Water distribution networks (WDNs) with other infrastructures constitute a complex and interdependent multi-utility system. Considering interdependencies between WDNs and other urban infrastructures, this work proposes WDN intervention planning using a dynamic multi-utility approach to tackle the challenges of pressure deficits and cascading failures by the decoupling of different infrastructure systems. For this purpose, the study develops reliability indices representing the hydraulic and decoupled statuses of WDNs with neighbor infrastructures; the hydraulic reliability represents the robustness of the network against the water pressure deficit, and decoupling reliability represents the extent to which WDN elements are decoupled from other assets elements. A multi-objective optimization algorithm is employed to develop rehabilitation strategies by introducing three approaches for WDN upgrade following a phased design and construction method. Evaluating intervention plans based on construction cost, reliability and cascade effects shows that, under budget limitation conditions, decoupling a WDN could significantly save the cascade cost such that 1% improvement in the decoupling reliability brings about 157.42 billion Rials cascade cost saving to asset managers. On the other hand, the decoupled network is weak against hydraulic reliability, which could make it by far less resilient network than the coupled network with around 75% hydraulic reliability difference.

Phased planning for municipal infrastructure is based on the time-dependent status of multiple networks, which is in contrast to the traditional approach, where one-phase construction and a single status are considered for planning system activities. This study integrates and optimizes the corridor-wise intervention planning of water, sewer, and road networks where the number of equally long phases and intervention decisions are among the decision variables showing the extent to which phase number optimization can impact the cost and coordination of the interventions in interdependent systems. Optimizing the phase number for municipal infrastructure optimization within an evolutionary algorithm is a challenging task due to the evolutionary recombination between numerous planning solutions with different decision variable lengths. A multi-phase design and construction approach is developed for the rehabilitation of the system in a real case study in Montreal, Canada. The study involves 20 corridors in which a street section is co-located with water and sewer pipes. A metaheuristic single-objective optimization engine is employed to minimize the total net present value of intervention plan costs for the whole integrated system. The results show that phased optimization could bring about a 25% cost saving for the rehabilitation master plan and coordinated multi-systems intervention activities.

Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the river Chiese catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease, or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features, and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of 10 RCMs are obtained through a proposed algorithm for different impact models’ calculations and projections, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28% and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration, respectively. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches, implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events’ features’ values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.