Water distribution systems are vital to the well-being of communities because they contribute to the functionality of all other infrastructure and lifeline systems. Earthquakes and other natural hazards can cause damage to the components of a water distribution system, causing far-reaching socioeconomic consequences. This research begins with the development of an end-to-end simulation framework to model post-earthquake functional loss and restoration of a water system, which encompasses seismic hazard characterization, component damage assessment, hydraulic performance evaluation, and network restoration modeling. The modeling framework is validated using data from the 2014 South Napa Earthquake and extended to a hypothetical scenario. The end-to-end simulation framework is then extended to consider stochastic event set assessments of the water network using the UCERF2 (Uniform California Earthquake Rupture Forecast, Version 2) earthquake rupture forecast model. Given that the end-to-end performance evaluation of distributed infrastructure for a large set of events is computationally expensive, a framework that uses Active learning to select a subset of ground motion maps and associated occurrence rates that reasonably estimates the water network risk is also developed. To deal with the temporal complexities that are embedded in the post-earthquake restoration process, a dynamic updating methodology is developed to reduce uncertainties in the outcomes of post-event recovery forecasts using Bayesian Inferencing, by exploiting real-time data. The specific example of updating predictions (post-earthquake functional recovery forecasts including total recovery time and complete recovery trajectory) is presented and validated on a real pipe network (Napa water system) and event (2014 earthquake and recovery). Ultimately, the frameworks and models developed as part of this work can inform risk-based decision making and resilience planning of water networks and other lifeline systems.