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HMT Hydrologic and Surface Processes

HMT Hydrologic and Surface Processes. NOAA ESRL PSD Water Cycle Branch April 2012. GOALS. Address hydrologic scientific questions and forecast operations implications Inform IWRSS National Water Center on hydrologic modeling and decision support. HASP OBJECTIVES.

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HMT Hydrologic and Surface Processes

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  1. HMTHydrologic and Surface Processes NOAA ESRL PSD Water Cycle Branch April 2012

  2. GOALS • Address hydrologic scientific questions and forecast operations implications • Inform IWRSS National Water Center on hydrologic modeling and decision support

  3. HASP OBJECTIVES • Conduct distributed modeling using high resolution precipitation fields • Primary model is the HL-RDHM - other models may be used as appropriate • Candidate basins: • Russian–Napa Rivers, CA; Babocomari River, AZ; N. Fork American River, CA • Parameter sensitivity, parameter identification, calibration and verification activities • Compare the distributed model results with those obtained from the lumped model • Apply versions of QPE and QPF hi-res precipitation fields • Examine soil moisture and ET dynamics and the role of in-situ measurements • Apply WRF ensemble for selected rainfall events • Characterize range of uncertainty associated with the various hydrometforcings • Determine what measurements of precipitation, soil moisture, evapotranspiration, and stream flow are most critical for accurate hydrological modeling • Examine scalability issues of distributed hydrologic input data and modeling in support of IWRSS-NWC

  4. NOAA’s HydrometeorologicalTestbed (HMT) Major Activity Areas • Quantitative Precipitation Estimation (QPE) • Quantitative Precipitation Forecasts (QPF) • Snow level and snow pack • Hydrologic Applications & Surface Processes • Decision Support Tools • Enhancing & Accelerating Research to Operations • Building partnerships  Recommended by USWRP 

  5. Participants • ESRL/PSD - Water Cycle Branch • Lynn Johnson • Chengmin Hsu • Rob Cifelli • Tim Schneider • Allen White • Robert Zamora • ESRL/PSD - Climate Analysis Branch • Reforecast Products • ESRL/Global Systems Division • Forecast Applications • NWS • CNRFC - Rob Hartman, Art Henkel • CBRFC – Andy Wood • NOHRSC – Andy Rost • OCWWS – Ed Clark • OHD - Mike Smith • WFO Monterey – Dave Reynolds • California • Dept Water Resources • Sonoma County Water Agency • SF PUC • SF Bay Flood Agencies

  6. Russian River Basin • Goals • More forecast points • Tributary flows • QPE / QPF • Soil moisture • Uncertainty • Assess lumped vs distributed model • 2003 – 2010 • CNRFC forcings and lumped model outputs • Compare to CONUS-scale (NOHRSC) • Water management applications

  7. SOIL MOISTURE 22 July 2008 rainfall brought the soil column to wetness values exceeding field capacity; setting the stage for the flood observed 23 July in the lower basin* River Gages Field Capacity *Zamora, R. et al. 2009: The NOAA Hydrometeorology Testbed Soil Moisture Observing Networks: Design, Instrumentation, and Preliminary Results. J. Hydromet. October.

  8. Soil Moisture Model Outputs Ed Clark, Hydrologist

  9. Russian River IWRSS Demonstration • Vision • Stakeholder Involvement • Data User Survey • Digital Watershed • Monitoring • Assimilation / Analysis • Prediction • System Integration and Decision Support • Demonstration • Assessment

  10. Digital Watershed • Geo-Database • Terrain, soils, vegetation • Hydrography, hydro model parameters • Impact features • Monitoring • Hydromet (P, SM, ET, RO) • Reservoir operations (RESSIM) • Water uses (M&I), irrigation, fisheries, recreation • Assimilation and Analysis • Database structure and design • QA / QC • QPE, QPF, reforecast • Prediction Modeling • Hydromet (QPF, hydro, inundation, water budget) • Water resources systems operations • System Integration and Decision Support • Geo-data standards • “On-the-margin” piloting • NWS integration (CNRFC, WFO) • Web services

  11. Improving Quantitative Precipitation Information (QPI) San Francisco Public Utilities Commission, Wastewater Enterprise • Accurate QPI needed to better manage storm water and combined sewer systems • QPI obtained at by combination of • Monitoring • Assimilation and analysis • Prediction • System integration • Leveraging HMT-West and CA‐DWR assets • Phased implementation: • Phase A – prototyping and detailed system design • Phase B – full implementation • Phase C – continuing operations

  12. Benefits of Improving Quantitative Precipitation Information Phase 1 Phases 2 & 3 Phase 4

  13. Thanks

  14. Frequency Discharge Modeled Historic Distribution Dec 04 – Mar 05: Large Scale Synoptic Events 2006 Monsoon season – record flooding 2007 Monsoon season 00z, Jan 1st, 2004 23z, Sept 30th, 2008 Ed Clark

  15. Ensemble Forecasting • Provide quantified estimates of uncertainty associated with hydrologic forecasts • Identify and address scientific and technical issues associated with providing ensemble inputs • Assess the performance of hydrologic models given various ensemble inputs

  16. Sonoma County Water Agency • Proof of Concept Study • Quantitative Precipitation Information (QPI) • Improving Frost Information

  17. Untreated combined sewer overflows (CSOs) create serious pollution problems in receiving waters • high storm flow loadings result in discharges exceeding interceptor sewer/ treatment plant capacities

  18. Real-Time Regulation • Real time regulation of in-system storage in CSSs successfully demonstrated in several cities • Inexpensive--relative to construction costs for expanding treatment plants, interceptor sewers, and detention storage • Ongoing implementations limited due to: • difficulties in required computer/information technology, robustness, and reliability • Incorporation of real-time data acquisition and control into construction planning for treatment plants, interceptors, and detention basins may reduce sizing requirements (and costs) by optimizing use of available in-system and detention storage for elimination of CSOs.

  19. CSO Integrated Real-Time Control • Real time control most effective if integrated over entire sewer network, but: • complex, large-scale, spatially distributed optimal control problem • highly nonlinear, dynamic optimization • need accurate simulation of stormwater runoff/sewer hydraulics • repeatedly solve optimal control problem within 5 to15 min. as rainfall forecasts and measured levels/flows updated • Optimal Control Algorithm for Real Time Regulation of CSOs • Ref: Darsono and Labadie, 2008 • Seattle case study

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