simulations of a hydrologic model, such as the Soil and Water Assessment Tool (SWAT), is widely considered as one of the most dependable approaches to assess the impacts of climate alteration on hydrology. The main objective of this study was to assess the potential impacts of climate alteration on the hydrology of the Yarra River catchment in Victoria, Australia, using the SWAT model. The climate projections from five GCMs under two Representative Concentration Pathway (RCP) scenarios—RCP 4.5 and 8.5 for 2030 and 2050, respectively—were incorporated into the calibrated SWAT model for the analysis of future hydrologic behaviour against a baseline period of 1990–2008. The SWAT model performed well in its simulation of total streamflow, baseflow, and runoff, with Nash–Sutcliffe efficiency values of more than 0.75 for monthly calibration and validation. Based on the projections from the GCMs, the future rainfall and temperature are expected to decrease and increase, respectively, with the highest changes projected by the GFDL-ESM2M model under the RCP 8.5 scenario in 2050. These changes correspond to significant increases in annual evapotranspiration (8% to 46%) and decreases in other annual water cycle components, especially surface runoff (79% to 93%). Overall, the future climate projections indicate that the study area will become hotter, with less winter–spring (June to November) rainfall and with more water shortages within the catchment.

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.

Pollution of a watershed by different land uses and agricultural practices is becoming a major challenging factor that results in deterioration of water quality affecting human health and ecosystems. Sustainable use of available water resources warrants reduction of Non-Point Source (NPS) pollutants from receiving water bodies through best management practices (BMPs). A hydrologic model such as the Soil and Water Assessment Tool (SWAT) can be used for analyzing the impacts of various BMPs and implementing of different management plans for water quality improvement, which will help decision makers to determine the best combination of BMPs to maximize benefits. The objective of this study is to assess the potential reductions of sediments and nutrient loads by utilizing different BMPs on the Yarra River watershed using the SWAT model. The watershed is subdivided into 51 sub-watersheds where seven different BMPs were implemented. A SWAT model was developed and calibrated against a baseline period of 1998–2008. For calibration and validation of the model simulations for both the monthly and annual nutrients and sediments were assessed by using the Nash–Sutcliffe efficiency (NSE) statistical index. The values of the NSE were found more than 0.50 which indicates satisfactory model predictions. By utilizing different BMPs, the highest pollution reduction with minimal costs can be done by 32% targeted mixed-crop area. Furthermore, the combined effect of five BMPs imparts most sediments and nutrient reductions in the watershed. Overall, the selection of a BMP or combinations of BMPs should be set based on the goals set in a BMP application project.

Excessive loadings of non-point source pollution from different landuse activities cause eutrophication in rivers and creeks, and are a major concern to water resource managers. Catchment-scale management programs have been proven to be efficient in reducing water pollution from landuse activities. Understanding the connection between landuse activities and water quality is important for developing these management programs. However, the lack of knowledge of nutrient and sediment mass loads and their correlation with landuses are major impediments in applying these management programs. The main objective of this study was to assess nutrient and sediment loads at various sections of the Yarra River catchment in Victoria, Australia for the period of 1998-2009, and to investigate the correlation between these loads and different landuse types. The specific objectives were to: (1) establish whether there is a statistically significant difference between concentrations in water quality samples representing two flow regimes i.e. baseflow conditions and surface runoff events, (2) estimate pollutant mass load: nutrients and sediment at various sections of the catchment, and (3) identify the significant and major sources of pollutant loads. A generic methodology was applied to achieve the above objectives using long-term in-stream water quality data and other readily available tools, which is also applicable to other catchments containing different landuses. The proposed methods and techniques addressed the issues of selecting water quality stations, catchment subdivision, identification of major landuse types, analysis of different flow regimes, and suitable methods to estimate loadings from the catchments containing different landuses. Although the methodology does not indicate the specific mechanisms causing surface runoff and pollutant transport, the data-based model is simple to apply and leads to good results, particularly in circumstances where limited time and data are available for assessment. In this study, 12 water quality stations were selected representing the Yarra River and its major tributaries of which 5 stations were on the main stem of the Yarra River. The dominant landuse type in the tributary stations was either agriculture or urban where as in the main stem stations; it was forest-agriculture mix type. Hydrograph separation allowed the separation of the water quality data into that collected during baseflow condition and surface runoff event. Baseflow conditions were defined as those water quality data collected when baseflow discharge was greater than or equal to 70% of mean daily discharge (total streamflow), and surface runoff events were defined as those data collected when baseflow discharge was less than 70% of mean daily discharge. The terms "baseflow conditions" and "surface runoff events" will be referred as "baseflow" and "events" respectively. At all water quality stations, the pollutant concentrations were significantly (p≤0.01) greater in events than in baseflow. Total nitrogen (TN), tot...

Catchment-scale hydrologic and diffuse source pollution models simulating a catchment are useful analysis tools to understand problems and find solutions through simulation of BMPs for particular catchment and agronomic settings. However, developing reliable catchment model and validating them on real-world catchment with monitored data is challenging. In this regard, model calibration and uncertainty analysis help to evaluate the ability of the model to sufficiently predict streamflow and constituent yields for specific applications. Complex physics-based distributed models contain many parameters that can complicate calibration process. In addition, the model when includes multi-variable at multi-site with multiobjective functions introduces more complexity to the calibration process. Over-parameterization is a wellknown problem in such distributed model. Sensitivity analysis methods reducing the number of parameters to be adjusted during calibration are important for simplifying the use of these models. The objective of this paper is to perform a sensitivity analysis for multiple variables (streamflow, sediment and nutrients) at three sites on a SWAT model developed in the agricultural part of the Yarra River catchment, Victoria (Australia) so that the model can be calibrated efficiently for water quality analysis purposes. SWAT is a continuous physics-based distributed model that operates on a daily time-step. The SWAT model requires the following data types: digital elevation model (DEM), land use, soil, land use management, daily climate, streamflow and water quality data. Australian catchments are data-rich in terms of hydroclimatic data, but data-poor especially for water quality and land use management. For this study, all the 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 in SWAT were curve number method for runoff estimating, Penman-Monteith method for PET and Muskingum method for channel routing. SWAT has an embedded automatic sensitivity, and calibration and uncertainty analysis tool. The sensitivity analysis method is a combination of Latin-Hypercube and One-factor-At-a-Time (LH-OAT) sampling that allows a global sensitivity analysis for a long list of parameters with only a limited number of model runs. SWAT has 26 streamflow, 6 sediment and 9 nutrient parameters. The LH-OAT sensitivity analysis was applied for streamflow (Q), Total Nitrogen (TN), Total Phosphorus (TP) and Total Suspended Solid (TSS) output variables at three sites in the study area for 1998-2008 periods. The LH-OAT sensitivity analysis provides a simple and quick way to assess parameter sensitivity for multiple variab...