Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index

Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting t...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) 2009-05, Vol.370 (1), p.155-162
Main Authors: Li, Hongxia, Zhang, Yongqiang, Chiew, Francis H.S., Xu, Shiguo
Format: Article
Language:English
Subjects:
Applied geophysics
Earth sciences
Earth, ocean, space
Evapotranspiration
Exact sciences and technology
Freshwater
hydrologic models
Hydrology
Hydrology. Hydrogeology
Internal geophysics
Leaf area index
MODIS
noderate resolution imaging spectroradiometer
prediction
rain
remote sensing
runoff
Runoff prediction
satellites
Ungauged catchments
vegetation
watershed hydrology
watersheds
Xinanjiang model
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Summary:Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting terra satellite-leaf area index (MODIS-LAI) data into Xinanjiang rainfall–runoff model and assesses the model performance on 210 catchments in south-east Australia. The results show that the inclusion of LAI data improves both the model calibration results as well as the daily runoff prediction in ungauged catchments. It is likely that more significant improvements to the model structure to integrate the remotely-sensed vegetation and other data can further reduce the uncertainty in runoff prediction in ungauged catchments.
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2009.03.003