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DSS for Integrated Water Resources Management (IWRM)

DSS for Integrated Water Resources Management (IWRM). DSS Implementation. DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen. What is a DSS ?. a computer based problem solving system

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DSS for Integrated Water Resources Management (IWRM)

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  1. DSS for Integrated Water Resources Management (IWRM) DSS Implementation DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen

  2. What is a DSS ? acomputer based problem solving system • Hardware, computing and communication • Software, DB/GIS, models, DSS proper • Data, information, knowledge • People, institutions that can assist non-trivial choice between alternatives in complex and controversial domains.

  3. Murphy’s Law No. …59 ?. The development of any software systems takes (much) longer than expected, even if this rule is taken into account

  4. Hardware requirements HW Changing very rapidly, not a real constraint, affordable standard PC/workstation/server powerful enough, most important: local technical support (global DHL delivery ?) Infrastructure: • Clean, climate controlled rooms • Stable power supply (UPS) • Internet connection (bandwidth, reliability)

  5. Architecture: Distributed redundant architecture: • Local control, needs more local resources, difficult to maintain, update, synchronize (will drift apart), more difficult to exchange/shared data and address common (regional) issues Networked (centralized server, distributed clients): • shared resources: server/cluster, RDBMS, backup, data, tools, easy to maintain and update, synchronize, but more limited local control

  6. DSS and model software Open Source Operating System (Linux), Ubuntu Application software: • Do it yourself: cheap, flexible, unreliable, inefficient, heterogeneous, difficult to manage/control • Commercial: expensive (long term support), but reliable and efficient if maintained (costs) • Combined: commercial stable core with open interfaces to integrate custom made components

  7. DSS and model software Usability, ease of use, error free operation, intuitive understanding (communication, participation): INTEGRATION Easy to change components in a distributed, open, modular architecture with standard protocols, interfaces, formats

  8. Architecture Distributed, web-based: • Shared central resources, administration • Easy access for distributed users, easy communication, distributed resources • Simple client requirements (PC with standard web browser, data import/export facilities) CONSTRAINT: Internet access, bandwidth, reliability (latency can be measured …) improves fast.

  9. Components (wishlist, part 1) • Object data base (nodes, reaches), DB administration (telemetry ?) • Monitoring data base (time series) • Embedded GIS (all objects geo-referenced) • Hydro-meteorological scenarios (prognostic 3D models: MM5, WRF) • Rainfall-runoff model, semi-distributed (ungaged catchments) erosion, flooding, lateral inflow, calibration ?

  10. Components (wishlist part 2) • Irrigation water demand estimation (incl. crop data base, production, yield) • Water resources model(s), dynamic water budget, allocation, economics (hydropower) • Groundwater model (3D ?) • Water quality model(s): DO/BOD, tracers, turbidity, for river and lakes/reservoirs; • Optimization (multi-criteria: water resources, water quality, socio-economics)

  11. Components (wishlist part 3) • Watershed management, wetlands (land use dynamics, site suitability analysis, optimal project location) • Fisheries management (Beverton-Holt ?) • Regional development dynamics (demography, socio-economics) • EIA (screening level project assessment) • Embedded user manuals and training, tutorials, distance learning ?

  12. Components (wishlist part 4) COMMON TOOLS (integrated): • Basic statistical analysis (non-parametric) • Plotting, mapping (compatible GIS) • Report generation (standard formats) • Data exchange protocols • User communication (groupware) • Discussion fora, FAQ, error log • Mailing lists, newsletter

  13. System integration (OOD) Modular architecture: • Several “independent” models for different topics, but shared input/output; • Smaller models easier to test ! • Cascading models, consistent coupling and integration (thematic, time and space)

  14. System integration (OOD) Modular architecture: Nested models (3 levels ?): • Overall Nile basin water budget • Major subcatchments (10-15 ?) • Local studies within the sub-catchments Each sub-model provides input (its sub-basin outflow and performance) to the next level

  15. System integration (OOD) Common protocols, interfaces, formats: • Ontology (terminology, variable definitions units, formats, META data) • Data base structure (object oriented, geo-referenced (shared GIS), time-stamped) • Data exchange (SQL,import/export to PC formats for local processing) • User interface (consistent style and logic)

  16. Data requirements Never enough (historical time series, spatial coverage) • Data organisation, META data, uncertainty, sampling statistics • Innovative sources: remote sensing • Model generated estimates for complete, high-resolution synoptic data fields (hydrometeorology)

  17. Data requirements Planning applications: • Historical data, long-term hydrometeorology for probabilistic analysis (climate change impacts ?) Operational management: • Needs real-rime monitoring data (sensors, local telemetry (GSM/GPRS, UHF radio), telemetry (investment and maintenance effort) • Model based forecasts (precipitation  flow)

  18. Monitoring sensors: • Meteorology • Water levels • Soil moisture • Flow monitoring • open channel, pipelines • Water quality • Telemetry: • UHF, GSM, GPRS

  19. People and institutions • Introduction of a DSS means institutional change (long term learning process) • Control of information is power, changes mean struggle, affect institutional structures • Training is essential: ON THE JOB training within the framework of relevant projects • Academic training takes YEARS (brain drain)

  20. People and institutions Training requirements: • Basics (academic) coordinated with local/regional universities ? • Topical and tool oriented courses • On-the-job training within specific projects • Distance learning tools ISSUES: tests, certification, accredidation ?

  21. DSS Implementation: Requires a well balanced consideration and careful integration of • Hardware, computing and communication • Software, DB/GIS, models, DSS proper • Data, information, knowledge • People (training), institutions, procedures Seriouslong term commitment

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