We present a cyber-physical-human distributed computing framework, AquaSCALE, for gathering, analyzing and localizing anomalous operations of increasingly failure-prone community water services. Today, detection of pipe breaks/leaks in water networks takes hours to days. AquaSCALE leverages dynamic data from multiple information sources including IoT (Internet of Things) sensing data, geophysical data, human input, and simulation/modeling engines to create a sensor-simulation-data integration platform that can accurately and quickly identify vulnerable spots. We propose a two-phase workflow that begins with robust simulation methods using a commercial grade hydraulic simulator - EPANET, enhanced with the support for IoT sensor and pipe failure modelings. It generates a profile of anomalous events using diverse plug-and-play machine learning techniques. The profile then incorporates with external observations (NOAA weather reports and twitter feeds) to rapidly and reliably isolate broken water pipes. We evaluate the two-phase mechanism in canonical and real-world water networks under different failure scenarios. Our results indicate that the proposed approach with offline learning and online inference can locate multiple simultaneous pipe failures at fine level of granularity (individual pipeline level) with high level of accuracy with detection time reduced by orders of magnitude (from hours/days to minutes).