Solutions for Hydrologic Analysis, Real-time Prediction, and Management of

Baxter E. Vieux Ph.D., P.E. Professor, School of Civil Engineering and Director, Natural Hazards and Disaster Research Center National Weather Center University of Oklahoma Norman, OK 73072 -- [email protected]

1 Overview

Technological innovation is creating unprecedented opportunities to apply information technology, gain new data from advanced sensor systems, and develop more effective urban infrastructure. Radar detection of precipitation and distributed modeling is a rapidly evolving technology that holds promise in making real-time predictions and for solving stormwater and drainage problems. Two key areas continue to present challenges: 1) representative precipitation over watershed areas in real-time; and 2) spatially distributed hydrologic prediction of stage and flow rates.

2 Hydrologic Prediction

3 Continental NEXRAD coverage

4 Rain gauge and radar

Radar rainfall— Radar rainfall measured by reflectivity depends on the number of drops and distribution of sizes Z=300R1.2 Z=300R1.4

, Z Rain gauge amounts depend ity Rainfall on time rate of arrival of each ctiv fle drop and its size Re Rate, R

Bias correction with quality controlled gauges 5 Combining strengths

Radar

Rain Gauge

Better rainfall estimates than either system alone

6 Quantitative Point Measurement Map of Intensities Radar considerations

Rain gauges –  Supply gauge only products  Gauge correction for bias  Gauges must be quality controlled Radar affected by –  Updrafts/downdrafts  Partial beam filling and blockages  Bright banding  Anomalous propagation  Clutter suppression  Low precision

7 Vflo™ Physics-Based Approach

 h  uh)(   IR  t  x

Rainfall  Distributed rainfall input Runon  /overland Hydraulics  Drainage network Runon  Soil /Impervious  Land Cover Runoff  Antecedent Moisture  Water Control Structures Runon  Recharge/channel losses Infiltration

Hydraulics= 8 Scour Critical Bridges ODOT/FHWA

Which bridge are at risk of scour? 9 Radar Precipitation Remnants TD Erin - August18-19, 2007

10 Radar Rainfall Totals Remnants TD Erin - August18-19, 2007

Daily total for 2007-08-18 CDT

Daily total for 2007-08-19 CDT Mean Bias Correction = 1.748 Average Difference = ± 11%

Without correction: 1 inch

would be 0.56 inches11 Real-time Monitoring of Scour Risk

12 Infrastructure Operations and Management

 Simultaneous simulation at 134 bridge locations with Real-time Vflo™  Accounts for bridge geometry and channel profile tracking

13 Recent Activity

14 NWS Headwaters Routing Remnants TD Erin - August18-19, 2007

 Real-time Vflo (9202 cells @ 300x300 meters)  Radar precipitation input

 Distributed model with interior watch points

 Kingfisher @ US 81 (320 sq. mi.)

 Kingfisher Cr. @ SH 33 (Two Locations)

 Winter Camp Cr @ SH33

15 Rainfall Totals Remnants TD Erin - August18-19, 2007

0.5 6

0.45 Rain (in) 5 0.4 Rainfall Over Kingfisher Accum (in) 0.35 4 0.3 0.25 3

0.2 2 0.15

0.1 1 0.05

0 0 0:00:00 1:00:00 2:00:00 3:00:00 4:00:00 5:00:00 6:00:00 7:00:00 8:00:00 9:00:00 10:00:00 11:00:00 12:00:00 Gage = 5.66 in

Rainfall Depth (in) Value High : 15 5 Miles Low : 1 Radar Storm Total Kingfisher @ SH 33 Aug 19 00:00-12:00 UTC 16 Radar Average = 8.28 in NWS Headwaters Forecast March 4, 2004

Reported Crest: 23.5 feet

17 Urban Forecasting

Background  Brays subject to intense and prolonged precipitation from tropical storms, continental fronts and coastal convergence  Relies on radar-based since 1997 to take flood protection measures Texas Medical Center/Rice  Hydrologic lead-time University, Houston Texas provides critical information for emergency management actions

18 Brays Bayou Model Setup

Brays Bayou Vflo Model 120x120m Area =100mi2 (260 km2)

19 Hydraulics

Action Level

JuneJune 8, 2004,2004, observed observed and and Vflo™ Vflo™ simulated simulated hydrographs at the atHarris the HarrisGully outlet outlet • 1x1 km NEXRAD 4.5 4.5 4.0 4.0 • Vflo 3.5 3.5 40x40m 3.0 3.0 2.52.5 • Harris Stage (ft) Stage Stage (ft)2.02.0

1.51.5 Gully 1.0 1.0 2 0.5 10 km 0.5 0.0 0.0 20

7:12 9:36 12:00 14:24 16:48 19:12 21:36 7:12 9:36 12:00Observed Simulated 14:24 16:48 19:12 21:36 Observed Simulated Time (CDT) Time (CDT) Radar Input TS Allison

Scatter Plot of Calibrated RG Pairs

400.0

Ri* = 1.00 * Gi R2 = 0.97 300.0

10 mm 500 200.0 Radar (mm) 100.0

0.0 0.0 100.0 200.0 300.0 400.0 Gauge (mm)

Allison 3rd

21 Validation of QPE

 Brays Bayou is highly urbanized with impervious Radar to Gauge Volume 200 areas and clay soils

180 Adjusted  Infiltration averages 7% of 160 y = 1.076x 2 140 R = 0.9646 rainfall ) 120  Streamflow Validation 100

80  Adjusted: Slope 1.076 2 60 Unadjusted (+) R =0.96 Radar (mm Rainfall Volume y = 1.1003x 40 2 R = 0.2129  Unadjusted: slope 1.10, 20 0 50 100 150 200 2 0 Stream Gauge Volume (mm) R =0.2129

Observed Streamflow and Radar Rainfall input comparison (R2=0.96)

Vieux, B.E., and P.B. Bedient, 2004. Assessing urban hydrologic prediction accuracy through22 event reconstruction. J. of Hydrology. 299(3-4), pp. 217-236. Special Issue on Urban Hydrology. Real-time Stage Prediction

23 Time Series of Bias Correction Factors, F

4.5 Mean 4 field bias 3.5 factor, F 3 for each 2.5 6-hr 2 moving 1.5 window.

Mean Field Bias Field Mean 1 0.5 Gi 0 FMR  Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Ri 2005 2006 2007 24 25 26 27 Rainfall Distribution

28 29 30 Flood Early Warning System City of Austin, TX

 Radar and rain gauge integration  Observed and simulated stage displayed  Cell-phone notification system based on user requested thresholds

31 Vflo Creek Austin, TX

32 Continuous Model Results

16 14 Observed 12 Simulated 10 8 6

Stage (feet) 4 2 0 Jan-13 Mar-12 Mar-13 May-3 Events 2007

33 Notifications

 Audible reception of Text/email messages  Icons and Blinking Icons with mouse-over messages  Threshold with color change and action list  Hydrographs  Inundation maps

34 Vflo™ Predicted Inundation in GoogleEarth

35 December 2, 2008 35 Maximum Predicted Inundation

36 36 37 38 39 40 41 42 Summary

Radar detection of precipitation and distributed modeling is a rapidly evolving technology that holds promise in making real-time predictions, and for solving Claudette in Vflo Interface stormwater and drainage problems.

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