Page Title Institut 002-035 MICRO LEVEL VULNERABILITY

INDEX

Page Title Institut

002-035 MICRO LEVEL VULNERABILITY ASSESSMENT OF ESTUARINE ISLANDS: A CASE STUDY FROM INDIAN SUNDARBAN

Tuhin Ghosh Jadavpur University, India

036-069 ASSESSMENT OF URBAN FLOOD RESILIENCE FOR WATER, SANITATION AND STORM WATER DRAINAGE SECTORS IN TWO CITIES OF INDIA

Sandeep Thakur & Uday Bhonde National Institute of Urban Affairs (NIUA), New Delhi

070-084 IMPACT OF CLIMATE CHANGE ON AGRICULTURAL, METEOROLOGICAL, AND HYDRO-LOGICAL DROUGHTS IN THE CENTRAL HIGHLANDS OF VIETNAM

Dao Nguyen Khoi VNUHCM – University of Science, Vietnam

085-105 ASSESSMENT OF STORM SURGE HAZARD, VULNERABILITY AND RISK OF THE AGRICULTURAL SECTOR IN LEYTE, PHILIPPINES

Engr. Jon H. Gaviola, The Oscar M. Lopez Center

106-119 SELECTING MULTI-FUNCTIONAL GREEN INFRASTRUCTURE TO ENHANCE RESILI-ENCE AGAINST URBAN FLOODS

A. Alves, A. Sanchez, B. Gersonius, Z. Vojinovic UNESCO-IHE, Delft, The Nether-lands

120-155 ADAPTATION TO CLIMATE CHANGE IN AREAS WITH CULTURAL HERITAGE

Z. Vojinovic1, D. Golub1, W. Keerakamolchai1, 2, W. Meesuk1, A. Sanchez Torres1, S. Weesakul 2

1UNESCO-IHE, Delft, The Nether-lands 2Asian Institute of Technology, Pathumthani, Thailand

156-170 WHEN HAZARDS BECOME DISASTERS: THE CASE OF COASTAL FISHING COMMUNI-TIES IN BANGLADESH

Mahmudul Islam, M. Mostafiz, P. Begum Sylhet Agricultural University

171-187 THE USE OF AGENT BASED MODELS FOR CLIMATE CHANGE ADAPTATION AND DE-VELOPMENT OF LARGESCALE EVACUATION STRATEGIES FOR FLOOD RISK MITIGA-TION

Neiler Medina (a) Arlex Sanchez (a) Zoran Vojinovic (a) Alida Alves (a)

(a) UNESCO-IHE, Westvest 7, Delft, Zuid Holland, 2611AX, The Netherlands

International Network on

Sustainable Water Management i n D e v e l o p i n g C o u n t r i e s

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MICRO LEVEL VULNERABILITY ASSESSMENT OF

ESTUARINE ISLANDS: A CASE STUDY FROM

INDIAN SUNDARBAN Tuhin Ghosh

Jadavpur University, India

WATER SECURITY AND CLIMATE CHANGE:CHALLENGES AND OPPORTUNITIES IN ASIA

Asian Institute of Technology, Bangkok, Thailand

29 November - 01 December 2016



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Indian Sundarban• World heritage site

• Largest mangrove patch(4.3%)

• Rich biodiversity- flora, fauna

• 4.6 million population

• 34% under poverty

• 99% rural areas

• Poor access to infrastructure



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Sundarban biodiversity



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Study area

• Three islands- Sagar, Ghoramara

and Mousani

• Western part of Indian Sundarban

Island system

• Extended from 21°37’ North to

21°55’North and 88°2’ East to 88°15’

East



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Sagar Island

Situated under administrative jurisdiction of district South 24 Paraganas of

West Bengal, have 42 mouzas/ villages

Largest among Sundarban Island- area of 245.33 km²

206844 population (Census, 2011)

Ghoramara Island

Situated under administrative jurisdiction of Sagar Block of district South 24

Paraganas of West Bengal

Has an area of 4.4 km² with population 5193 (Census, 2011)

Mousani Island

Mousuni Island is under administrative jurisdiction Namkhana CD Block of West

Bengal, have 4 mouzas/ villages

Covering 29 km² area with population 22073 (Census, 2011)



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Vulnerability context: study islands

• The temperature increase rate has been reported about 0.019˚c with a projected 1 ˚C by the year 2050 (Hazra et al, 2002)

• Sea level rise 1990- 2000: 3.14 mm/year (Hazra et al.,2002)

• Change in river hydrodynamics

• During 1969 to 2009 Indian Sundarban had total landloss of around 210 km2 (Hazra et al, 2013)

• During the last part of decade (2006-2009) : experienced four major cyclones viz. Sidr, Nargis, Bijliand Aila

• Cyclone Aila of 2009 was the most hazardous of the climatic disasters to have recently hit the Sundarban Delta

• High population growth

• Development constraints: road connectivity, access to health services



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Research questions

• What are the trends in physical and anthropogenic changes?

• What are the key elements of vulnerability in the study area in respect to both

the natural and socio- economic factors?

• What is the extent of social and environmental vulnerabilities in the study

area?



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Data

Primary and secondary data sources:

• Survey data: Direct interviews with

783 households, selected by cluster

random sampling

• Published data: Indian

Meteorological Department;

Directorate of Census, GoI; WWF

• Satellite images: Landsat data

• The data analysis have been done in

two stages

• Stage 1: Trend analysis to get change

pattern as background of

vulnerability analysis

• Stage 2: assessment of actual

scenario along with vulnerability

mapping

Analytical approach



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Climatic Trend analysis

Temperature

(1901 – 2000)

• Average temperature increase: 0.011°C per year

Data source: IMD, Govt. of India

y = 0.0111x + 30.769R² = 0.2178

y = 0.0097x + 21.386R² = 0.1095

y = 0.0104x + 26.078R² = 0.2347

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y = 2.0783x + 1536.9R² = 0.0345

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Data Source: IMD, Govt. of India

• Amount of rainfall has increased :rate of 2.08 mm per year (1901 to 2000)• Number of rainy days have decreased implies the increase in intensity of

rainfall.

Rainfall



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Morphological change in study islands(1990 to 2015)

Erosion

• Sagar Island: Erosion rate

estimated 0. 2 km2 per year

• Mousani Island: Considerable

land loss: rate of erosion almost

0.08 km2 per year

• Ghoramara Island: Experienced

huge land loss, maximum loss

between 1975 to 1990



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Island Time window 1990 - 1995 1995 - 2000 2000 - 2005 2005 - 2010 2010 - 2015

Sagar

Erosion in sq km 0.43 9.35 3.8 0.55 5.79

Accretion in sq km 17.13 1.59 0.86 7.56 0.39

Ghoramara

Erosion in sq km 0.11 0.61 0.61 0.09 0.46


Mousani

Erosion in sq km 0.18 2.85 0.86 0.42 1.02


Erosion/Accretion and Net Land Loss during 1990 to 2015



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y = -0.0187x + 10.873R² = 0.0435

0

10

20

Cyclonic Depression

y = -0.0184x + 5.2625R² = 0.1296

0

5

10

Cyclonic Storm

y = 0.0046x + 1.5533R² = 0.0152

0

2

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Severe Cyclonic Storm



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Population growth

• Sagar Island: population growth

of 1.4% per annum in 2011

• Ghoramara Island experienced

little growth, 0.55% per annum

and negative growth rate of -0.08

at 2011

• Mousani Island experienced

population increase at 1%growth

rate per annum in 2011

86769110672

149222180408

206844

2.53.03

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4163 43364972 5,236 5193

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Sagar Island

Ghoramara Island

Mousani Island



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Crop productivity

Production decreased due to salinization of soil, fertilizer and labour cost, non-marketing of the produce, etc.

y = -5.2645x + 701.54

400

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2000-01 2001-02 2002-03 2003-04 2004-05 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

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Vulnerability assessment

• Vulnerability= f (exposure, sensitivity, adaptive capacity)

• Risk depends on the exposure of the system and adaptive capacity

of the system reduces the risk from threats.

• Residual Threat = Adaptive capacity – Risk

• Residual threat determine the extent of vulnerability



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• Composite Vulnerability Index: assessment based on both physical and

socio- economic variables

• Assessment done following the methodology developed by Ramakrishna

Mission in 2009 and report prepared by Hazra et. al., 2013.

• Normalization of variable by percentage

• Ranking as high- medium- low

Vulnerability assessment methodology (1)



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• The geometric mean of different vulnerability classes with rank has been

derived to assess mouza level Vulnerability Rank (VR) -

VR= 7√V1*V2*V3……..V7

• Vulnerability maps of study islands have been prepared separately in

interactive GIS platform.

• Finally overlying the maps to get Composite vulnerable zones or ‘hot spot’

mouzas

Vulnerability assessment methodology (2)



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Erosion

• Vulnerable mouzas: Ghoramara, Baliara,

Radhakrishnapur, Chandipur,

Chemaguri

• Low: < 0.04 Km2 land loss per year

Moderate: 0.041 to 0.08 Km2

High: >0.081 Km2 per year



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• 33% household are severely being

affected by erosion

• Loss of land, Homestead, livelihood

Severe Impact Moderate Impact No Impact

Impact of erosion on local inhabitants

0 5 10 15 20 25

land

homestead

livelihood

%of households

Types of Loss Due to Erosion



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House structure

• Low- <40% kachcha house,

moderate- 41- 70% kachcha, high-

71% kachcha houses

• Sapkhali (50%), Kusumtala (44%),

Ghoramara (43%): most vulnerable to

damage during storms and coastal

flooding.



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Electrification

• Low- <10% left electrification, moderate- 11-

30%, high- more than 31% left electrification

• Most of the villages don’t have electric

connection

• Few mouzas of Sagar have grid connection

since 2011



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Population density

• Low- less than 900 persons/ Km2, high-

greater than 1301 persons/ Km2 area,

moderate- 901- 1300 persons per Km2

area



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Education achievement level

• Low- more than 41% adult

educational attainment, high- less

than 20% attainment, moderate-

rest (21- 40)

• Lack of alternate skill and low

educational attainment leads to

more vulnerable situation



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Level of sanitation

Low: more than 81% houses having

good sanitation

High: less than 50% houses having good

sanitation

Moderate: rest (51- 80)



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• Only around 40% of population is employed- major share unemployment

• Female work participation is very low around 25%

• Decreasing productivity- less profit in agriculture

• Increasing poverty

• Shift to daily labour- quick money; out migration

• Increasing income inequality

• 38%, 37% and 25% surveyed families of Sagar Island, Ghoramara Island andMousuni Island are under below poverty level respectively

Economic status



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Cumulative Percentage of Total Income

Gini Coefficient- 0.22

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Cumulative Percentage of Total Income

Gini Coefficient: 0.18

Lorenz curve & Gini- coefficient showing income inequality among surveyed mouzas of

Sagar Block Mousani Island

Striking income inequality has been found in study islands



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Low: < 50% BPL families

Moderate: 51- 65% BPL families

High: > 66% BPL families

• BPL families- average income, assets

considered

Poverty



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Estimation of composite vulnerability

• Composite vulnerability map of 27

surveyed mouzas

• Ghoramara highly vulnerable

• Sapkhali, Baliara, Shibpur mouzas are

also closer to highly vulnerable

condition



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Delta in distress!!



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Conclusion & recommendations

• 21% mouzas highly vulnerable to erosion

• Social vulnerability is higher among the surveyed villages

• Educational attainment is lacking for 46% mouzas

• 21% mouzas need immediate economic assistance

• Around 66% mouzas are standing at the edge of vulnerability



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Recommendations (1)

• Regular maintenance of embankment in the coastal villages

• Proper warning and evacuation plan for cyclone

• Planned housing structure: Ghoramara, Sapkhali, Kusumtala

• Expansion of non- conventional source of energy to meet the deficiency: tidal energy, wind energy, solar energy

• Night school, vocational training, technical schools for adults; involvement of school children of locality

• Raising awareness about sanitation; monitoring and upgrading sanitation condition from gram panchayat



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Recommendations (2)

• Initiatives from local authority to give prior attention to economically

vulnerable mouzas to reduce poverty

• Reduction of income inequality: stable occupation; labour law for informal

sectors

• Focus on traditional practices- improve productivity through climate-

resilient agricultural practices

• Stakeholder participation



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Thank you for your kind patience

Questions please????



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ASSESSMENT OF URBAN FLOOD RESILIENCE

FOR WATER, SANITATION AND STORM WATER

DRAINAGE SECTORS IN TWO CITIES OF INDIA

Sandeep Thakur & Uday Bhonde,

National Institute of Urban Affairs (NIUA), New Delhi






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Yellow area – flood prone

Physiography of India and locations of important cities



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Sources: Planning commission & central water Commission reports

Flood Expenditure in India



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Climate change threats

• Long term trends indicates 5-8% increase in rainfall at upper catchments

• Short duration (24/hrs.) rainy days are increasing chances of urban flooding

• Threats on infrastructure like storage reservoirs, water supply, transportation already indicated in national reports



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Year Urban Flooding2000 Mumbai, Chennai, Bangalore Kolkata, Hyderabad

2001 Ahmadabad, Bhubaneswar, Thane, Mumbai

2002 Delhi

2003 Delhi, Ahmadabad, Vadodara

2004 Chennai

2005 About 10 cities; Mumbai was the worst affected.

2006 Number of affected cities rose to 22. Surat was the worst affected.

Vishakhapatnam airport was inundated for more than 10 days.

2007 Number of affected cities rose to 35. Kolkata was the worst affected.

2008 Jamshedpur, Mumbai, Hyderabad were worst affected.

2009 Delhi, Mumbai, Kurnool

2010 Delhi, Guwahati, Ahmadabad, Leh, Mumbai

2015 Chennai, Mumbai

2016 Gurgaon, Bhopal, Nashik, Guwahati (NE states, MP, Gujarat, Bihar, UP, WB,

Rajasthan)

Source: various web resources

Highlighted impacts Of urban flooding particularly on economy



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Flood Resilience

• The United Nations International Strategy for Disaster Reduction (UNISDR) defines resilience as “an ability of a system, community or society exposed to hazards to resist, absorb, accommodate and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions” (https://www.unisdr.org/we/inform/terminology#letter-r).

• Term “resilience” is now widely referred particularly in natural hazard risk reduction and climate change adaptations programmes.

• Use of term has become prominent post year 2005 (Serre D., & Barroca, B.,2013)

https://www.unisdr.org/we/inform/terminology



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Flood Resilience – Benchmark studiesCORFU - 2007 (EU)

CIRIA - 2010 (UK)

USEPA- 2014 (USA)

ZFRA- 2014 (Zurich)

• Aerts, J. et.al. (2014) estimated Cost of Flood Resilience in coastal megacity of New York for different strategies.

• Three strategies were proposed to make coastal areas flood resilient viz. • measures to enhance building codes in NY city, a non-structural approach • different structural measures like levee barriers, beach nourishment in coastal areas of city and • hybrid approach which combines building codes measures, and barrier construction only for very

high risk areas as per probabilistic hazard estimation.• Interesting policy recommendations like collection of US$10 resilience fees from tourists

(50million/year) visiting city to recover an investment in building resilience measures are proposed in the study.



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Flood Resilience - India• Disaster Management Act – 2005, formed Nat.DM. Authority

• NDMA – 2008 - flood resilience guidelines

• NDMA – 2010 – urban flood resilience guidelines

2005 2016..Improved

• In India, understanding of resilience (urban flood) is limited and now increasingly used by academia and development sector.

• Especially, the term is frequently used post climate change and natural hazard studies carried out by national agencies (MoEF, NDMA) under resilience programmes and with support from international donors.



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Flood resilience and national missions of India• Atal Mission for Rejuvenation and Urban Transformation (AMRUT)

• Swachh Bharat Mission (SBM) to improve urban infrastructure and sanitation conditions in Indian cities.

• Smart cities mission

• All programmes under Ministry of Urban Development, Govt. of India

This research study aimed to propose broad framework to measure cost of urban flood resilience which would be useful to city officials in preparing projects in WSS sectors under such missions.

It is anticipated that by incorporating the resilience dimension for future WSS projects with some cost details; damages to critical infrastructure would be minimized and services will be less affected during urban floods by achieving some degree of urban flood resilience (http://smartcities.gov.in/ )

Urban areas (cities) are playing vital role in economic growth of country as their share in gross domestic product (GDP) is increasing. High Powered Expert Committee (HPEC) indicated that it is expected to be 75% by 2030 (HPEC, 2011) in India. Thus, any adverse impact on urban economy due to natural or manmade disasters directly affects economy of India

http://amrut.gov.in/http://www.swachhbharaturban.in/sbm/home/

http://smartcities.gov.in/



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Aim & ObjectivesStudies/research in India on climate change impacts in urban areas have emphasised on risk

profiling, vulnerability assessment of population & service sectors and then recommends suitableadaptation/resilience options. However, there are limited information on financial aspects ofresilience particularly of WSS sectors.

• Therefore, important aim of this article is ,

• To highlight status of urban flood resilience in general and particularly of WSS sectors in two select cities in India namely Guwahati and Vishakhapatnam

• The specific objectives are ,

• To discuss gaps in WSS infrastructure, associated challenges and how the gap influences flood resilience in selected cities

• To appraise WSS infrastructure, institutional set up and financial status in study cities from flood resilience perspective



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Infrastructure: Gap assessment, emergency plans, innovative technologies, new infrastructurearrangements, retrofitting, relocation plans

Institutions: Good governance, roles and responsibilities, enforcement of laws, devolution of powers, bye-laws, skilled staff, capacity building and training

Finance : Revenue receipts/expenditure, grants (centre, state), loans (international institutes)

Urban Floor Resilience

WSS Sectors

Framework to test urban flood resilience



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Guwahati

Vishakhapatnam

Indo-Gangetic PlainDesert

Himalaya Highlands

Coastal Plains

This is part of research study whereinFour cities were studied

Two cities are discussed in this article

Guwahati – NE hilly terrainVishakhapatnam – SE coastal plain



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Profile of study cities

• Guwahati, North East Hilly terrain has an average annual rainfall 1,600 mm

• Severe urban flooding since 1980

• Vishakhapatnam, South Eastern Coastal Plain has an average annual rainfall 1,200 mm

• Urban flooding during HUDHUD Cyclone, 2014

• Vulnerable to cyclone, storm surges, sea level rise, tsunami

• Both cities have saucer/bowl shape, due to typical topographical set both cites are vulnerable to urban flooding

Cost of Inaction to Public Utilities:-

• Guwahati – Rs. 224 crore (US$35 million) in two years (2014-15)

• Vishakapatnam – Rs. 255 crore (US$40 million) in single extreme event of Hudhudcyclone (2014)



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Flood vulnerability



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Guwahati, Assam - Baseline

• As per Census 2011, population living in the city was 957,352 (~1.2 million now considering Urban Agglomeration).

• In 2012, slum population residing in slums noted was 139,000 (14%)

• Decadal growth rate – 19%

• GMC area - 216 sq.km., GMDA -262 sq.km.

• Master plan available for 2021

1890 19672010



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DEEPOR BEEL

BRAHMAPUTRA RIVER

BASISTHA RIVER

PAMOHI RIVER

FATASIL HILL

UNIVERSITY HILL

SILAPAHAR

NILACHALHILL

NARKASURHILL

SONAIGHULIHILL

JAPORIGOG HILL

UDAIGIRIHILL RAMCHAI

HILL

KHAGHULIHILL

NARENGIHILL BURAGOSAIN

HILL

RANI RESERVEHILL

KHANAPARARESERVEHILL

KALIHILL

CONTOURS

WATER BODY

NATURAL DRAINAGES

RAILWAYLINE

LEGEND

AIRPORT

NATIONAL HIGHWAY

MAJOR ROADS

Meghalaya Plateau

Slope direction – South to north

N



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Courtesy : GMDA



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Primary reasons of urban flooding

• High rainfall in river catchment areas of southern plateau

• Backflow of flood water from Brahmaputra river – ultimate discharge

• Reduction in carrying capacities of local rivers/drainage with high silt load from deforested hills

• Degradation of natural wetland areas and local water bodies

• Poor sanitation conditions in city – no sewerage infrastructure

• Increased paved areas in the city with urbanization



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Urban flooding impacts

• Shifting of water intake barges in Brahmaputra River

•Damage to water supply infrastructure in hills

• Silt load in septic tanks - overflow

• Submergence of hand pumps and bore wells in flood water

•Water-borne diseases risk



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City Sector Planning Infrastructure Institutions Finance Implementation/O&M

Guwahati

Water Good Poor Very Poor Good Very Poor

Sewage Poor Very Poor Very Poor Very Poor Very Poor

Solid waste Average Poor Good Average Average

Storm water Good Poor Very Poor Good Poor

Appraisal of Guwahati for urban flood resilience



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Current approach - More engineering

Courtesy : GMDA



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Current approach – cont.

Courtesy : GMDA



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Conclusions• 18 Automatic Weather Stations for the City

• EWS & Alarm System for Disaster preparedness

• Drainage Design on basis of Peak Discharge

• SoP for city agencies

Overall –

• Guwahati has started towards achieving resilience as ..

Initiatives of storm water drainage and some resilient measure like raising power supply transformers,

New technologies for de-silting city drains are seen on ground

Short term duration measures have relieved from the urban flooding in 2015-16 Courtesy : GMDA



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Vishakhapatnam, Andhra Pradesh - Baseline

• As per Census 2011, population living in the city was 1.8 million (~2.0 million now considering Urban Agglomeration).

• Slum population 0.56 million (30%) highest in India

• There are 741 slums in the city out of that 286 are notified and 455 non-notified slums.

• Decadal growth rate – 19%

• GVMC – 566.95 sq.km.

• VUDA - 1701sq.km. covering entire GVMC area

• Master plan available for 2025



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19001950

1970

2003 2015



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Damage due to urban flooding

• Rs. 255 crore (US$40million) - Cost of inaction-Public Utilities

• 700 km of water supply line damaged

• 350 km of open drains and UGD line damaged

• Water supply affected with failure of electricity supply and non-availability of generator sets

• Groundwater withdrawal not possible due to no power supply

• Shortage lasted for a week in some areas



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Primary reasons of urban flooding

• Typical topographical set up of city due to which high velocity water gushes from surrounding hills

• Low elevation coastal areas making city vulnerable to storm surge inundation during high cyclonic winds

• Poor coverage of storm water drainage network

• Ignorance of local water bodies in urban planning



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City Sector Planning Infrastructure Institutions FinanceImplementation/

O&M

Vishakhapatnam

Water Average Very Good Very Good Good very good

Sewage Average Poor Very Good Good Poor

Solid waste Poor Very Poor Very Good Good Poor

Storm water

Average Average Very Good Good average

Appraisal of Vishakhapatnam for urban flood resilience

Overall - Vishakhapatnam seems comparatively better positioned to adopt flood resilient measures as the Infra and Finances are good.



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Challenges & Key Learning

• Poor information availability from small and medium sized cities so size range increased

• Lack of systematic documentation of infrastructure damage in cities due to floods

• Lack of information on infrastructure damage/repair and O&M for urban floods . It is mixed and not separated

• No collated information on expenditure to analyze financial cost of resilience

• Lack of awareness on flood resilience among city officials• Projects prepared with engineering solutions and lack eco -system based

holistic / integrated approach• Need for wider outreach of climate resilience knowledge



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Financial cost of resilience

• Five years city budgets were analyzed to see revenue receipts from different sources like own and external sources like central, state and other grants and loans from financial institutions.

• The revenue expenditure for WSS sectors were analyzed for capital, O&M and other expenditure.

• Infrastructure gaps in WSS sectors (critical infrastructure) are important in determining cost of flood resilient WSS sectors.

• Cost of resilience is higher for those cities which have wider infrastructure gaps because this will be over and above the infrastructure gap filling cost.



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Comparison

• In Guwahati there are multiple agencies that manages WSS sectors in the city. Budgets of all the agencies are separately done and there is no collated data available with single agency on income and expenditure incurred by different agencies for new infrastructure projects, O&M etc.

• The data indicates that share of water sector for capital and O&M is quite negligible when compared to entire GMC budget. All new infrastructure projects are being constructed international financial support on loan basis.

• Since there are multiple agencies doing O&M and absence of collated data precise conclusion on financial aspect is difficult.



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Comparison cont..

• In Vishakhapatnam, analysis of budgets was easy as there are no multiple agency

• Per capita annual capital expenditure in WSS sectors infrastructure projects is only 9% considering HPEC

• Per capita annual O&M cost is only 52% of recommendations by HPEC

• This indicates that the O&M is comparatively better in the city and also validates benefit of having single agency in managing city



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Concluding remarks

• The appraisal of three pillars infrastructure, institutions and finance of study cities indicates that the financial cost to make WSS sector flood resilient will include costs for filling existing and future infrastructure gaps.

• In addition to this, the resilience costs will include cost of innovative Green Infrastructure, Institutional strengthening by capacity building to implement new cost effective options and relying on own financial strength.

• City can only afford to spend additional infrastructure expenditure for resilience, if its existing financial burden is low.



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IMPACT OF CLIMATE CHANGE ON

AGRICULTURAL, METEOROLOGICAL, AND

HYDROLOGICAL DROUGHTS

IN THE CENTRAL HIGHLANDS OF VIETNAM

Dao Nguyen Khoi

VNUHCM – University of Science, Vietnam






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Outline

1. Background and objectives

2. Study area

3. Methodology

4. Result and discussions

Calibration and validation of the SWAT model

Climate change scenarios

Analysis of changes in SDF of the hydro-meteorological and agricultural

droughts

5. Conclusions



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Background In recent years, the frequency and severity of the flood and drought due to climate

change have considerably increased (IPCC, 2013)

Under the impact of climate change, studies on monitoring and predicting droughts on along-term scale are necessary to find countermeasures to cope with extreme droughtconditions that may occur in the future (Kim et al., 2014).

The method to monitor and predict drought is using observed hydro-meteorological dataand projected data through outputs of the general circulation models (GCMs) andhydrological model.

Vietnam has faced severe and prolonged droughts, which causes water scarcity and lossof agricultural production with damaged costs of hundreds of billion Vietnamese Dong.

• In the dry season 2015-2016 with the effects of El Niño phenomenon, the CentralHighlands region has faced the most severe droughts in the past 100 years, causingsevere damage to agriculture (FAO, 2016)



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Background (cont.)

Objective

To investigate the impacts of climate change on meteorological, agricultural, andhydrological droughts in the Srepok River Basin in the Central Highlands of Vietnam



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Study area

Area: 12,000 km2;

Climate: tropical climate monsoon,

with 2 seasons: rainy season (May-

Oct) and dry season (Nov – Apr);

Annual rainfall: 1,700-2,400 mm

Soil: 75% basalt soil;

Population: 2.5 million inhabitants

(2013).

Srepok River Basin



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Methodology

Observed climate data

5 GCMs(IPCC-AR5)

RCP 4.5

RCP 8.5Downscaling

(Bias-correction)

Climate change scenarios

(Precip and tmp)

Drought indices(SPI, SSWI, SRI)

Change in SDF of droughts



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Methodology (cont.)Input data

Data type Data description Scale Data sources

DEM Topographical features 90m USGS

Land-use Land-use classification such as

agricultural land, forest, and urban, 2003

1km MRC

Soil Soil types and physical properties 10km FAO

Meteorology Daily precipitation, min and max

temperature in the 1981-2009 period at 9

stations

Daily Hydro-Meteorological Data

Centre (HMDC)

Hydrology Streamflow in the period 1981-2000 Daily Hydro-Meteorological Data

Centre (HMDC)



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Results and discussion



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Calibration and validation of the SWAT model



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Climate change scenarios• In this study, GHG emission scenarios

adopted RCP4.5 (middle) and RCP8.5(high) were used.

• GCM simulation outputs ofSEACLID/CORDEX member countrieswithin the SEA

Centre, country developed GCMs Centre abbreviationCountry used

GCMs

Canadian Centre for Climate Modeling & Analysis, Canada CanESM2 Malaysia

Centre national de Recherches Meteo., France CNRM-CM5 Vietnam

Hadley Centre, UKMO HadGEM2-AO South Korea

Institute Pierre-Simon Laplace, France IPSL-CM5A-LR Malaysia

Max Planck Institute for Meteorology MPI-ESM-MR Thailand



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Climate change scenarios (cont.)

(a) RCP 4.5 scenario

(b) RCP 8.5 scenario



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Analysis of changes in the drought SDF

Historical time series of SPI, SSWI, and SRI



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Analysis of changes in the drought SDF (cont.)

(a) SPI (b) SSWI (c) SRI

Future changes in the severity, duration, and

frequency of the droughts in the RCP 4.5 and 8.5

scenarios



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Conclusions

Climate change scenarios (precipitation and temperature) were generated for the period2045-2070. It is indicated that the climate in the study area would generally warmer andwetter in the future;

Under the possible climate change, the annual and seasonal streamflow would increasesignificantly in the future;

The drought events would generally increase in the future, while the drought severityand frequency would decrease.



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Thank you for your attention!



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ASSESSMENT OF STORM SURGE HAZARD,

VULNERABILITY AND RISK OF THE

AGRICULTURAL SECTOR IN LEYTE, PHILIPPINESEngr. Jon H. Gaviola, The Oscar M. Lopez Center






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INTRODUCTION

http://images.gmanews.tv/v3/webpics/v3/2014/10/640_2014_10_17_09_38_36.jpg

http://www.mb.com.ph/wp-content/uploads/2014/08/1_fish-edt.jpg

This sector shares about 10% of the country’s gross domestic product (GDP)and around 30% for its work force (Philippine Statistics Authority, 2016).

Agriculture plays a significant role in the Philippines.



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http://irri.org/images/blogs/20131113-typhoon-impact.jpg

http://philnews.ph/wp-content/uploads/2013/11/NN1.jpg

Typhoon Haiyan (local name Yolanda)

• 600 fatalities (91% from Leyte) and

• damage exceeding US$ 2 billion

Specific to the agricultural sector

• damage exceeding US$ 200 M

• about 77 000 hectares of land



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DOST Project NOAH (2014)

Most vulnerable coastal areas

Samar, Leyte, Iloilo, Palawan, Cebu, Negros, Bohol, Bicol, Quezon, Metro Manila, Bulacan, and Surigao

Recommendation from this study

DETAILED STORM SURGE STUDIESTO IMPLEMENT APPROPRIATESITE SPECIFIC SOLUTIONS



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• This study utilizes light detection andranging (LiDAR) technology in modellingthe storm surge hazards

• It targets to produce high resolution stormsurge model and maps that will help invisualizing the hazard, vulnerability andrisk.



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MATERIALS AND METHODS

Study Area

The study was conducted in Palo, one of the forty municipalities

of the province of Leyte. Located in the northeastern section of

the province and south of the capital city of Tacloban, it has a

total area of 22127 hectares with an estimated population of

70,052 as of 2016 (Philippine Statistics Authority, 2016). The

farming barangays (villages) are Baras, Candahug, Cogon,

Guindapunan, Salvacion, San Fernando, San Joaquin and

Tacuranga.



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Data Used

1 meter resolution 1 km resolution

ELEVATION DATA



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Haiyan as meteorological dataFrom JWTC Best Track Data

+

Other Data Used

spatially-varying amplitudes and phases of tidal forces



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The development of the storm surge models for the study site

was done using Delft3D. Computational grids along the

coastline of the study area were used to set-up the

hydrodynamic model. Delft3D-RGFGRID was used to generate

and refine the grids using splines. The bathymetric and

topographic elevation data were then interpolated on the

computational grids using Delft3D-QUICKIN.

Storm Surge Modeling

Finally, using DELFT3D-FLOW, the flow of water in the

coastal area was simulated. It was generally dictated by the tidal

forces on the open boundaries as well as wind stress of the

meteorological data.



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Hazard

The results of the modeling phase were then exported into an

ArcGIS format for the post processing and analysis. To

transform the depth values into a hazard level, the classification

of storm surge heights was implemented. This was in reference

to an average height of a Filipino.

Height

(m)

Class GIS Value

0-0.50 Low 1

0.51-1.50 Medium 2

> 1.5 High 3



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Vulnerability

• Vulnerability (V) is a function of exposure (E), adaptive

capacity (AC), and sensitivity (S) (IPCC, 2014).

• ArcGIS Spatial Analyst will be used to facilitate the rating

procedure involving ranking of multiple datasets into one

representative dataset which would be the final output, the

vulnerability map.

• Each barangay will then be rated and ranked according to

their exposure, adaptive capacity and sensitivity scores.

• Sources for these data will be focus group discussions (FGD)

to be conducted as well as secondary data obtained from

barangays and locality concerned.

V = % E + % AC + % S



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• Adaptive capacity will be rated based on focus group

discussions involving the smallholder farmers and fisher

folks exposed to storm surge hazards.

• General questions regarding awareness of climate change,

access to climate and weather information, access to capacity

building programs, among others will be asked to the

participants.

• The answers will be patterned to the description and rank

prepared for them for uniformity and standardization of

answers.

• After post-processing of FGD results, fields for each

question shall be added to the attribute table barangay

shapefile and be ranked based on the table.

Rank GIS Value

Not at all (No) 5

A little 4

Somewhat 3

A lot 2

Extremely (Yes) 1

Ranking of data for adaptive capacity measure

Value Class

1 Very High

2 High

3 Moderate

4 Low

5 Very Low

Adaptive Capacity Index



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The indicators to be considered in exposure index were

distance from coast and elevation of the barangays.

These factors will be based from the spatial data available for

analysis such as LiDAR data, base maps and administrative

boundaries of barangays in the locality.

For the sensitivity index, series of informant interviews (KII)

and secondary data gathering will be conducted to collect

necessary data such as percentage of agricultural land, fraction

of household engaged in the agriculture sector, crops and

fishery among others.

𝐈𝐢 =𝐗𝐢 −𝐌𝐢𝐧𝐗𝐢

𝐌𝐚𝐱𝐗𝐢 −𝐌𝐢𝐧𝐗𝐢

Normalized

Value, %

GIS Value

(exposure)

GIS Value

(sensitivity)

Class

0-20 5 1 Very Low

21-40 4 2 Low

41-60 3 3 Moderate

61-80 2 4 High

81-100 1 5 Very High

Exposure and Sensitivity Indices.

V = % AC + % E + % S



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Risk

• Since storm surge risk is a function of storm surge hazard

and storm surge vulnerability, the formula will be used below

to finally determine the risk values for each barangay.

• It will finally be classified into specific risk level based on

the table.

RiskSS = Hazardss * Vulnerabilityss

Range Classification

0-3 Very Low

4-6 Low

7-9 Moderate

10-12 High

13-15 Very High

Storm surge risk classification.



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RESULTS AND DISCUSSION

• The figure displays the storm surge

simulation results for Palo using the

parameters from Typhoon Haiyan. The

inundated area was displayed reaching

up to 2 kilometers from the shore. The

depth caused the storm surge brought

about the super typhoon has reached up

to 7.9 meters.

• The agricultural barangays namely

Baras, Candahug, Cogon,

Guindapunan, Salvacion, San

Fernando, San Joaquin and Tacuranga

were affected by the storm surge.



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• The simulated storm surge heights were

translated to a hazard map that visualize

areas that are exposed different levels of

hazards specific to storm surge.

• A flood height of 0.5 meters and below is

classified as low; 1.5 meters and above

as high; and between 0.5 and 1.5

meters as medium.



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Series of focus group discussion with the smallholder farmers and fisher folks as well as gathering of secondary data to

assess their vulnerability to the climate change and expected extreme events as manifested by stronger and higher storm

surges in the future.



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3 years after Haiyan…



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Agricultural lands and

smallholder farmers in Leyte

Damaging impacts of storm

surges

Using LiDAR technology in the

process of identifying vulnerable

areas to storm surges

Future scenarios under climate

change (how sea level rise will

intensify storm surges)

Documentation of potential

adaptation strategies

EXPECTED OUTCOMES:

Science-informed policy makingEnhanced adaptive capacity

Gender-sensitive adaptation measures

Better yield

Higher productivity

Increased income

Stabilized food supply

TARGET OUTCOMES



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CONCLUSION

This study has exhibited the use of light detection and ranging (LiDAR) technology for producing

higher resolution inundation maps to expose the agricultural areas most prone to storm surges in the

future.

The flood heights caused by the storm surge can actually be translated into hazard levels. Low, medium

and high level of hazards were mapped out to show different exposure levels to the climate risk being

studied which is storm surge.

Hazard maps can actually be analyzed with vulnerability factors such as the interaction of human

population to the physical and social environment to display the risk of the area being studied.

Vulnerability and risk assessments specific to storm surges are very important to the agricultural sector

in order for them to better adapt and prepare to the changing climate.



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for more information, contact:

Engr. Jon H. GaviolaResearcher, Oscar M. Lopez Center [email protected]

755-2332 loc. 2652

End of Presentation

mailto:[email protected]



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SELECTING MULTI-FUNCTIONAL GREEN

INFRASTRUCTURE TO ENHANCE RESILIENCE

AGAINST URBAN FLOODS

A. Alves, A. Sanchez, B. Gersonius, Z. Vojinovic

UNESCO-IHE, Delft, The Netherlands






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• BACKGROUND

• CONCEPTUAL FRAMEWORK

• MEASURES SELECTION TOOL

• SELECTION TOOL APPLICATION AND PRELIMINAR RESULTS

• NEXT STEPS

• CONCLUSION

OUTLINE



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BACKGROUND

Urbanization growth:

Climate Change:

Projections of relative changes in runoff by the end of 21st century (IPCC, 2007)

Main urban agglomerations, with more 50% of world population (WB, 2007; UN, 2014)Runoff increment as a consequence of urbanization

Changing trends of precipitation

Number of reported flood events per year (WB, 2012)



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Traditional approach:

• Mainly based on grey infrastructure.

• Single oriented approach, focused on removing water from streets as fast as possible.

• Not flexible systems, based on design event methods which offer low future adaptation capacity.

• Reliable and largely tested systems.

• Enough methods, tools and resources for designing.

• High technical and social acceptability.

BACKGROUND



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Sustainable approach:

• Based on green infrastructure.

• Multiple benefits approach, focused on runoff reduction besides other co-benefits for the urban environment.

• Flexible systems, present high adaptation capacity under uncertain future.

• Lower reliability in front of extreme events.

• Tools, design methods and performance evaluation still under development.

• Are not easily chosen by decision makers.

BACKGROUND



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STORAGE MEASURES

GREENINFRASTRUCTURE

BLUE INFRASTRUCTURE

BUFFERING AND INFILTRATION

BUILDINGS FLOOD PROOFING

TRADITIONAL MEASURES

Knowledge Base PEARL Project – All possible measures

Local constraints analysis:• Soil type• Groundwater depth• Drainage area slope• Type of sewer system• Urban configuration

Hazards Analysis

Flood Type: • Fluvial• Pluvial• Coastal• Flash• Groundwater

Measures Screening

Suitability analysis:• Public spaces• Population density• Land use• Treatment plant /

CSO

Multiple benefits /Local preferences:• Water quantity reduction• Water quality improvement• Environmental benefits• Liveability enhancement• Cost minimization• Economic benefits• Socio-cultural benefits

Measures Ranking

CONCEPTUAL FRAMEWORK FOR MEASURES SELECTION



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MEASURES SELECTION TOOL: SCREENING

Flood type

Local constraints for measures application

Shorter list of measures



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MEASURES SELECTION TOOL: SUITABILITY RANKING

Local characteristics for suitability analysis

First ordered list of measures

Scores



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MEASURES SELECTION TOOL: BENEFITS/PREFERENCES RANKING

Co-Benefits weights according to local preferences

Second ordered list of measures

Scores



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SELECTION TOOL APPLICATION AND PRELIMINARY RESULTS

Ayutthaya

Medium population density, heritage and touristic site, fluvial and pluvial floods.

(Meesuk et al., 2015)



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SELECTION TOOL APPLICATION AND PRELIMINARY RESULTS

SukhumvitHigh urbanization and population density, mainly commercial and business area, frequent pluvial floods.

(Shrestha, 2013)



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NEXT STEPS

How to combine selected measures?

• Type of applicability: permanent or temporal measures.

• Implementation scale: centralized or decentralized approach.

• Hydrological impact: source control or end of pipe solutions.

• Main runoff reduction process: storage, evapotranspiration, infiltration, etc.

Criteria for measures combination:



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CONCLUSIONS

• This work presents a methodology for screening and ranking measures against urban floods.

• The screening process is based on the analysis of local flood problems and local conditions and

features of the urban space.

• The method also considers the co-benefits achievable through the implementation of green

infrastructure and local preferences for those benefits.

• In order to facilitate the implementation of the developed methodology, a decision support tool

has been developed.

• This tool was applied in two different study areas, obtaining different preferred measures which

are in accordance with the characteristics of each case.

• The outcome of this work is seen as useful for helping decision making processes focused on

reducing urban flood risk in a sustainable way, allowing the improvement of other

environmental aspects.

• Further work will continue with the validation and improvement of the process, as well as with

the analysis of measures combination in order to develop long term sustainable strategies.



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THANK YOU!!

Alida Alves: [email protected]



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ADAPTATION TO CLIMATE CHANGE IN AREAS

WITH CULTURAL HERITAGE

Z. Vojinovic1, D. Golub1, W. Keerakamolchai1, 2, W. Meesuk1, A.

Sanchez Torres1, S. Weesakul 2

1UNESCO-IHE, Delft, The Netherlands2 Asian Institute of Technology, Pathumthani, Thailand






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• FRAMEWORK

• PROJECT

• STAKEHOLDER ANALYSIS

• HAZARDS, VULNERABILITIES, RISK

• MEASURES

OUTLINE



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• Flood risk analysis for urban areas with cultural objects

• Tangible and Intangible aspects

• Social and cultural contexts

Framework



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Framework



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N Limit: 14 ⁰ 23’26”S Limit: 14 ⁰ 18’49”E Limit: 100 ⁰ 35’20”W Limit: 100 ⁰38’00”

Slide 12

Project Area – Ayutthaya Island



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2011 Rainfall Analysis: 5 storms occurred, namely: HAIMA (26 Jun.), NOCK-TEN (31 Jul.),

HAITANG (28 Sep.), NESAT (30 Sep.) and NALGAE (5 Oct.)

HAIMA

NOCK-TEN

HAITANGNESAT

NALGAEAyutthaya

Rainfall from HAIMA (25 June 11)

Rainfall from NOCK-TEN (31 July 11)

Source : Hydro and Agro Informatics Institute, Thailand



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2011 Floods - Ayutthaya



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2011 Floods - Ayutthaya



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Framework - steps

Stakeholder analysis Risk analysis Communication



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identification → categorisation → interdependencies analysis

Stakeholder Analysis



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Hazard AssessmentModels 1 & 2:1 . Tachin and Chao Phraya River.2. Only Chao Phraya River.

New 1D Model (3):3. Ayutthaya Heritage Site and proximities.

1 2

3



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Water depth (m)

1D/2D: Maximum Flood-depth Resultswith bypass

Improvement options 1

Current situation

Animation!!!

Hazard Assessment

Improvement options 2 Improvement options 3



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Hazard Assessment



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Vulnerability Assessment



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SOCIAL VULNERABILITY

Source: Google Images

- Large scale survey;

- Eight parameters were used

to calculate social vulnerability;



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SOCIAL VULNERABILITY




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PHYSICAL VULNERABILITY – vulnerability of the built environment

Cultural properties

Residential buildings

Identification of buildings' types

• residential

• commercial

• public

• cultural

Identification of vulnerability parameters

• type of structure (e.g. single or two -storey, flood protection, etc)

• condition

• building content

Categorisation

• low

• medium

• or high level of vulnerability



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IMPACT ASSESSMENT – tangible damages: depth-damage curves

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

0 0.5 1 1.5 2 2.5 3 3.5 4

Esti

mat

ed d

amag

es [

THB

]

Depth of inundation [m]

Single-storey houses Two-storey houses Pillar houses

Property’s area:< 50 m2 ≥ 50 m2

Type of structure



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PHYSICAL VULNERABILITY




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Social vulnerability assessment at the community level

Flood awareness

14%

Flood preparedness

16%

External Support

9%Community cohesiveness

and education11%

Health14%

Property15%

Income /livelihood12%

Vulnerable groups

9%

Cap

acity

Susc

epti

bili

ty

(flo

od

eff

ect

on

dif

fere

nt

do

mai

ns)

8 groups of social vulnerability parameters and their weights.

Identification of communities

• 33 communities

Data collection

• Focus Group Discussions with communities’ representatives

• Questionnaire

Evaluation of vulnerability

• Qualitative and quantitative analysis of questionnaire responses



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Parameters for economic vulnerability assessment:

• time period of complete shutdown of the business due to a flood event;

• time period when a business experiences downsizing caused by a flood event; and

• an operational capacity during downsizing phase.

ECONOMIC VULNERABILITY

Identification of economic activities

• tourist–oriented / non-tourist

Data collection

• Interviews with business owners

• Government and insurance

Evaluation of vulnerability

• Analysis of three parameters

VS denotes vulnerability score,

I denotes income level before the 2011 flood event [Thai Baht per month],

Tsd denotes shutdown phase [months],

Td denotes downsizing phase [months],

Cd denotes operational capacity during a downsizing phase [in comparison to I].



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ECONOMIC VULNERABILITY




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CULTURAL VULNERABILITY

59525252

5145

4342

3132

2525

2019

1513

1211

88

66

544

22

11

0 20 40 60

Chantharakasem National Museum

The Ancient Palace

Wat Phra Si Sanphet

Phet Fortress

Old City Wall

City Shrine

Wat Suwan Dararam

Somdet Phra Srinakarindra Park

Wat Rattanachai

Million Toy Museum

Wat Yan San

Wat Som

Wat Pra Sat Thong

Wat Khun Mueng Jai

Wat Je di Thong

Number of responses

The degree of a property's sensitivity to flooding

very low low medium high very high

Level of a

property's

significance

very low very low low low medium medium

low low low medium medium high

medium low medium medium high high

high medium medium high high very high

very high medium high high very high very high

A matrix which defines different levels of cultural vulnerability based on the properties' significance and their sensitivity to flooding.

Residents’ opinion about cultural significance

Assessment of individual significance of different cultural properties in Ayutthaya

(UNESCO and ICOMOS, Structural Assessment)



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CULTURAL VULNERABILITY




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COMBINED VULNERABILITY



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COMBINED VULNERABILITY



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RISK ASSESMENT



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RISK ASSESMENT - CALCULATED



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RISK PERCEPTION“Areas at higher risk of floods?”

Group mapping exercise

3 colours represent different levels of risk:

light tint – low level,

moderate tint – medium,

rich tint – high level.

“Critical depth of flood waters?”

“Acceptable levels of risk?”



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RISK PERCEPTION



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RISK COMPARISON



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RISK ASSESMENT – Calibration # 1



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RISK ASSESMENT – Calibration # 2



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RISK COMPARISON



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http://www.pearl-fp7.eu/



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THANK YOU!!

Zoran Vojinovic: [email protected]



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WHEN HAZARDS BECOME DISASTERS: THE CASE

OF COASTAL FISHING COMMUNITIES IN

BANGLADESHMAHMUDUL ISLAM, M. MOSTAFIZ, P. BEGUM Sylhet Agricultural

University






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Background

• Bangladesh is the 5th most at risk country in the world in terms of disasters (World Risk Report 2016)

• About 97.1% of the total area of Bangladesh and 97.7% of its total population are at risk of multiple hazards (World Bank, 2005)

• Cyclones, saline water intrusion, water logging, landslides, and arseniccontamination pose substantial threats to the livelihoods of the coastal inhabitants (Lazar et al., 2015)

• 14 percent of GDP Bangladesh is exposed to disasters. Each year the country incurred 1.8 percent of GDP loss due to natural disaster (CDMP II 2016)



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Background

• About 80–90% of global losses and 53% of global cyclone-related deaths occurred in Bangladesh (GoB, 2008)

• Hazards do not necessarily cause a disaster. Instead disasters occur by a mix of physical exposure and socio economic pressures

• Small-scale fishers are among the most vulnerable professional groups . Because they

live close to coastal water, heavily dependent on climate sensitive fisheries usually “poorest of the poor” involved in “low profile” profession



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Study areas and methods

•Using DPSIR as analytical tool, this study explores the mechanism of disaster vulnerability of coastal fishers in the Southern Bangladesh

• Semi-structured interviews (59)

• Four communities (from Borguna and Patuakhali districts) dependent on Hilsa fishing

• Secondary Data from Bangladesh Meteorological Department and Department of Fisheries



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DPSIR Framework

The DPSIR (Drivers-Pressures-State-Impacts-Responses) framework was developed for assessing the causes, consequences and responses to change in a holistic way (EEA, 1999; Atkins et al. 2011).



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Drivers • River mouth system in the low-lying coastal ecosystem. Thus vulnerable to climate related hazards, coming from

both sea and catchment areas

• Majority of the interviewed households are found functionally landless, a section them live on the government (khas) land in densely settlement



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Drivers

• Repeated exposure to cyclones originating in the Bay of Bengal

• From 2007, seven cyclones made landfall on the Bangladesh coast suchThe track of major cyclones (1985-2009) (Chowdhury et al. 2012)

•

• The track of cyclone



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Drivers

• Increasing numbers of low pressure system means that increasing number of days of rough weather which hinders traditional fishing in the open sea

Total Duration of Depression Events (X axis) and Number of Total Days of Rough Sea (Y axis) per Month between 1975 and 2015 (Islam 2015)



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Drivers• Patron-client relationship is most dominant in the communities that wielded most social

power in the area

• Socio-economic profiling, for most of the respondents, monthly income is close to poverty line

• Longer distance from cyclone centers and reluctance to move

• Stability and effectiveness of the embankments in the face of repeated extreme events.

© P. Gain



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Pressures• Cyclones destroyed standing crops, productive assets for fishing, collapsed houses and other

physical infrastructures that shattered local economy,

• Frequent cyclones and depression in the bay often force fishers to abandon their fishing trip and return to coast. Incomplete fishing trips incur a substantial financial loss

• Loss to be 76,000 BDT (950 US$) for a 10-day fishing trip



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State

• The coastal population of Bangladesh has doubled since the 1980s

• Agricultural productivity drastically reduced , coastal fisheries ecosystem suffer over-exploitation

• Intense competition cause reduction in catch-per-unit-effort



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Impacts and response

• Overall ecological environment degraded

• Prolonged saline water stagnation cause agricultural land unsuitable

• Loss and damage fishing assets decimated the capacity to go for fishing and immediate survival

• Destructive fishing practices rises significantly since majority of the fishers are indebted and are desperate to restore their livelihoods

• Displacement and migration

The population density (754/square km) of the southern zone is relatively lower compared to Bangladesh (964/square km) (BBS, 2011), largely because of out migration (GoB/FAO, 2013)



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Summary

• River-mouth system in coast is particularly vulnerable to disaster coming from both sea and catchment areas

• Hazards are recurring and cumulative, making vulnerable to disaster

• Communities suffer several shocks within a short period, and/or multiple simultaneous pressures,

• The pace of change outstrips adaptive capacity of suffered population and local institutions



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Reflection• The fishing communities, by their very nature are the first victim of cyclone

and more prone to water borne hazards

• The implementation of the implementation of the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries will be a right step forward

• The Guidelines asks for urgent and ambitious action, in accordance with the objectives, principles, and provisions of the United Nations Framework Convention on Climate Change (UNFCCC)

• Better coordination, between GoB and NGOs and among the NGOs response and recovery programs could have produced better utilization of limited resources and resulted in distribution of services and resources equally to all the affected parties that would ultimately benefit the vulnerable population in the coast



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Thank you



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THE USE OF AGENT BASED MODELS FOR CLIMATE

CHANGE ADAPTATION AND DEVELOPMENT OF LARGE-

SCALE EVACUATION STRATEGIES FOR FLOOD RISK

MITIGATIONNeiler Medina (a) Arlex Sanchez (a) Zoran Vojinovic (a) Alida Alves (a)

(a) UNESCO-IHE, Westvest 7, Delft, Zuid Holland, 2611AX, The Netherlands






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Outline

• Motivation of the work

• Scope of the research

• Methodology

• Preliminary Results

• Conclusions



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Motivation!



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The scope of this research is to explore new andnovel techniques that can be used to betterunderstand which are the key components in theformation of risk and which of those are the mostsignificant in order to reduce people’s risk tofloods. More specific the research focus on howcan we improve evacuation strategies

Scope



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Methodology

To better understand the key components of people’s risk toflood and determine which are the most sensitive parametersto have better evacuation plans we are using a state of the artAgent Base Model (ABM) to allow us to study and modelindividual and crowd behaviour under extreme Hydro-Meteorological events (Floods) in coastal Areas before andduring a disaster is unfolding.



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Methodology

The key challenge for a successfulEvacuation is Information…. In cityevacuation Not only the message contentmatters but the way the message is deliver,accepted and understood by individualsand the community plays a major role inthe effectiveness of the evacuation itself. Inaddition lead time plays a major role in theeffectiveness of the evacuation itself.



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Methodology



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Methodology

Social Model

Hydrodynamic Model

Flood Warning

How people willbehave under floodthreat

Dissemination StrategyDissemination MeansMessage Content



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MethodologyBuilding the Behavioral Model

Agent and Environment General Characterization



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Building the Behavioral ModelModel Formalization and parameterisation

Methodology

Phase 1 – Daily Behaviour

Threat

PERCEPTION SENSOR

BEHAVIOUR

Social Status

Z F

Cognition

Z F

Emotion

Z F

Physis

Z F

ACTOR

Z: State Causal DependenciesF: State Transition Function Information Flow

Phase 2 – Cognitive Behaviour

Internal structure of PECS reference model (from [1]) [1] C. Urban and S. Bernd, ‘PECS–Agent-Based Modelling of Human Behaviour’, in Emotional and Intelligent–The Tangled Knot of Social Cognition, North Falmouth, MA, 2001



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To explore and evaluate how the provision of warning information can affect theevacuation response and behaviours, 4 different scenarios were set up in the ABM builtin using Repast-Simphony:

Baseline scenario: The agents evacuate according with the initial set of rulesof the ABM, no new information is given to the agents in how to evacuateand the evacuation will be performed based on the "existing" knowledge ofthe agent.

Scenario 1: A message with the warning and evacuation was sent at thesame time to all the population in the island.

Scenario 2: A message with the warning and evacuation was send graduallyto all the population in the island, known as stage evacuation.

Scenario 3: A message with the warning and evacuation was sent only tothose inhabitants that reside and/or work in the areas to be expected beaffected by the hazard event.

Methodology - Implementation



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ABM – Sint Maarten.



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Preliminary Results – Scenario Analysis




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Adjusted mortality functions based on:

People Vulnerability à F[Age(h, w, As), gender, health]

Building Vulnerability à F[Material, Year]

Car “Vulnerability” à F[Stability on Water] - Type of Car

- Terrain Slope - Hazard

“Mortality” Function




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1. ABM has proved to be a powerful tool to test several evacuation strategiesregarding Warning messages content, dissemination Means anddissemination strategies.

2. ABM for city evacuation allows authorities to detect the need to improve(update) evacuation plans, also to identify the need to improve currentinfrastructure (roads, shelters capacity and location, etc.)

3. A good synergy between social sciences and engineers is needed to havebetter evacuation models that can be used in real life.

4. At this point a generic ABM for city evacuation seems to be an impossibletask.

Conclusions



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[email protected] Medina P.

Documents

Page Title Institut 002-035 MICRO LEVEL VULNERABILITY