The impact of bushfires on water quality

Population growth in urban areas leads to a higher demand in water use. Quality of water is an important factor not only from an aesthetic view, but also for ecological health purposes. This paper presented research that is designed to develop a spatial approach to support the planning of the water quality in the areas subjected to bushfires, using a case study from state of Victoria. In particular, this research involved the implementation of a hydrological model in order to predict the river water quality, to assist in the decision-making process. The impact of bushfires on water quality can be highly variable for the most of the individual water quality parametres. This variability is caused by a number of landscape influences and climatic factors, most notably rainfall. High magnitude and intense rainfall events soon after fire generate the largest impacts on water quality and sometimes trigger extreme erosion events. There are many important water quality parameters that must be taken into account when the water is delivered to the population. For some of the water quality parameters there is very little information available, which makes it difficult to draw conclusions about bushfire impacts. The monitoring campaigns are very expensive, and better options are the modeling tools. The model used in this research is eWater, a conceptual, semi-distributed model, which applies the flow accumulation principles. eWater Source - Australia's National Hydrological Modelling Platform (NHMP) – is developed by eWater CRC, Australia. It is designed to simulate all aspects of water resource systems to support integrated planning, operations and governance from urban, catchment to river basin scales including human and ecological influences. The catchment analyzed can be divided into sub-catchments and functional units. The model integrates rainfall runoff, constituent generation and filter models, which are parameterized. The user must find the best set of parameters that is suitable for that catchment. After calibration and validation, the model can be used in the same catchment for any period of time, and it will be able to predict the pollution levels in the catchment, with a good accuracy. Also, a user can follow the same steps, to calibrate the model for any other catchment. This method is time consuming, but it doesn’t require many input data.The fires and the rain are classified in 3 classes each. Then, the landuse, the burnt areas and the areas with rain are combined and parameterized separately. The outputs from the developed model are good correlated with the measured data, and show higher concentrations of suspended sediment and nutrients after bushfire followed by rain. To improve the model performance, the measured water quality data must be daily data with a better accuracy