Current Activities for Flood Forecasting
Enhancement in Korea

2007. 12. 4

Deg-Hyo Bae (dhbae@sejong.ac.kr)
Professor, Dept. of Civil & Env.
Env. Engrg.,
Engrg., Sejong University, Korea

Current Flood Damage and Their Causes

Billion Won

2560

Short Forecasting
Lead Times
979

Abnormal Weather & Climate Change

143

553

Urban Flood Damage

291

Flash Flood Damage Abnormal Weather & Climate Change Impacts
Flood vulnerability analysis, structural and nonstructural
adaptation measures

 Short Forecast Lead TimeUse of numerical weather forecast information for real-time
short-term flood forecast Urban Flood Damage Reduction
Development of the coupled radar rainfall and urban flooding
modeling system

 Improvement of the Existing Forecasting Models
Develop a state space form of stochastic dynamic Kalmanfiltering model for real-time prediction and updating system

 Flash Flood Damage Reduction
In addition to classical forecasting system,
develop a new flood forecasting model system

Current Flood Forecasting System ( FFS ) FFS– Hydrologic and Meteorologic Observation System– Watershed and River Modeling System
Minister of Ministry ofConstruction & Transportation
Request of approval forreleasing water from dam

Administrator of Dam• To make plans forreleasing water fromdam• To start releasingwater

Flood Control Office• To estimate possible outflow bydivided basin areas• Flood forecast• Announcement and cancellationof flood forecast

Approval for
releasing waterfrom dam

Notification of plans forreleasing water from dam

Report of results

Telemetering
Observation Station• Monitoring Stations
for water level andrainfall• Water level of dam
and volume of release

Notification ofresults

Central Disaster Prevention and
Safely Countermeasures
Headquarters

Notification offlood-forecasting
announcement

Announcement to Public(Broadcast or newspaper)
Related Agencies
Regional Disaster Prevention andSafely Countermeasures
Headquarters

Flood Control Office
FFC Organization

Residents in the Regions

Flood Forecasting and Warning Process FFS Observation Networks
LEGEND
Raingage station
Stage station
Dam siteBasin

Area(km2)%

RaingageNo.

StageNo.

Han R.

26,018

26.1

153

96

Nakdong R.

23,817

23.9

165

140

Geum R

9,810

9.9

99

100

Young. &Seom.R

3,371

4.9

8192

Total

63,016

68,2498302※ Up to 2006

 Watersheds and River Routing Models– Watershed Routing•Storage function method– River Routing••••Storage function method
Kinematic Wave method
Muskingum method
DWOPERds (t + Ti )= rave (t ) − q (t + Ti )dts (t ) = k ⋅ q(t ) p⎧ f1rave (t )rave (t ) = ⎨⎩rave (t )• Difficult to find unique parameter set• Require experts for operational use∑r < R∑r ≥ R
ave

sa

avesa

Activity I

Use of Numerical Weather Forecast forReal-Time Flood Forecasting Numerical weather prediction at KMA/MOST– GDAPS for 110×110 and 220×220km– RDAPS for 30×30km (5-km res. Test)– GTS network for global observations of on-site and RS data
– Short-term(48 hrs), weekly(48hr-7days), long-term (monthly, seasonal,bi-annual) weather forecast information are provided

Evaluation of Weather Forecast Accuracy
RDAPS 48 hours forecast– Until 12 hours: underestimate due to spinup time– 24~48 hours: overestimate– 12~24 hours: most accurate
bias score: 0.8~0.9 threat score: 0.4
accuracy: 0.8
rmse: 2 mm/12hr+ 48 hours

Obs.

+ 36 hours

 GDAPS 10 days forecast– Threat score: 0.7 until 3 days
Han Basin
GeumBasin
Nak Basin
YoungsinBasinSeomjin
Basin

JulyThreat score+ 24 hours+ 12 hours

RDAPS numerical weather forecast (data : 2003.7.18.002003.7.18.00-07.24.12)
Forecast time : 21hr

Ensemble Combine Approach

Forecast time : 30hr

- Need to increase the spatial resolution from 30 km to 5km- Increase the numerical weather forecasting accuracy and develop
some adjustment techniques- Use of weather radar and satellite data for flood management

Activity II
NowcastDevelopment of Radar Rainfall & Flood
Forecasting System for Urban Watersheds
 Objectives– To develop radar rainfall and forecasting system
for Jung-rang urban watershed Method
0.5hr forecast– Consists of four processes: Meteorological Forecasting Process,Hydrologic Observation Process, Hydrologic Modeling Process,and Urban Flood forecasting & Waning Process

1hr forecast

2hr forecast

 Major outcomes– To estimate and forecast real-time radar-driven rainfalls– To provide algorithms for real-time flood forecast Nowcast and short-term forecast with radar data
Radar operation and signal dataprocess (KMA)

Incorrect radar data treatment
Quantitative Precipitation Estimation(QPE)기상청 레이더 네트웍광덕산

2003.12.10

광덕산

2000. 8 설치백령도영종도

영종도
2001

관악산

동해

군산

면봉산
부산

진도

9 Marshall-Palmer Z-R Eq.
9 Window Probability Matching Method
9 Least-Square Fitting Method

9 POD frequency analysis
9 Beam blockage remove
9 Ground Clutter remove

백령도

면봉산

진도

Real-time radar rainfall bias adjustment제주 성산포2003 12시험 운영 중

2001. 7 설치θ = 0.95o

Real-time data
relation system(planning)

9 Bias adjustment method application
9 Real-time stochastic bias adjustment
method application

Quantitative Precipitation
Forecast (QPF)

pl = 0.002 , ph = 0.30

Radar Clutter Mask: θ = 0.95o, pl = 0.002, ph = 0.30

9 Translation Model 9 VSRF from KMA

033030300

FP6050100150200270FFG250

Hydrologic Modeling Process

90

9 SWMM model240

9 HEC-HMS120210

9 Distributed hydrological Model

150180 Rainfall & discharge estimates
Key Map 240 km
150 km
50 km

CASE I: M&P Radar Rainfall

WPMM Radar Rainfall

Rainfall(mm/hr)

120

AWS
Adj. M&P Z-R
Adj. WPMM Z-R
Adj.Least-square Z-R

80

4000

LSF Radar Rainfall

1020AWS 1hr Accumulated Rainfall

3040506070

Hours. Since 2003. 8.22 00:00
12Adj.Least-Square MAP

4Adj.M&P MAP

84012Adj.WPMM MAP

8AWS MAP

402468101208DISCHARGE(cms)

Hydrologic application ofradar-driven rainfalls forflow simulation

Adj.CAS E I_Discharge
Obs. Q(Ju ng-ran ggyo)
Sim . fro m AWS
Sim . fro m Radar_Adj.W PMM
Sim . fro m Radar_Adj.M&P
Sim . fro m Radar_Adj.LS F

64200102030
Hours. Si nce 2003.6.11 00:00

4050RAINFALL(mm)RAINFALL(mm)8012 Real-time stochastic bias adjustment and forecasting algorithm
WSRWSR-88D Precipitation
AdjustmentAlgorithm

Adaptive Error
Parameter
Algorithm

Maximum Likelihood
AutoregressiveAlgorithm

9 Process model of the meanfield bias

9 Stochastic logarithmic bias
model

9 First-order autoregressive
process

β t +1 = β t + wt +1

At +1 = At + wt +1

zt +1 = β t +1 + vt +1

9 Observation equation⎡ Ng⎤⎢ ∑ Rg (t , j ) ⎥⎥β t = ln ⎢ jN=g1⎢⎥⎢ ∑ Rr (t , j ) ⎥⎢⎣ j =1⎥⎦Gt +1 = Rt +1 At +1 + et +1
9 BiasNg

At =∑ R (t , j ) R (t , j )rj =1

9 Bias prediction equation

β t +1 = a1β t + wt +1

9 Kalman Filter for log-biasg{Bˆt−+1 = exp βˆt−+1 + 0.5 Pt +−1Ng∑ R (t , j )
j =1⎤
⎡ Ng
⎢ ∑ Rg (t , j ) ⎥⎥β t = ln ⎢ jN=g1⎥⎢⎢ ∑ Rr (t , j ) ⎥⎦⎥⎣⎢ j =12r∑−t +1}

9 Error variances

Q = a2 (1 − a12 )( ) (exp {P }−1)

= Bˆ t−+12R(η ) = a3 η a4

−t +1

Development of State Space Form ofActivity III Stochastic Dynamic Flood Forecasting Model Stochastic dynamic flood forecasting modelds (t + Ti )= rave (t ) − q (t + Ti )dts(t ) = k ⋅ q(t ) pP

⎧ f1rave (t )rave (t ) = ⎨⎩rave (t )(a)∑r < R∑r ≥ R
avesa

avesa

RainfallTime LagtQ

Computed Flow
Observed Flow

tP

t-3

t-2

t-1

t

t+1

t+2

Rainfall(b)tQdq(t )
1=(rave (t − Ti ) − q(t ))dtkpq(t ) p −1dq(t + Ti )1=(rave (t ) − q (t + Ti ) − w(t + Ti ))dtkpq(t + Ti ) p −1

Predicted Flow
Updated Flow
Observed Flow

tt-3

t-2

t-1

tt+1t+2Z k = hk (qk ) + Vk Prediction and updating processes– Prediction stepdq~ (t +1)
1= ~(rave (t +1−Tl ) − q~ (t +1))
dtk pq (t +1)p −1
dP (t +1) 2= { (1−p )rave (t +1−Tl )q −p − (2 −p )q1−p } P(t +1) + Q(t +1)
dtkp– Updating step
q~ k ( +) = q~ k ( −) + K k [ z k − h k ( q~ k ( −) ) ]P

Pk ( +) = [ I − K k H k ( q~ k ( −) ) ] Pk ( −)Q

Kk =Rainfallt
Predicted Flow
Updated Flow
Observed Flow

Pk ( −)Pk ( −) + R kt
t-3

t-2

t

t-1

t+1

t+2

 Developed model application– Selected watershed: Nakdong river basins– Estimated parameters
Andongdam

Floodevent

Basin

1983 ~
1986
1998 ~
2003
1998 ~
2003

WhechenAndongdam
Imhadam

Whechen

KpParameters
Tlf1

Rsa

37.00

0.437

2.73

0.035

18.616

37.08

0.411

3.440.461

112.76

40.32

0.412

2.00

0.394

107.70– Whechen0
HOICHUN
Obs Flow

Q (cm s)Det. Run
RAIN

800

4060

40080

0100

8/9/85

8/10/85

8/11/85

8/12/85

Stoc. Run

1200

20Det. Run
RAIN

40
800
60
400

80

0
7/24/86100
7/25/86

TIME(hours)

TIME(hours)

1985. 8. 9-12

1986. 7.24-26

7/26/86

R A I N F A L L ( m m /ho ur )20

Stoc. Run

1200

0
HOICHUN
Obs Flow

R A I N F A L L ( m m /ho ur )

1600

1600

Q (cm s)2000

ImhadamDevelopment of Flash Flood Guidance System

Activity IV in Korea

 Flood Prediction (FP) and Flash Flood Guidance (FFG)
Rainfall

FFG

Soil Moisture Accounting ModelSAC, TOP

Excess Rainfall

Threshold Runoff

Routing Model

Unit Hydrograph

Peak Flow

Qpeak

Flood Stage>Bankfull What is FFG ?
FFG is the amount of rainfall
needed in a specified periodof time to initiate flooding
on small streams

 Forecast Lead Time ?
FFG is computed for 1-,3-, and 6-hour durations
optionally for 12-, 24hour durations

 Component of FFG system
Hydrologic Components

MeteorologicalComponents

Threshold Runoff

MM5• Manning’s Bankfull Discharge• Two Years Return Period Flow• Snyder’s Synthetic Unit Hydrograph• GIUH

Flash Flood
Watching&Warning

Observed

Estimation of Soil Moisture• Sacramento SMA Model• Mesoscale TOPMODEL• VIC Model

RadarSatellite

FFG Computation

 Estimation of grid-based FFG
Current Time
Rainfall( mm/∆t )Rainfall-Runoff
Relationship

Runoff

Runoff( mm/∆t )Bankfull Stage

Flash Flood
Watching &Warning

Rainfall Forecast
Current Water Level
RainfallFFG
THRESHOLD RUNOFF: Runoff needed
to raise water level to bankfull stage

Compare between FFG
and forecasted rainfall

tThreshold Runoff

Soil Moisture The development of a forecasting system such as Flash
Flood Guidance (FFG) is essential for mitigation of flood
disaster
Radar Rainfall

Flash Flood Guidance

Forecast Rainfall

Flash Flood Threat– Need to develop FFG system considering man-madechannel and urban drainage network– Need to increase accuracy of flash flood forecast– Need to combined technology among mesoscale forecast,radar and satellite information

Activity V

Flood Vulnerability Analysis for Obtaining
Reasonable Adaptation Measures Definition of flood vulnerability

Flood vulnerability proxy variables
Index

Proxy variables

Description

PN80

A number of days precipitation ≥ 80mm/day

PX1D

Maximum precipitation of sum for 1-day interval each year

MDF

Maximum daily discharge each year

FN90

Frequency of discharge exceeding long-term 90th percentile

ELEV

Mean basin elevation (m)POPBain population density (inhabitants/km2)

ASA

Density of assets (won/m2)

CI

Channel improvement (%)

PUMPdrainage pump capacity (ton/min.)

DFC

Flood control capacity of dam (106 ton)

Sensitivity

Exposure

Adaptation Sensitivity proxy variables on current
climate condition

 Flood Vulnerability vs. Flood Damage(＇7171- ＇00)

Summary and Future Directions
GEOSS/AWCI Downscaling Process

General Circulation Model

Data
Assimilation

Data
Assimilation

Improved prediction
Satellite data
Regional/Meso Model

Improved
InitialCondition
In-situ data
Centralized Data System

Flood Peak Reduction
3000

Optimized rules
2500

Outflow eq. inflow

3

discharge [m /s]

Outflow eq. 0
2000
1500
1000
500

DHM

0

7/8.1z7/9.1z7/10.1z

7/11.1z

Improved
Prediction

7/12.1z

SocioSocio-Economic Data

2002

Summary and Future Directions
GEOSS/AWCI Downscaling Process

General Circulation Model

Data
Activity I
Assimilation

Use of Numerical
DataWeather Forecast
Assimilation
for RealReal-Time Flood
Forecasting

Improved prediction
Satellite data

Flood Peak Reduction

Development of

Optimized rules
2500

Activity IV Flash Flood Guidance

Outflow eq. 0
2000

Outflow eq. inflow

3

Development of Radar
Rainfall & Flood Forecasting
Systemfor Urban
Watersheds
Regional/Meso
Model

Improved
InitialDevelopmentof State
Condition
Activity III Space Form ofIn-situ data
Stochastic Dynamic
Centralized Data System
Flood Forecasting Model

3000

discharge [m /s]

Activity II

System in Korea

1500
1000
500

Activity V0

7/8.1z7/9.1z7/10.1z

7/11.1z

2002

7/12.1z

Flood Vulnerability Analysis
DHM
Improved
for Obtaining Reasonable Prediction
Adaptation
Measures
SocioData
Socio-EconomicThank you for your attention