Combined Use of Radar and Gauge Measurements for Flood Forecasting - - PowerPoint PPT Presentation

▶
combined use of radar and gauge measurements for flood
SMART_READER_LITE
LIVE PREVIEW

Combined Use of Radar and Gauge Measurements for Flood Forecasting - - PowerPoint PPT Presentation

Apr 12, 2023 184 likes •556 views

Combined Use of Radar and Gauge Measurements for Flood Forecasting Using a Physics-based Distributed Hydrologic Model National Hydrologic Warning Council Dallas Texas October 23, 2003 Baxter E. Vieux, Ph.D., P.E. Vieux & Associates, Inc.

slide-1
SLIDE 1

Combined Use of Radar and Gauge Measurements for Flood Forecasting Using a Physics-based Distributed Hydrologic Model

Baxter E. Vieux, Ph.D., P.E. Vieux & Associates, Inc. Norman, Oklahoma USA www.vieuxinc.com And Professor of Civil Engineering and Environmental Science University of Oklahoma

National Hydrologic Warning Council Dallas Texas October 23, 2003

slide-2
SLIDE 2

Technological Advances in Rainfall Measurement

  • Advances in rainfall measurement technology

have made new approaches to hydrologic prediction possible, and with more accuracy than ever before.

  • Technological advances in precipitation

measurement (radar/satellite/gauge) and hydrologic modeling allow us to better plan, design, and forecast performance of drainage infrastructure in preparation for the next flood.

slide-3
SLIDE 3

Distributed Radar Input

NEXRAD 10 cm Doppler Radar—

  • 160+ installed
  • ~130 in US
  • Elsewhere

internationally

slide-4
SLIDE 4

Twin Lakes, Oklahoma

  • The first operational

WSR-88D

  • Installed in May 1990

at Twin Lakes, Oklahoma

  • Prototyped at

National Severe Storms Laboratory (NSSL), Norman, OK

  • Movie ‘Twister’
slide-5
SLIDE 5

Radar measures reflectivity

slide-6
SLIDE 6

Reflectivity and rainfall rate

  • Reflectivity

depends on drop size distribution

  • Rainfall rate

depends on drop size distribution

Rainfall Rate, R

R e f l e c t i v i t y , Z

Radar rainfall—

Z=300 R1.4 Z=250 R1.2

slide-7
SLIDE 7

Combining Systems

Better Rainfall Estimates than either system alone Rain Gauge Rain Gauge Radar

slide-8
SLIDE 8

Physics-based distributed modeling

  • “Physics-based” means that conservation laws of

mass momentum and energy are used to make hydrologic predictions

  • Hydrodynamics are used to generate both flow

rates and flood stage

  • Represents spatial variability of parameters and

inputs

  • Distributed modeling is accomplished by

subdividing the domain of interest

  • Fully distributed models use computational

elements such as grid cells

slide-9
SLIDE 9

Classifying hydrologic models

Deterministic Stochastic Hydrodynamics Distributed Black Box (Neural Nets) Conceptual Fully Distributed Grid/Unit Semi-Distributed Subareas Statistical Distribution Lumped

Adapted from— Rhodda and Rhodda, Proceedings of the Royal Society, 1999.

Models that benefit from using radar inputs and geospatial data

slide-10
SLIDE 10

Distributed Hydrologic Modeling

Factors controlling runoff: 1. Rainfall/Snowmelt Input 2. Channel/overland Hydraulics 3. Drainage network 4. Soil Infiltration/Impervious 5. Land Cover 6. Antecedent Moisture 7. Water Control Structures

Runon Runon Runon Runoff Rainfall Infiltration I R x uh t h − = ∂ ∂ + ∂ ∂ ) (

slide-11
SLIDE 11

Vflo™ Distributed Hydrologic Analysis and Prediction

www.vieuxinc.com

slide-12
SLIDE 12

Blue River— Importance of channel hydraulics

  • Basin located in south central Oklahoma.
  • Subject of longstanding research and the National

Weather Service experiment to compare distributed models (DMIP)

  • 1200 km2 modeled with 270 m resolution
  • NWS gauge-adjusted radar (NEXRAD Stage3)
  • Model simulations for 23 events (18 calibration

and 5 verification)

  • Event based simulation initialized by simple soil

moisture scheme.

slide-13
SLIDE 13

Achievable Accuracy Case Studies

  • Within a distributed modeling framework,

an important question is: How accurately can hydrographs be simulated using physics-based hydrologic models and gauge-adjusted radar?

slide-14
SLIDE 14

Blue River Model setup

slide-15
SLIDE 15

Blue River October 21, 1996

Discharge - Blue River Basin Uncalibrated, No Rating Curves, No Mod Puls Routing Initial Saturation of 30%

100 200 300 10/21/1996 0:00 10/22/1996 0:00 10/23/1996 0:00 10/24/1996 0:00 10/25/1996 0:00 Date (UTC) Discharge (cms) Observed Simulated

slide-16
SLIDE 16

Blue River March 25, 1997

Discharge - Blue River Basin Uncalibrated, No Rating Curves, No Mod Puls Routing Initial Saturation of 50%

50 100 3/25/1997 0:00 3/26/1997 0:00 3/27/1997 0:00 3/28/1997 0:00 Date (UTC) Discharge (cfs) Observed Simulated

slide-17
SLIDE 17

Blue river volume and peak

Vflo™ RMSE= 52.0 m3s α=0.75 and β=1.0. Vflo™ RMSE= 9.8 mm α=1.0 and β=1.0.

Blue Volume

y = 0.9464x R2 = 0.7412 0.00 20.00 40.00 60.00 80.00 100.00 0.00 20.00 40.00 60.00 80.00 100.00 Observed (mm) Simulated (mm)

Peak Blue

0.00 200.00 400.00 600.00 0.00 200.00 400.00 600.00 Observed (m

3/s)

Simulated (m

3/s)
slide-18
SLIDE 18

Texas Medical Center/Rice University Flood Alert System

Urban real-time flood forecasting—

  • Texas Medical

Center relies on an operational distributed model flood forecasting

  • Radar + Vflo™

www.floodalert.org Texas Medical Center Brays Bayou Main Street

slide-19
SLIDE 19

Real-time prediction

Response Flood Information Observations

slide-20
SLIDE 20

Vflo™ Brays Bayou

$ Z $ Z

Roughness 0.01 - 0.015 0.015 - 0.018 0.018 - 0.025 0.025 - 0.05 > 0.05 No Data Brays Bayou

20 Kilometers N E W S

Gessner Main Street Main Street

Drainage area 260 km2 Model resolution 120 x120 m

slide-21
SLIDE 21

Testing reliability

  • Optimizing the rising limb—

Select a threshold and measure observed and simulated time to cross the threshold called time to flood (TTF).

  • Adjust parameters to optimize TTF, peak

and time to peak for three calibration storms

  • Validate performance
slide-22
SLIDE 22

Forecasts based on Hydrograph rising limb

0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 14000.00 16000.00 18000.00 6/5/2001 15:00 6/5/2001 16:00 6/5/2001 17:00 6/5/2001 18:00 6/5/2001 19:00 6/5/2001 20:00 6/5/2001 21:00 6/5/2001 22:00 6/5/2001 23:00 6/6/2001 0:00 Date (CDT) Discharge (cfs)

Only optimizing for peak and time to peak does not necessarily match the rising limb making forecast thresholds accurate Optimizing for TTF improves rate of rise that will be used in a real-time flood alert system

0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 14000.00 16000.00 18000.00 6/5/2001 15:00 6/5/2001 16:00 6/5/2001 17:00 6/5/2001 18:00 6/5/2001 19:00 6/5/2001 20:00 6/5/2001 21:00 6/5/2001 22:00 6/5/2001 23:00 6/6/2001 0:00 Date (CDT) Discharge (cfs)

slide-23
SLIDE 23

Verification 1st wave August 15

slide-24
SLIDE 24

Verification 2nd wave August 15

slide-25
SLIDE 25

Main Street Verification event

15 August 2002

8/15/2002 7:00 8/15/2002 12:00 8/15/2002 17:00 8/15/2002 22:00 8/16/2002 3:00 8/16/2002 8:00 8/16/2002 13:00 8/16/2002 18:00 8/16/2002 23:00 8/17/2002 4:00 8/17/2002 9:00 Date (EST) Discharge m 3/s

Verification— Gauge adjusted radar No model adjustment

slide-26
SLIDE 26

Historic event performance

Radar to Stream Gauge Volume Adjusted y = 1.076x R2 = 0.9646 Unadjusted (+) y = 1.1003x R2 = 0.2129

20 40 60 80 100 120 140 160 180 200 50 100 150 200 Stream Gauge Volume (mm) Radar Rainfall Volume (mm)

  • Verification
  • f QPE using

stream gauge volumes

  • Radar

adjustment improves efficiency from R2=0.2129 to R2=0.9646

slide-27
SLIDE 27

Rainfall Runoff Prediction in Real- Time

  • Rainfall-runoff prediction is particularly

important for a variety of applications such as water resources management, flood prediction, emergency management.

slide-28
SLIDE 28
slide-29
SLIDE 29

Hydrographs

Measured Simulated

Greenville Louisberg

TS Allison

slide-30
SLIDE 30

Vflo™ Predicted Inundation Web Display

slide-31
SLIDE 31

Hurricane Floyd Transportation Impacts

Pitt-Greenville Airport (PGV), Pitt County

Photo Courtesy of North Carolina Emergency Management

slide-32
SLIDE 32

Stage Sensitivity Summary

Calibration sensitivity

78.9 30.7 15.0 9.1 8.3 7.6 1.3 10 20 30 40 50 60 70 80 90 rainfall channel width channel side slope

  • verland

slope infiltration channel slope hydraulic roughness % difference

slide-33
SLIDE 33

Summary

  • 1. Physics-based distributed modeling can produce

accurate predictions in real-time at any location in a drainage network.

  • 2. Made possible by technological advances in radar

rainfall measurement

  • 3. Consistent performance across storm sizes/type
  • 4. Physically realistic parameters from geospatial

data

  • 5. High achievable accuracy in peak and rising limb

predictions given good channel hydraulic data

  • 6. Event reconstruction tests reliability of operational

flood forecasting systems

slide-34
SLIDE 34

Further information

Vieux B.E. 2002. “Predictability of Flash Floods Using Distributed Parameter Physics-Based Models.” Report of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems, Committee on Hydrologic Science, Water Science and Technology Board, Board on Atmospheric Sciences and Climate, National Research Council, ISBN 0-309-08347-8. pp. 77-82. Vieux, B.E., and F.G. Moreda, (2003). Ordered Physics-Based Parameter Adjustment of a Distributed Model. Chapter 20 in Advances in Calibration of Watershed Models, Edited by Q. Duan, S. Sorooshian, H.V. Gupta, A.N. Rousseau, R. Turcotte, Water Science and Application Series, 6, American Geophysical Union, ISBN 0-87590- 355-X pp. 267-281.

  • Vieux. B.E., (2001) Distributed Hydrologic Modeling Using GIS, ISBN 0-

7923-7002-3, Kluwer Academic Publishers, Norwell, Massachusetts, Water Science Technology Series, Vol. 38. p. 293. Second Edition expected 2004 English and Chinese

slide-35
SLIDE 35

Questions?

  • -Ganges River Distributary, Bangladesh

www.vieuxinc.com