Much of the discolouration research has focused on managing cohesive material layers that are endemic to all pipes in DWDS. In addition to cohesive layers, there is good evidence that many sedimentation zones exist close to customers based on widespread yet seemingly isolated incidents, compared to the mobilisation of cohesive material layers, that can produce significant clustering of discolouration contacts across multiple locations (Husband and Boxall, 2016, Cook et al., 2015, Pothof and Blokker, 2012). The low-level issues from material sedimentation zones have been largely ignored or considered addressed, yet without understanding, through general network flushing strategies (Husband and Boxall, 2018, Vreeburg and Boxall, 2007).

Therefore, there is little knowledge of how extensively spread sedimentation zones are and their impact on discolouration is unclear. Understanding what causes sedimentation to occur and therefore the ability to identify risk zones is hence a crucial step to managing discolouration proactively.

The standard practice in the industry is to use DWDS network models at a low spatial (usually 10-50 houses per demand node) and temporal scale (15min demand patterns). Historically this has been fine as models were mostly used to check continuity of supply and pressures across the system. Discolouration risk (in this case sedimentation), however, is unique to specific pipe sections due to the hydraulic conditions in that specific pipe section. Therefore, this research aims to convert low spatial scale models to high spatial scale models (Figure 1 below). This is coupled with high-resolution flow profiles as UU 2002 data shows that much of the hydraulic characteristics are lost if flow demands are averaged over 15 minutes. Figures 2 and 3 below highlight the difference in peak flows where the blue line represents flows at 1-second interval and the black line averages the same data over15 minutes.

It is unknown what model spatial and temporal scale is required to predict sedimentation zones (hence discolouration risk). In addition, it has not been confirmed which metrics show sedimentation risk in specific pipe sections. This research aims to fill that gap by investigating both the previous and using the findings to develop a framework to map pipes according to discolouration risk.