Radar-based flood forecasting: Quantifying hydrologic prediction uncertainty

Transcription

1 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 1 University of Oklahoma, School of Civil Engineering and Environmental Science, National Weather Center, 120 David L. Boren Blvd., Suite 3600, Norman, OK 73072; PH (405) ; bvieux@ou.edu

2 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?

3 Hydrologic Prediction Problem KEEGANS BAYOU BRAYS BAYOU WILLOW WATE R HOL E 1. Radar QPE Action Level 2. Model 3. Stream Forecasts Sensing Predicting

4 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 modeled as a single layer with variation throughout the basin. Kinematic wave grid-grid and in channels defined by surveyed cross-section, rating curves or trapezoids Channel hydraulics include cross-section, rating curves, trapezoidal, modified Puls and looped rating curves Continuous soil moisture tracking by adjusting climatological ET according to available soil moisture and radar rainfall in each grid cell. Vieux. B.E., Distributed Hydrologic Modeling Using GIS, Kluwer Academic Publishers, Runon A t Runon ( Q) + = q x Rainfall h ( uh) + = R I t x SO u = n R ( e) CA 12 / h 23 / Runon Infiltration Runoff T h q = N + ( R I) Ω t x new old = 0 [(1 θ ) Q + θ Q ] + Δt [(1 θ F + θ F ] = CA ΔtS ) old new old new

5 Real-time Forecasting Radar input Operational Distributed Model, Vflo Forecast stage in realtime for operational decisions Used in the Rice University/Texas Medical Center Flood Alert System (

6 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 10km 2 subwatershed of the 260 km 2 Brays Bayou located in Houston TX. Parts of the stormwater sewer draining Harris Gully runs through Rice University and the TMC.

7 Evaluative Study Model prediction accuracy with NEXRAD input Radar Quantitative precipitation estimates (QPE) derived from radar and rain gauge. KEEGANS BAYOU BRAYS BAYOU WILLOW WATE R HOL E

8 Distributed hydrologic prediction Stage (ft) June 8, 2004, observed and Vflo simulated hydrographs at the Harris Gully outlet 7:12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated 1. Harris Gully June 8, 2004, observed and Vflo simulated hydrographs at the Harris Gully outlet 2. Improved Hydraulics 3. Scaling of predictability 4. NEXRAD 1x1 km input Stage (ft) :12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated Vflo Harris Gully 40x40m 10 km 2

9 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 Stage (ft) :12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated Stage (ft) :12 9:36 12:00 14:24 16:48 19:12 21:36 Time (CDT) Observed Simulated

10 Prediction Performance Simulated Stage (ft) Difference in Peak (ft) Observed Peak Time Simulated Peak Time Difference in Peak Time (min) Event Observed Peak (ft) Simulated Peak (ft) 8-Jun :25 12: Jun :55 18: Oct :00 11: Jul :45 14: Jul :10 18: Jul :05 14: Jul :55 15: Jul :25 5: Apr :55 8: May :50 7: Jun :15 12:55-20 Stage prediction accuracy = 1.05 ft RMSE Measured Stage (ft) Peak arrival time accuracy = 10.0 min. RMSE

11 Distributed Hydrologic Modeling Vflo Distributed Runoff Model 500m - Blue River (1200km2) 200m Connerville (420km2) Flow Direction Effective Porosity Soil Depth Hydraulic Conductivity Slope

12 Channel Parameterization Cross-sections

13 Blue River Selected Hydrographs

14 Model Calibration Objective Functions RMSE Volume (in) RMSE Peak (cfs) Search parameter space to find minima

15 Rainfall Products Gauge Only 30,000 Simulated G = (Observed) R 2 = Simulated Peak (cfs) 20,000 10, ,000 20,000 30,000 Observed Peak (cfs) Density of Mesonet Stations Number of Stations = 13 Average distance = 40 Km

16 Re-Analysis of NWS Stage III/MPE MFB statistics 9 Gauges MFB AD MFB CAD LB CAD MIN AVE MAX

17 Rainfall Products Radar MFB 30,000 Simulated MFB = (Observed) R 2 = Simulated Peak (cfs) 20,000 10, ,000 20,000 30,000 Observed Peak (cfs) 14yr Storm total=122,640 hourly maps

18 Hydrograph Predictions Observed Gauge Only Radar Local Bias Radar MFB

19 Quantitative Precipitation Estimate Verification by Streamflow Event Runoff Volume Event Peak Discharge 0.80 Coefficient of Determination (R2) Volume Improvement over Gauge-Only Peak Improvement over Gauge-Only MFB ABRFC MPE Gauge Only Re-analysis Raw Stage III Mesonet

20 Temporal Scaling of Hydrologic Prediction Accuracy Daily Streamflow Monthly Streamflow Blue River near Connerville (420 km 2 )