Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

Abstract

Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.

Keywords: Bayesian networks; South Africa; water resources.

Fig 1

An example of a conceptual model (a), highlighting the exposure and effect pathway representing a socioecological system, where risk to recreational water use is assessed.

Fig 2

Ecological classification of different components of the RQOs is based on the ecological categories along a continuum. (Adapted from Kleynhans & Louw, .) The corresponding colors are used for easy reference when depicting ECs of river reaches on maps.

Fig 3

Basic conceptual model representing the variables in the River Health Programme, South Africa (DWS, 2016b).

Fig 4

Framework for determination of RQOs through BN application. Each RQO component is represented by a BN submodel, which considers criteria as per the South African EcoClassification indices, used in the current RQO determination process. Specified criteria as outlined in the indices provide the parent node data. The endpoint of each submodel may be linked to ECs.