Water data are permanently collected from a variety of sources. Primary data sources are established water monitoring networks commonly maintained by government agencies responsible for the hydrological, meteorological or geological services. For urban water systems such monitoring networks are maintained by responsible water utilities. Further to these ‘standard’ data sources there is an increased availability of other, more heterogeneous data sources: flood marks, sensors, open data sites and data repositories, smart phones, reports, citizens, etc. Smart phones have important advantages: they are equipped with physical sensors such as pedometers, breath analysers, accelerometers, image analysers, etc.; are attached to humans and can collect information from different locations; are increasingly becoming transmitters of human-sensed data. The field of ICT for water has the challenge of merging these different water data sources in new applications that will deliver additional value to the end users.

Water data refer to different aspects, which depend on the applications’ requirements: water quantity, quality, rainfall, movement of water within the soil, groundwater movement, pollution, etc. In urban water systems, data refer to water pressure, energy consumption, water quality and others.

Data can have different formats; data can be structured (such as those collected from sensors) or unstructured (e.g. textual data received in social networks). Many water data are time series. Data can also be related, meaning that two variables can have a functional relationship.

Water systems (geological units, rivers, lakes, soil distribution, precipitation) are distributed in space. So, water data are related to the spatial context and, implicitly with the geographical information and GIS (Geographical Information Systems). GIS has a broader scope, and covers not only the water “world” but also the transportation, urban planning, and others. GIS data are available from various sources, in different formats and with different sharing policies. These are in fact very similar issues to the water data. To deal with these challenges the GIS community has recently developed standards for sharing spatial data, primarily within the Open Geospatial Consortium (OGC – see Section 6.2). This community also developed the concept of SDI (Spatial Data Infrastructure) that supports easy deployment, discovery and access to geospatial data. These developments are very relevant for the water domain. In fact, other standards for different types of water data (such as WaterML, for sharing time series data, for example) are being developed within OGC. However, while the progress with developing and establishment of new data standards continues, new challenges emerge with respect to the effective usage of the new, heterogeneous data coming from the ‘non-standard’ sources mentioned above.