Using a comparative of DRASTIC and Bayesian weights of evidence approach to assess transboundary aquifer vulnerability in a data scarcity region: Tuli-Karoo aquifer

The Tuli-Karoo Transboundary Aquifer system is shared among Botswana, South Africa and Zimbabwe. It hosts a complex aquifer predominately of sand, heavily tapped in South Africa and sandstone substantially used in Botswana threatening it’s quality. Two vulnerability models, the classic DRASTIC (Depth to water table [D], Recharge to the aquifer [R], Aquifer media [A], Soil media [S], Topography [T], Impact of vadose zone [I], Conductivity [C]) and the bayesian based, Weights of Evidence (WofE), were used to delineate zones vulnerable to groundwater contamination under limited nitrate observations. The study revealed that the generic DRASTIC vulnerability results were comparable to the WofE, for the highly vulnerable zones, as both had an area under the curve (AUC) below 0.6. The high vulnerability was attributed to the shallow depth of water level, fractured geological condition (based on transmissivity) and high net recharge rate in and around the Tuli-Karoo Aquifer. However, there are differences in spatial variability in the low/medium vulnerability zones. WofE was superior in capturing spatial variability of groundwater vulnerability. Considerations must therefore be provided, under data scarcity, to incorporate proxies that include landuse-landcover and population density as evidential layers. Overall, WofE performs better due to better prediction power under data-scarcity conditions. This study recommends that under data scarcity, high predictive models like WofE should be used for environmental planning and groundwater protection strategies. • This study evaluates the reliability and usefulness of DRASTIC methodology compared to WofE. • Both DRASTIC and WofE support environmental planning and groundwater protection strategies. • DRASTIC captures the high vulnerability well as validated by elevated nitrate concentration even under data scarcity. • WofE detects the spatial variability efficiently which could be further improved with other evidence like population. • Under data scarcity conditions, WofE is appropriate for regional vulnerability assessment.