Journal
HYDROLOGICAL PROCESSES
Volume 27, Issue 16, Pages 2325-2338Publisher
WILEY
DOI: 10.1002/hyp.9217
Keywords
environmental factors; hydrological processes; overland flow generation; spatial and temporal variations
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Funding
- Water Resource Commission of South Africa [K5/1904]
- School of Bioresources Engineering and Environmental Hydrology
- Farmer Support Group, University of KwaZulu-Natal
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Improved knowledge on overland flow (OF) generation and its dynamics (i.e. spatial and temporal variations) is essential to understand catchment hydrology, a prerequisite for better water resources and soil management. In this study, our main objective was to quantify the dynamics of OF during rainfall events and to assess its main factors of control. The research study was undertaken in an agricultural 23-ha catchment of a communal pasture in KwaZulu-Natal (South Africa) experiencing Mediterranean climate and with variations of soil, topography and vegetation conditions. The dynamics of OF was evaluated during three rainfall seasons (2007 to 2010) by using 1x1-m(2) microplots (n=15) located at five landscape positions. At each location, a microplot was equipped with an automatic tipping bucket linked to a logger to estimate the delay between the start of the rain and the start of OF [i.e. the time to runoff initiation (TRI)]. Multivariate analysis was applied to the total OF and TRI data and the information on selected environmental factors (rainfall characteristics; soil type; soil clay content, Clay; proportion of the soil surface covered by vegetation, Cov; proportion of the soil surface covered by crusting, Crust; mean slope gradient, S; soil bulk density, (b); soil water tension at different depths, SWT). The average OF rate over the 3-year study period varied 2.3-fold across the catchment (from 15% footslope to 35% backslope), whereas the average TRI varied by a 10.6-fold factor (between 0.6min at bottomland and 6.4min at footslope). TRI temporal variations correlated the most with event duration (r=0.8) and cumulative amount of rainfall since the onset of the rainy season (r=-0.47), whereas TRI spatial variations were controlled the most by Crust (-0.97<-0.77). Ultimately, TRI spatial variations were modelled and mapped in an attempt to model OF dynamics over the entire microcatchment. Copyright (c) 2012 John Wiley & Sons, Ltd.
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