Aller au contenu principal

page search

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

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

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

Resource information

Date of publication
Décembre 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400181511
Pages
332-341

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 38mm/yr. It indicates that incorporation of vegetation dynamic data into Xinanjiang model may show more benefits for catchments located in the wet regions

Share on RLBI navigator
NO

Authors and Publishers

Author(s), editor(s), contributor(s)

Zhou, Yanchun
Zhang, Yongqiang
Vaze, Jai
Lane, Patrick
Xu, Shiguo

Publisher(s)
Data Provider