DSS for Integrated Water Resources Management IWRM DSS

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DSS for Integrated Water Resources Management (IWRM) DSS Implementation DDr. Kurt Fedra kurt@ess. co.

DSS for Integrated Water Resources Management (IWRM) DSS Implementation DDr. Kurt Fedra [email protected] co. at ESS Gmb. H, Austria http: //www. ess. co. at Environmental Software & Services A-2352 Gumpoldskirchen

What is a DSS ? a computer based problem solving system – Hardware, computing

What is a DSS ? a computer 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. 2

Murphy’s Law No. … 59 ? . The development of any software systems takes

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

Hardware requirements HW Changing very rapidly, not a real constraint, affordable standard PC/workstation/server powerful

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) 4

Architecture: Distributed redundant architecture: • Local control, needs more local resources, difficult to maintain,

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 5

DSS and model software Open Source Operating System (Linux), Ubuntu Application software: • Do

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 6

DSS and model software Usability, ease of use, error free operation, intuitive understanding (communication,

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 7

Architecture Distributed, web-based: • Shared central resources, administration • Easy access for distributed users,

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. 8

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Components (wishlist, part 1) • • • Object data base (nodes, reaches), DB administration

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 3 D models: MM 5, WRF) Rainfall-runoff model, semi-distributed (ungaged catchments) erosion, flooding, lateral inflow, calibration ? 11

Components (wishlist part 2) • • • Irrigation water demand estimation (incl. crop data

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 (3 D ? ) Water quality model(s): DO/BOD, tracers, turbidity, for river and lakes/reservoirs; Optimization (multi-criteria: water resources, water quality, socio-economics) 12

Components (wishlist part 3) • • • Watershed management, wetlands (land use dynamics, site

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 ? 13

Components (wishlist part 4) COMMON TOOLS (integrated): • Basic statistical analysis (non-parametric) • Plotting,

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 14

System integration (OOD) Modular architecture: • Several “independent” models for different topics, but shared

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) 15

System integration (OOD) Modular architecture: Nested models (3 levels ? ): • Overall Nile

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 16

System integration (OOD) Common protocols, interfaces, formats: – Ontology (terminology, variable definitions units, formats,

System integration (OOD) Common protocols, interfaces, formats: – Ontology (terminology, variable definitions units, formats, META data) – Data base structure (object oriented, georeferenced (shared GIS), time-stamped) – Data exchange (SQL, import/export to PC formats for local processing) – User interface (consistent style and logic) 17

Data requirements Never enough (historical time series, spatial coverage) – Data organisation, META data,

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) 18

Data requirements Planning applications: • Historical data, long-term hydrometeorology for probabilistic analysis (climate change

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) 19

Monitoring sensors: • Meteorology • Water levels • Soil moisture • Flow monitoring open

Monitoring sensors: • Meteorology • Water levels • Soil moisture • Flow monitoring open channel, pipelines • Water quality Telemetry: UHF, GSM, GPRS 20

People and institutions • Introduction of a DSS means institutional change (long term learning

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) 21

People and institutions Training requirements: • Basics (academic) coordinated with local/regional universities ? •

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 ? 22

DSS Implementation: Requires a well balanced consideration and careful integration of • Hardware, computing

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 Serious long term commitment 23