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Determination of below-ground vegetation and water use model parameters for a revised South African hydrological baseline land cover.

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The combination of both natural and anthropogenic activities have caused significant changes to the natural land cover which have impacted on the hydrological responses. The assessment of the magnitude of these land use change impacts on the hydrological response is important for sound water resource management, and is largely dependent on the baseline land cover used. The development of an updated natural vegetation map of South Africa by SANBI (2012), together with improved field based measurements of natural vegetation water use in recent studies, has led to the assessment of this map as a new hydrological baseline for South Africa. The proposed new baseline provides an opportunity to address the concerns raised about the current Acocks’ (1988) baseline used in South Africa. This study has provided estimates of the below-ground related vegetation and water use ACRU parameters for the proposed new baseline. These below-ground parameters estimated include the seasonal variations of the distribution of active roots in topsoil and subsoil horizons (ROOTA and ROOTB), the effective rooting depth (EFRDEP). The new and refined set of below-ground land cover ACRU input parameters will contribute to an improved and reliable baseline against which to assess any changes. As it was impractical to produce field-based measurements for the large number of natural vegetation species, and as it was not possible to form new spatial observations of theses below-ground root structures, the refined parameterisation of the below-ground component in ACRU was based primarily on review of measured values from past literature. The ROOTA values were estimated based on the vertical root distributions for various vegetation growth forms from previous studies together with the A-horizon soil depths of the vegetation clusters that constitute the baseline land cover. The effective rooting depth (EFRDEP) values were estimated by applying a linear regression relationship, relating rooting depths to Mean Annual Precipitation (MAP) for each baseline cluster. The study also involved a sensitivity analysis of the land cover input parameters to the ACRU Agrohydrological Model to determine the parameters to which the model is most sensitive.


Master of Science in Hydrology. University of KwaZulu-Natal. Pietermaritzburg, 2018.