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Integration of sensor networks and decision support tools for basin-scale, real-time water quality management

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Presentation given by Nigel Quinn, HydroEcological Engineering Advanced Decision Support, Berkeley National Laboratory, USA, at the 2011 Cybera Summit / Sensor Web Enablement Workshop.

Presentation given by Nigel Quinn, HydroEcological Engineering Advanced Decision Support, Berkeley National Laboratory, USA, at the 2011 Cybera Summit / Sensor Web Enablement Workshop.

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  • 1. Integration of sensor networks and decision support tools for basin-scale, real-time water quality management  Nigel W.T. Quinn PhD, P.E., DWRE HydroEcological Engineering Advanced Decision Support Berkeley National Laboratory, Berkeley, CA 94720 Division of Planning, US Bureau of Reclamation Sacramento, CA 95825    CYBERA GeoSpatial/Open Data Conference Banff Centre, Banff, CANADA  October 6-8 2011
  • 2. URBAN WETLANDSAGRICULTURE
  • 3. SALINITY REGULATION IN WESTERN SAN JOAQUIN VALLEY OF CALIFORNIAl  The Central Valley Regional Water Quality Control Board has adopted an alternative stakeholder-centric approach to salinity planning and regulation “real-time salinity management”l  Requires dischargers that are otherwise subject to WDR’s to adopt a “Board approved” real-time salinity management programl  Program to include monitoring, real-time data access, modeling and decision supportl  High reliance on sensor networks and the development of a stakeholder supported sensor webl  Compliance date in late 2014
  • 4. EXEMPLAR : SEASONALLY MANAGED WETLANDS IN THE GRASSLANDS ECOLOGICAL AREA 170,000 acre wetland footprint within the San Joaquin Basin
  • 5. DEFINITIONS ASSIMILATIVE CAPACITYThe mass load of a pollutant that can be safely discharged to areceiving water without exceeding the water quality objective orstandard for that pollutant. REAL-TIME WATER QUALITY MANAGEMENT A coordinated and cooperative set of actions based on real-time forecasts of river water quality to consistently meet water quality objectives
  • 6. COMPARISON OF WEB-BASED SENSOR NETWORK TECHNOLOGIES1.  Web-based sensor network using Campbell Scientific Loggernet software and Real-Time Data Management (RTDM) toolbox2.  Web-based sensor data access and reporting using YSI- Econet and Aquatic Informatics Aquarius software3.  Integrated web-based sensor data access, QA data processing and reporting using Kisters WISKI software APPLICATION TO SEASONALLY MANAGED WETLANDS
  • 7. WEB-BASED SENSOR NETWORK USING CSILOGGERNET AND RTDM TOOLS
  • 8. WEB-BASED SENSOR NETWORK USING CSI LOGGERNET AND RTDM TOOLSADVANTAGES•  Capable of being customized to the application•  Robust and easy to troubleshootDISADVANTAGES•  Difficult to integrate cellular, GOES and land line telemetry•  Time consuming to operate and troubleshoot even with automation offered in LoggerNet•  Graphics from RTDM application stored daily as permanent jpeg or gif images – very storage intensive•  Wetland biologists reluctant to spend time indoors doing data processing or system troubleshooting•  Lag in data processing compromised effectiveness for RTDM
  • 9. MONITORING
  • 10. FLOW AND WATER QUALITY FORECASTING
  • 11. YSI-ECONET SENSOR WEBTOPOLOGY FOR WETLAND MONITORING !
  • 12. WEB-BASED SENSOR DATA ACCESS USING YSI-ECONETADVANTAGES•  Simple to install and become operational•  Ability to restrict data access on public website to QA censored data•  Web site customizable for display of sensor parameters, graphic visualization formats and backdrop GIS station maps•  Rapid tech transfer among wetland community – new paradigmDISADVANTAGES•  Cannot download directly from either access or data nodes in network•  Lack of integration with QA software•  Difficult to overwrite preliminary data with QA-censored data•  Inability to mix and match other telemetered data logging hardware•  Excellent for small networks but expensive scale up to enterprise level
  • 13. DATA QUALITY ASSURANCE : DATA VALIDATION AND CORRECTIONl  Need to automatically flag suspect data and identify : –  Outliers –  Unusual rate of change –  Poor correlation with past or adjacent sensor time seriesl  Visual flagging allows to quickly spot problemsl  Corrections should be performed either visually or numericallyl  Tracking and annotation of all corrections and changesl  Original data must be retained
  • 14. AQUARIUS DATA QA OBJECT MODEL DATA PROCESSING WHITEBOARD
  • 15. FEATURES OF AQUARIUS SOFTWARE FOR REAL-TIME DATA PROCESSINGl  Over 30 toolboxes for most signal processing functionsl  Whiteboard concept allows users to easily build their own workflowsl  Simple drag and drop interfacel  Single-click visualization of data at any stage in workflowl  Whiteboards can be saved for re-use, and can be run automaticallyl  Not well integrated with sensor web for data downloading and QA data uploading to websitel  Excellent software help files, user online tutorials, case study examples
  • 16. AQUARIUS FOR CONTINUOUS WEB-BASED DATA QA AND ERROR CORRECTING
  • 17. FEATURES OF WISKI TOOLBOX FOR REAL-TIME DATA MANAGEMENTADVANTAGES•  Fully integrated toolbox combining data downloading, data processing, data dissemination and modeling support•  Installed user base within irrigation water district community and USFWS (Alaska)•  Local presence within Northern California for user support and training•  Robust system capable of handling thousands of network data nodes•  Ability to perform low-cost SCADA control functionsDISADVANTAGES•  Increased software functionality requires commitment for effective use•  Significant effort required to access data from existing YSI-Econet system
  • 18. AUTOMATED DRIFT CORRECTION OF REAL-TIME DATA IN WISKI Manual Readings
  • 19. REVIEWING QA INFORMATION FOR REAL-TIME DATA IN WISKICONTROL BARS FOR GRAPHICAL REVIEW OF DATA QUALITY AND COMMENTS
  • 20. SETTING VALIDATION RULES FOR REAL-TIME DATA IN WISKI
  • 21. STANDARDISATION OF INTEROPERABILITY PROTOCOLS TO ENHANCE DATA SHARINGData Integration with WISKI Web Services WISKI user can access SOS Water ML2 Services and load KITSM – scalable multi-tier data into the WISKI database Time Series Data Metadata architecture to to organize, compute and share time series Downloaded data can be data included in further calculations (agents) and KiTSM analysis (statistics/ operations) KiWIS SOS/ Data Consumer WaterML2 Class FrameworkOne API which combines severalinteroperability standards – allowswetland data to be brought into Cloud
  • 22. NEXT STEP : IMPLEMENTATION OF WISKI WEB SERVICES INTERFACEWEB PRO for Intranet WEB PUBLIC for Internet Data copied from screen or direct downloaded Display salinity concentration exceedence levels Utilizes graphical user interface to access data
  • 23. FLOW AND WATER QUALITY SIMULATION MODELING – WARMF-SJR n
  • 24. HUMAN FACTORS IN WETLAND REAL-TIME SALINITY MANAGEMENT ADOPTIONl  Recognize institutional constraints of participating stakeholders : Federal and State agencies have autonomy over their decisions : water districts and private wetlands answer to their Boardsl  Private entities that are not as well funded as State and Federal agencies. Incentive programs could be combined with existing habitat programs as agents of change.l  Collaborations with regulators to develop interim salt load targets - creating a transition period for wetland management to learn by doing and improve drainage salt load scheduling incrementally (adaptively)
  • 25. ADAPTIVELY MANAGING WETLAND REAL-TIME MANAGEMENT INVESTMENTSl  Adaptive management dictates a feedback mechanism to prevent irreversible damage to wetland resource through real-time salinity management while promoting and sharing successful outcomesl  Learning by doing develops experiential knowledge base that can guide future actions and operations. This is necessarily a long-term strategy give the inter-annual variability of climate and water supply allocations. Provides hedge against uncertainty.l  By its nature a long-term planning strategy – 10 to 15 year planning horizon for technology transfer and institutional adoption.l  Need to plan for long term financing of essential components such as enhanced data sharing and management technologies.
  • 26. INSTITUTIONAL ASSURANCES TO INCREASE PACE OF ADOPTIONl  California has well-financed stakeholder interest lobbies - impossible to satisfy all stakeholder interests. Every information management and decision support system is, by nature, compromised at the design phasel  Assurances necessary to reduce perceived risk of adoption – otherwise easier to employ litigation to avoid changel  Assurances can only be given by statutory bodies with institutional clout to make long-term promisesl  Assurances need to be backed up with data collection to better understand long-term trends – otherwise no proof of harml  Needs to be understood that system impacts can take years to develop - though physically reversible may be difficult to remedy institutionally
  • 27. SUMMARY AND CONCLUSIONSl  Real-time water quality (salinity) management allows greater salt export than traditional load-based TMDL’s.l  For seasonally managed wetlands RTSM is the only long-term option if waterfowl habitat is to be sustainedl  RTSM will require integration of data acquisition, processing, model forecasting, information dissemination and decision supportl  Technical progression in capability of sensors and supporting software over past decade essential for implementation of RTSMl  Full TMDL compliance required by 2014 – major challenge for cooperative data sharing and coordination of actions between agriculture, wetland interests, municipal and industrial stakeholders