Development of a land use-based spatial water requirements model for the Berg Water Management Area - Van Der Walt, Marthinus (School of Architecture, Planning and Geomatics, 2018)
This study was conducted to investigate the requirements for the spatial modelling of current and future water demand in the Berg River Water Management Area in the Western Cape of South Africa in order to produce a prototype model from which annual water requirements could be computed and spatially visualised. To accomplish this the spatial distribution of water demand within the study area was first investigated. The data required to perform spatial water demand modelling of diverse land uses and socio-economic activities were evaluated. Finally, the question of improving spatial water demand modelling at the catchment scale was considered from both a systems design and a technical perspective. The resulting model consists of two main modules; one performing a rudimentary monthly soil water balance to obtain monthly and annual irrigation requirements, and another applying preconfigured determinant layers derived from land use to town zone layers in order to determine annual urban water use intensities per areal unit. The resulting model prototype follows a sequential workflow based on a series of components that combine to produce a spatial overview of water use intensity within the study area. Water demand was found to be predominantly irrigated agriculture in the upper reaches of the Berg (mainly wine grape) and was found to be dominated by intensive industrial users in the central and lower reaches. The model was designed so that new data could be introduced in order to expand the system where required, as well as allowing for updated datasets to be incorporated as they become available. Due to the uncertainties inherent in the modelling and approximation of real world phenomena, the importance of establishing a set of structured, stable, predefined user requirements and system specifications were noted as a fundamental requirement for improving model development and design efficiency and ensuring model validity. It was further found that incorporating additional datasets, covering parameters related to the system, may serve to improve model accuracy, but could easily lead to compounded errors if not correctly parameterised or adequately validated.