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