Sub-theme: Hydrology, Submitted for poster presentation.
MODELLING THE EFFECTS OF LAND COVER CHANGE ON
DRY SEASON FLOWS IN THE HEADSTREAMS OF THE ZAMBEZI RIVER IN
ZAMBIA.
Jonathan M. Kampata,
University of Botswana,
Department of Environmental Sciences,
P/Bag UB 00704. Gaborone. Botswana.
E:Mail: jkampata@orc.ub.bw
jkampata@yahoo.com
Tel:+267-72474632 or +260-95806049
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ABSTRACT
Headstream environments are threatened by environmental changes due to human activities.
Previous studies have found that the effects of land cover change on water resources,
specifically dry season flows are still not well understood. In this poster presentation the
strategy of the proposed research is highlighted.
The main objective of this research is to enhance the understanding of the effects of land
cover change on the dry season flows of sub-humid tropical catchments. The specific
objectives being: i) to examine how different land cover types affect the partitioning of
rainfall into surface runoff and sub-surface flow.; ii) to determine the effects of land cover
change on the dry season flow characteristics; and iii) to examine how the knowledge derived
at the micro (plot) scale of the effects of land cover on the hydrological processes can be
applied at the macro (catchment) level.
The research will be carried out within the western and north-western part of the Zambezi
River basin headstream in Zambia. This area is the source of the Zambezi River which is a
major river in the SADC region. It is reported to be under threat due to land cover changes
arising mainly from deforestation and agriculture which is affecting the sustainability of the
water resources.
The research will involve land cover mapping and change detection; physical modelling by
setting up plot experiments to monitor the hydrological processes under various land cover
types; mathematical modelling using a rainfall- runoff model to investigate the effects of land
cover change on the hydrological regime; analysis of the dry season flows in order to
ascertain the behaviour of the low flow characteristics and application of an artificial neural
network model to make prediction on effects on the low flows arising from various scenarios
of land use and land cover change.
The significant gaps to be addressed by this research are the need to understand the low flow
generating processes; how the low flow characteristics are affected by changes in the land
cover in tropical sub-humid headstream catchments and how to upscale plot level
measurements to the catchment level.
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