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Distributed Estimation of Water Severe Storms Prediction and Global Climate Impact on the Gulf Coast, Rice University, October 29‐31, 2008 Radar-based flood forecasting: Quantifying hydrologic prediction uncertainty Baxter E. Vieux, Ph.D., P.E. J.M. Imgarten, Graduate Research Assistant 1University of Oklahoma, School of Civil Engineering and Environmental Science, National Weather Center, 120 David L. Boren Blvd., Suite 3600, Norman, OK 73072; PH (405) 325-3600; email: [email protected] Overview Stormwater runoff significantly impacts flooding and water quality in urban catchments. Weather radar captures the variability of rainfall over watersheds from catchment to river basin scale What is the accuracy that can be achieved for catchments and river basins, and how does it scale with space/time? Hydrologic Prediction Problem OU BRAYS BAY KEEG E AN L S B O AY H O R U TE WILLOW WA 1. Radar QPE Action Level 2. Model 3. Stream Forecasts Sensing Æ Predicting River Basin Hydrologic Modeling Vflo Continuous Drainage network and hydraulics determine hydrologic response without presumptive unit hydrographs. Setup with geospatial data and physically realistic parameters Saturation and infiltration rate excess is Raifanll modeled as a single layer with variation Runon throughout the basin. Runon Kinematic wave grid-grid and in channels Runoff defined by surveyed cross-section, rating Runon Infiltration curves or trapezoids ∂ h ∂()uh Channel hydraulics include cross-section, + =RI − rating curves, trapezoidal, modified Puls and ∂ t ∂ x ∂ A ∂()Q looped rating curves + = q ∂ t ∂ x Continuous soil moisture tracking by S 12/ u = O h23/ adjusting climatological ET according to n available soil moisture and radar rainfall in ⎡∂ h ∂ q ⎤ RN()e = T + −(RI − )= 0 each grid cell. ∫Ω ⎢ ⎥ ⎣ ∂t ∂ x ⎦ Vieux. B.E., 2004. Distributed Hydrologic ModelingCA CA= − tΔ S([ 1−θ ) Q+θ ]+ QΔ([ 1−θ t ) +θ F] F Using GIS, Kluwer Academic Publishers, new old old new old new Real-time Forecasting Radar input Operational Distributed Model, Vflo™ Forecast stage in real- time for operational decisions Used in the Rice University/Texas Medical Center Flood Alert ? System (www.fas.org) Urban Catchment Radar data used in this analysis are derived from both S- band (NEXRAD) with accuracy enhanced through bias correction. Model accuracy is assessed using radar QPE derived from the existing WSR-88D (KHGX) as input to a physics-based hydrologic model. The study catchment, Harris Gully, is a 10km2 subwatershed of the 260 km2 Brays Bayou located in Houston TX. Parts of the stormwater sewer draining Harris Gully runs through Rice University and the TMC. Evaluative Study Model prediction accuracy with NEXRAD input Radar Quantitative precipitation estimates (QPE) derived from radar and rain gauge. OU BRAYS BAY K E EEG AN L S B O AY H OU R TE WILLOW WA Distributed hydrologic prediction June 8, 2004, observed and Vflo™ simulated hydrographs at the Harris1. Gully Harris outlet 3. Scaling of 4.5 4.0 Gully predictability 3.5 3.0 2.5 2.0 Stage (ft) Stage 1.5 1.0 0.5 0.0 7:12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated 4. NEXRAD June 8, 2004, observed and Vflo™ simulated hydrographs at the Harris2. Gully Improved outlet 1x1 km input 4.5 4.0 Hydraulics 3.5 3.0 2.5 2.0 Stage (ft) 1.5 1.0 0.5 0.0 7:12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated Vflo Harris Gully 40x40m 10 km2 Radar storm total for June 14, 2005 over Harris Gully. June 8, 2004, observed and Vflo™ simulated hydrographs at the Harris Gully outlet June 8, 2004, observed and Vflo™ simulated hydrographs at the Harris Gully outlet 4.5 4.5 4.0 4.0 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 Stage (ft) Stage (ft) 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 7:12 9:36 12:00 14:24 16:48 19:12 21:36 7:12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Time (CDT) Observed Simulated Observed Simulated Prediction Performance 10 Difference Observed Simulated Difference Observed Simulated in Peak Peak Peak in Peak Event Peak (ft) Peak (ft) (ft) Time Time Time (min) 9 8-Jun-04 4.2 4.2 0 12:25 12:35 10 13-Jun-04 6.1 11 4.9 18:55 18:55 0 8 23-Oct-04 3.8 5.1 1.3 11:00 11:05 5 11-Jul-05 2.3 3.5 1.2 14:45 14:55 10 7 12-Jul-05 4.6 4.5 -0.1 18:10 18:15 5 13-Jul-05 6.7 6.5 -0.2 15:05 14:55 -10 14-Jul-05 6.4 8.9 2.5 14:55 15:25 30 6 15-Jul-05 4.8 5 0.2 5:25 5:25 0 29-Apr-06 4.5 5.1 0.6 8:55 8:55 0 5 6-May-06 4.6 2.3 -2.3 6:50 7:25 35 17-Jun-06 4.8 5.1 0.3 13:15 12:55 -20 4 Simulated Stage (ft) 3 2 Stage prediction accuracy = 1 1.05 ft RMSE 0 0246810 Measured Stage (ft) Peak arrival time accuracy = 10.0 min. RMSE Distributed Hydrologic Modeling Vflo™ Distributed Runoff Model 500m - Blue River (1200km2) 200m – Connerville (420km2) Flow Direction Effective Porosity Soil Depth Hydraulic Conductivity Slope Channel Parameterization Cross-sections Blue River Selected Hydrographs Model Calibration Objective Functions RMSE Volume (in) RMSE Peak (cfs) Search parameter space to find minima Rainfall Products Gauge Only 30,000 SimulatedG = 0.6892(Observed) R2 = 0.1032 ) 20,000 Simulated Peak (cfs 10,000 0 0 10,000 20,000 30,000 Observed Peak (cfs) Density of Mesonet Stations Number of Stations = 13 Average distance = 40 Km ReRe--AnalysisAnalysis ofof NWSNWS StageStage III/MPEIII/MPE MFB statistics MFB AD MFB CAD LB CAD 99 GaugesGauges MIN 0.226 1.2 0.1 0.0 AVE 0.992 20.8 15.8 7.0 MAX 2.600 395.0 139.9 55.9 Rainfall Products Radar MFB 30,000 SimulatedMFB = 1.0202 (Observed) R2 = 0.4229 ) 20,000 Simulated Peak (cfs 10,000 0 0 10,000 20,000 30,000 Observed Peak (cfs) 14yr Storm total=122,640 hourly maps Hydrograph Predictions Observed Gauge Only Radar Local Bias Radar MFB Quantitative Precipitation Estimate Verification by Streamflow 1.00 Event Runoff Volume 0.90 Event Peak Discharge 0.80 0.70 0.60 Volume Improvement 0.50 over Gauge-Only 0.40 Coefficientof Determination (R2) 0.30 Peak Improvement over Gauge-Only 0.20 0.10 0.00 MFB ABRFC MPE Gauge Only Re-analysis Raw Stage III Mesonet Temporal Scaling of Hydrologic Prediction Accuracy DailyDaily StreamflowStreamflow MonthlyMonthly StreamflowStreamflow Blue River near Connerville (420 km2).
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