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

„ runoff significantly impacts flooding and 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 . „ Setup with geospatial data and physically realistic parameters

„ Saturation and rate excess is Rainfall 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, + −= IR rating curves, trapezoidal, modified Puls and ∂ t ∂ x ∂ A ∂ Q)( looped rating curves + = q ∂ t ∂ x „ Continuous tracking by S 12/ u = O h23/ adjusting climatological ET according to n available soil moisture and radar rainfall in ⎡∂ h ∂ q ⎤ e)( = NR T + −− IR = 0)( each grid cell. ∫Ω ⎢ ⎥ ⎣ ∂t ∂ x ⎦ Vieux. B.E., 2004. Distributed Hydrologic Modeling = − Δ [ −θ )1( +θ ]+ Δ [ −θ )1( +θ FFtQQStCACA ] 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 hydrographs1. at Harris the Harris Gully outlet 3. Scaling of Gully predictability 4.5 4.0 3.5 3.0 2.5

Stage (ft) Stage 2.0 1.5 1.0 0.5 0.0

7:12 9:36 12:00Observed Simulated 14:24 16:48 19:12 21:36 Time (CDT) 4. NEXRAD June 8, 2004, observed and Vflo™ simulated hydrographs at2. the HarrisImproved Gully outlet 1x1 km input

4.5 Hydraulics

4.0

3.5

3.0

2.5

Stage (ft) 2.0

1.5

1.0

0.5

0.0

7:12 9:36 12:00 14:24 16:48 19:12 21:36 Observed Simulated 2 Time (CDT) Vflo Harris Gully 40x40m 10 km 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 Stage (ft)

Stage (ft) 2.0 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) ObservedTime (CDT)Simulated Observed Simulated Prediction Performance

10

9 Difference Observed Simulated Difference Observed Simulated in Peak Peak Peak in Peak Event Peak (ft) Peak (ft) (ft) Time Time Time (min) 8 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 7 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 12-Jul-05 4.6 4.5 -0.1 18:10 18:15 5 6 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 5 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 6-May-06 4.6 2.3 -2.3 6:50 7:25 35 4 17-Jun-06 4.8 5.1 0.3 13:15 12:55 -20

Simulated Stage (ft) 3 2 Stage prediction accuracy = 1 02468101.05 ft RMSE Measured Stage (ft) 0 Peak arrival time accuracy = 10.0 min. RMSE

Distributed Hydrologic Modeling

Vflo™ Distributed 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 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)