Estimating catchment-scale impacts of wildfire on sediment and nutrient loads using the E2 catchment modelling framework
Approximately 1.3 million ha of forested and agricultural land in south-eastern Australia was burnt by wildfires in early 2003. This paper describes a modelling process to assess the impacts of the fires on the quality of receiving waters and river systems in the fire-affected catchments. First, we construct and parameterise the E2 catchment modelling framework to represent the flow, and sediment and nutrient loads for the water storages and river systems in fire-affected sub-catchments and second, we assess likely impacts of the fires on loads of total suspended sediments (TSS), total nitrogen (TN) and total phosphorus (TP). Very good calibration (with coefficient of efficiency values generally greater than 0.8) of the rainfall-runoff models to observed flow at several gauging stations within each catchment was achieved. Digitised land use layers were reclassified to form functional units representing unburnt and burnt land uses. Pre- and post-fire loads of TSS, TN and TP predicted by the model at end of catchment outlets and water storages were then compared relative to pre-fire loads. Compared to pre-fire conditions, the models predicted that the Ovens, Kiewa, Upper Murray and Snowy catchments would deliver, on average, approximately 30 times greater TSS, 5 times greater TN, and 8 times the amount of TP. Proportional increases in predicted loads at the catchment outlets were generally smaller than increases observed at water quality monitoring sites. These differences reflect the proximity of the monitoring stations to the burnt areas, the total percentage of catchment burnt, and the amount of rainfall. The predictions of load increases carry important assumptions and limitations, and such an approach can only be used for making relative assessments of the impacts of the fires on average suspended sediments and nutrient loads.
wildfire nutrient loads e2 catchment modelling water quality sediment loads