Urban drainage network models (UDNMs) have been widely used to facilitate flood management. Typically, a UDNM is developed using data from Geographic Information Systems (GIS), and hence it consists of many short pipes and connection nodes or manholes. To improve modeling efficiency, a GIS-based model is generally skeletonized by removing many elements. However, there has been surprisingly a lack of knowledge on to what extent such skeletonization can affect the model's simulation accuracy, resulting in uncertainty in flood risk estimation. This paper makes the first attempt to quantitatively evaluate multidimensional impacts of different skeletonization levels on hydraulic properties of UDNMs. This goal is achieved by a new evaluation framework comprising of eight existing and new metrics that make use of hydrographs, main pipe hydraulics and flood distribution properties. A real-life UDNM is used to illustrate the new framework under various rainfall conditions and different skeletonization levels. The new framework is also used to compare the performance of two compensation methods in mitigating impacts caused by model skeletonization. Results obtained show that: (a) model skeletonization can significantly affect the magnitude of peak flow at the outfall, with a maximum overestimation of up to 20%, (b) hydraulics in main pipes can also be affected by model skeletonization with the maximum flow increasing up to 35%, and (c) model skeletonization may significantly alter the flood distribution properties which has been largely ignored in past studies. These findings provide guidance for UDNM skeletonization, where their associated impacts should be aware in engineering practice.

Most of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi-parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab-sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre-specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub-network as equal as possible, where sub-networks are determined by the selected hydrants and current flow pipe directions. The CS's size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real-world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade-off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors.