HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 1

HarmoniRiB Workshop on Uncertainty in Data and Models HarmoniRiB Workshop on Uncertainty in Data and Models Brussels, September 21, 2006Brussels, September 21, 2006

Design of a Water Information Design of a Water Information System for future needsSystem for future needs

Thomas Bech (DHI Water & Environment)Thomas Bech (DHI Water & Environment)Roger V. Moore (CEH)Roger V. Moore (CEH)

Giuseppe Passarella (IRSA)Giuseppe Passarella (IRSA)

HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 2

OutlineOutline

Trends and Future Needs The HarmoniRiB database Conclusions

HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006 3

Trends in Information and Decision Trends in Information and Decision Support SystemsSupport Systems

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Recap - Limitations of current Recap - Limitations of current databasesdatabases

No possibility to store information on data uncertainty

Mostly national datasets– different terminology (dictionaries) across countries– different variables and measurement techniques

across countries

Mostly single domain data– need for all domain data (meteorology, hydrology,

geology, ecology, socio-economics, etc) in one database allowing easy cross-sectoral analysis

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TrendsTrends

We are moving from dealing with– Single, isolated problems

To– Complex, multi-disciplinary issues

Symptoms– Data -> Information -> Knowledge -> Decision – Reports -> Operational Systems– Models -> Information Systems -> Decision Support Systems

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Example: Hydropower DSSExample: Hydropower DSS

The user: Operates the hydropower facility

The user want a system that:

Provides the predicted revenue from power sales for the next 48 hours, with a given release strategy.

Provides the predicted revenue from power sales for the next 48 hours, with a altered release strategy, power prices and precipitation rate (what if scenarios)

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Example: HydropowerExample: Hydropower

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DSS Key CharacteristicsDSS Key Characteristics

Data integration across domains, types and sources Combines, aggregates, analyses data Presents information useful for decision making Keywords: Integration – Analysis – Presentation

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Future Needs - SummaryFuture Needs - Summary

We need frameworks that allows us to link “DSS Components” in a nearly seamless manner (such as OpenMI)

We need data integration platforms which are adaptable, scalable and Open

We need a portfolio of “Plug-and-play DSS components” Uncertainty must be addressed – in the data integration

platform as well as the tools

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The HarmoniRiB DatabaseThe HarmoniRiB Database

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The HarmoniRiB database The HarmoniRiB database

Data Centre

Rep Basin managers

Rep Basin managers

Rep Basin managers

Rep Basin managers

Rep Basin managers

Users

SearchRequestRetrieve

Su

pp

lie

rs

Office basedOffice basedGeographically dispersed suppliers and usersGeographically dispersed suppliers and users

Europe

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The HarmoniRiB databaseThe HarmoniRiB database

Design is based on CEH’s extensive experience in Water Information Management Systems and CEH’s existing systems

Is implemented using proven technologies and software (ORACLE, ArcSDE/ArcGIS)

Implements a generic approach to data storage Uncertainty information added using the DUE Is operational at the project data centre (IRSA at Bari, Italy) Project data from river basins across Europe available and can

be retrieved through Web interface or SQL

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Software DevelopedSoftware Developed

The database Database administration software Data Loader Software Web site for Uploading / Loading data Web site for data retrieval

HarmoniRiB Workshop on Uncertainty, Brussels, September 21, 2006

A generic approach to storageA generic approach to storage

All data stored as attributes of

objects

All data are assumed to change

over time

No distinction made between

spatial and non-spatial attributes

Dictionaries of attributes and

objects can be extended. i.e.

Scalable

ObjectObject

AttributeAttribute

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Attributes and attribute dictionariesAttributes and attribute dictionaries

Attributes are used to record “whatwhat” has been described or observed at an object

Examples of attributes are:– Object class– Site ID– Site name– Location (X,Y, Z)– Width– Water level– Concentration of mercury– Fish count– Mean daily river flow– Species ……. abundance

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Attributes and attribute dictionariesAttributes and attribute dictionaries

Attributes can be numerous – e.g. species lists or chemicals

Attribute definitions are therefore grouped in to dictionariesdictionaries

Example dictionaries are:– Chemical– Hydrological– Hydrogeological– Algal– Macrophytes– Invertebrates– Weather– Universal– System

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Number of records loaded per Dictionary

The Data Centre StatusThe Data Centre StatusDatasetsDatasets

Dictionary name Number of attributes Number of records

Biological 8 7731

General 44 83726

Geological 3 74862

Hydrological 16 1319790

Morphological 2 4035

Physical and Chemical

579 1359589

Socio-economical 12 2609

Soil 7 21729

Tidal 0 0

Weather 32 7102285

Total

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The Data Centre StatusThe Data Centre StatusDatasetsDatasets

River Basin / Country Number of records Number of Dictionaries Number of attributes

Svratka / Czech Republic 203.720 5 33

Weisse-Elster / Germany 1.555.965 4 21

Odense / Denmark 322.468 7 82

Jucar / Spain 5.119.946 6 51

Geropotamou /Greece 206.485 2 16

Candelaro / Italy 255.771 6 63

Vecht / Nederland 1.112.226 8 613

Thames / United Kingdom 1.190.126 5 82

Total 9.966.707

Number of records loaded per River Basin

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Quantitative modelQuantitative modelUncertainty recorded as a PDFUncertainty recorded as a PDF

PDF = NormalPDF = Normal

Mean = Mean =

SD = SD = σσ

??

??0

1.0

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ConclusionsConclusions

Confirmed: The HarmoniRiB database has proven sustainable as the data

platform of the HarmoniRiB project (including WFD data) The HarmoniRiB database platform is a suitable data

integration platform for future DSS / Water Information Management systems

Challenges: The database is not suitable for low-end systems To operate it successfully requires IT-proficient staff – and

thorough understanding of the data model

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Future Needs - recapFuture Needs - recap

We need frameworks that allows us to link “DSS Components” in a nearly seamless manner (such as OpenMI)

We need data integration platforms which are adaptable, scalable and Open

We need a portfolio of “Plug-and-play DSS components” Uncertainty must be addressed – in the data integration

platform as well as the tools

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Next steps - exploitationNext steps - exploitation

Transformation to operational product– Improved GUI– Improved data loading facilities

Keeping the database alive– Hosting– Maintenance

Initial set of applications by building collaborations with e.g. – LOCAR (UK)– AQUASTRESS (EU)– CUAHSI (US)

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MessagesMessages

Integration of data across types / domains is a component of every Decision Support System

Uncertainty must be addressed The database developed in HarmoniRiB deals with both