Water distribution systems reliability has typically been studied using single-period simulations of the hydraulic behavior of the system, due to high computational requirements. The aim of this work was to develop and test a framework for the evaluation of mechanical reliability, hydraulic reliability, and firefighting reliability under extended-period simulations. Four functionality functions were proposed to quantify the serviceability of the system under the perturbation scenarios. The functions were tested in five case studies based on real-world networks and compared with the previously developed mechanical reliability estimator (MRE) and hydraulic reliability estimator (HRE), as well as with similar definitions for single-period simulation. Results showed that both MRE and HRE consistently correlate with the supply/demand ratio functionality, and are easy to compute even in optimization routines. Comparison results with single-period definitions of functionality showed that, on average, evaluating hydraulic conditions at the peak hour works is a good estimation of the extended-period behavior, but in some cases, discrepancies up to 50% can be found.

Estimating future demands with a high degree of accuracy in water distribution network design remains an elusive goal. The desired outcome is to match the design demands to the demands that are eventually "realized" in the built system. To this end, this paper explores the cross correlation between demands in an existing system in order to gain a better picture of the representative spatial and temporal patterns of design demands. The aim of the paper is to analyze the cross correlation in the residential demand data collected in the city of Milford, Ohio. More specifically, the paper begins to answer five important questions concerning the cross correlation of the Milford demand data: how strongly cross correlated are indoor, residential demands? How strongly correlated is the deterministic, diurnal component of residential demand? How strongly correlated is the random noise component of residential demand? To what extent does the choice of time step influence the strength of correlation between these 3 demand components? Does the correlation between these 3 demand components differ significantly between weekdays and weekends? To answer these questions, aperiodic regression model was used to isolate the deterministic and the random noise components from the residential demand data collected in Milford. Correlation indices were formulated to measure the cross correlation of residential demand, its deterministic, diurnal component, and its random noise component. The Milford results pointed to a number of preliminary findings: (1) both residential demand and its deterministic, diurnal component had a positive and moderate to high correlation, while the random noise component of demand had a low level of correlation for the cases investigated; (2) increasing the time step length (from 600 s to 3,600 s) did increase the strength of the correlation in residential demand and its deterministic, diurnal component. This suggests that a longer time step increases both the coherence in diurnal demand patterns and their synchronicity. It is unclear whether time step length had any influence on the correlation of the random noise component of demand; (3) both residential demand and its deterministic, diurnal component were more strongly correlated during weekend periods than during weekday periods. This finding suggests that weekend periods may be characterized by less erratic water use patterns between customers leading to more coherent and synchronous diurnal patterns. It is unclear whether the random noise component was influenced by day-of-week effects. The implications of these preliminary results are discussed in the context of extended period simulation (EPS) and water quality modeling as they pertain to cost-effective design. This paper was presented at the 8th Annual Water Distribution Systems Analysis Symposium which was held with the generous support of Awwa Research Foundation (AwwaRF).

A three-dimensional coupled hydrodynamic and water quality model (Deflt3D-WAQ) was applied to evaluate the ability of a computational simulation to reproduce seasonal water quality conditions over a seven-month period in a wastewater stabilization pond located in eastern Ontario, Canada. Trends in effluent nutrient concentrations, pH, and dissolved oxygen were visually reproduced, and root-mean square errors, in comparison with weekly observations (alkalinity, $43{\mathrm{gHCO}}_3{\mathrm{m}}^{-3}$ ; dissolved oxygen, $4.0{\mathrm{gm}}^{-3}$ ; ${\mathrm{NO}}_3$ , $2.2\mathrm{gN}{\mathrm{m}}^{-3}$ ; ${\mathrm{PO}}_4$ , $0.10\mathrm{gP}{\mathrm{m}}^{-3}$ ; ${\mathrm{NH}}_4$ , $0.14\mathrm{gN}{\mathrm{m}}^{-3}$ ; pH, 0.65), were consistent with literature values. Three phytoplankton groups (green algae, diatoms, and flagellates) were also simulated. The calibrated model was extended to investigate the effects of changes in pond design on treatment efficiency, including removing the baffle, increasing the pond depth, increasing wind sheltering, and relocating the influent pipe from the pond bottom to the surface. Increasing the pond depth and wind sheltering reduced vertical mixing, sequestering nutrients near the sediments and improving effluent water quality. Removing the baffle had no impact on removal efficiency, and relocating the inlet to the surface reduced pond efficiency. Three-dimensional biogeochemical models thus provide a virtual process-based means for testing pond prototype design.