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Initial Results and Future Applications of a CONUS - wide Flash Flood Prediction System Zachary Flamig University of Oklahoma/School.

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Presentation on theme: "Initial Results and Future Applications of a CONUS - wide Flash Flood Prediction System Zachary Flamig University of Oklahoma/School."— Presentation transcript:

1 Initial Results and Future Applications of a CONUS - wide Flash Flood Prediction System Zachary Flamig University of Oklahoma/School of Meteorology NOAA/National Severe Storms Laboratory In collaboration with: JJ Gourley Suzanne Van Cooten Yang Hong Humberto Vergara NOAA/NSSL NOAA/NSSL OU/CEES OU/CEES October 25 th, 2010 National Flood Workshop, Houston, TX

2 Looking inland… Flash Flood on June 10 th Albert Pike Campground, AR 20 Fatalities AP Photos Floods & Flash Floods around May 1st Nashville, TN >$1 Billion in damage Flash Flood on June 14 th Oklahoma City, OK >$1 Million in damage

3 Flash Flood Prediction? State of the Art: Gridded Flash Flood Guidance*  Distributed hydrologic model for soil moisture accounting  Rainfall/runoff model for runoff potential prediction  Static model for critical runoff threshold estimation *Schmidt, J., A. J. Anderson, and J. H. Paul, 2007: Spatially-variable, physically-derived flash flood guidance. Preprints 21st Conference on Hydrology, San Antonio, Amer. Meteor. Soc., 6B.2. Ultimately derives rainfall threshold which if exceeded means flash flooding is occurring or will occur! X

4 Flash Flood Prediction Observed Precipitation Forecast Precipitation Distributed Hydrologic Models Streamflow time Return Period time Flood Exposure Model $0 >$1M Crop Damage Probability $0 >$5M Property Damage Probability 0 1,000 People Affected Probability Requirements: Flash Flood Scale (1 km 2, Sub-Hourly Time Scale) Probabilistic (Ensemble) Prediction National Mosaic and Multi-Sensor QPE (NMQ-) Flooded Locations And Simulated Hydrographs (FLASH )

5 Getting Observed Precipitation  NMQ Q2, radar only product  0.01º x 0.01º (~1km x 1km)  2.5 minute update  Long term reanalysis (soon!)

6 Precipitation Forecasts  Cloud resolving NWP from 4km 2 to 1km 2  HRRR primary candidate because it assimilates NMQ 3D radar reflectivity field

7 Hydrologic Models CREST HL-RDHM  Jointly developed by OU/NASA  Runs operationally over globe  Developed by NWS  Runs operationally at RFCs

8 Simulated Threshold Frequency  Requires a long archive of precipitation (10+ years)  Run the model using the precipitation archive  Compute Log Pearson-III flood frequencies for each grid cell Return Frequency Full Archive USGS Q 14 Year USGS Q 14 Year Simulated Q 2 Years555 cms549 cms655 cms 5 Years1119 cms925 cms841 cms 10 Years1594 cms1215 cms959 cms 25 Years2297 cms1625 cms1103 cms 50 Years2888 cms1962 cms1207 cms 100 Years3540 cms2322 cms1309 cms USGS Illinois River near Tahlequah, OK

9 Flood Exposure Model  Risk = Hazard (dynamic) * Vulnerability (static)  Property damage from ABRFC area for flash floods  Utilizes StormDat polygon data from  Only hazard information used in shown figure

10 Event Type: Flood Start Time: 6/27/ :30 A.M. Latitude: Longitude: County: Butler State: Kansas Flood Nature: Overflow road other Depth: 0.3 m Lateral Extent: 300 m Comments: Horse corral on location was flooded. Creek flooded 1/4 mile west from location. Road closed at 150th and Highway 77. Verification Methods 1. NWS flash flood reports (StormDat) + Designed to encompass all events in forecaster’s area of responsibility - Dependent upon NWS warning process, population density minute streamflow data from USGS + Objective measurement of discharge - Need flashy basins with basin area < 260 km 2 (flash flood scale) - Flash flood defined as 2-year return period 3. SHAVE flash flood reports + High spatial and temporal resolution + Flood characteristics - Database is storm-targeted; does not encompass all flash flood events - Dependent on population density Event Type: Flash Flood WFO: OUN Begin Date: 3/20/2007 Begin Time: 9:30 A.M. CST County: Kay State: OK

11 Severe Hazards Analysis and Verification Experiment (SHAVE)*  SHAVE reports are more dense than NWS reports (e.g., 50:1)  Unique data collected in SHAVE  Reports of no flooding  Specific impact  Lateral extent/depth/motion of water  Respondent-estimated frequency  Lightning Creek flooding, OKC July 2010 *Ortega, K.E., T.M. Smith, K.L. Manross, A.G. Kolodziej, K.A. Scharfenberg, A. Witt, and J.J. Gourley, 2009: The severe hazards and verification experiment, Bull. Amer. Meteor. Soc., 90,

12 Lets see it!

13 Back to the Coast!  Distributed hydrologic model (HL-RDHM) run for Tar & Neuse basins.  Green dots represent verification points  Red dots are hand-off points for hydrodynamic ocean model (ADCIRC)  NMQ-FLASH will allow for distributed hydrologic model results from anywhere in the CONUS including other coastal areas (Texas, South Carolina, etc)

14 Real-time Simulations  Non-optimized ensemble produces reasonable spread already at this early stage; optimized ensemble from NMQ-FLASH will produce a spread that completely envelopes the observed hydrograph.

15 National Mosaic and Multi-Sensor QPE (NMQ-) Flooded Locations And Simulated Hydrographs (FLASH) - A CONUS-wide flash-flood forecasting demonstration system NMQ/Q2 Rainfall Observations - 1km 2 /2.5 min Stormscale Rainfall Forecasts Stormscale Distributed Hydrologic Models Probabilistic Forecast Return Periods and Estimated Impacts June 2010, Albert Pike Rec Area, Arkansas 10” 8” 6” Q2Q2 Q5Q5 5 hr Hydrograph of Simulated and Observed Discharge Simulated surface water flow 20 fatalities 80% 60% 40% Probability of life-threatening flash flood t=2300 t=0000 t=0100


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