Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step toward sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events, and in an organized manner, to prioritize the use of available resources to restore service rapidly while minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve the resilience of a postdisaster WDS through identifying optimal sequencing of recovery actions. To address this gap, the authors propose a new dynamic optimization framework in this study in which the resilience of a postdisaster WDS is evaluated using six different metrics. A tailored genetic algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include the following: (1) the near-optimal sequencing of a recovery strategy heavily depends on the damage properties of the WDS; (2) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time; and (3) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS.

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.