Probabilistic Flood Risk ModellingNovel methods and models for South-East Asia

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Erickson LanuzaEnvironmental Scientist, DHI Singapore

Avinash ChakravarthyWater Resources Engineer, DHI Singapore

Team DHIGregers JorgensenMark FieldingMichael MeadowsElena PisonTan Moi KhimOle LarsenJesper GroossMads RasmusenLu Li LeiJulien OliverRasmus Borgstrom

Introducing DHIThe expert in WATER ENVIRONMENTS

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DHI in short

We’re a research organisation21% of our resources are allocated to R&D to further our knowledge

Our people are highly qualified80% of our 1,100 employees hold an MSc or a PhD degree

We’re globalWe have more than 30 offices worldwide and experience from projects in over 140 countries

We’re an independent, private and not-for-profit organisation

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Our areas of expertise

Our in-house facilities and tools: physical scale model testing, laboratories, ballast water centres, survey and monitoring systems for field studies

Aquaculture and agriculture Energy Climate change

Coast and marine Surface and ground water Urban water

Industry Environment and ecosystems Product safety and environmental risk

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MIKE by DHI software for water environments

The gold standard for modelling of all water environments

An extensive and global user community

Local support by experts

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MIKE CUSTOMISED by DHI tailor-made software solutions

Manage, organise and analyse large amounts of data

Make wise and robust water management decisions

Get the full benefit of real-time monitoring & early-warning systems

Optimise operations and planning

Early warning and forecast system in Slovenia

Optimised river operations inNew South Wales, Australia

Improved information

management in the Lake

Victoria Basin

Sharing water resources fairly in

the Nile river basin

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THE ACADEMY by DHI training and knowledge sharing

• Training courses and capacity building in more than 40 countries• Knowledge sharing through events• Global partnerships, including cooperation with major universities• Serious games

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Case Study – 2011 Thailand Flood

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2011 Flood• Worst flood in the last 5 decades. • Flooding persisted in some areas until mid-

January 2012.• Affected 65 out of 77 provinces, and resulted in a

total of 884 deaths.

• More than two-thirds of the country was inundated by flood waters

• The World Bank estimated a total economic loss of 47.5 Billion $

• One of the top five costliest natural disaster events in modern history worldwide.

2011 FloodExceptionally wet year over the whole Chao Phraya Basin

Source: Thai Met DepartmentTRMM Daily accumulation rainfall 05 Sept to 15 Sept 2011

Return period of monsoon rain: ~ 50 year?

Objectives

• Quantifying the flood risk in Thailand, Vietnam, Malaysia, Indonesia and Singapore

• Key activities include

Ø Development and application of probabilistic hazard modelling methodsØ Development of flood hazard models for large scale and complex floodplains (e.g. Chao Phraya

basin) making use of datasets from the public domainØ Development of a stochastic weather generatorØ Integrate hazard description with MR exposure and vulnerability data for underwriters

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Deterministic VS Probabilistic approach1 event scenario VS ensemble of scenarios to account for uncertainty and variability of events

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Sea level

Defense systems

Chao Phraya Basin• 162.000km2

• Mountainous in the north, flat alluvial plain in the south (app. gradient 1.5m/100km)

• 20 million people• High industrialization and urbanization rate

Topography of Bangkok area

Chao Praya Basin

Modelling challenges - Flood protection measures• Structural (e.g. dykes, storage areas) • Non-structural measures (e.g. diversion schemes flood retarding areas,

pumping stations, dams)

DHI Industrial Estates Flood Risk assessment 2008• Embankments protect for frequent events only (˂ 50 yr. RP)• Embankments not soundly engineered, maintained and accessible• Need for holistic flood management plans considering the basin scale• Preparedness and recovery planning has to be carefully considered

Hi-Tech IE

Hi-Tech IE

Hi-Tech IE

Nava Nakorn IE Bangkadi IE Bangkadi IE

Conceptual floodplain model

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SRTM not able to describe main flood plain features

Analysis and segregation of study areas

Workflow

Publicly available datasets

River and floodplain modelling

Hydrology Digital terrain and floodplain model

Stochastic structure failure

Stochastic weather generator

Result Processing and analysis

Result integration (MR Hazard)

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Analysis & Segregation of the Study Area

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GIS Processing

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Population density raster (30m resolution) derived from:

• Settlement mapping (automated satellite imagery interpretation or readily-available existing classified imagery datasets settlement mapping –e.g. Landsat

• Enhanced Thematic Mapper, in some cases Open Street maps

• Gazetteer population number

Source and Methodology:http://www.andytatem.webspace.virginmedia.com/index_files/AsiaMethods.htm

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Population/Build-up Mask

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Analysis and segregation of priority areas

• 20% of exposed population covered by standard 2D model (small basin – medium exposure)

• 20% of exposed population covered by coupled RR-1D model (large basin – low exposure)

• 60% of exposed population covered by coupled RR- Quasi-2D model (large basin – high exposure)

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Fully Dynamic 2D

Modelling approaches

Coupled RR-1D Coupled RR- Quasi 2D

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River network and catchments

River network and cross sections

Interpolation of water level between cross sections superimposed on digital elevation model

Coupled hydrological and 1D hydraulic model – automated hydrodynamic model set-up

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Coupled hydrological and 1D hydraulic model – development of flood maps

Catchment ”backbone” approach of water level interpolation- Creates smoother interpolated raster- Ability to map wider floodplain

Flood Cell Processing

Improved Delineated Network

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Hydrodynamic model schematization

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• Automated: easy to replicate and update• Fast simulation time• Capture structures and simulate structure behavior

Detailed flood plain modelling – flood cell approach

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Detailed flood plain modelling – from index to flood map

Meso-scale Exposure modelsResidential

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Meso-scale Exposure modelsIndustrial

Input Data Weight

Industrial Commercial

Population Density 0.10 0.30 Industrial Estate 0.60 0.10 OSM Industrial 0.30 0 OSM Commercial 0 0.6

Example of potential weighting

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Hydrological modelling

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Modelling approaches – Fully dynamic 2DDeveloping extreme rain events for catchment scales based on TRMM and Rain Gauge data

IDF Curves (Source: DID, TRMM) Areal reduction factors (Source: DID, AR&R)

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Modelling approaches – Fully dynamic 2D (MIKE 21)100Y rainfall event – application of area reduction factors – example from Malaysia

Flood depthNo areal correction applied

Flood depthAreal correction applied

Continuous Rainfall-Runoff/hydrological (RR) modelling

• Catchment based (lumped) conceptual model

• Adapted to long-term time varying runoff simulation

• Able to describe different hydrological regimes (steep slopes and flat plains)

• Linear reservoirs for ground- and surface water

• Calibrated against observed data

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Model discretization • Definition of 500+ sub-

catchments larger than 500 km2

• Each model individually parameterized based on land-use, slope and soil

• Models create 2000+ inflow points from sub-catchment > 70 km2 to 20.000+ km of rivers modelled

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Ground data - Discharge

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P73 [m^3/s]

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 0

200

400

600

800

1000

1200

1400

1600

E23 [m^3/s]

1970 - 79 1980 - 89 1990 - 99 2000 - 09 0

200

400

600

800

1000

1200

Models are calibrated to observed data

Calibration

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NSE RMSE ME

Rain gauges (10 stations) 0.72 148.94 -0.16 TRMM 3B42 0.81 123.67 -0.01

TRMM 3B42RT 0.68 151.35 -0.24

Rain gauges TRMM 3B42 TRMM 3B42RT

Example of parameter adjustment as function slope, size and land-use characteristics

Limits for model parameters

Source: ESA

Distribution of hydrological model parameters

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Validation

Maximum Discharge @ Ubon Ratchathani

Modelled : 10,047 m3/sObserved : 10,015 m3/s

Event on Sept-Oct 2006 (Source: RID)

Simulated Runoff (TRMM)© DHI

GIS and spatial modelling for risk analysis and management

Hazard modellingStatistical tools and numerical engines (MIKE)

DSSProcessed informationRisk, Loss modellingCBA, MCATailored interfaceScenario management

Spatial modellingHazard screeningExposure, vulnerabilityPre-processingPost-processing

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Thank you !!