Soil erosion and deposition modelling in a semi-arid grazing catchment in North Central Queensland
File version
Author(s)
Ciesiolka, C.
ilburn, D.
Littleboy, M.
Yu, Bofu
Rose, Calvin
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
S.R. Raine, A.J.W. Biggs, N.W. Menzies, D.M. Freebairn and P.E. Tolmie
Date
Size
272681 bytes
File type(s)
application/pdf
Location
Brisbane, Australia
License
Abstract
The Springvale study site is a small 10 hectare, second order catchment in a badly degraded part of the Nogoa River basin that lies in the 600 to 700 mm rainfall belt of tropical Australia. Sheet and rill erosion are associated with highly dispersible subsoils developed from flaggy sandstones, siltstones and mudstones. Over grazing and unfavourable climate variability have been given as reasons for land degradation and reduced grass production within the catchment. It has been postulated that in addition to the onsite effects of degradation of the catchment due to erosion associated with declining grass production, the millions of tons of soil removed from the Nogoa basin, in which the Springvale catchment is nested, would be a substantial threat to the economic life of the Fairbairn dam, which supports irrigation, industry and urban needs in the area. Rainfall, runoff, soil loss and vegetation cover data have been continually collected both at hillslope and catchment scales in the Springvale catchment since 1979. In this study, data from hillslopes have been used to calibrate an erosion model, which has been modified and upscaled to simulate erosion and deposition at a small catchment scale using a raster based Geographic Information Systems (GIS). Data at the catchment outlet are used for model verification. Two erosion index models are also used to compare the spatial pattern of predicted net erosion and net deposition in the catchment and show that, while the insitu erosion is high, the exiting sediment load from the catchment is quite low by comparison. Sensitivity analyses were conducted to evaluate uncertainties in model predictions due to uncertainties in input values. It was shown that the model was more sensitive to changes in some model parameters than others.
Journal Title
Conference Title
ISCO 2004 Conference Proceedings
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
DOI
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
© The Author(s) 2004. The attached file is posted here with permission of the copyright owners for your personal use only. No further distribution permitted. For information about this conference please refer to the publisher's website or contact the authors.