SWOT measurements for understanding water related disasters

Jun Magome, Kuniyoshi Takeuchi, Kazuhiko Fukami and Pham Thanh Hai

ICHARM International Center for Water Hazard and Risk Management under the auspices of UNESCO hosted by PWRI, Tsukuba

2

Short IntroductionRelated Research to SWOT

- Reservoir Storage Monitoring using Satellite Observations.

1st Dam Database Workshop 3

Methodology concept of monitoring S by satellite

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Schematic diagram of water storage monitoring by satellite (Magome et al., 2003)

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1st Dam Database Workshop 4

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1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

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1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V(km3）

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M. S America

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Europe

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(1992-2004, time-step 10days) (Magome et al, 2005)

湖沼 19地点ダム貯水池 15地点

Magome et al., 2005

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Validation

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実測値

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1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

ΔV　(km3)

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Volta Lake （Akosombo Dam ）

ΔV

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9.9 km3

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(7.1%)Volta Lake （Akosombo

Dam ）

1.05 km3

(9.5%)0.47 km3

(6.0%)Lake Powell

（Gren Canyon Dam ）

誤差最大値誤差平均

9.9 km3

(25.5%)2.8 km3

(7.1%)Volta Lake （Akosombo

Dam ）

1.05 km3

(9.5%)0.47 km3

(6.0%)Lake Powell

（Gren Canyon Dam ）

誤差最大値誤差平均Error (Avg.) Error (Max.)

ObservationEstimate

SWOT measurements for understanding water related disasters

Jun Magome, Kuniyoshi Takeuchi, Kazuhiko Fukami and Pham Thanh Hai

ICHARM International Center for Water Hazard and Risk Management under the auspices of UNESCO hosted by PWRI, Tsukuba

-Early warning and forecasting system for water related disasters such as flood and drought, storm serge, tusnami is advancing constantly, taking advantage of satellite surface water monitoring technology.

-Especially, satellites, radars and telemetered ground precipitation measurements are getting integrated and delivered to society through internet together with results of analyses using these data.

-Besides, information dissemination by efficient and user friendly interface such as Google Earth is becoming generalized and newer communication ways of sharing water disaster risk information between water sectors and local society are starting.

-These satellite observations are useful and desired even in various poorly-gauged regions.

Introduction

Objectivel To provide and assist implementation of the best

practicable strategies to localities, nations, regions and the world to manage the risk of water related hazards including floods, droughts, land slides, debris flows and water contamination.

• At the first stage, the priorities is flood-related disasters.(development of flood disaster early warning/forecasting system especially for poorly gauged regions)

Advanced Early Warning & Hazard Mapping

Owned & operated by local practitionersGlobally Available Datasetwith Simulation Model

Concept of IFAS (Integrated Flood Analysis System) -Toward Prompt Implementation of Flood Forecasting / Warning Systems with the Sense of Ownership of local users in Developing Countries -

Global observation of rainfallby earth observation satellites Other GIS data for runoff mode

(Land use, soil, etc.)

IFAS( A basis for flood forecasting/warning system)

Real- time input: Satellite & ground rainfallGIS data input for setting parametersGIS analysis to build runoff modelRunoff analysis and flood simulationUser- friendly interfaces for output

5

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2007/7/9 0:00

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2007/7/9 4:00

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2007/7/9 12:00

2007/7/9 14:00

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2007/7/9 19:00

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450

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雨量

50

流量 250

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Project:ABC D EFG D ate Tim e:2007/7/9G rid No:482

上流域平均雨量

河道流量 (G 482)

○○実績河道流

Data download throughInternet, free of charge

Flood disaster prevention & mitigation

Topographic dataSatellite-based near real-time rainfall data

Current situationDespite of the needs for flood forecasting/warning,

No rainfall, GIS data, nor analytical toolsà Required much money & time for implementation

After the application of IFAS:Prompt & efficient implementationNo need to develop original core systemStep-by-step improvement of accuracy with

hydrological observational network

Ex.) IFNet-GFAS, NASA-3B42RT, JAXA-GSMaP

Mekong River Basin ApplicationDHM (YHyM/BTOP Model) Simulation Period daily, grid size: 2min

1972 - 2000 ( Calibration) gauged P. & gauged Q.1998 - 2007 ( Application ) satellite P. (TMPA-3B42V6)2008 - (Application) satellite P. (TMPA-3B42RT)

2D-Inuundation simulation (ICHARM’s 2D-FEM flood inundation model): 2000, 2003, 2007, 2008

Type Description Source Original spatial resol. Remarks

Phys

ical

Dat

a

DEM USGS-GTOPO30 30 sec. Global dataSoil Map FAO-DSMW 0.25 deg. Global data, 1995Soil Properties FAO - USGS soil triangle (Rawls et al., 1982, 1985)Land Cover Type GLCC/IGBP V2 30 sec. Global data, April 1992 to March 1993

Root depth - - Sellers et al., (1994, 1996)River width - - Calculated based on the upstream area;

(Lu et al.,1989)NDVI NOAA-AVHRR 8 km Monthly data, 1981–2000

Hyd

ro-m

et d

ata

Precipitation (Daily) MRCTMPR: 3B42V6

Gauged0.25 deg

65 stations, from 1972 to 2000

Mean Temperature (Daily) MRC Gauged 24 stations, from 1972 to 2000Cloud coverDaylight durationDiurnal temperature rangeExtraterrestrial radiationVapour pressureWind speed

CRU TS 2.0 0.5 deg.Global, monthly data from 1901 to 2000.Used for he potential evapotranspirationmodel in YHyM

Observed discharge (Daily) MRC Gauged 11 stations, from 1972 to 2000.

Satellite Observation à DHM(YHyM/BTOP Model) à Discharge(TMPA-3B42RT(2008)

Flood inundation Simulation around Tone sap Lake

II. Flood inundation for Tone River (Japan) analysis for case of levee breach in Tokyo Area

Laser Profiler 100 m resolution data FEM Elevation

II. Flood inundation for Tone River (Japan) analysis for case of levee breach in Tokyo Area

• to implement effective water related disaster management (for flood, drought (incl. cyclone) , storm serge, tusnami and so on) by local water sectors, measurements of water surface elevation and water storage change or discharge in higher temporal resolution and spatial resolution by SWOT are very useful especially data poor and large international river basins (e.g. Mekong River Basin, Euphrates river)

- It will be very helpful for water related disaster management to detect and monitor the inundated area and depth directory for flood disasters without influence of clouds with more reliable.

- For early warning and forecasting system, accurate elevations as well as hydrological information by SWOT make DHMs and flood inundation models more effectively for flood disaster management sectors.

SWOT for water related disaster

- SWOT output will be utilize as high resolution verification datasets, more realistic hydrological input data, initial condition data and boundary condition.- In addition, new physical way of parameter determination of these models will be also desired.

Thank you very much

Jakarta, May 2007 by K. Takeuchi

Real causes of disasters: Poverty & Governance

Mangahan Floodway in Metro Manila (28 May 2007 K. Takeuchi)

The fundamental problems are poverty and Governance.