Accurate calibration of hydraulic models of water distribution systems (WDSs) is essential for reliable simulations. After eliminating gross errors, including those in estimated demands, pipe roughness coefficients (PRCs) are the most often used calibration parameters. Although numerous PRC calibration methods exist to ensure model simulations align well with field observations, they typically assume error-free pressure gauge elevations and overlook the compensatory interactions between elevation errors and PRC uncertainties. This often results in biased PRC calibration outcomes. To overcome this limitation, this paper introduces a novel framework that decouples elevation errors by minimizing the standard deviation of pressure residual time series, rather than relying on traditional residual minimization techniques. Additionally, a clustering-based data preprocessing approach is employed to reduce the impact of uncertain nodal demands and measurement noise. Tests on three benchmark networks demonstrate that the proposed method accurately calibrates PRCs, even when accounting for elevation inaccuracies, nodal demand uncertainties and measurement noise simultaneously. This establishes a new paradigm that leverages the statistical characteristics of residual time series to enable error-decoupled model calibration. Crucially, the method also quantifies pressure gauge elevation errors through post-calibration residual analysis, eliminating the need for costly field surveys. This advancement is particularly valuable for regions with missing or erroneous elevation data, significantly improving WDS calibration practices.

Bursts in water distribution systems (WDSs) lead to water wastage and negative environmental impacts. Optimizing pressure sensor placement (PSP) for effective burst detection is crucial for prompt response and adverse event mitigation. While many optimization methods are available for this purpose, their resulting PSP strategies often exhibit large variability caused by background noise or metering accuracy, representing low robustness. This consequently hinders the wide application of existing PSP methods for burst detection in WDSs. This study proposes a novel multi-objective PSP method aimed at maximizing burst detection while minimizing the number of sensors. Within this method, a new threshold metric is introduced to quantify the minimum detectable burst outflow across all pipes. The metric has the key advantage of taking into account the fact that the relative magnitude of the detectable threshold for different pipes is independent of noise, thereby addressing the low robustness issue. The proposed method is applied to three WDSs, and results show that it can consistently achieve robust optimal PSP strategies across diverse noise conditions. Comprehensive comparisons between the proposed method and the other benchmark approaches are conducted using five metrics, including detection performance and layout characteristics. These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. It is anticipated that the proposed method can be useful in engineering practice due to its great robustness in determining the optimal PSP strategies for burst detection in WDSs compared to other alternatives.