Hydrologic forecasting for flood risk managementrivers.bee.oregonstate.edu/sites/default/files/...Sep 30, 2009 · “Ondoy” 8 16/10/2009 ... PowerPoint Presentation Author: Kibler

Hydrologic forecasting for flood risk management

Predicting flows and inundation in data-limited catchments

Dr. Kelly KiblerAssistant professor

Civil, Environmental, & Construction EngineeringUniversity of Central Florida

Roadmap1 Hydrologic forecasting for early warning

• Lead time• Accuracy• Challenge of the data-limited basin

2 Remotely sensed precipitation data • Opportunities and limitations• Designing observation networks to integrate remotely sensed

data3 Inundation

• Calibrating/validating hydrologic models

2

Two critical components :1. Forecast accuracy 2. Forecast lead time

Flood Forecasting for Early Warning

3



HOURS

4



HOURS DAYS

5



HOURS DAYS WEEKS

6



HOURS

Increasing forecast lead time, increasing potential response and loss reduction

DAYS WEEKS

7

Flood Forecasting for Early WarningEconomic benefit- damage/loss reduction

“In other words, for every USD 1 invested in this EWS, there is a return of USD 558.87 in benefits.” Subbiah and Bilden, 2008, World Bank Report

8

Flood Forecasting for Early WarningEconomic benefit- damage/loss reduction: risk assessment in Surma River basin, Bangladesh

“Benefit-cost ratio of early warning system for a 5-year event was 2.71, 12.44 and 33.21 for 24 hours, 48 hours and 7 days lead time, respectively.” Hyder, 2013

9

Important to accurately predict:1. Onset of flooding

flood stage


Precipitation,

Discharge

time

Event

It started to rain.

Flooding

Peak of water level

Peak of rainfall

10

Important to accurately predict:1. Onset of flooding2. Peak discharge

Precipitation,

Discharge

time

Event

It started to rain.

Flooding

Peak of water level

Peak of rainfall

flood stage


11

Important to accurately predict:1. Onset of flooding2. Peak discharge3. Duration of flood

Precipitation,

Discharge

time

Event

It started to rain.

Flooding

Peak of water level

Peak of rainfall

flood stage


12

Important to accurately predict:1. Onset of flooding2. Peak discharge3. Duration of flood4. Spatial extent of inundation, through time


13

Flood Forecasting for Early WarningTwo critical components :1. Forecast accuracy 2. Forecast lead time

Accuracy of forecast with 10 days lead time

Webster et al., 2010

Flood forecasting in poorly gauged basins

Challenges:• Insufficient implementation and maintenance of ground-based,

real-time hydrologic observation.– Lag time between data observation and availability for flood forecasters.– Lead time compromised, poor forecasting skill.

• Management across boundaries.– Administrative barriers to data availability.

Manual data observation Basins cross boundaries Lack of transmission15

Geopolitically ungauged catchment area

Geopolitically ungauged catchment area:Areas where comprehensive data observation networks may exist, but due to geopolitical constraints, catchments are effectively ungauged.

Hydrologic prediction in ungauged basins: a wicked problem for hydrologists

16

Water-related disaster in transboundary river basins

Flood disasters in transboundary river basins:• Historically more severe, affect larger areas and result

in higher costs of human life and economic damages. • Suggests that international river basins may be

uniquely vulnerable to flood hazards.Bakker, 2009

Limitations in capacity for preparedness –a potential source of vulnerability in transboundary

basins? 17

Integrated Flood Analysis System

Ground-gauged and/or satellite rainfall

Model creationRun-off analysis

River discharge, Water level, Rainfall distribution

Courtesy of JAXA

Global data: topography, land use, etc.

Aquifer modelRiver coursemodel

Surfacemodel

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• Several remotely-sensed precipitation products have global coverage. • Resolution (time and space) and observation accuracy are low

compared with ground observation rainfall.

SATELLITE RAINFALL IS NOT A SUBSTITUTE FOR AN OBSERVATION NETWORK!

Product name 3B42RT CMORPH QMORPH GSMaPBuilder NASA/GSFC NOAA/CPC NOAA/CPC JAXA/EORC

Coverage 50N～50S 60N～60S 60N～60S 60N～60SSpatial resolution 0.25° 0.073° 0.073° 0.1°Time resolution 3 hours 30 minutes 30 minutes 1 hour

Delay of delivery 6 hours 18 hours 3 hours 4 hoursCoordinate system WGS

Data archive Dec. 1997～ Recent 1week Recent 1week Dec.2007～

Data source(sensor)

Aqua/AMSR-E, AMSU-B,

DMSP/SSM/I and TRMM/TMI and IR

TRMM/TMI, Aqua/AMSR-E, AMSU-B, DMSP/SSM/I and IR

TRMM/TMI, Aqua/AMSR-E,

DMSP-F13-15/SSM/I, DMSP-F16-17/SSMIS,

IR data

IFAS – Satellite rainfall data

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present 1hour later 2 hours later

>

≒

3hour total

雨域の移動速度（停滞度）指標とピーク雨量比率の関係

Rain event with slow movement of rainy area

Rain event with quick movement of a rainy area

present 1hour later 2 hours later

3hour total

2

58 761 3 9

4

1 289

4

3 7

18923 5

6 7

56before

after

Yoshino river

Gro

und-

base

d / S

atel

lite

rain

fall

SlowquickMovement of rain area

correction methodto describe the difference of rainfall events

in terms of wind speed

Sate

llite

rain

fall

(GSM

aP) (

mm

/3h)

Ground rainfall (mm/3h)

IFAS – Satellite rainfall data calibration

20

GSMaP(original) GSMaP(corrected)Ground gauged rainfall

3 hourly rainfall in Taiwan on typhoon Morakot, 2009 Aug.08 20-23（UTC）

• Corrected GSMaP is more accurate than raw data.

• User may specify any calibration equation.


21

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

9/6 9/7 9/8 9/9 9/10Date (2004,GMT)

Dis

char

ge (m

3 /sec

)0

20

40

60

80

100

Rai

nfal

l (m

m/h

our)

Ground-gauged rainfallSatellite rainfall(3B42RT)　　　〃　　　　 (original GSMaP)　　　〃　　　　 (corrected GSMaP)Calculated discharge(Ground)　　　〃　　　　 (3B42RT)　　　〃　　　　 (original GSMaP)　　　〃　　　　 (corrected GSMaP)Measured discharge

SendaigawaRiver length =137km Basin Area =1,600km2 Correction of satellite data is successful


22

0

500

1,000

1,500

2,000

2,500

7/4 7/5 7/6 7/7Date (2006,GMT)

Dis

char

ge (m

3 /sec

)0

20

40

60

80

100

Rai

nfal

l (m

m/h

our)

Ground-gauged rainfallSatellite rainfall(3B42RT)　　　〃　　　　 (original GSMaP)　　　〃　　　　 (corrected GSMaP)Calculated discharge(Ground)　　　〃　　　　 (3B42RT)　　　〃　　　　 (original GSMaP)　　　〃　　　　 (corrected GSMaP)Measured discharge

KikuchigawaRiver Length =71 km Basin Area =996 km2


Why was correction of satellite data unsuccessful?

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0

500

1000

1500

2000

2500

3000

3500

9/6 0:00 9/6 6:00 9/6 12:009/6 18:00 9/7 0:00 9/7 6:00 9/7 12:009/7 18:00 9/8 0:00

Date (UTC)

Dis

char

ge [

m3/s]

0

10

20

30

40

50

60

70

80

90

100

Rai

nfa

ll [m

m/h]

MWR observation

Ground gauged rainfall

GSMaP

Corrected GSMaP

Calculated discharge(GSMaP)Calculated discharge(Corrected GSMaP)Observed discharge

0

200

400

600

800

1000

1200

1400

1600

7/4 0:00 7/4 6:00 7/4 12:00 7/4 18:00 7/5 0:00 7/5 6:00 7/5 12:00 7/5 18:00 7/6 0:00

Date (UTC)

Dis

char

ge [m

3/s]

0

10

20

30

40

50

60

70

80

90

100

Rai

nfa

ll [m

m/h]

MWR observation

Ground gauged rainfall

GSMaP

Corrected GSMaP

Calculated discharge(GSMaP)Calculated discharge(Corrected GSMaP)Observed discharge

MWR observedfrequently

No MWR during peak rainfall

successful case : Sendai river unsuccessful case : Kikuchi river

Accuracy of rainfall area distribution depends on frequency of MWR observation

(& accuracy of IR motion vectors)

Image Source : JAXAOzawa et al. (2010)


24

Forecasting Inundation for Early Warning

Bicol River basin, Philippines: simulated inundation extent and depth for a) 2-year, b) 5-year, c) 10-year, d) 25-year, e) 50-year and f) 100-year return periods.

d) 25 year

a) 2 year b) 5 year c) 10 year

e) 50 year f) 100 year

25

The Rainfall-Runoff Inundation (RRI) model

Forecasting Inundation for Early Warning

Rainfall-Runoff Model

River Routing Model

Flood Inundation Model

• Rainfall-Runoff Model: simulating streamflow discharge with rainfall input.

• River Routing Model: tracking flood wave movement along an open channel with upstream hydrograph.

• Flood Inundation Model: simulating flooded water spreading on floodplains with inflow discharge.

26

18Inundation map (2009-2010),

Pampanga river basin. Observed and simulated discharge

(2009-2010) at Arayat station.

Dis

char

ge (m

3 /h)

Traditional calibration with discharge

Dis

char

ge (m

3 /h) R

ainfall (mm

/h)

NSE=0.58

Calibrating by spatial extent of inundation: RRI vs MODIS

19

NDVI

22-30/09/2009“Ondoy”

8-16/10/2009“Pepeng”

8/10/2010-1/11/2010“Juan”

22-30/09/2009“Ondoy”

8-16/10/2009“Pepeng”

8/10/2010-1/11/2010“Juan”

NDFI1

22-30/09/2009“Ondoy”

8-16/10/2009“Pepeng”

8/10/2010-1/11/2010“Juan”

LSWI

22-30/09/2009“Ondoy”

8-16/10/2009“Pepeng”

8/10/2010-1/11/2010“Juan”

MLSWI

Comparison of inundation extent from RRI model and MODIS images using different NDSIs during selected flood events in Pampanga river basin. MODIS

RRI

0.3 0.29

0.140.30

0.25

0.100.25

0.24

Sumary

1 Hydrologic forecasting for early warning• Objective to achieve long(er) lead time with confidence

(accuracy, or at least understanding of uncertainty) in data-limited basins.

2 Remotely sensed precipitation data • We still need ground-gauged precipitation, but do we need

the same information as before?3 Spatial inundation patterns

• Calibrating/validating hydrologic models using discharge or inundation through space and time?

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Hydrologic forecasting for flood risk managementrivers.bee.oregonstate.edu/sites/default/files/...Sep 30, 2009 · “Ondoy” 8 16/10/2009 ... PowerPoint Presentation Author: Kibler

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