Flood Forecasting &

Decision Support System for Flood

Control and Early Warning

Dr. LIU Zhiyu

Bureau of Hydrology, Ministry of Water Resources of China

Homepage: http://xxfb.hydroinfo.gov.cn/EN/eindex.jsp

Email: liuzy@mwr.gov.cn

International Training Programme 2010

Management of Flood Control and Disaster Mitigation

June 2010 China

影响人口

死亡人口

灾害损失

Global Major Disasters

Death toll due to flooding

Slides

Droughts

Earthquakes

Epidemics

Extremes tem

peratures

Floods

Wild Fires

Wind Storm

s

Volcanoes

Other N

atural Disasters

Non-N

atural Disasters

TOTAL

Oce

ania

US &

Can

ada

Res

t of A

meric

asEur

ope

AfricaAsi

aTot

al

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Type of disaster

Nu

mb

er

of

peo

ple

kill

ied

Oceania

US &

Canada

Rest of

Americas

Europe

Africa

Asia

Total

Total number of people reported killed, by continent and by type of phenomenon (1990 to 1999, source: IFRC World

Disaster Report 2000)

Floods

Seasonal (riverine) flood monsoon rainfall, snowmelt, etc.

Flash flood heavy short-term storm, snowmelt, etc.

Urban floodDebris & mud flow (often as a flash flood)

Snowmelt flood Ice jam flood Glacier lake outburst flood Dam-break flood

Flood Risk Management

Structural structuresConstruction of dams and river embankments River improvement works, floodway, flood

retarding basin, etc. Slope protection (erosion control)High-floored houses, shelters, riverside forests

and bamboos, etc.

Non-structural structures Legal framework, coordination among stakeholders Land use management flood monitoring, forecasting & early warning

systems Preparedness –hazard mapping, improving

communication, education to create awareness Insurance and mutual aid

Data Collecting

Decision

Making

Meteorology

Hydrological

Engineering

Drought

Damage

Loss

Forecasting &

Prediction

Non-structure Measure for Flood Management

4 step

（洪水管理4环节）

Em

erg

en

cy

Re

sp

on

se

Activitie

s

+ rebuilding

预测预报 调度决策

救灾重建

Flood forecasting plays a key role in flood management

Questions:

1. WHAT IS REAL-TIME FLOOD FORECASTING ?

2. UNDER WHICH CONDITIONS IS IT POSSIBLE ?

3. WHAT ARE COMMON APPROACHES OF FF ?

4. WHAT IS THE STATE-OF-THE-ART OF FF?

5. China NFFC’s decision support systems?

WHAT IS REAL-TIME

FLOOD FORECASTING?

•A Flood Forecasting System is a tool aimed at reducing

uncertainty on the future evolution of a flood event..!!!

•It is the reduction of uncertainty that allows for more reliable

decisions.

•Real-time flood forecasting provides a possibility of providing

the authorities in charge of the management of catastrophic

situations such as floods, timely and sufficiently accurate

information on the possible future evolution of the events

within a pre-determined time horizon.

“Timely” means

early in time to allow for the implementation of the

possible interventions.

“Sufficiently accurate” means

to be associated to a measure of its uncertainty

and within a tolerable error band (e.g.10-20%).

“Pre-determined time horizon” means

time horizon must be sufficiently long (e.g. 12hr)

to allow for the implementation of decisions.

UNDER WHICH CONDITIONS

IS IT POSSIBLE ?

Large-size catchments (>10,000 km2) - flood forecast can be

performed up to 12 hours in advance, only by upstream level

and/or discharge measurements.

89/63

Medium-size catchments (between 10,000 and 1,000 km2),

flood forecast can be performed if a telemetering rain gauge

network is available.

超声波水位计

Small-size catchments (< 1,000 km2), - flood forecast is

possible only if a precipitation forecast is available.

Very small-size or urban catchments (< 200 km2) - it is

impossible to provide an accurate real-time forecast, radar

precipitation estimates are essential.

Radar DataPrecipitation

Estimates

WHAT ARE COMMON APPROACHES

OF FLOOD FORECASTING?

A schematic of a flood forecasting system (NERC, 1994)

• a precipitation forecasting

model (deterministic and/or

stochastic)

• a catchment model

(deterministic and/or

stochastic)

• a flood routing model

• A flood plain model

• A geographical information

system (GIS)

• A geo-referenced data

bank

• An expert system shell

Fiver steps taken in operational flood forecasting

(1) In the synoptic weather situation, when rainstorms occur, the flood is

roughly estimated and forecasted according to the quantitative

precipitation forecasting, or the possible rainfall.

(2) Flood forecasts are successively made with the rainfall-runoff model

duration by duration according to rainfall.

(3) When flooding has appeared in the upper reach, the flood will be

forecasted for the lower reach based on the corresponding water level (or

discharge).

(4) If there is much difference between the observed values and forecasted

values, the forecasts should be real-time adjusted.

(5) If taking structural measures, such as flood diversion or flood storage,

or if dyke break occurs, for flood control policy-making, regulation of

flood forecasts should be made.

Principle of operational flood forecasting

•Different Approaches,

• Group Consultation, and

• Comprehensive Analysis

Empirical forecasting

schemes

Mathematic models for

flood forecasting

Tw

o m

ajor ap

pro

aches o

f FF

降雨径流相关 上下游水位(流量)相关

Rs

RI

E P and EM

IM

RB

Qs

CI

CG

KG

RG

QI

QG

K B,WM

R

1-FR FR

W

WU

WL

WD

S

SM

EX

KI

UH

∑Q

EU

EL

ED

UM

LM

C

XINANJIANG MODEL新安江蓄满产流模型

Operational flood

forecasting

WHAT ARE COMMON APPOACHES OF FLOOD FORECASTING?

Empirical flood forecasting methods

o API Model (P～Pa～R + UH)

P

t初损后渗法

P

tP～Pa～R

Q

t

实测

计算

P~Pa~R Unit Hydrograph

Empirical flood forecasting methods

上游

实测

演算

Q

t

Q

t

实测

演算

涨落急剧型 涨落平缓型

),(

),(

),(

)(

1

1

1

1

PZfZ

QZfZ

ZZfZ

ZfZ

UtDt

UtDt

UtDtDt

UtDt

Improved accuracy

Empirical flood forecasting methods

o Corresponding stage (discharge) method

Resultant discharge method

)(

)(

1

1

Utm

UtDt

QfZ

QfQ

Resultant discharge (m3/s)upQ Peak stage at Pingle station (m)

Stage (discharge) fluctuating rate method

),(

),(

1

1

tttt

tt

QZfZ

KZfZ

Multi-factors combined axes correlation method

),,(

),,(

0

0

TZPfZ

PZPfZ

m

cm

1

2

3

4

5

6

7

72

60

48

3624

12

57 58 59 60 61

Mean area precipitation（mm）

150 100 50

Peak stage at Yangshan station（m）

0 59 60 61 62 63 64 65 66

Rainfall runoff correlation method

),(

]),[(

am

am

PPfQ

QPPfQ

P+

Pa (m

m)

Runoff (mm) Peak discharge(m3/s)

Mathematic models for flood forecasting

Statistical

(black

box)

Lumped

Conceptual

(grey box)

Distributed

Physically-based

(white box)

Deterministic Stochastic

Watershed Hydrological Models

Joint

Stochastic-Deterministic

Semi-Distributed

Conceptual

(grey box)

Mathematic models for flood forecasting

• Tank model

• Snowmelt-runoff model (SRM)

• Inflow-storage-outflow (ISO) function models

• Conceptual watershed model for flood forecasting (China)

• Conceptual watershed model (the HBV model)

• Sacramento soil moisture accounting model (NWSRFS-SAC-SMA)

• Snow accumulation and ablation model (NWSRFS-SNOW-17)

• Synthesized constrained linear system (SCLS)

• Non-linear rainfall runoff model – URBS

And, the combined streamflow forecasting and routing models

• Streamflow synthesis and reservoir regulation (SSARR)

• Manual calibration program (NWSRFS-MCP3).

HOMS website: http://www.wmo.int/web/homs/projects/HOMS_EN.html

Models for forecasting streamflow from

hydromeorological data

The main factor is:

• the understanding and the correct definition of the

purposes, for which the method or the model will be used.

•the availability of data, that heavily conditions the selection

of the type of modelling approach, which ranges from the

simple statistical methods to the extremely detailed physical

process models.

Choosing an appropriate flood forecasting model

The following additional factors have to be considered when selecting a

model:

1) Forecasting lead time vs time of concentration (or travel time when

dealing with routing problems)

2) Robustness of the approach, in the sense that, when dealing with

real time forecasting, sudden instabilities or large forecasting errors must be

avoided at all cost, even by resorting to slightly less accurate approaches.

3) Computational time, in that the forecast must be made available in

time to the flood managers and dependent responders, to guarantee the

effectiveness of their decisions. Frequently this requirement discourages the

use of sophisticated and accurate, but time consuming, approaches.

Choosing an appropriate flood forecasting model

WHAT ARE THE STATE-OF-THE ART

OF FLOOD FORECASTING?

IMPROVEMENT OF RAINFALL MEASUREMENT

New approach: multi-sensor rainfall data assimilation

using Bayesian approaches with an aim to improve

rainfall measurements

RAINGAUGES METEO SATELLITEMETEO RADAR

Direct measurement of precipitation

Point measurement

At point quantitatively good while spatially low quality

Indirect measurement of precipitation

Good spatial description but quantitatively poor

Precipitation intensity

Wind and positioning

Evaporation

Clutter o Ground Clutter (non-meteorological echoes)

Attenuation

Beam Blocking

Orographic Enhancement

Bright Band, snow, etc.

Presently poor algorithms for QPS

Several under development

Discontinuous in time

Indirect measurement of precipitation

Good spatial description but coarse and quantitatively poorer than RADAR

RAINGAUGES RADAR & SATELLITE BAYESIAN COMBINATION

BLOCK KRIGING OF THE RAINGAUGES + RADAR

UPSCALING

BK GAUGES + RADAR at satellite scale

SATELLITE

KALMAN FILTER

BK GAUGES + RADAR + SATELLITE at satellite scale

DOWNSCALING (KALMAN SMOOTHING)

BLOCK KRIGING OF THE RAINGAUGES + RADAR + SATELLITE

y yP~

2.8

0.0

April 15 1998 – @ 19:00

BLOCK KRIGING

BK + RADAR BK + SATELLITE BK+RADAR+SATELLITE

RADAR SATELLITE

BAYESIAN COMBINATION & TOPKAPI

13-18 April 1998

Discharge at CASALECCHIO

0

50

100

150

200

250

300

15 21 27 33 39 45 51 57 63 69 75 81 87 93 99 105

time (h)

Dis

char

ge

(m3/

s)

OBSERVED

BK

RADAR

BK+RADAR

BK+SATELLITE

BK+RADAR+SATELLITE

RADAR

UNDER-ESTIMATION

Discharge at CASALECCHIO

0

500

1000

1500

2000

2500

3000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139

time (h)

Dis

ch

arg

e (

m3

/s)

OBSERVED

BLOCK KRIGING

RADAR

BK + RADAR

BK + SATELLITE

BK + RADAR + SATELLITE

Discharge at CASALECCHIO

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67

time (h)

Dis

ch

arg

e (

m3

/s)

RADAR OVER-ESTIMATION

BAYESIAN COMBINATIONS & TOPKAPI

13-22 November 2000

IMPROVEMENT OF HYDROLOGICAL MODELS

The new millennium: the derived from topography

models, e.g.

THE SCN + GIS MODEL

THE TOPMODEL MODEL

THE TOPKAPI MODEL

THE LISFLOOD MODEL

THE tRIBS MODEL

Chq

x

qr

t

h

Point eq.

Watershed eq.

s

T

T

sss

sVba

t

V

s

T

T

ooo

oVba

t

V

c

T

T

ccc

cVba

t

V

Distributed Model Lumped Model

Large Scale eq.

trhh tx 0,0,

*

0,0, trhcq tx trhh tx 0,0,

*

0,0, trhcq tx

Integrated eq.

TOPKAPI Model and parameter lumping

+

=

+

TOPKAPI: minimum data requirements

Data

Model

D.E.M. Soil Types Map Land Uses Map

TOPKAPI is developed and calibrated from widely available maps

TOPKAPI is one of the first distributed models that can run in real time

TOPKAPI can be directly linked to Radar,Satellite, Meteorological Models to provide reliable flood forecasts

the Yihe River basin

A=5 318 km2

the Huaihe River basin A=10

900 km2

the Yangtze River basin

A=15 500 km2

Chinese basins

Soil

DEM

Landuse

Huaihe River

A=10,900km2 at the Xixian station

GLDAS global soils dataset of Reynolds :

http://www.ngdc.noaa.gov/seg/eco/cdroms/reynolds/reynolds/reynolds.htm

UMD 1km Global land cover :

http://www.geog.umd.edu/landcover/1km-map/meta-data.html

USGS GTOPO30:

http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html

Flood Forecasting for the Huaihe River basin by

using TOPKAPI

0

1000

2000

3000

4000

5000

6000

7000

80001998-5-1 0:00

1998-5-8 0:00

1998-5-15 0:00

1998-5-22 0:00

1998-5-29 0:00

1998-6-5 0:00

1998-6-12 0:00

1998-6-19 0:00

1998-6-26 0:00

1998-7-3 0:00

1998-7-10 0:00

1998-7-17 0:00

1998-7-24 0:00

1998-7-31 0:00

1998-8-7 0:00

1998-8-14 0:00

1998-8-21 0:00

1998-8-28 0:00

1998-9-4 0:00

1998-9-11 0:00

1998-9-18 0:00

1998-9-25 0:00

1998-10-2 0:00

1998-10-9 0:00

1998-10-16 0:00

1998-10-23 0:00

1998-10-30 0:00

日期和时间

流量 (m3s-

1)

0

20

40

60

80

100

120

140

160

流域平均降水

计算

实测

Calibration

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

100002002-5-1 0:00

2002-5-8 0:00

2002-5-15 0:00

2002-5-22 0:00

2002-5-29 0:00

2002-6-5 0:00

2002-6-12 0:00

2002-6-19 0:00

2002-6-26 0:00

2002-7-3 0:00

2002-7-10 0:00

2002-7-17 0:00

2002-7-24 0:00

2002-7-31 0:00

2002-8-7 0:00

2002-8-14 0:00

2002-8-21 0:00

2002-8-28 0:00

2002-9-4 0:00

2002-9-11 0:00

2002-9-18 0:00

2002-9-25 0:00

2002-10-2 0:00

2002-10-9 0:00

2002-10-16 0:00

2002-10-23 0:00

2002-10-30 0:00

日期和时间

流量 (m3s-

1 )

0

20

40

60

80

100

120

140

160

180

200

流域平均降水

计算

实测

Validation

Flood Forecasting for the Dongjin reservoir by

using TOPKAPI

A=1080km2 at the Dongjin reservoir

SoilDEM

Landuse

Digital Soil Map of the World and Derived Soil Properties on CD-ROM

(FAO, 1998)

USGS Global Land Cover Characteristics Data Base :

http://edcdaac.usgs.gov/glcc/global_int.html (2000)

USGS HYDRO1k data set

http://edcdaac.usgs.gov/gtopo30/hydro/ (2000)

Reservoir-inflow

In-reservoir

Grid map of

“reservoir-

inflow” cells

Calibration

0

500

1000

1500

2000

2500

6-7

6-10

6-13

6-16

6-19

6-22

6-25

6-28 7-1

7-4

7-7

7-10

7-13

7-16

7-19

7-22

7-25

7-28

7-31 8-3

8-6

8-9

Time（△t=3hr）

discharge（

m3/s）

Cal.

Obs.

Validation

0

200

400

600

800

1000

1200

4-1

4-2

4-4

4-5

4-7

4-8

4-10

4-11

4-13

4-14

4-16

4-17

4-19

4-20

4-22

4-23

4-25

4-26

4-28

4-29 5-1

Time（△t=3hr）

Discharge（

m3/s） Obs.

Cal.

Flood event statistics (time step=3 hrs)

Principal calibrated model parameters

USE OF METEOROLOGICAL QUANTITATIVE

PRECIPITATION FORECASTS

(2) Application of NWP/QPF into Flood Forecasting

QPF FFS

FFS

reportingdata

Decision-

making

QPF

NWP

20×20km,

3-hour,

up to 5 days

Flood

forecasting

(with rainfall

forecasts)

Forecasted Observed

China NFFC’s

Decision Support System?

MISS — Meteorological Information Service System

(“天眼”气象信息综合业务系统)

TCS — Tropic Cyclones System （热带气旋系统）

NCHIOS — National Comprehensive Hydrological

Information Operational System

(全国水情综合业务系统）

FFS — Flood Forecasting System (洪水预报系统）

ISS — Information Service System

(水文信息综合服务系统）

Operational Systems

59

NCHIOS

Rainstorm Monitoring: auto warning

自动报警

Flood Monitoring: auto warning

库容曲线

统计信息

Integrated Information Enquiry

历史过程比较

测站视频

Integrated Information Enquiry

Comprehensive Hydrological Analysis

Forecasting-based Flood Early Warning

Forecasting-based Flood Early Warning

Flash flood forecasting

and warning

6/22/2010 68

Flash flood forecasting

and warning

National Flood Forecasting System (NFFS)

National Flood Forecasting System (NFFS)

The empirical relationship method

Automatic calibration of model parameters

Real-time operational flood forecasting

Interactive flood forecasting program

Various forecasts by different forecasters

Forecasts are made along the river trees

Utility modules

Thanks

谢谢!