Karim C. Abbaspour, Philippe Quevauviller, Massimo Craglia, Gregory Giuliani, Kazi Rahman, Stefano Nativi, Anthony Lehmann, Martin Beniston, Douglas Cripe, Nicolas Ray, and Hydrology and Hydraulic Engineering
Hydrological modeling and water management intrinsically ask for better integration of data, information and models due to their complex and interdisciplinary nature. The challenge is to inform policy- and decision-makers with reliable data and efficient tools based on scientific models. The latest technological advances in Earth observation and Web technologies allowed the development of Spatial Data Infrastructures (SDIs) that are accelerating the pace of data sharing at regional and national scales, with the long-term vision of creating a global SDI. A series of recent European projects (e.g. ACQWA, enviroGRIDS, GEOWOW) are contributing to promote innovative Earth observation solutions to favor the uptake of scientific outcomes in water management and policy. Currently, hydrological, meteorological and climatological data still remain difficult to find, access, and integrate because of various accessibility and incompatibility issues. New data sources from remote sensing or crowd sourcing are also producing valuable information to improve our understanding of the water cycle, while field sensors are developing rapidly and becoming cheaper. International initiatives such as OGC, GEOSS and INSPIRE catalyze data sharing by promoting interoperability to maximize the use of data and by supporting easy access to and utilization of geospatial data. Emerging standards (e.g., WaterML, netCDF) are enhancing interoperability between hydrology and other scientific disciplines, allowing to share models and specific algorithms using the Web Processing Service (WPS). The System of Systems (SoS) approach appears to be a valuable and promising concept in order to lower the entry-level barrier to a real multi-disciplinary framework. The UncertWeb modeling framework is an attempt to quantify and efficiently communicate uncertainty of data and models, an essential pre-requisite for decision-making of adaptation strategies. Distributed computing infrastructures can handle complex and large hydrological data and models, while Web processing services bring the flexibility to develop and execute simple to complex workflows over the Internet. The brokering approach allows binding heterogeneous resources published by different data providers and adapting them to tools and interfaces commonly used by users of these resources. Successful SDIs rely on various aspects: a shared vision between all participants, necessity to solve a dominant problem, adequate data policies, incentives, and sufficient resources. Capacity building at human (education and training of individuals), infrastructure (installing/configuring/managing of the needed technology) and institutional (enhancing the understanding within organization and/or governments) levels is also a major element for reinforcing the commitment to SDI concepts. Consequently, SDIs certainly represent an important step toward removing barriers to data availability, accessibility, integration and modeling, which are needed to improve decisions on water resources. With all these technological improvements, large steps are being made to improve the science-policy interface. The most difficult step however remains to bring scientists and decision makers around the same table to build their project together with an adaptive strategy., JRC.H.6-Digital Earth and Reference Data