Hydroinformatics and decision support: current technological trends and future prospects

1. Hydroinformatics and decision support: current technological trends and future prospects AndrejaJonoski

2. Contents • Brief Introduction to Hydroinformatics • Data sharing standards and interoperability • Cloud and Grid Computing • DIANE-CM project: stakeholder collaboration in flood risk management

3. Hydroinformatics • Origins: early 1990-ies • Encapsulated water-related knowledge for continuously increasing user categories • Key development: Modelling systems • Generic computational codes with powerful user interfaces (pre- and post-processors) • Users – different than the code developers • Markets for modelling systems software products

4. Hydroinformatics • Current situation – driven by the Internet • Market still dominated by modelling systems products • Modelling services are emerging • New data sharing standards are emerging that enable interoperability • User base broadened to stakeholders, citizens using sophisticated technologies (web, mobile phones) • New kinds of applications targeting new users

5. Data sharing standards • Data needs to be shared between peopleand between software components • Many attempts in the past focused on either of these targets • Solution seemingly able to simultaneously target both: eXtensible Markup Language (XML)

6. Data sharing standards • XML is similar to HyperText Markup Language (HTML), but the focus is not on presentation, but on semantics (meaning) of data • Hierarchical presentation of data in a tree-like structure • Data are provided together with their description • Easily readable by computers, and understandable by humans • Increasingly used in software applications

7. Data sharing standards • However, XML is not enough for standardisation • Different domains need to be described in a standardised way in terms of their objects, properties and relations • Result: emergence of many XML-based domain descriptions • Usually recognisable by the ‘ML’ at the end • AdsML: Markup language used for interchange of data between advertising systems • FpML: Financial Products Markup Language • GML: Geography markup language for expressing and exchanging geographical features • WaterML: Language for expressing and sharing time series of water-related data

8. Data sharing standards • Open Geospatial Consortium (OGC) http://www.opengeospatial.org/ • Many standards for geo-spatial data: • Web Map Service ( WMS) • Web Feature Service (WFS) • Web Coverage Services (WCS) • Water ML – emerging standard for time series data • Originated in USA, further developed by OGC Hydrology working group

9. Data sharing standards • Example WaterML file

10. Interoperability • Modelling systems may increasingly start using standardised formats • Example HEC software DSSVue (USACE)

11. Interoperability • Interoperability with dynamic coupling • Open Modelling Interface (OpenMI) • www.openMI.org • Coupling in which different models exchange data during runtime (per time step) • Software components (modelling systems) require code adaptation to become OpenMI compliant

12. Cloud and Grid computing Two aspects: 1. Possibility for deploying applications, (including models and data) on the web, without maintaining them as stand alone applications 2. Harnessing the power of many parallel computational elements • Cloud computing – service orientation model: • Infrastructure as a service • Platform as a service • Software as a service

13. Cloud and Grid computing Example from existing software provider

14. Cloud and Grid computing New possibilities • Cloud computing framework for a hydro information system • (Delipetrev et al., this conference)

15. Cloud and Grid computing Grid computing: • Organisations pool autonomous computing resources together for common purpose • Usually for scientific research • They create Virtual Research Communities that form Virtual Organisations (VOs) • All participants can benefit from the shared computing resources • Less costly alternative to supercomputers • European Grid Infrastructure http://www.egi.eu/

16. Cloud and Grid computing • Example: www.envirogrids.net SWAT SWAT-CUP

17. ExampleDIANE-CM PROJECT Decentralised Integrated ANalysis and Enhancement of Awareness through Collaborative Modelling and Management of Flood Risk

18. DIANE-CM PROJECT Collaborative flood risk management

19. Case studies

20. Collaborative platform (CM) and Collaborative Modelling Exercise (CME) http://hikm.ihe.nl/diane_cm/cranbrook/ http://hikm.ihe.nl/diane_cm/alster/ TIMELINE OF THE DIANE-CM PROJECT

21. Example objectives (UK)

22. Example alternatives (UK)

24. Steps in Collaborative Modelling Exercise (CME)

25. Steps in Collaborative Modelling Exercise (CME) • Visualisation of the individual positions versus the group as a whole • Darker blue colours represent more preferred alternatives by the whole group • Individual ranking of the same alternative with markers • Different markers for different stakeholder group • Similar individual rankings grouped in clusters

26. Preliminary conclusions(feedback from stakeholders) • Collaborative modelling approach is much appreciated by stakeholders • Demands for more information • Diverse stakeholders require diverse presentation forms of the same information • Potential for increased flood risk awareness in communities

27. Thank you !