Improving runoff estimates using remote sensing vegetation data for bushfire impacted catchments

Rainfall-runoff modelling is widely used for runoff estimation at the catchment scale. However, its simulation capability is sometimes influenced because of rapid land cover changes occurring in catchments. This paper investigates whether modification of a rainfall-runoff model, Xinanjiang, by the incorporation of dynamic remote sensing data (MODIS leaf area index (LAI) and albedo) can improve runoff estimates for four south-east Australian catchments which experienced severe bushfire impacts. The results show that incorporation of remote sensing LAI and albedo data into the modified Xinanjiang model can improve model performance in three wetter bushfire impacted catchments, compared to the modified model using mean annual vegetation data as model inputs. The improvement is indicated by a slight increase (0.01-0.07) in the Nash-Sutcliffe efficiency of daily runoff and noticeable decrease (3-11%) in volumetric errors. However, use of vegetation dynamics does not improve runoff time series simulation in a dry catchment for which mean annual runoff is only 38 mm/yr. It indicates that incorporation of vegetation dynamic data into Xinanjiang model may show more benefits for catchments located in the wet regions Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.