The recent advances in satellite, sensor, automation, networking, and computation capabilities resulted in an ever-increasing avalanche of data and observations about the water systems. Thus we must use these data to build more accurate and integrated representations of these water systems.

Big Data implies large-volume with heterogeneous and diverse dimensionality, complex and growing data sets from multiple sources. With the fast development of solutions for efficient transfer, data collection capacity and storage, Big Data is now rapidly expanding in most of the engineering and science domains, including hydroinformatics (see Section 2.1).

One of the critical areas for management of water resources is hydrometry, a discipline under the hydro-science guiding protocols for data acquisition, processing, and delivering of quantitative estimation of the variables associated with the hydrological cycle (from rainfall to flow in rivers). Recently, the World Meteorological organization identified and implemented a standardized method for conducting uncertainty analysis using rigorous and robust approaches [Muste, 2012] (see Section 2.2).

According to the World Economic Forum [Global Risk, 2015], the International Energy Agency projects water consumption will increase by 85% by 2035 to meet the needs of energy generation and production. Global water requirements are projected to be pushed beyond sustainable water supplies by 40% by 2030. Also, nowadays utilities collect millions of pieces of data each day. This asks for finding new methods and technologies to efficiently process and use this data, and to build more accurate and integrated representations of smart water systems (see Section 2.3).