Bhubaneswar Final Report DRM

United Nations Development Program
Enhancing Institutional and Community Resilience to

Disasters and Climate Change
Hazard Risk and Vulnerability Analysis (HRVA) of

the City of Bhubaneswar (Odisha)
Final Report
November 2014
Multi-Hazard Risk and Vulnerability Analysis for the City of Bhubaneswar, Odisha
For the attention of:
Emergency Analyst and Officer-in-

Charge, DM Unit, UNDP,
55, Lodi Estate, New Delhi – 110 003
Final Report Confidential Page 2 of 174

Document Control
Version Date Author(s) Brief Description of

type of changes
1.0 Oct 2014 Sushil Gupta, Draft

Muralikrishna,
Rupesh Sinha,
Niva Srivastava,
Ujjwal Sur, Vijaya
Lakshmi, Indu
Jain, Uttam Singh,
Kishor Dhore,
Murthy MVRL,
Upamanyu
Mahadevan
2.0 Nov 2014 Sushil Gupta, Final

Muralikrishna,
Rupesh Sinha,
Niva Srivastava,
Ujjwal Sur, Vijaya
Lakshmi, Indu
Jain, Uttam Singh,
Kishor Dhore,
Murthy MVRL,
Upamanyu
Mahadevan

Acknowledgements
The consulting team extends its appreciation to UNDP, India for awarding this assignment to
us. We would also like to acknowledge the valuable guidance and support of Mr. G.
Padmanabhan, Mr. Ashok Malhotra, Ms. Abha Mishra and Mr. Prasad Babu of UNDP, India
for their discussions and sharing of thoughts during the various meetings on the project.
We would also like to acknowledge our sincere gratitude to Dr. Krishan Kumar, IAS, City
Commissioner, Municipal Corporation for extending all possible support in conducting this
assignment. We extend our sincere thanks to Dr. Kamal Lochan Mishra, Chief General
Manager, OSDMA for extending all help in getting data from different departments. We
extend our sincere thanks to all the city officials for providing their valuable support during
data collection activity and final workshop and to Mr. Meghanad Behera, City Project
Coordinator, UNDP for his kind help in coordinating with various Govt. departments.
We also acknowledge GIS division of Bhubaneswar Municipal Corporation (BMC) for sharing
of the GIS data.

Executive Summary
The Hazard Risk and Vulnerability Analysis (HRVA) for the city of Bhubaneswar, Odisha has
been carried out as part of the on-going GOI-UNDP project “Enhancing Institutional and
Community Resilience to Disasters and Climate Change.” It aims to reduce disaster risks in
urban areas by enhancing institutional and community resilience to disasters and climate
change by integrating Disaster Risk Reduction (DRR) measures in the development
programs as well as to undertake mitigation activities based on scientific analysis.
This report provides findings of the hazard risk and vulnerability assessment of key natural
hazards the city is exposed to, namely – Cyclonic wind, Flood, Earthquake, Heat wave, and
Epidemics. Quantitative modeling techniques based on GIS were used for mapping and
analysis using standard public domain models. Based on these results, recommended
actions for various mitigation and adaptation measures were provided in the last section.
City Profile:
Bhubaneswar, the capital of Odisha state, is also popularly known as the "Temple City of
India". It is located on the Eastern Ghats, about 40 km west of North Bay of Bengal (with an
average elevation of 45 meters above mean sea level) in Khordha district. It lies on the west
bank of river Kuakhai, which is a tributary of River Mahanadi that flows about 30 km
southeast of Cuttack. The river Daya branches of Kathjodi and flows along the southeastern
part of the city. The city has a spatial spread 135 sq. km with 67 Census wards and a
population of more than 8 Lakhs. It has a population density of 6,228 person/ sq km.
City Asset:
The city has more than 197,000 households and about 75% of the buildings in the city are
under residential use. The city has several commercial and industrial establishments; about
14% and 2.5% of the total buildings are under commercial and industrial use, respectively.
The city has a total road network of about 1,642 km of which about 51 km is national
highway passing through the city. The total length of rail network in Bhubaneswar city is
about 34 km and the only international airport is located at a distance of 3 km from the city
center. The city has 1,171 educational institutions and 667 health centers. The city, being a
temple city, has a large number of religious places (more than 117 sites). The total estimated
value for main exposure elements for Bhubaneswar city is more than INR 151,452 Crores,
out of which Residential, Commercial, and Industrial exposure is about INR 91,085 Crores,
INR 43,707 Crores, INR 4,353 Crores, respectively. The exposure value of essential facilities
(health care centers, educational institutions, etc.), and transportation systems are estimated
at about INR 4,985 Crores, and INR 7,186 Crores, respectively.
Hazard mapping and analysis:
For the past three decades, the state of Odisha and in particular, Bhubaneswar city has
been experiencing unprecedented contrasting extreme weather conditions; from heat waves
to cyclones; from droughts to floods. In the year 1998, the State of Odisha faced an
unprecedented heat wave situation, because of which 2,042 persons lost their lives. Though
extensive awareness campaigns have largely reduced the number of casualties during post
1998 period, a good number of casualties are still reported each year. It has become a
menace during peak summer. The temporal analysis of daily temperature data for the past
three decades shows a steady increase in city temperature. During May 2013, a maximum
temperature of 47oC was recorded at Bhubaneswar. Most of the districts in Odisha, on an
average, recorded 40ºC during April 2014 and, the temperature across a few districts in
coastal Odisha reached 46ºC by the end of May. Very severe heat stress conditions
prevailed in May / June in 2014. In fact, Bhubaneswar has become one of the hottest Indian
cities in recent times.

Floods and water logging in the low-lying areas of the city have also become common due to
unplanned growth of the city. The south-west monsoon contributes a considerable portion of
heavy rainfall in both onset and withdrawal phases, which generally lead to flash floods in a
short time. Analysis suggests that rainfall amount per event has varied from 100 mm to 520
mm and the frequency of flash floods across coastal Odisha has increased considerably
from 1960 onwards. Most of the heavy rainfall events occur in July and August. The region,
extending from the central part of coastal Odisha in the southeast towards Sambalpur district
in the northwest, experiences higher frequency and higher intensity of heavy rainfall with
less inter-annual variability. It is because low-pressure systems develop over north-west Bay
of Bengal with minimum inter-annual variation and the monsoon trough extends in west
northwesterly direction from the centre of the system.
During the period from September to November each year, a number of cyclonic storms
originating in the Bay of Bengal overrunning the city also bring gales and heavy rains during
the northeast monsoon. According to a United Nations Development Programme (UNDP)
report, its wind and cyclone zone is at a "very high damage risk". During late withdrawal
phase of the south-west monsoon season on Oct. 30, 1999, Bhubaneswar city received a
record 43 cm of rainfall in a few hours. Again, in the year 2009, rapid formation of meso
scale disturbances due to strong low-level convergence of moist air from the Bay of Bengal
in the presence of convectively unstable stratification was responsible for heavy rainfall,
which led to widespread heavy rainfall events. The 1999 Odisha cyclone caused major
damages to buildings and city infrastructure, and loss of human lives.
The flat coastal belts with poor drainage, high degree of siltation of the rivers, soil erosion,
breaching of embankments and spilling of floodwaters over them cause severe floods in the
river basin and delta areas. This problem becomes even more acute when floods coincide
with high tide. The entire coastal belt in Odisha is prone to storm surges. However,
Bhubaneswar city, being about 40 km from the coast, does not suffer from storm surge. The
storms that produce tidal surges are usually accompanied by heavy rainfall making the
coastal belt vulnerable to both floods and storm surges. A good number of people, nearly
300 persons, succumb to death due to thunderstorm lightning in the State of Odisha every
year.
According to the Bureau of Indian Standards (BIS) IS:1893 (2002, 2014) on a scale ranging
from I to V in order of increasing susceptibility to earthquakes, the city lies in seismic zone
III, which is a moderate seismic risk zone (BMTPC, 2006). In spite of the moderate, non
damaging earthquakes observed so far in and around Bhubaneswar, it cannot be said
confidently that higher intensity earthquakes are unlikely. Recently, on May 21, 2014 an
earthquake of magnitude 6 on the Richter scale occurred in the Bay of Bengal, which was
severely felt in different parts of Bhubaneswar city due to local soil-amplifications, though
there was no report of any significant damage in the city.
Recent studies have indicated that the Mahanadi river valley is faulted and could be a
potential earthquake source. Besides this, the Sumatra fault zone and tectonic plate setting
along the Andaman and Nicobar Islands and Burma Micro-plate boundaries in the eastern
part of the Bay of Bengal pose potential threats of tsunami for the coast of Odisha. However,
the city, being about 40 km from coast, is not prone to Tsunami.
Application of hazard maps for city development and disaster management:
Hazard mapping and analyses help in identifying areas that are prone to various hazards –
both in terms of intensity and in terms of probability. This also helps the City in taking
appropriate site-specific short, medium, and long-term mitigation measures, which include
both structural and non-structural measures. It would also help the city administration to
mainstream DRR activities in the city development process.

Vulnerability Assessment:
Assessment of physical vulnerability to various hazards, and social vulnerability and
environmental vulnerability in general of the city was assessed based on identified indicators
and field observations. Different vulnerability functions for cyclonic winds and earthquakes
were used for risk assessment, based on analysis of the building typology in the city. For
social vulnerability, socio-economic indicators were considered based on secondary and
field based information. The Social Vulnerability Index (SoVI) is developed for Bhubaneswar
city. Poverty and gender seem to have a high influence on the social vulnerability in the city.
There is a high correlation between high social vulnerable groups to low income areas,
particularly slum settlements of the city. In livelihood vulnerability analysis, three types of
analysis, i.e. impact of heavy rainfall, high temperature and heat wave were carried out. The
city’s rapid growth has converted vegetative areas, low-lying water bodies, and open spaces
into built-up spaces. Built up environment has increased the rainfall run off, leading to water
inundation problems in many parts of the city and during summer month increase the heat
wave situation of the city due to back radiation.
Risk Assessment
The risk assessment is based on the hazard, exposure and vulnerability analysis. Risk
assessment has considered five hazards (cyclone winds, earthquakes, floods, epidemics,
and heat waves). However, due to non-availability of storm water drainage network data, and
ward level data on epidemics, risk assessment could not be carried out for these hazards.
The capacity assessment analyzed needs of the city administration and community in terms
of response and preparedness. The risk analysis and capacity assessment helped
in deriving the key recommendations for the city for risk mitigation and preparedness to
devise short, medium, and long-term strategies for the city as well as communities at large.
Risk assessment was carried out using probabilistic method for cyclonic winds and
earthquakes. For earthquakes, probable Maximum Losses1(PML) were estimated for the 475
years return-period hazard scenario. For cyclonic winds, PML losses were estimated for 5,
10, 25, 50, and 100 years return-period hazard scenarios. Risk matrix by hazard was
developed based on estimated losses and damages attributable to each hazard.
Earthquake risk assessment: The PML for buildings were estimated based on occupancy
and replacement costs for different building types. The estimated losses are to the order of
INR 6,009 Crores for residential buildings, INR 3,892 Crores for commercial buildings and
INR 297 Crores for industrial buildings in a 475-year return period earthquake hazard
scenario. The estimated PML losses are highest for schools/college buildings (to the order of
INR 346 Crores). For hospitals and places of worship, the estimated PML losses are to the
order of INR 64 Crores and INR 95 Crores, respectively. In this scenario event, the
estimated casualties (serious injuries including fatalities) are estimated between 1,933 –
2,075 people for the entire city.
Cyclone wind hazard risk assessment: Due to 100-years return period cyclonic wind
hazard, the PML for buildings were estimated based on occupancy and replacement cost for
different building types. The estimated losses are to the order of about INR 111 Crores for
residential buildings, INR 56 Crores for commercial buildings and INR 6 Crores for industrial
buildings, respectively. Cyclonic wind hazard losses are not significant for 5, 10, and 25
years return-period events. Average Annual Losses (AAL) losses are to the order of about
INR 1.2 Crores for residential buildings, INR.0.59 Crores for commercial buildings and INR
0.06 Crores for industrial buildings, respectively.
1
The economic loss numbers presented in this report are structural losses and cost of land and content values
are not taken in such analyses.

Epidemic impact: For epidemic risk assessment, sufficient data at ward level was not
available in the city. The State malaria statistics for the period 2007-2011 showed that
Khordha district is in the group of districts with low incidence of malaria disease. However,
the incidence statistics of Acute Diarrheal Disorders (ADD) show that about 2% of the city
population is annually affected, which is a significant number. The recurring natural hazards,
such as water logging due to flood and cyclone also act as a triggering factor for waterborne
and vector borne diseases.
Climate Change impact on Health Sector: The Intergovernmental Panel on Climate
Change (IPCC) Fourth Assessment Report (AR4) concluded that climate change would have
adverse impacts on human health. As the city is vulnerable to climate related hazards, this
would have significant implications unless appropriate mitigation measures are adopted. The
temperature variation due to climate change may have adverse effects on both morbidity
and mortality (particularly on elderly people) including cardiovascular, respiratory, and heat
stroke mortality as well as increase in incidence of vector borne diseases - malaria, dengue
and chikungunya. The changes in the rainfall pattern would also increase the incidence of
vector borne diseases.
Capacity Assessment: Capacity and needs assessment was carried out for both city
administrations as well as for communities. The capacity in terms of response and relief with
respect to knowledge, skill and awareness towards mitigation and adaptation measures
were analyzed. The city administration has reasonably good infrastructure, knowledge, and
resources for disaster management. In addition, to support and be part of the State DRR
activities, the city administration is active in developing measures towards a climate risk
resilient urban center. The city is part of the UNISDR global campaign of “Making Cities
Resilient Campaign” and is the recipient of SASAKAWA Recognition 2011 and Role Model
for Community Preparedness.
For the health-sector, there is a need to develop a long-term, comprehensive epidemic
contingency plan by taking into consideration the impacts of climate change. In addition,
moving forward, the city health department should develop location-specific data of epidemic
hazards. This will be helpful in analyzing and promptly responding to epidemic hazards. At
community-level, awareness on building codes (byelaws), land use restrictions, hazard
zones etc. are needed. There should be sensitization programs on hygiene and preventive
measures for minimizing epidemics particularly during post disaster situations. NGOs and
community organizations need to be further encouraged to be part of community capacity
building activities.
Recommended Actions:
Based on the city level risk assessment, recommended actions are suggested for DRR of
the city. These include both structural and non-structural measures. Sector specific short,
medium, and long-term strategies are suggested based on the assessment carried out in
this study. It necessitates mainstreaming DRR in city development planning to reduce the
risks and protect the population and assets of the city. This needs coordination among
sectors, an integrated approach ensuring mitigation and adaptation measures that would not
cause adverse impacts. The mitigation and adaptation measures need to be phased
appropriately and integrated into the City's short, medium, and long-term plans.
Cyclone adaptation and mitigation measures: Among buildings, the economic losses of
residential buildings are highest due to cyclonic winds. Among utility networks (Electric lines,
Railway lines, Water lines, and Sewerage lines) the estimated economic losses are highest
due to cyclonic winds. The following measures are needed to reduce these losses:
∙ Enforce building codes (byelaws) for various types (residential, commercial, and
industrial) of buildings in general and residential buildings in particular, to reduce the
cyclonic wind risk in the city.

∙ Significant damage to buildings also happens due to fallen trees/their branches from
high cyclonic wind speeds. Hence, city administration should have a proper tree
pruning policy for the city.
∙ Evaluate tinned/asbestos roof buildings for their resistance to cyclonic wind by certified
structural engineers in a phased manner. This should be followed up by appropriate
retrofitting measures.
∙ Gradually covert the overhead lines in general, and electric power lines in particular, to
underground cables. This will help in avoiding damage and loss due to cyclonic
winds.
Earthquake risk mitigation measures: The estimated economic losses to residential
buildings are highest followed by commercial and industrial buildings. Among
schools/colleges, hospitals, and religious places, the estimated economic losses are highest
for schools/colleges followed by religious places and hospitals. Since earthquake risk
mitigation measures are directly related to life-safety, the city administration should take
these up on priority for their strict compliance. The following are some of the measures to
mitigate losses to life and property from earthquakes:
∙ Create regular public awareness campaigns on “Earthquake safety - Dos and Don’ts”
through seminars and quizzes in schools/colleges, and through print and electronic
media
∙ Review and enforce strict building codes (byelaws) compliance in design and
construction of various types of new buildings and infrastructures.
∙ Evaluate old buildings from structural engineering point of view, especially starting from
schools/colleges, religious places and hospitals for their structural resistance to
earthquakes. This should be followed up by appropriate retrofitting measures.
∙ All the residential, commercial and industrial buildings should be evaluated for their
structural safety in a phased manner and appropriate retrofitting measures should be
taken up from building code perspectives.
∙ To mitigate non-structural damages, several measures can be adopted, such as: o
Fasten shelves, cupboards etc. securely to walls,
o Secure water heaters, LPG cylinders etc., by strapping them to the walls or
bolting to the floor
o Anchor overhead lighting fixtures and fans to the ceiling properly
o Secure hanging objects, such as ACs, heavy glass paintings etc., as hanging
objects may cause loss to life and property
Flood/Water logging mitigation measures: Taking into consideration the growth in the
city, the following measures are recommended for urban flood management:
∙ Remove encroachment of natural drains as this helps in mitigating flood/ water logging
problem of the city
∙ Develop and connect storm water network for the entire city including peripheral areas of
the city
∙ Develop high resolution (preferably 0.5 m) Digital Elevation Model (DEM), which will be
helpful to model and predict city flooding/water-logging accurately at sub-ward level and
for planning mitigation measures.
∙ Periodically clean existing storm drains, which are clogged due to waste dumping and
indiscriminate developmental activities
∙ Improve the existing solid-waste disposal system and enforce non-dumping of solid waste
in drains

The service delivery of the city administration, especially the solid waste management,
sewerage network and public transport system should also take into consideration
population growth of the city in future.
Heat wave adaptation and mitigation measures: Though Bhubaneswar city has good
cover of trees and vegetation, however, in view of the future growth of the city and predicted
increase in temperature due to impact of climate change, the following measures are
recommended for heat-wave adaptation and mitigation:
∙ City administration should develop a ward-level plan to check on vulnerable populations

during heat waves, especially the elderly and poor.
∙ Additionally, city administration should arrange for portable water tanks in the event of
heat waves.
∙ Create awareness among communities towards “Green buildings”2 ∙ While revising
building codes for residential buildings, it is also important to consider the heat wave risk
in the city. The design specifications should take into account guidelines on the design of
green buildings
∙ Building owners should be encouraged to use heat-reflecting material on roof-tops of
existing buildings
∙ Green building designs should be adopted for government and public buildings ∙ Green
cover should be further improved in the city in a phased manner ∙ Increase awareness in
people to take pre-emptive measures during heat waves, for
example, drinking enough water, avoiding alcohol consumption, etc. and in
understanding warning symptoms of heat exhaustion and how best to keep cool. ∙
Training masons for constructing buildings following building codes and design
specifications that cover features of green buildings
Epidemics adaptation and mitigation measures: Health is a key sector that needs priority
considerations as part of DRR activities in both short and medium-term planning. These
include:
∙ Public awareness for improving hygiene and sanitation.
∙ Monitoring of commercial eating places to enforce quality standards and ensuring good
supply of quality drinking water
∙ Imparting hygiene and sanitation education in schools
∙ Desilting drains to avoid water logging during rainy season
∙ Land use planning needs to take into consideration water logging issues during and
after construction and development activities
∙ Coordinate with the railways and PWD to regularly fumigate railway yards and trains in
train yards, particularly during rainy seasons
∙ The drinking water supply department to the city should test water system for adequate
chlorination levels and for bacterial and viral counts
∙ Inspection across the City to identify potential mosquito breeding grounds and take
necessary steps before and during rainy season
Climate change adaptation measures
∙ Land use and infrastructure development plans of the city need to take into
consideration the short and long- term climate change trends
∙ Low-lying areas of city can be best protected from water logging by developing suitable
drainage system. The storm water drainage system of the city need to be
2
GRIHA – green building ‘design evaluation system’– A tool to design, operate, evaluate and maintain resource
efficient ‘healthy’ and ‘intelligent’ building
(http://www.cccindia.co/corecentre/Database/Docs/DocFiles/rating_system.pdf)

developed taking into consideration the flood scenarios and the rainfall variation
trends based on climate change scenarios.
IT and database development measures
∙ The city Municipality and Bhubaneswar Development Authority has some GIS data of
the city. ORSAC and OSDMA are other State agencies who have/are developing GIS
data for the entire Odisha state. These agencies should develop the Utility network
layers (electricity network, drinking water network, storm water network, sewerage
network, and communication network) in GIS platform to help various decision
makers to integrate DRR activities. There should be a central database, which is
accessible to various departments through defined data sharing policies
∙ The city needs to have a mechanism to develop disease incidence data from both
government and private hospitals. This can be done through an online module in the
city portal where access can be given to users (government and private hospitals) to
enter tested and positively identified cases at their institutions with their spatial
locations. Similar to birth and death registry, registering disease incidence for
identified diseases needs to be made mandatory.
∙ Health contingency planning should be based on disease incidence data ∙ Damage
assessment reports need to follow the format developed and circulated by NDMA and
need to be decentralized. In the case of the City, it should be at ward level. Mobile based
applications can be developed for ward officials to make online entry of damage
information.
Mainstreaming integrated DRR in city development planning
∙ The city master plan needs to consider hazard risks from various natural hazards and
integrate mitigation measures in its vision document
∙ City, with the support of the political representatives, needs to enforce land use zoning
and building codes based on hazard and risk maps
∙ Implement incentives and disincentives for climate proofing – tax subsidies for houses
with climate proofing and disincentives like climate risk penalties for people
encroaching hazard prone areas.
∙ Awareness of political representatives will help regulate community encroachment in
hazard prone areas
∙ As a medium and long-term measure, the city should build a storm water drainage
system for entire city to avoid urban flash floods/water-loggings.
Risk Atlas: The outcomes of the study are presented in graphic form in a Risk Atlas and
provided as a separate document. The atlas is a compilation of all the base and analytical
maps generated as part of this study. The risk atlas is presented at ward level, which would
help in understanding the spatial distribution of hazards, exposure and risks.
Table of Contents
Document Control................................................................................................................. 3
Acknowledgements............................................................................................................... 4
Executive Summary .............................................................................................................. 5
Table of Contents................................................................................................................ 12
List of Figures ..................................................................................................................... 15
List of Tables ...................................................................................................................... 19
Abbreviations Used............................................................................................................. 20
1 Background.................................................................................................................. 22
1.1 Scope of the Assignment..................................................................................... 23 1.2
Bhubaneswar City Profile .................................................................................... 24 2 Multi
Hazard Mapping and Analysis ............................................................................. 25 2.1
Cyclonic Wind Hazard ......................................................................................... 25 2.1.1
Cyclone hazard in city of Bhubaneswar ........................................................... 25 2.1.2 Data
availability and sources ........................................................................... 30 2.1.3
Methodology for cyclone hazard assessment .................................................. 30 2.1.4 GIS
Mapping and Analysis of Cyclonic Wind hazard........................................ 32
2.1.5 Application of Cyclonic Wind Hazard Maps in Disaster Management and City
Planning....................................................................................................................... 36
2.2 Flood Hazard Assessment................................................................................... 37
2.2.1 Hydrology of Floods......................................................................................... 37
2.2.2 Probabilistic Simulation of Runoff .................................................................... 39
2.2.3 Hydraulic Modeling (Inundation Model)............................................................ 40
2.2.4 Mapping of Flood Extents ................................................................................ 41
2.2.5 Analysis of Flood Hazard................................................................................. 42
2.2.6 Localized Flooding/Water logging .................................................................... 42
2.3 Earthquake Hazard.............................................................................................. 44
2.3.1 Seismotectonics of the area around Bhubaneswar .......................................... 44
2.3.2 Seismic hazard at rock level ............................................................................ 45
2.3.3 Modeling Soil Amplification .............................................................................. 46
2.3.4 Application of Earthquake Hazard Maps in Disaster Management and City
Planning....................................................................................................................... 50
2.4 Heat Wave Hazard .............................................................................................. 51
2.4.1 Data source ..................................................................................................... 51
2.4.2 Methodology .................................................................................................... 51
2.4.3 Analysis results................................................................................................ 51

2.4.4 Application of Heat Wave Hazard Studies in Disaster Management and City
planning ....................................................................................................................... 55
2.5 Epidemics............................................................................................................ 56
2.5.1 Data availability and sources ........................................................................... 56
2.5.2 Methodology for hazard assessment ............................................................... 56
2.5.3 Disease Hazard Mapping................................................................................. 57
2.5.4 Application of Disease Susceptibility Mapping in Disaster Management and City
planning ....................................................................................................................... 60
2.6 Climate Change and its Impact on Hazards......................................................... 60
2.6.1 Literature review .............................................................................................. 60
2.6.2 Data source ..................................................................................................... 61
2.6.3 Methodology .................................................................................................... 62
2.6.4 Analysis results................................................................................................ 62
2.6.5 Application of Climate Change studies in City Development ............................ 66
3 Development of Exposure Database for Bhubaneswar City ......................................... 68
3.1 Data sources ....................................................................................................... 68 3.2
Inventory of vulnerable demographics, buildings and infrastructure ..................... 68 3.3
Methodology Adopted for Exposure Database Development............................... 69 3.4
Analysis of Exposure Elements ........................................................................... 71
3.4.1 Population........................................................................................................ 71
3.4.2 Housing ........................................................................................................... 73
3.4.3 Essential Facilities and Public Buildings .......................................................... 82
3.5 Transportation Network ....................................................................................... 89
3.5.1 Roads .............................................................................................................. 89
3.5.2 Bridges and Flyovers ....................................................................................... 91
3.5.3 Railways .......................................................................................................... 92
3.5.4 Airports ............................................................................................................ 93
3.6 Utility Networks.................................................................................................... 93
3.6.1 Potable Water Network .................................................................................... 93
3.6.2 Electric Power Network.................................................................................... 93
3.7 Estimation of Exposure Values ............................................................................ 94
3.7.1 Estimation of Built-up Floor Area ..................................................................... 94
3.7.2 Estimation of Unit Replacement Costs............................................................. 94
3.7.3 Calculation of Exposure Values ....................................................................... 94
4 Vulnerability Assessment ............................................................................................. 99
4.1 Data Sources and Availability .............................................................................. 99 4.2
Physical Vulnerability........................................................................................... 99 4.3
Social Vulnerability ............................................................................................ 100

4.3.1 Slum population............................................................................................. 100
4.3.2 Methodology for social and livelihood vulnerability assessment ..................... 102
4.3.3 Social vulnerability distribution....................................................................... 104
4.3.4 Livelihood Vulnerability Analysis .................................................................... 106
4.4 Environmental Vulnerability ............................................................................... 106 5
Risk Assessment ....................................................................................................... 110 5.1
Introduction........................................................................................................ 110 5.2 Risk
Assessment Methodology.......................................................................... 110 5.3 Risk Matrix
by Hazard........................................................................................ 114 5.3.1
Earthquake .................................................................................................... 115 5.3.2
Cyclonic Wind................................................................................................ 119 5.3.3 Other
Hazards ............................................................................................... 127 5.4 Potential Risk
Based on Climate Change to Health Sector ................................ 127 5.5 Estimation of
Affected Population and Casualties.............................................. 129 5.6 Composite
Vulnerability Analysis ....................................................................... 129 5.7 Risk
Atlas .......................................................................................................... 130 6 Capacity
Assessment at Community, Ward and City Levels ...................................... 132 6.1
Methodology for Capacity Assessment.............................................................. 132 6.2
Capacities of Existing Government Institutions .................................................. 133 6.2.1
Capacity Assessment of Government Institutions .......................................... 133 6.2.2
Capacity Requirements of Government Institutions ....................................... 134 6.3
Capacity of Social Institutions ............................................................................ 134 6.3.1
Community Capacity and Awareness ............................................................ 134 7
Recommendations ..................................................................................................... 136
References ....................................................................................................................... 140
8 Annexure 1: Hazard Maps ......................................................................................... 142 9
Annexure 2: Detailed Exposure Data ......................................................................... 145 10
Annexure 3: Questionnaires and Forms ................................................................ 170

List of Figures
Figure 2-1: Storm tracks of past events from year 1877 – 2013 (Source: IMD and JTWC)..
29 Figure 2-2: Flowchart showing approach for cyclone hazard
assessment ........................... 31 Figure 2-3: Average Wind Speed Vs Return Period based
on Gumbel Distribution............. 32 Figure 2-4 : Steps for cyclone hazard
assessment.............................................................. 32 Figure 2-5: Cyclone hazard Map for
5-year return period .................................................... 33 Figure 2-6: Cyclone hazard Map for
100-year return period ................................................ 34 Figure 2-7: Basin Boundary Map of
Mahanadi with Location of Bhubaneswar .................... 38 Figure 2-8: Flood hazard
assessment framework................................................................ 39 Figure 2-9: Annual
Maximum Discharge for Tikarapara Flow Gauge Station ...................... 40 Figure 2-10:
Simulated Return Period Discharges .............................................................. 40 Figure 2-11:
HEC RAS model for Mahanadi Delta .............................................................. 41 Figure 2-12:
Flood Hazard Map for 100-year return period along with 2003 event flooding. 42 Figure 2-
13: Wards reported water logging problems during the 2014 ................................ 43 Figure
2-14: Seismotectonic map of areas around Bhubaneswar ....................................... 45 Figure
2-15: Ward level PGA map of Bhubaneswar city at hard rock-level (after GSHAP).. 46 Figure
2-16: Spatial variation of (a) Vs30 values and (b) Soil-Index for Bhubaneswar city .. 48
Figure 2-17: Site Amplification Factors for different Soil Index Values (=Vs30 Values) .......
48
Figure 2-18: Ward level PGA based Probabilistic Seismic Hazard Map For 10% Probability in
50 Years (475-Year Return Period) for Bhubaneswar city ....................................... 49
Figure 2-19: Temporal trends in observed annual mean surface air temperatures at
Bhubaneswar, India................................................................................................. 52
Figure 2-20: Temporal trends in observed annual mean maximum (day-time high) surface air
temperature at Bhubaneswar, India......................................................................... 52
Figure 2-21: Temporal trends in observed annual mean minimum (night-time low) surface air
temperature at Bhubaneswar, India......................................................................... 53
Figure 2-22: Anomalies in observed maximum (day-time high) surface air temperature during
summer season (with respect to the 1951-1980 mean) at Bhubaneswar. An
accelerated increasing trend is evident in recent decades....................................... 54
Figure 2-23: Anomalies in observed minimum (night-time low) surface air temperature during
summer season (with respect to the 1951-1980 mean) at Bhubaneswar. The
increasing trend in recent decades is not pronounced as the observed maximum
temperature trend.................................................................................................... 54
Figure 2-24: Percent deviation in observed rainfall with respect to 1991-2010 mean at
Bhubaneswar during monsoon season.................................................................... 55
Figure 2-25: Incidence of ADD cases across the year in Bhubaneswar city (derived from
district level data). ................................................................................................... 58
Figure 2-26: Seasonal incidence of malaria in Odisha State ............................................... 58
Figure 2-27: Malaria incidence trend during the last three years in Bhubaneswar city.........
58

Figure 2-28: Incidence of diseases across seasons based on the sample survey,
2014Disease Susceptibility Mapping ....................................................................... 59
Figure 2-29: Distribution of disease cases reported across the wards of Bhubaneswar city 59
Figure 2-30: Disease incidence across income group (household survey 2014) .................
60
Figure 2-31: A comparison of rate of increasing trends in surface air temperature since
historical times in Bhubaneswar, Odisha and India.................................................. 61
Figure 2-32: Projected rise in mean maximum and minimum surface air temperatures during
hot summer months for 2040s in Khordha District of Odisha (Bhubaneswar city is
marked with black boundary here)........................................................................... 63
hot summer months for 2080s in Khordha District of Odisha (Bhubaneswar city is
marked with black boundary here)........................................................................... 64
Figure 2-34: Projected change in annual and monsoon season rainfall (in mm/ day) for
2040s in Khordha District of Odisha (Bhubaneswar city is marked with red boundary
here)........................................................................................................................ 64
here)........................................................................................................................ 65
Figure 3-1: Broad categories of exposure elements considered in the study....................... 69
Figure 3-2: Approach to exposure development.................................................................. 70
Figure 3-3: Distribution of population by gender and SC/ST (caste).................................... 71
Figure 3-4: Distribution of population by literacy.................................................................. 72
Figure 3-5: Distribution of non-working population and child population (<6 Years).............
72 Figure 3-6: Ward-wise population density ...........................................................................
73 Figure 3-7: Distribution of built-up area by occupancy and major building
use .................... 75
Figure 3-8: Percentage distribution of occupied and vacant house (left), Distribution of the
Census houses based on usage in Bhubaneswar city (right)................................... 76
Figure 3-9: Different types of residential houses in Bhubaneswar ....................................... 78
Figure 3-10: Distribution of residential houses by use (left), Condition of residential houses in
Bhubaneswar city (right).......................................................................................... 78
Figure 3-11: Distribution of residential houses by building construction materials and
structure .................................................................................................................. 79
Figure 3-12: Different types of commercial buildings/ centers in Bhubaneswar ...................
80 Figure 3-13: Distribution of commercial buildings by use in Bhubaneswar
city .................... 81
Figure 3-14: Ward-wise distribution of industrial built-up area by wards w.r.t. the total
industrial area of the city.......................................................................................... 82
Figure 3-15: Different types of industries in Bhubaneswar city ............................................ 82
Figure 3-16: Ward-wise distribution of schools in Bhubaneswar city ................................... 83
Figure 3-17: Distribution of Health Facilities in Bhubaneswar City by types of services
offered ...........................................................................................................................
..... 84
Figure 3-18: Ward-wise distribution of hospitals in Bhubaneswar city ................................. 85
Figure 3-19: Distribution of police stations in Bhubaneswar City ......................................... 85
Figure 3-20: Distribution of fire stations in Bhubaneswar City.............................................. 86
Figure 3-21: Distribution of government offices in Bhubaneswar City.................................. 87
Figure 3-22: Distribution of religious places in Bhubaneswar City ....................................... 88
Figure 3-23: Cultural heritage sites of Bhubaneswar city..................................................... 89
Figure 3-24: Distribution of road network (left) and replacement cost by road types (right) .
90 Figure 3-25: Road Network in Bhubaneswar city.................................................................
91 Figure 3-26: Bridges, flyovers, railway network and airport locations in Bhubaneswar
city.. 92
Figure 3-27: Total exposure values for different housing/ infrastructure types in
Bhubaneswar city .................................................................................................... 95
Figure 3-28: Distribution of ward-wise total residential exposure value ............................... 96
Figure 3-29: Distribution of ward-wise total commercial exposure value.............................. 97
Figure 3-30: Distribution of ward-wise total industrial exposure value ................................. 98
Figure 4-1: Population growth in Bhubaneswar, Odisha.................................................... 100
Figure 4-2: Population and slum population growth in Bhubaneswar city during the last 4
decades................................................................................................................. 101
Figure 4-3: Distribution of slums in Bhubaneswar city ....................................................... 102
Figure 4-4: SoVI along with slum pockets of Bhubaneswar city (SOVI range is represented
by different choropleth).......................................................................................... 105
Figure 4-5: Reported accident injuries at Capital hospital, Bhubaneswar ..........................
106 Figure 4-6: LULC 2000 of Bhubaneswar city.....................................................................
107 Figure 4-7: LULC 2005 of Bhubaneswar city.....................................................................
107 Figure 4-8: Change in LU/LC between 2005-2011 of Bhubaneswar city ...........................
107 Figure 4-9 LULC composition of Bhubaneswar city (2000)................................................
108 Figure 4-10 LULC composition of Bhubaneswar city (2005)..............................................
108 Figure 4-11: LULC composition of Bhubaneswar city (2011).............................................
108 Figure 4-12: LULC map of Bhubaneswar city (2011).........................................................
109 Figure 5-1: Loss Exceedance Curve .................................................................................
110 Figure 5-2: An example site-specific exposure (Railway lines, Roads, Flyovers,
Bridges). 113
Figure 5-3: Distribution of Structural Losses (PML) corresponding to 475-years return period
hazard scenario event for residential buildings in Bhubaneswar city...................... 116
hazard scenario event for commercial buildings in Bhubaneswar city.................... 117
earthquake hazard scenario event for industrial buildings in Bhubaneswar city ..... 118
cyclonic wind hazard scenario event for residential buildings in Bhubaneswar city 120
cyclonic wind hazard scenario event for commercial buildings in Bhubaneswar
city .............................................................................................................................. 121
cyclonic wind hazard scenario event for industrial buildings in Bhubaneswar city.. 122
Figure 5-9: LEC and AAL for Cyclonic Wind Hazard ......................................................... 123
Figure 5-10: Risk Map: AAL for cyclonic wind hazard for residential buildings ..................
124 Figure 5-11: Risk Map: AAL for cyclonic wind hazard for commercial
buildings................. 125 Figure 5-12: Risk Map: AAL for cyclonic wind hazard for industrial
buildings .................... 126 Figure 5-13: Composite vulnerability map of Bhubaneswar
city ........................................ 130 Figure 6-1: Steps involved in capacity needs
assessment ................................................ 132 Figure 8-1: Cyclone hazard Map for 10-year
return period ................................................ 142 Figure 8-2: Cyclone hazard Map for 25-year
return period ................................................ 143 Figure 8-3: Cyclone hazard Map for 50-year
return period ................................................ 144
List of Tables
Table 2-1: India Meteorological Department cyclone classification by sustained wind
speed ............................................................................................................................
.... 25
Table 2-2: List of storm events used for the study (1877-2013)........................................... 26
Table 2-3: List of notable cyclones, areas affected and lives lost (SMRC 1998 & IMD)...... 29
Table 2-4: Ward-wise cyclonic wind hazard statistics .......................................................... 35
Table 2-5: Soil Classification Scheme based on Shear Wave Velocities .............................
47 Table 3-1: Building Categories by construction materials and Structural Types ..................
76 Table 3-2: Residential built-up area by structural types .......................................................
79 Table 3-3: Unit Replacement Cost of Different Building Types ............................................
94
Table 4-1: Growth in population and slum population in Bhubaneswar city during the last 5
decade .................................................................................................................. 101
Table 4-2: Social indicators selected for social vulnerability analysis ................................ 103
Table 4-3: Incidence of major diseases across various income groups .............................
105
Table 5-1: Probable Maximum Losses (PML) for the Earthquake Hazard in Bhubaneswar
city......................................................................................................................... 115
Table 5-2: Estimation of projected losses to various sectors for the earthquake hazard for a
475-year return period hazard ............................................................................... 119
Table 5-3: PML for the Cyclonic Wind Hazard in Bhubaneswar city .................................. 119
Table 5-4: Estimation of projection of losses to various sectors for the Cyclonic Wind
hazard ...........................................................................................................................
... 127
Table 5-5: Summary of health effects of weather and climate, IPCC (2007) and WHO
(2009) ...........................................................................................................................
... 128
Table 5-6: Estimated numbers of affected people and causalities (serious injuries including
fatalities) for 475 years return-period earthquake hazard scenario event............... 129
Table 9-1: Ward-wise distribution of population................................................................. 145
Table 9-2: Ward-wise distribution of population based on literacy rate ..............................
147 Table 9-3: Distribution of the Census houses based on the condition of the
houses ......... 149 Table 9-4: Ward-wise distribution of Census houses based on
uses................................. 152 Table 9-5: Ward-wise distribution of Census houses by
building structural types .............. 155 Table 9-6: Estimated built-up floor area for different
housing types (in sq. m.) .................. 158 Table 9-7: Unit replacement costs of different
building/ infrastructure types ...................... 159
Table 9-8: Ward-wise estimated exposure value for different houses by occupancy and uses
(INR in Crores) ...................................................................................................... 160
Table 9-9: Ward-wise estimated length and exposure values for different types of roads..
163 Table 9-10: Ward-wise estimated length and exposure values for railway
network ........... 166 Table 9-11: Infrastructure details and estimated exposure value for
Bhubaneswar Airport 167 Table 9-12: Ward-wise estimated length and exposure values for
bridges and flyovers .... 168

Abbreviations Used
AAL Average Annual Loss
ADD Acute Diarrheal Disorders
AR4 Fourth Assessment Report
BDA Bhubaneswar Development Authority
BMC Bhubaneswar Municipal Corporation
CDMP City Disaster Management Plan
CDMP Community Disaster Management Plan
DRR Disaster Risk Reduction
DM Disaster Management
DRM Disaster Risk Management
DRR Disaster Risk Reduction
EP Exceedance Probability
EWS Early warning System
GAA Gopabandhu Academy of Administration
GIS Geographic Information Systems
GOI Government of India
HRVA Hazard Risk Vulnerability Assessment
INR Indian Rupees
IPCC The Intergovernmental Panel on Climate Change
IRS Incidence Response System
km Kilometer
LEC Loss Exceedance Curve
LISS Linear Imaging Self-Scanner
MDR Mean Damage Ratio

MHA Ministry of Home Affairs
mm Millimeter
NDMA National Disaster Management Authority
NDRF National Disaster Response Force
NGOs Non Government Organization
NHAI National Highways Authority of India
NIDM National Institute of Disaster Management
NRHM National Rural Health Mission
ODRAF Odisha Disaster Rapid Action Force
OSDMA Odisha State Disaster Management Authority
PML Probable Maximum Loss
PPP Public- Private- Partnership
PWD Public Works Department
RCC Reinforced Cement Concrete
RCF Reinforced Concrete Frame
SC/ ST Scheduled Caste/ Scheduled Tribe
SIRD State Institute for Rural Development

SOP Standard Operation Procedures
UN United Nations
UNDP United Nations Development Programme
WHO World Health Organization

1 Background
India has experienced exponential urban growth in the last few decades with more than 70%
of its urban population residing in Class-I cities. As per 2011 Census, there are 468 Class-I
cities compared to 399 such cities in 2001. Fast growth in these urban centers also leads to
increased exposure of the urban population and infrastructure to natural hazards. The
impact of climate change has accentuated the risk of urban centers to natural hazards,
particularly, the hazards related to hydro-meteorological phenomena.
Bhubaneswar, the capital city of Odisha, has a population of about 8,40,834 with a
population density of 6,228 per sq km (Census, 2011). The city is experiencing very high
growth both in terms of urban built as well as population. The city is exposed to various key
hazards- cyclonic winds, floods, earthquakes, heat waves, and epidemics. As per the
Bureau of Indian Standards (BIS) code IS:1893 (2002) and BMTPC Atlas (2006), the city is
located in the seismic zone III that is a moderate earthquake risk zone class.
In addition, to support and be part of the State Disaster Risk Reduction (DRR) activities, the
city administration is active in developing measures towards a climate risk resilient urban
center. The city is part of the UNISDR global campaign of “The Making Cities Resilient
Campaign” and is Recipient of “SASAKAWA Recognition 2011” and Role Model for
Community Preparedness.
The city has taken several proactive steps towards climate change adaption, in particular to
the power, roads, and drainage infrastructure development activities. The new building
byelaws are in place with design safety norms (BDA, 2008). Development plans also look
into the risk zones of the city while considering any new development projects.
Bhubaneswar City has been selected as one of the eight cities in India for implementing the
Climate Risk Management Project on a pilot basis under the framework of the Urban
Disaster Risk Reduction project of GOI-UNDP.
The ongoing Government of India (GOI)-UNDP Disaster Risk Reduction (DRR) program aims
to strengthen the capacities of government, communities and institutional structures by
undertaking DRR activities at various levels and develop preparedness for recovery. Under
this program, eight cities (including Bhubaneswar) prone to multi-hazards in the country
were identified, and selected for detailed hazard vulnerability and risk assessment studies.
These studies will support the local administration and the community to develop risk
resilience through understanding of hazards, vulnerability and risk and integrate appropriate
mitigation and management practices to protect the community and its assets. The project
emphasizes a participatory approach for developing the capacity of the cities in integrating
Climate Change Adaptation (CCA) and DRR concerns at city level plans.
The main objective of the proposed multi-hazard risk and vulnerability analysis assignment is
to assess the extent of risk and the vulnerabilities of Bhubaneswar city particularly to climate
related hazards. The outcome of the exercise is expected to help identify a set of structural
and non-structural steps that UNDP, City Administration and other stakeholders can take to
mitigate the risks posed by various hazards. It also aims to consider the future climate
change scenarios such that the development activities accommodate this understanding to
reduce the impact in the medium and long- terms.
The present study provide quantified hazard, vulnerability and risk of prominent hazards
prevalent in the city; development of short, medium and long term mitigation strategies for
DRR in the city and develop capacity of city stakeholders in mainstreaming DRR activities in
the city development activities.

1.1 Scope of the Assignment
The study has various components as detailed below:
1. Component 1: Multi-Hazard Mapping and Analysis
2. Component 2: Development of Exposure Database at City level with resolution of ward
level
3. Component 3: Vulnerability Assessment (Physical, Economic, Social and Environment)
4. Component 4: Risk Assessment
5. Component 5: Capacity Assessment at community, ward and city levels 6. Component 6:
Recommended Actions that can be taken to mitigate multi-hazard related risks through City
level Disaster Management Plan, sectoral planning and implementation of
projects/initiatives at community, ward and city levels
1.2 Bhubaneswar City Profile
Bhubaneswar, the capital of Odisha, is also popularly
known as the "Temple City of India".
Area 135 sq km
Number of wards 67
Socio economic profile
Population 840,834 (Census, 2011)
Population density 6,228 person/ sq km (Census,

2011)
Key economic activity Industry, Tourism, Trade and

Commerce
No. of households 1,97,661
Literacy rate 83%
Slum details
Authorized slums 116 Un-Authorized slums 320
Slum Population 3.01 No. of slum households 80,630

Lakhs
Weather characteristics
Average annual rainfall: 1,542 mm
Mean Annual 270 C

Minimum
Temperature
Mean Annual 320 C

Maximum
Temperature
Rainy seasons June-Oct
Mean Annual 70%

Humidity
Infrastructure
Road length 1,642 km
Railway (length) 33.8 km
Industries 1,794 (2011)
Hospitals 667 (2011)

Educational 1,171 (2011)
institutions
* Source: Bhubaneswar Municipal Corporation, Census of India, http://www.odisha.gov.in/

2 Multi Hazard Mapping and Analysis

2.1 Cyclonic Wind Hazard
Tropical cyclones are large atmospheric vortices, which form over the tropical warm oceans.
A severe cyclone can extend horizontally from 150 km to 1,200 km with fierce winds
spiraling around a central low-pressure area. An intense cyclone carries a belt of strong
winds and heavy rain clouds, which could cause destruction when the cyclone crosses the
coast and makes landfall.
Hazards associated with tropical cyclones are long duration rotatory high velocity winds,
which could cause massive damage not only limited to coastal regions but also to areas
within about 100 km from the coast. In view of this, Bhubaneswar, one of the cities of
Khordha district and located about 45 km from the coast, commonly experiences significant
losses to property and infrastructure due to severe cyclonic storms. The wind speed of
various categories of cyclonic disturbances provided by India Meteorological Department
(IMD), are given in Table 2-1.
Table 2-1: India Meteorological Department cyclone classification by sustained wind speed
Sl. No. Storm category (Intensity) Abb. Wind speed (knots) Wind speed (kmph)
1. Low Pressure Area LPA <17 <31
2. Depression D 17-27 31-49
3. Deep Depression DD 28-33 50-61
4. Cyclonic Storm CS 34-47 62-88
5. Severe Cyclonic Storm SCS 48-63 89-118
6. Very Severe Cyclonic Storm VSCS 64-119 119-221
7. Super Cyclone SC >120 >222

The cyclone hazard assessment evaluates the frequency and severity of various cyclonic
events at different recurrence intervals or return periods ranging from more frequent to rare
events, based on a historical cyclonic database. The city of Bhubaneswar, due to its
geographical position, has limited exposure to Very Severe Cyclonic Storms and Super
cyclones. However, various storms of different intensities affected the city, especially those
that made landfall in neighboring districts such as Ganjam, Puri, Jagatsinghpur and
Kendrapara. The 1999 Paradip cyclone was one of the most severe cyclones that caused
extensive damage to property and loss of lives in the city. Therefore, a comprehensive
modeling approach was adopted for cyclone hazard assessment of Bhubaneswar city for the
computation of wind speeds associated with various extreme events.
2.1.1 CYCLONE HAZARD IN CITY OF BHUBANESWAR
The tropical cyclones originate in the Bay of Bengal and affect the coastal region of Odisha
during two seasons in a year: Pre-monsoon (April-May) and Post-monsoon (October
December). In addition, intense depressions during the monsoonal periods and especially
during the southwest monsoon are also affecting the Odisha coast. The peak frequency is
found to be in the months of July, August and September. In the 137-year period between
1877 and 2013, 110 tropical disturbances passed within 150 km of Bhubaneswar City that
include 39 cyclonic events, an average of one cyclone in 3.5 years (Table 2-2). However, a

Multi-Hazard Risk and Vulnerability Analysis for the City of Bhubaneswar, Odisha majority of the
cyclones have weak effects. Exceptions to this rule are found for severe cyclonic storms of
November 1973, May 1982, very severe cyclonic storms of September 1888, November
1891, October 1912, September 1936, October 1967 and October 1999 super cyclone. In
the present study, historical tropical cyclones that passed the region surrounding the City of
Bhubaneswar and presented in Table 2-2, were analyzed to determine the locations of high
hazard incidence at ward-level.
Table 2-2: List of storm events used for the study (1877-2013)
Sl. No. Day Month Year Category/Grade
1 3 8 1878 Deep Depression
6 11 7 1882 Cyclonic Storm
11 13 9 1888 Very Severe Cyclonic Storm



46 15 7 1920 Depression



96 3 11 1973 Severe Cyclonic Storm
101 30 5 1982 Severe Cyclonic Storm
105 25 10 1999 Super Cyclonic Storm
The data of past cyclonic disasters was collected from India Meteorological Department
(IMD) reports, SAARC Meteorological Research Centre (SMRC, 1998), and from several
research publications and is presented in Table 2-3. These events affected Bhubaneswar
City in the last 137 years and resulted in loss of lives and property. Based on an analysis of
historical data, Bhubaneswar City witnessed several storms ranging from Tropical
Depressions (31 – 61 km/hr) to very strong storms (88-167 km/hr). The storm tracks of past
events from 1877 to 2013 are depicted in Figure 2-1.

Figu
re 2-1: Storm tracks of past events from year 1877 – 2013 (Source: IMD and JTWC)
Table 2-3: List of notable cyclones, areas affected and lives lost (SMRC 1998 & IMD)
S No. Date Description of the meteorological event
1 September 7- Crossed South Odisha coast and adjoining North Andhra coast on
14, 1971 September 10 and moved up to eastern Delhi. 90 People died and
8,000 Cattle heads perished. This system caused considerable damage
to crops, houses, telecommunications and other property in the coastal
districts of Odisha. viz., Ganjam, Puri, and Cuttack.
2 September Crossed South Odisha coast near Gopalpur on September 22. Caused
20- 25. 1971 considerable damage to crops and houses due to flood and heavy rain
at Vamsadhura village in Srikakulum and Koraput districts.
3 October 26- Crossed Odisha coast near Paradip early in the morning of October 30.
30,1971 Maximum wind speed recorded was 150-170 kmph .Lowest Pressure
recorded 966 hPa near the center of the storm. 10,000 People died and
more than one million people were rendered homeless. 50,000 Cattle
heads perished and 8,00,000 houses were damaged.
4 September Crossed extreme South Odisha coast near Gopalpur on the afternoon
20- 25, 1972 of 22nd and weakened into a depression by the morning of the 23rd.

S No. Date Description of the meteorological event
Max. wind recorded in gust was 136 kmph at Gopalpur at about 07:40
UTC on 22nd. Caused damage to crops & houses but no loss of life
was reported.
5 November 3-9, Crossed Odisha coast close to and north of Paradip on the early
1973 morning of 9th. It weakened rapidly and lay as a trough over Odisha the
same day. Maximum wind reported was 100 kmph at Paradip and
Chandbali experienced surface winds of 100 kmph .This cyclone
caused some damage to standing crops in the coastal districts of
Odisha between Paradip and Chandbali.
6 September,2 Crossed Odisha coast near Puri on the early morning of September 26,
4- 28. 1981 weakened into a depression on that evening over interior Odisha, and
adjoining East Madhya Pradesh. 5 Launches were lost in the Bay and
many houses were damaged in Midnapur district of West Bengal and
Cuttack district of Odisha.
7 May 31 to Crossed on 3rd June near Paradip, Odisha. As a result of high tides
June 5th 1982 damage caused all along this coastal stretch. This cyclone caused
heavy damage in the coastal districts of Puri, Cuttack and Balasore.
8 October 9- Crossed North Odisha coast near Chandbali in the forenoon of 14 th. This
14.1984 system caused some damage in Cuttack and Balasore districts of
Odisha and Midnapore district of West Bengal
9 17-21 Sept. Crossed on 20th Sept. close to Puri .For three consecutive days, due to
1985 1.5 m sea-wave, Puri coast was inundation.
10 13-17 Oct. 1985 Crossed near Balasore on 16th Oct. High tidal crossed near Balasore
on16th October. High tidal wave of about 16’ to 18’ was observed.
12 23-27 May 1989 Crossed 40 km northeast of Balasore. 61 persons died in Odisha and
West Bengal.
13 26-27 Oct 1909 Ganjam district was severely affected. Puri and Balasore were less
affected with violent winds and had lower rainfall. 22 humans and many
cattle were killed. Damage ran into several lakhs of rupees. INR 15
lakhs damage was in Gopalpur alone.
14 13-18 Nov 1923 High floods occurred in the rivers in Ganjam district. Puri district was
also affected. Immense destruction to communication services including
railways. Considerable damage to crops. 20 humans and a few hundred
cattle killed. A large number of public and private properties including
irrigation works were damaged.
15 1-6 Oct 1906 Cyclonic storm crossed the coast north of Puri. Considerable damage to
trees, roads, houses, even Pucca buildings in Puri.
16 15-16 Nov 1942 Less severe than the one on 16 October 1942. The cyclone was close
to Odisha coast and weakened.
2.1.2 DATA AVAILABILITY AND SOURCES

The vulnerability of Bhubaneswar City due to cyclonic winds has been assessed by
analyzing the past cyclone datasets. For this purpose, tropical cyclone track data for the
period from 1877 to 2013 was obtained from IMD (tropical cyclone Atlas and other scientific
reports and research papers from journals) and JTWC (NOAA). The other available sources
like Unisys Hurricane Database (2013), SMRC (1998), and several research publications
were also considered in preparing a master database of cyclonic tracks and their intensity
information for Bhubaneswar City. The compiled master database of storm tracks was used
for calculating the expected return period maps of cyclone wind hazard for Bhubaneswar
City.
2.1.3 METHODOLOGY FOR CYCLONE HAZARD ASSESSMENT
Cyclonic wind hazard assessment identifies and demarcates areas, which are exposed to
strong winds associated with tropical cyclones. It provides information on the extent and
wind speed throughout cyclone prone areas for a range of wind magnitudes. The cyclone
hazard assessment framework adopted for this study is depicted in Figure 2-2.

Data Collection Literature Survey

(Historical cyclonic Events, Meteorological parameters)
Creation of Historical
Event Catalogue
Development of
Probabilistic Scenarios
Cyclone Hazard Models
Calibration and Validation

of Hazard Model
Application of Hazard
Models for probabilistic
Scenarios
Hazard Maps for Various

Scenarios
Figure 2-2: Flowchart showing approach for cyclone hazard assessment

From the generated database, frequencies, locations of landfall, and the maximum pressure
deficit (ΔP) have been tabulated for each cyclone, and this input has been used to perform a
suitable statistical analysis (Gumbel distribution) (Figure 2-3) to calculate maximum value of
ΔP for key return periods. Climate change and other man-made changes add some degree
of uncertainty beyond assessed projections of the model.
For the historical tracks, surface winds associated with a tropical cyclone have been derived
from a dynamic storm model based on the formulation of Jelesnianski and Taylor (1973).
Meteorological inputs used for this model include positions of the cyclone, pressure drop and
radii of maximum winds at fixed intervals of time. The main component of the storm model is
a trajectory model and a wind speed profile approximation scheme. The trajectory model
represents a balance among pressure gradient, centrifugal, Coriolis, and surface frictional
forces for a stationary storm. A variable pressure deficit, forward speed, and radius of
maximum winds have been used in location specific storm model for computation of wind
fields at model grid (250 m X 250 m) points. The storm strength is reduced after the cyclone
crosses the coast. The above process has been repeated for each time-step along the track
and the maximum wind at each location throughout the duration of the storm has been
computed and retained for loss calculations. The maximum wind speed computed with the
model has been calibrated/validated against observed data. Figure 2-4 explains the step-by
step approach adopted for cyclone hazard modeling. Finally, scenarios of maximum wind
speed are prepared for all the key return periods (5, 10, 25, 50 100 years).
)r
e
100
Y
i
75
r
50
p
25
r
0
R
150
0 25 50 75 100 125 150 175 200 225 Wnd Speed (kmph)
125
Figure 2-3: Average Wind Speed Vs Return Period based on Gumbel
Step 2
Distribution Step 1 Step 3
• Storm Parameters Before Landfall – Duration of the storm cell of the grid
• Wind Foot Print ‒ Calibration
– Track (Lat, Long)
– Pressure drop ‒ 250 m grid cells over area of interest ▪ Hazard mapping
– Radius of maximum wind – Forward ‒ Simulate storm motion using time
step process ‒ Repeat step 2 for all storms
velocity
‒ Record maximum wind speed in each‒ Generate wind hazard map
Figure 2-4 : Steps for cyclone hazard assessment

Results from this task will be used in arriving at location specific vulnerability and risk
associated with cyclonic winds. Losses include physical damage due to wind. The output of
the cyclone hazard analysis provides for spatial description of winds involving various GIS
themes at ward level.
2.1.4 GIS MAPPING AND ANALYSIS OF CYCLONIC WIND HAZARD
As mentioned above, the maximum wind speed at 250 m x 250 m grid resolution at ward
level for key return periods is determined with the help of 2-D dynamical storm model. Wind
hazard extent maps are prepared by integrating model results with various GIS themes to
produce maps with varying wind magnitudes and are depicted in different colors. The wind
hazard maps show the wind extent and wind magnitude for various return periods. The
highest return period indicates the worst-case of wind hazard. Each of the wind hazard maps
contain ward boundaries, city boundary, and wind extent and wind magnitudes. Ward
boundaries have been labeled with ward numbers, which have been used for the study. The
cyclone wind hazard maps for 5-year return period and 100-year return period are shown in
Figure 2-5 and Figure 2-6, respectively. Wind hazard maps for other return periods are given
in Annexure 1: Hazard Maps .
The colors designated for the specific wind speed ranges are shown in the legend of each
map. The wind speeds shown with orange color indicate higher wind speed and light yellow
indicates lower wind speeds. The cyclone hazard maps for different return period events are
overlaid with the ward boundaries for analyzing detailed susceptibility in specific regions.
From the maps, it can be seen that for lower return periods (5 to 50 years) lower wind speed
extents are limited to areas over the western wards whereas the relatively higher wind
speeds cover a large extent of the eastern part of Bhubaneswar. However, in the case of
100-year return period, higher wind speeds cover a large extent of the western city and
relatively lower wind speed extent is limited to the eastern side of city. As per IMD
Guidelines, wind speeds associated with tropical depressions of 50-61 kmph may cause
minor damage to loose and unsecured structures. Whereas, wind speeds associated with
cyclonic storms (62- 87 kmph) or storms of higher categories, can cause extensive damage
to thatched roofs and huts, minor damage to power and communication lines due to
uprooting of large avenue trees, etc. In the higher return periods (more than 50 Years) under
most severe scenario, wind magnitude and extent start increasing and cover many areas of
the city.
These maps will help identify the high vulnerability zones for Bhubaneswar City. Assessment
of cyclone risk and vulnerability at ward level will be useful to evolve a sustainable local level
development action plan for preparedness and mitigation.
Figure 2-5: Cyclone hazard Map for 5-year return period

Figure 2-6: Cyclone hazard Map for 100-year return period
Analysis of cyclonic wind hazard shows average wind speed and associated extent of
affected areas in various wards of the city. For analysis purpose, the five return period maps
and area covered in various wards are worked out. While considering 50 and 100- year
return periods, the entire city is prone to severe cyclonic storms (> 110 kmph). Ward-wise
statistics are given Table 2-4.

Table 2-4: Ward-wise cyclonic wind hazard statistics
Ward Average wind Average wind Average wind Average wind Average wind
No. speed (kmph) speed (kmph) speed (kmph) speed (kmph) speed (kmph)
for 5-Year for 10-Year for 25-Year for 50-Year for 100-Year
Return Period Return Period Return Period Return Period Return Period
1 66 69 78 113 165
2 65 69 78 113 166
3 65 69 78 115 170
4 66 69 78 117 178
5 66 69 77 117 174
6 65 69 77 113 166
7 65 69 77 113 165
8 65 69 77 113 165
9 66 69 76 115 169
10 66 69 77 116 172
11 65 69 77 115 169
12 66 69 77 115 169
13 65 69 77 114 165
14 65 68 77 113 164
15 65 69 78 113 164
16 65 69 77 114 165
17 65 69 77 116 170
18 65 69 76 117 175
19 65 68 77 116 173
20 65 69 77 113 164
21 65 69 77 113 163
22 65 69 78 112 159
23 65 70 78 112 159
24 65 69 78 113 161
25 65 69 78 113 163
26 65 69 77 114 164
27 65 69 77 114 165
28 65 68 77 115 167
29 65 68 77 115 168
30 65 68 77 116 170
31 65 68 77 116 171
32 65 68 77 117 173
33 65 68 77 116 171
34 65 68 77 116 170
35 65 68 77 115 168
36 65 69 78 115 166
37 65 69 78 114 165
38 65 69 78 114 164
39 65 69 78 114 165
40 65 69 78 115 167
41 65 69 78 116 169
42 65 69 78 116 170

Ward Average wind Average wind Average wind Average wind Average wind
No. speed (kmph) speed (kmph) speed (kmph) speed (kmph) speed (kmph)
for 5-Year for 10-Year for 25-Year for 50-Year for 100-Year
Return Period Return Period Return Period Return Period Return Period
43 65 68 77 116 170
44 66 69 78 117 172
45 65 68 78 116 168
46 65 69 78 115 165
47 65 69 78 114 164
48 65 69 78 114 162
49 65 70 78 112 160
50 65 69 78 113 162
51 65 69 78 114 163
52 65 69 78 115 165
53 65 69 78 115 166
54 65 69 78 115 165
55 65 69 78 115 167
56 65 69 78 116 168
57 65 69 78 116 169
58 65 69 78 116 168
59 65 69 78 115 166
60 65 69 78 115 166
61 65 69 78 115 164
62 65 69 78 114 162
63 65 69 78 113 160
64 65 69 78 113 161
65 65 69 78 113 159
66 65 69 78 113 159
67 65 70 79 115 163
2.1.5 APPLICATION OF CYCLONIC WIND HAZARD MAPS IN DISASTER MANAGEMENT AND

CITY PLANNING
Cyclonic wind hazard maps have been developed at ward scale for Bhubaneswar city.
Cyclonic wind affected areas have been delineated at ward level based on the return period
of past events. These cyclonic wind hazard maps are developed for several purposes, such
as:
∙ Cyclonic wind hazard maps will help the policymakers and decision makers to understand
the severity of potential storms and allow them to take necessary action to ensure
sustainable development by introducing necessary programs and measures.
∙ All the map results would be useful for the planning and design department to make
decisions. These maps would provide a basis for the government for storm prediction
and estimation of damage due to cyclonic winds.
∙ Most important sectors like education, health, housing, lifelines and transportation need
special attention for storm safety. The cyclonic windstorm zones will provide fair
understanding about expected performance of structures during cyclonic windstorms and
necessary measures to protect the structures.
∙ The zones will further help the local urban government to introduce and enforce building
byelaws and building codes to protect the urban infrastructure.

∙ These maps will also be helpful to national and international NGOs to prioritize disaster risk
reduction strategies.
∙ The cyclonic wind hazard assessment maps will help policy makers, planners, decision
makers, and related actors to better plan and implement an effective system related to
storm hazard management. However, to get a clear and more detailed picture of the
cyclonic wind hazard assessment in Bhubaneswar, it is recommended to integrate and
utilize the networking system of storm monitoring and observations, which include the
neighboring districts of Bhubaneswar.
2.2 Flood Hazard Assessment
The flood hazard assessment evaluates the frequency and severity of various flood events
at different recurrence intervals or return periods ranging from more frequent to rare events,
based on hydrological and physical information. Due to various flood control measures in
upstream areas, overall flood risk for city of Bhubaneswar is very low. In fact, flood resilience
of the city area was one of the driving factors for shifting the capital from Cuttack.
Continuous heavy rainfall during the monsoon season can cause water logging in some
areas of the city. A comprehensive modeling approach has been adopted for examining the
riverine flood hazard for city areas.
2.2.1 HYDROLOGY OF FLOODS
The city of Bhubaneswar is situated on one of the anabranch of river Mahanadi in its delta
area. The Mahanadi River forms the northern boundary of the city. The Mahanadi basin
extends over an area of 1,41,589 km2, which is nearly 4.3% of the total geographical area of
the country and drains across Chhattisgarh, Odisha, Bihar, and Maharashtra (Figure 2-7).
About 46% of the drainage area of Mahanadi lies within Odisha State. The upper basin is
saucer shaped and mostly lies in Chhattisgarh state. Complete Mahanadi river basin is
almost circular in shape with a diameter of about 400 km and an exit passage of about 160
km length and 60 km breadth. Mahanadi River discharges into the Bay of Bengal in Odisha
forming significantly larger delta area. The annual average rainfall in the basin is about 1,500
mm. The heavy rainfall received by the basin during the monsoon months is mostly caused
by the monsoon depressions. The depressions often cause heavy to very heavy rainfall
along and near their tracks. These depressions originate in the Bay of Bengal, cross the
eastern coast of the country, and move further inland in a west to northwesterly direction.
The mean annual rainfall over the entire basin is around 1,400 mm and more than 60% of it
is contributed by the southwest monsoon season.
As described above floods due to overflow from Mahanadi River is a major cause of flooding
in the entire Mahanadi Delta including Cuttack. This section examines the flood hazard for
the city of Bhubaneswar.

Figure 2-7: Basin Boundary Map of Mahanadi with Location of Bhubaneswar
Flood hazard assessment identifies and demarcates areas, which are exposed to floods. It
provides information on the extent and depth of flooding throughout flood prone areas for a
range of flood magnitudes. The flood hazard assessment framework adopted for this study is
given in Figure 2-8, which comprises of the following:
∙ Identification, acquisition, compilation and review of all relevant hydro meteorological and
biophysical data. These data includes terrain, soil, land use land cover, runoff/river
discharge and flood protection measures to form the input for the model.
∙ Probabilistic analysis of runoff to simulate various return period events (from frequent to
rare events) for flow gauge station upstream of the city.
∙ Hydraulic modeling to estimate flood levels throughout the flood plain areas in the city for
various flows generated from key return period events
∙ Flood hazard mapping to show flood extent and flood depth for a range of events, which is
the end result of hazard assessment.
Figure 2-8: Flood hazard assessment framework
2.2.2 PROBABILISTIC SIMULATION OF RUNOFF

Probabilistic simulation is necessary due to non-availability of historical observations for long
periods. Generally, historical observations are available for a relatively short period (say 20
to 50 years). Probabilistic simulation helps in generating events to capture extremes that
might not have present in the available historical data sets. Probabilistic event sets have
been generated using river discharge/runoff data at Tikarapara flow gauge station located
upstream of the Mahanadi delta area. In the first step, daily flow data for Tikarapara flow
gauge station have been collected from Central Water Commission (CWC). The annual
maximum flows have been presented in Figure 2-9. The Probabilistic simulation for annual
maximum flow discharge has been carried out after identifying the appropriate probability
distribution. The linear moment technique (Hosking, 1990) has been used to determine the
most appropriate distribution. Various L moment parameters (ratios) have been estimated
using the annual maximum flows. Various flow discharges have been simulated using
appropriate distribution for the long-term periods to capture extremes. The probabilistic flow
discharge at key return periods (2, 5, 10, 25, 50, 100 years) have been estimated and are
given in Figure 2-10. These sets of probabilistic event flows have been given as inputs to the
hydraulic model for determining flood extents for each probabilistic event.
Figure 2-9: Annual Maximum Discharge for Tikarapara Flow Gauge

Station
Figure 2-10: Simulated Return Period Discharges

2.2.3 HYDRAULIC MODELING (INUNDATION MODEL)
Flood flows estimated in the probabilistic analysis have been provided as an input to the
hydraulic modeling. The hydraulic modeling calculates flood elevations along streams and
rivers for flood flows of various return periods ranging from the most frequent to rare events.
Flood elevations are then used to delineate the aerial extent of flooding adjacent to the
streams and rivers. This technical effort serves to identify areas of flood inundation within the
floodplain that are at risk and subject to flood damages.
Derivation of flood extent, flood depths and flood velocity have been determined using 1D
hydraulic modeling through the river system for all return period events.1D model using
HEC-RAS have been applied. In many applications of river flood modeling, a one
dimensional full hydrodynamic modeling system is used. In the riverine areas, water surface
profiles for reaches have been determined using one-dimensional steady flow analysis using
Hydrologic Engineering Centre’s River Analysis System (HEC-RAS) software. HEC-RAS is
an integrated system that contains one-dimensional hydraulic analysis components for

steady and unsteady flow simulation for a full network of natural and constructed channels.
The basic computational procedure is based on the solution of the one-dimensional energy
equation. Energy losses are evaluated by friction (Manning’s equation) as also expansion
and contraction losses. The momentum equation is utilized in situations where the water
surface profile is rapidly varied. The situations include a mixed flow regime (USACE 2010).
Basin geometric data consist of the river system connecting all segments, cross-section
data, reach lengths, energy loss coefficients, and stream junction information. The river
system schematic defines how the various river reaches are connected, as well as
establishes the naming conventions for referencing all the other data. The connecting river
reaches are important for the model to understand how the computations should proceed
from one reach to the next. The river system schematic has been determined using HEC
Geo-RAS (an arc view extension for pre and post processing of RAS) in GIS environment
using ESRI’s Arcview. Estimated runoffs have been routed through the river system using
one-dimensional hydraulic analysis to delineate flood extents and depth. Figure 2-11 shows
the HEC RAS model developed for Delta area of river Mahanadi.
Figure 2-11: HEC RAS model for Mahanadi Delta

2.2.4 MAPPING OF FLOOD EXTENTS
Based on the return period rainfall and corresponding flow values, the boundaries of the
flood plains are determined using one-dimensional hydraulic modeling. Flood extent maps
have been prepared by integrating model results with GIS data to produce a map with
varying flood depths depicted in different colors. The flood hazard maps have been
developed simulated flow discharges. The flood hazard maps show the flood extent and
flood water depth for various return periods of 2, 5, 10, 25, 50 and 100 Years. In descriptive
terms, highest return period indicates the worst case of flooding. Each of the flood hazard
maps contains ward boundary, district boundary, river, flood extent and flood depths. Ward
boundary has been labeled with ward number, which has been created for the study. The
flood hazard map for 100-year return period is shown in Figure 2-12. The flood depths are
shown in blue color ramp with dark blue indicating higher depth and light blue indicates the
lower flood depths. From the maps it can be seen that the city of Bhubaneswar is least
affected by riverine floods except a small portion in the south of the city in Ward Number 67.

Similar observations can be derived from the historical flood extent map of 2003 event.
Flood extent map of 2006 event also showed similar trends.
Figure 2-12: Flood Hazard Map for 100-year return period along with 2003 event flooding 2.2.5
ANALYSIS OF FLOOD HAZARD
The flood hazard maps show flood extent and flood depths. From simulated and observed
flood extent maps it is evident that a majority of city areas are not prone to flood even in the
most severe events.
2.2.6 LOCALIZED FLOODING/WATER LOGGING
Since the last couple of years, the city of Bhubaneswar has been experiencing localized
flooding or water logging in some areas. Bhubaneswar has a system of natural drainage
comprising of 10 drains. Due to rapid growth in infrastructure, encroachment, siltation, and
dumping of debris, the natural carrying capacity of these drains has been reduced
considerably. The reduced carrying capacity creates barriers to the natural flow of water
during heavy rains.

The city has 10 major (primary) drains/nallas and several secondary and tertiary drains
running along the roads. Many of the flood plains/open areas adjacent to these drains have
been recently converted into residential areas – some are approved residential areas and
some are encroached by city dwellers.
The wards reported to have water logging problems during the 2014 flood are shown in
Figure 2-13.
Figure 2-13: Wards reported waterlogging problems during the 2014
The areas such as Acharya Bihar, Jayadev Bihar, Chandrasekharpur, Rangamatia, Salia
Sahi (Slum), GGP Colony, Dumuduma, Satya Nagar, Laxmisagar, Bamikhal, Old Town,
Rasulgarh – Kalpana NH, Mancheswar, VSS Nagar, Sundarpada, Chakeisiani, Mainsikhala,
Niladribihar, Sailashree Bihar, Patia, and Raghunathpur regularly experience localized
flooding.
As outlined in the city development plan, a comprehensive drainage system may help in
reducing the problems of localized flooding by utilizing the alignment of the existing drainage
and natural drainage system. This may call upon a need for storm water management by
prevention of encroachments, periodic maintenance, and land use regulations.

2.3 Earthquake Hazard
Bhubaneswar and surrounding regions lie in a Stable Continental Region (SCR) that is not
seismically active. However, minor to moderate earthquakes have occurred now and then at
different localities, which are not damaging in nature. In the recent past, the maximum
magnitudes of these earthquakes have been reported around 4.5 to 5.3 on the Richter’s
scale and the maximum-recorded intensity in Bhubaneswar city so far is about VI on the
MSK Intensity scale.
Seismic hazard assessment identifies and demarcates areas, which are exposed to different
levels of earthquake ground motion. It provides information on the expected levels of peak
ground motion that might be experienced in different parts of a city for a particular value of
probability of exceedance by taking into account all the seismic sources in and around the
city. Most of the seismic hazard assessment studies estimate the expected hazard at hard
rock level. However, it is important to know that ground motion experienced by structures is
not necessarily at hard rock level, and hence should be estimated at the surface level. Since,
local soil also plays an important role in ground motion amplifications, especially when Vs30
(average shear–wave velocity up to a depth of 30 meter) values are much lower 760
meters/second. From the data analysis, it was observed that Vs30 values in Bhubaneswar
city varies from about 200 m/sec to 550 m/sec. Hence, for proper estimation of seismic
hazard, modeling of local soil amplification is important. The seismic hazard assessment
approach for Bhubaneswar city comprises of the following:
∙ Seismotectonics of the study area
∙ Review of published probabilistic seismic hazard analyses for a key return periods and
choose the hazard value(s) at hard rock level
∙ Model the soil-amplification on a finer grid cell of 0.1 km x 0.1 km using NEHRP
(2007)/HAZUS-MH soil classification scheme
∙ Convolute the hazard value(s) at hard rock level with soil amplification factors, and
generate earthquake hazard maps for 10% probability of exceedance (475 year return
period)
∙ Compute the seismic hazard values at Uniform Resolution Grids (URG) at 0.1 km x 0.1 km
for Bhubaneswar city
∙ Generate GIS based seismic hazard map at ward level
Seismic hazard mapping to show expected peak ground motion (Peak Ground Acceleration,
PGA) for 10% probability of exceedance (475 year return period), which is the end result of
hazard assessment.
2.3.1 SEISMOTECTONICS OF THE AREA AROUND BHUBANESWAR
In areas around Bhubaneswar, several faults have been identified in the region and some
have shown evidence of movement during the Holocene epoch (SEISAT, 2000). The
Brahmani Fault in the vicinity of Bonaigarh is one among them (SEISAT, 2000). The
Mahanadi River also flows through a graben structure. As per Seismotectonic Atlas of India
(SEISAT, 2000), several deep-seated faults are situated beneath the Mahanadi delta.
The Mahanadi and Brahmani graven, Mahanadi delta and parts of Balasore and Mayurbhanj
districts come under earthquake risk zone –III (moderate damage risk zone) as per the
earthquake risk zonation map prepared by Bureau of Indian Standards and published by
Building Material Technology Promotion Council of India (BMTPC, 2006). As per Seismic
Zoning Map of India (IS: 1893, 2002, 2014), Bhubaneswar city is located in Seismic Zone-III.
In spite of the moderate, non-damaging earthquakes observed so far in and near
Bhubaneswar, it cannot be confidently said that higher intensity earthquakes are unlikely.
Recently, on May 21, 2014 an earthquake of magnitude 6 occurred in the Bay of Bengal,
which was severely felt in different parts of Bhubaneswar city. However, there was no report

of any significant damage in the city. The following figure presents the Seismotectonic map
of the areas in and around Bhubaneswar.
Figure 2-14: Seismotectonic map of areas around Bhubaneswar
2.3.2 SEISMIC HAZARD AT ROCK LEVEL
As per Seismic Zoning Map of India (IS: 1893, 2002, 2014; BMTPC, 2006), Bhubaneswar
city lies in Seismic Zone-III, with a seismic zone factor of 0.16g, where a maximum intensity
VII on MSK Intensity scale can be expected. The Global Seismic Hazard Analysis Program
(GSHAP; www.seismo.ethz.ch/gshap/ ), provides probabilistic seismic hazard values in and
near Bhubaneswar city ranging from 0.129 g to 0.13 g corresponding to 10% probability of
exceedance in 50 years (475 years return period) at base rock level. This clearly indicates
that PGA values are almost same for the entire city, while, in reality, different parts
experience different levels of ground motion due to local soil condition effects.

Figure 2-15: Ward level PGA map of Bhubaneswar city at hard rock-level (after
GSHAP) 2.3.3 MODELING SOIL AMPLIFICATION
Local soil conditions can significantly affect earthquake ground motion of an earthquake. The
soil top layers act as filters that can modify the ground motion as a function of their dynamic
characteristics. Soft, weak soils tend to amplify long-period seismic motions and thus
generally impart large ground displacements to structures, while very stiff soil and rock tend
to de-amplify the ground motion.
For dynamic purposes, soils are classified in terms of their shear wave velocity. A majority of
authors, including the European and NGA developers (for example, Schott et al., 2004;
Campbell et al., 2009; Boore et al., 2011; Sandikkaya et al., 2013) have used the average
shear-wave velocity in the upper 30 meters of sediments, Vs30, as the parameter for
characterizing effects of sediment stiffness on ground motions. Use of this parameter is
considered to be diagnostic in determining site amplification than the broad and ambiguous
soil and rock categories used in earlier studies with the exception of the relation of Boore et

al. (1997), who used Vs30. Therefore, the site amplifications of ground motions relative to a
reference rock condition are continuous functions of Vs30 and have been used for the study
area, due to the absence of Bhubaneswar-specific relationships between site classes and
amplification effects, and coarse surficial geology at 1:250,000 scale. The widely used
NEHRP’s site amplification procedure based on shear wave velocities (Wills et al, 2000,
BSSC, 2001) has been applied in this study (Table 2-5).
Table 2-5: Soil Classification Scheme based on Shear Wave Velocities

Soil Index NEHRP Brief Description Shear Wave Velocity
value /CDMG Class (Vs,30) m/s
1.0 AB Very hard to firm rocks mostly >760

metamorphic and igneous rocks
1.5 BC Firm sedimentary rocks (mid Miocene age) 760

and weathered metamorphic
2 C Sedimentary Formation Mid-Lower 550-760

Pleistocene age
2.5 CD Weak rock to gravelly soils - Deeply 270-550

weathered and highly fractured bedrock
3.0 D Holocene Alluvial soils 180-270
3.5 DE Young alluvium / Water-saturated alluvial 90-180

deposits
4.0 E Non-engineered artificial fill, soft clays, <90

peat and swamp deposits
Using Wald et al. (2004) and Wald and Allen (BSSA, 2007) approach, gridded (0.1 km x 0.1
km) Vs30 map and corresponding soil-index map have been generated using NEHRP
(Figure 2-16) classification.
The site–dependent amplification factors followed the non-linear two-dimensional soil
amplification factors modified from Choi and Stewart (2005); and Walling, M, Walter Silva,
and Norman Abrahamson (2008), which relate non-linear multipliers based on the level of
ground motion (PGA) and averaged soil index assigned for a given location.
Figure 2-16: Spatial variation of (a) Vs30 values and (b) Soil-Index for Bhubaneswar city
The plot of amplification factors for different soil index classes (corresponding to respective
Vs30 values) normalized by the amplification for reference BC soil Vs30=760 m/s (soil index
1.5), used in the study is shown in Figure 2-17.
Figure 2-17: Site Amplification Factors for different Soil Index Values (=Vs30 Values)
The site amplification factors were then calculated based on the high resolution Vs30 based
soil index map, and these have been multiplied with the PGA Rock values derived (as given
in Figure 2-15) for the study area.
The final seismic hazard map generated at ward level contains seismic ground motion
estimates at surface level, by taking into account the local soil-amplification factors in
different parts of Bhubaneswar city (Figure 2-18).
Figure 2-18: Ward level PGA based Probabilistic Seismic Hazard Map For 10% Probability in
50 Years (475-Year Return Period) for Bhubaneswar city
As discussed earlier, from Figure 2-18 it is clear that different parts of the city are expected
to experience different levels of ground motion due to local soil amplification.

2.3.4 APPLICATION OF EARTHQUAKE HAZARD MAPS IN DISASTER MANAGEMENT AND
CITY PLANNING
As mentioned earlier, the Seismic Zoning Map of India (BMTPC, 2006; IS: 1893, 2002,
2014) only provides a uniform seismic coefficient of 0.16, and the published probabilistic
seismic hazard map presents an almost same value of about 0.13 g for the entire city. From
Figure 2-16 and Figure 2-17, it is evident that local soil amplification, especially when Vs30
values are lowers than 760 m/sec, plays a significant role and must be taken into
consideration in seismic design and structures. As per the ward level map, Figure 2-18, PGA
based probabilistic seismic hazard analysis of Bhubaneswar city provides spatially varying
values of 0.158g to 0.228g, which should be taken care of while designing new buildings as
well as in taking up seismic retrofitting of old buildings.

2.4 Heat Wave Hazard
Bhubaneswar has become one of the hottest Indian cities with scorching summers in the
recent time. Extremely high increase in average monthly mean maximum temperature,
continuous increase in the number of hot days and rising temperature difference between
Bhubaneswar and the nearby cities provide an impression of gradual emergence of the city
as an urban heat island.
2.4.1 DATA SOURCE
Historical weather data was collected from the India Meteorological Department (IMD) and
other available sources in order to analyze the rising trends in surface air temperature in
Bhubaneswar city during the past few decades.
2.4.2 METHODOLOGY
Temporal trends in daytime maximum and nighttime minimum surface air temperatures were
assessed using historical weather data.
2.4.3 ANALYSIS RESULTS
For the past three decades, the state of Odisha has been experiencing unprecedented
contrasting extreme weather conditions; from heat waves to cyclones; from droughts to
floods. In Bhubaneswar city of Odisha, the annual mean surface air temperature has risen
during the past two centuries (Figure 2-19). However, the rate of increasing trend has
sharply increased in the last few decades of the 20 th century, which could be attributed to
global climate change due to anthropogenic forcings. Further analysis of data also suggests
that the rate of increase in temperature is found to peak in May and June months of the year.
In the year 1998, the State of Odisha faced an unprecedented heat wave situation, because
of which 2042 persons lost their lives. Though extensive awareness campaigns have largely
reduced the number of casualties during post 1998 period, still a good number of casualties
are being reported each year. In the year 2009, the Odisha state experienced the worst heat
wave since the one in 1998 that killed more than 2,000 people, of which 1,500 died in
coastal Odisha alone. Severe heat stress conditions prevailing recurrently in Odisha in this
century have put the State Government in a very difficult situation. It has become a menace
during peak summer causing insurmountable human suffering. The temporal analysis of
daily temperature data in Bhubaneswar for the past three decades has shown a steady
increase in the city temperature over the years. In fact, Bhubaneswar has become one of the
hottest Indian cities in the recent times. Extremely high rise in annual average maximum
temperature, continuous increase in the number of hot days and rising temperature
difference between Bhubaneswar and the nearby cities provide an impression of gradual
emergence of the city as an urban heat island. During May 2013, the maximum temperature
of 47oC was recorded at Bhubaneswar. Subsequently, heat stress conditions prevailed in
Bhubaneswar. Most of the districts in Odisha, on an average, recorded 40ºC during April
2014 and the temperature across a few districts in coastal Odisha reached 46ºC by the end
of May. Very severe heat stress conditions prevailed in May / June this year. The increasing
trends in observed maximum (daytime high) and minimum (nighttime low) surface
temperature at Bhubaneswar are depicted in Figure 2-20 and Figure 2-21 respectively.

Figure 2-19: Temporal trends in observed annual mean surface air temperatures at
Bhubaneswar, India
Figure 2-20: Temporal trends in observed annual mean maximum (day-time high) surface air
temperature at Bhubaneswar, India

Figure 2-21: Temporal trends in observed annual mean minimum (night-time low) surface air
temperature at Bhubaneswar, India
It is interesting to note from Figure 2-20 and Figure 2-21 that the rate of increase in daytime
maximum temperature at Bhubaneswar is higher in comparison to the nighttime minimum
temperatures meaning thereby that the diurnal temperature range at this site is increasing in
recent decades. This is further illustrated in Figure 2-22 and Figure 2-23 wherein the trends
in summer time maximum and minimum temperatures at Bhubaneswar are shown.

Figure 2-22: Anomalies in observed maximum (daytime high) surface air temperature during
summer season (with respect to the 1951-1980 mean) at Bhubaneswar. An accelerated
increasing trend is evident in recent decades
Figure 2-23: Anomalies in observed minimum (nighttime low) surface air temperature during
summer season (with respect to the 1951-1980 mean) at Bhubaneswar. The increasing
trend in recent decades is not pronounced as the observed maximum temperature trend
Physical considerations indicate that tropospheric warming due to observed rate of

temperature rise should lead to an enhancement of moisture content of the atmosphere and
is associated with an increase in heavy rainfall events. Therefore, even though an overall
decrease in annual mean rainfall anomalies has been monitored at Bhubaneswar (see
Figure 2-24), more frequent incidences of high intensity rainfall could be expected in coming
years and decades. Extreme rainfall events should result in landslides, flash floods, and crop
damage that would have major impacts on society, the economy, and the environment.
Figure 2-24: Percent deviation in observed rainfall with respect to 1991-2010 mean at
Bhubaneswar during monsoon season
2.4.4 APPLICATION OF HEAT WAVE HAZARD STUDIES IN DISASTER MANAGEMENT AND

CITY PLANNING
Heat waves can affect communities by increasing summer-time peak energy demand, air
conditioning costs, air pollution and greenhouse gas emissions, heat-related illness and
mortality, and water quality. These impacts include:
∙ Increased energy consumption;
∙ Elevated emissions of air pollutants and greenhouse gases;
∙ Compromised human health and comfort; and
∙ Impaired water quality
Urban heat islands increase overall electricity demand, as well as peak demand, which
generally occurs on hot summer weekday afternoons, when offices and homes are running
cooling systems, lights, and appliances. During extreme heat events, which are exacerbated
by urban heat islands, the resulting demand for cooling can overload systems and require a
utility to institute controlled rolling brownouts or blackouts to avoid power outages.
Apart from impact on energy-related emissions, elevated temperatures can directly increase
the rate of ground-level ozone formation. Ground-level ozone is formed when NOx (mono
nitrogen oxides NO and NO2) and volatile organic compounds (VOCs) react in the presence
of sunlight and hot weather. If all other variables are equal, such as the level of precursor
emissions in the air and wind speed and direction, more ground-level ozone will form as the
environment becomes sunnier and hotter.

Increased daytime temperatures, reduced nighttime cooling, and higher air pollution levels
associated with urban heat islands can affect human health by contributing to general
discomfort, respiratory difficulties, heat cramps and exhaustion, non-fatal heat strokes, and
heat-related mortality. Sensitive populations, such as children, older adults, and those with
existing health conditions are at particular risk from these events.
2.5 Epidemics
The demographic characteristics (mainly population density), weather conditions (mainly
humidity, temperature and rainfall), and access to health infrastructure are some of the key
factors that influence disease incidence/epidemic. The high variation in temperature, heavy
and continuous rain, and tropical temperature can contribute to increase in the epidemic
hazard in the city. Fast and unplanned growth of the city can often lead to poor drainage and
overcrowding. The poorly maintained sewerage, old and poorly maintained drinking water
system, etc. can trigger vector infestation and lead to outbreaks of diseases in urban system.
Several cities of Odisha have a poor mechanism for sewage treatment and sewage is let into
the natural river system without any treatment. This increases the risk of water borne
diseases in the state, particularly Acute Diarrheal Disorders (ADD) and Gastroenteritis. The
State has also reported very high incidence of malaria cases. Taking this into consideration,
the study attempts to analyze key vector borne diseases - malaria, chikungunya, and
dengue, and water borne diseases - typhoid, diarrhea, jaundice and gastroenteritis based on
the available historical reported cases.
2.5.1 DATA AVAILABILITY AND SOURCES
Historical disease incidence data was collected from City Capital Hospital, Integrated
Disease Surveillance Project (IDSP) and from the State Malaria control unit of the National
Rural Health Mission. BMC does not maintain a disease data of its own, unlike many other
urban centers in the country. The city’s health services are being supported by the State
Health Department and for that reason data available are aggregated at district level. Some
of the issues of the IDSP program include lack of adequate resources, technology issues,
and non-achievement of program results even though it was launched in 2001. There is a
dearth of ward level disease specific data available for the city of Bhubaneswar. There is no
mechanism for private hospitals and clinics in the city to share data with the city
administration. Monthly historical disease data for the last five years were available at the
City Capital hospital for selected diseases.
In addition to the secondary data collected, the team also carried out a household survey in
which disease incidence details of the households were collected through enumeration.
Even though it was difficult for respondents to recall the disease events (including year) that
occurred in the past in the family, the respondents have provided events that happened in
the family in the near past. Disease data from the household sample provides a lead in
understanding the incidence characteristics across the socio-economic classes in the city.
2.5.2 METHODOLOGY FOR HAZARD ASSESSMENT
The epidemic incidence data is available at district level and there is a dearth of city specific
(ward level) data from the above mentioned sources. Statistical interpolation technique
based on population data is used for assessing the estimated incidences at city/ward level.
The service facilities at urban center may influence the health and reduce the disease
incidence in cities, at the same time, overcrowding in the city and poor living condition can
cause higher incidence of disease. However, both these aspects are factored in the
statistical process of data.
The following steps were followed for disease hazard assessment:

1. Literature review: Literature available at the city and district levels related to various
health and monitoring programs along with media articles and news were reviewed to
understand the prominent diseases and outbreak histories of diseases in the city.
2. Consultation: The city/State health officers were consulted and the City hospital records
were referred. Published documents of various national and state level health programs
and media articles were also referred to understand what kind of diseases were
reported in the city in the past
3. Disease selection: Prevalence and incidence are criteria that were considered for
selection of diseases for analysis.
4. Collection of disease data: The disease data available with various government agencies
were collected and compiled. The disease data is available at aggregate level for
selected diseases for the last 4 years. For malaria and ADD, which are the key diseases
pertaining to the city are only available at aggregate level – at district level. District level
data was interpolated at city/ward level using population (incidence rate) as criteria
5. Household survey: As part of the social vulnerability analysis, a sample household
survey was conducted in which questions related to disease incidence were also asked. 6.
Analysis of disease data: The secondary and primary data collected were analyzed to
understand the disease characteristics in terms of spatial distribution, seasonality and how
it changed over the year. Based on the incidence data, a disease incidence calendar was
prepared, which shows the vulnerable months for that particular disease.
2.5.3 DISEASE HAZARD MAPPING
The disease mapping for the city was carried out with the available disease data. The key
objective is to understand if there is any high incidence of disease in specific months
(season) of the year or if there any hotspots in the
city in terms of any diseases. To develop a good the heart of the city reported H5N1 positive
analytical result of the spatial and temporal cases in the poultry birds
distribution of diseases it is essential to have Disease Risk in the city
good quality monthly ward level data, which is
not available in the city. Based on the available Occurrence vector borne and water high during
data, the following inferences are made. monsoon season
In 2010, there was an epidemic outbreak of High incidence of disease recorded in the slum
Influenza H1N1 reported in Khordha district with pockets of the city
32 persons killed in the State (IDSP 2012). Malaria cases are dipping while there is an
However, the city did not report any alarming increase in waterborne diseases
numbers. In 2012, the Central Poultry
Development Organization (CPDO) located in Warns take adequate preventive steps to avoid
disease outbreak
and the city declared a 3 km radius as surveillance zone. The Salia Sahi, the biggest slum in
Odisha, falls under the alert zone and the alter zone spreads across 21 of the 67 wards in
the city. One person was reported H1N1 positive in the city in 2012.
The incidence of water borne and vector borne diseases has correlation with heavy rainfall.
However, there is a high incidence of water borne diseases across the year (Figure 2-25)
and also malaria incidence (Figure 2-26), even though reported cases of both these
diseases are high during July and August months.

s N
s
3500 3000 2500 2000 1500 1000 500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct
a
Nov Dec
0
c
2012
r
2013
b
Figure 2-25: Incidence of ADD cases across the year in Bhubaneswar city (derived from
district level data).
Figure 2-26: Seasonal incidence of malaria in Odisha State

Source: MM Pradhan, 2012
Figure 2-27: Malaria incidence trend during the last three years in Bhubaneswar city
The state level statistics on malaria show a decreasing trend.

Based on the household survey conducted, the diseases mostly reported include diarrhea
(22% of reported cases), gastroenteritis (23%), malaria (31%), jaundice (23%). The
household survey results also indicate that the occurrence of water borne diseases is high
during the rainy season. Even though there is high incidence of malaria reported in the rainy
season, there are cases reported across the year. Jaundice incidence shows a high rate of
occurrence during the summer months. Gastroenteritis is common throughout the year,
which is mainly to do with the unhygienic conditions, particularly in handling food. During the
survey, very few dengue and no chikungunya cases were reported.
Figure 2-28: Incidence of diseases across seasons based on the sample survey,
2014Disease Susceptibility Mapping
It is interesting to note that there is no hotspot for any of the diseases in the city; rather the
whole city has high incidence of water borne diseases and malaria. All the wards surveyed
irrespective of income/economic classes and house types they are living in reported high
incidence of both water borne diseases and malaria.
12 10
p
8
Diarrhea Gastroenteritis Jaundice malaria
6
s
s
4
a
2
d
0
e
4 9 10 14 16 20 21 22 50 53 Wards
Figure 2-29: Distribution of disease cases reported across the wards of Bhubaneswar city
The city Capital hospital records document reported cases like snake bite, accidental
injuries, ADD, measles and acute respiratory infections. Monthly sex-wise data is available
for these diseases for the last 5 years. However, spatial distributions of the disease cases
are not available for spatial analysis. There is an increase in accident injuries during the last
three years and more male cases have been reported as compared to female cases.

s
2001-5000
a
c
25
d
20
e
r
15
o
10
p
<2000 5
30
0
Diarrhea Gastroenteritis Jaundice Malaria
Figure 2-30: Disease incidence across income group (household survey 2014)
2.5.4 APPLICATION OF DISEASE SUSCEPTIBILITY MAPPING IN DISASTER MANAGEMENT

AND CITY PLANNING
1. Understanding of disease characteristics (both incidence and prevalence) can help in
predicting the risks and in appropriate mitigation planning.
2. Understanding occurrence time is important so that required prevention measures can be
put in place before the events occur.
3. Correlating disease incidence with social and biophysical factors, for instance distribution
of slums and reported cases, and waterlogged areas can provide insight into triggering
factors that increase and spread diseases, thereby allowing them to be dealt with at the
root cause level.
2.6 Climate Change and its Impact on Hazards
2.6.1 LITERATURE REVIEW
Global warming in response to human-driven emissions, particularly of carbon dioxide (CO 2)
has accelerated since 1970s and broke more countries' temperature records than ever
before in the first decade of the new millennium. A new analysis from the World
Meteorological Organization says average land and ocean surface temperatures from 2001
to 2010 rose above the previous decade, and were almost a half-degree Celsius above the
1961-1990 global average3. The decade ending in 2010 was also an unprecedented era of
climate extremes as evidenced by heat waves in Europe and Russia, droughts in the
Amazon Basin, Australia and East Africa, and huge storms like Tropical Cyclone ‘Nargis’ and
Hurricane ‘Katrina’. The 2012 annual global temperature across the land and ocean surface
was among the 10 warmest years on record4. In 2013 again, the global land surface
temperature was 0.77°C above the 20 th century average, the 11th warmest August on record.
Global average ocean surface temperature was higher than the 1981–2010 average value.
Experts say a decade is about the minimum length of time to study when it comes to spotting
climate change. From 1971 to 2010, global temperatures rose by an average rate of 0.17°C
per decade. The pace also picked up in recent decades. Average temperatures were 0.21°C
warmer during the past decade (2001 to 2010) than from 1991 to 2000, which were in turn
0.14°C warmer than from 1981 to 1990. Natural cycles between atmosphere and oceans
make some years cooler than others, but during the past decade, there was no major event
associated with El Niño, the phenomenon characterized by unusually warm temperature in
the equatorial Pacific Ocean. Much of the decade was affected by the cooling La Niña, which
comes from unusually cool temperatures there, or neutral conditions.
3
WMO, 2013: The Global climate 2001-2010: A decade of climate extremes, WMO No. 1103, 61 pp.
4
State of the Climate report - 2012: NOAA’s National Climatic Data Center, Published August 2013.

As stated under section 2.4, the state of Odisha has been experiencing unprecedented
contrasting extreme weather conditions for the past three or more decades; from heat waves
to cyclones; from droughts to floods. In Bhubaneswar city of Odisha, the annual mean
surface air temperature has risen during the past two centuries (see Figure 2-19 above).
However, the rate of increasing trend has sharply increased in the last few decades of the
20th century (Figure 2-31), which could be attributed to global climate change due to
anthropogenic forcings. Figure 2-31 clearly illustrates that during the past three decades of
the 20th Century, the rising trend in surface air temperature over Bhubaneswar is higher than
all India and Odisha state averages thus explaining the likely contribution of global climate
change to the unprecedented heat wave conditions in Bhubaneswar during hot summer
months. These climatic extremes could further aggravate in future thus requiring an
assessment of regional and local climate change and its impacts.
Trends in surface temperature increase over the last two
century in India and Bhubaneswar
1.6
)
1.4
u
1.2
n
1.0
C
0.8
°
e
0.6
0.4
r
2.4
0.2
Bhubaneswar Odisha India
2.2 0.0
1760-1810 1810-1860 1860-1910 1910-1960
2.0
1960-1990 Year
1.8
Figure 2-31: A comparison of rate of increasing trends in surface air temperature since
historical times in Bhubaneswar, Odisha and India.
2.6.2 DATA SOURCE

Following data sets were used in order to execute this task:
∙ Historical weather data was collected from the India Meteorological Department (IMD)
and other available sources.
∙ Projected climate data obtained from one of the state-of-the-art Global Climate Models,
namely HadGEM2-ES model (UK). The reason for selection of this GCM is that this
model has demonstrated reasonable degree of skill in simulating the baseline
climatology over the Indian region. The emission scenario considered for
development of future climate change scenarios is RCP 6.0 (Representative
Concentration Pathways) emission pathways and identified as modest future
emission scenario bracketing plausible future climate change without stringent
mitigation policies.

2.6.3 METHODOLOGY
Two types of analysis were carried out under this task, which are detailed as under:
∙ Temporal trends in rainfall and surface air temperature were assessed using historical
weather data.
∙ Spatial distribution patterns in maximum and minimum surface air temperatures and
rainfall over Bhubaneswar were developed using above-mentioned HadGEM2-ES
model data in GIS platform (ArcGIS 9.2). These analyses provide the likely shifts in
spatial changes of temperature and rainfall during 2040s (2026-2055) and 2080s
(2061-2090) with respect to baseline time period (1961-1990). The results of this,
together with the trend analysis, can be used to assess the implications of climate
change on various meteorological and hydro-meteorological hazards (e.g., drought,
flood, and heat wave etc.).
2.6.4 ANALYSIS RESULTS
India, as a whole, has experienced its average annual surface air temperature rise by about
0.5°C during the past century as also observed across the continents and globe and thus
supports its attribution to anthropogenic influences on global scale. The rise in surface
temperature seems to have accelerated since 1960s and particularly so during the past
decade. Past studies5reported that surface air temperatures over India are going up at the
rate of 0.4°C per decade, with peaks during the post-monsoon and winter seasons. Summer
temperatures over the State of Odisha in India are projected to increase by 2.5°C during
2040s and 3.5°C during 2080s. Winter temperatures could increase by as much as 3.0°C
during 2040s and 4.5°C by 2080s. According to a more recent study, south Asian summer
temperatures are projected to increase by 3°C to nearly 6°C by the end of 21 st Century with
the warming most pronounced in the northwestern parts of India 6. By the time 1.5°C warming
is reached, heat extremes that are unusual or virtually absent in today’s climate in the region
are projected to cover 15% of land areas in summer. Some regions are projected to
experience unprecedented heat during more than half of the summer months.
The recent climate modeling results (CMIP5 simulations) suggest that greenhouse gases
have contributed a global mean surface warming in the range of 0.5°C to 1.3°C over the period
of 1951−2010, with the contributions from other anthropogenic forcings, including the cooling effect
of aerosols, likely to be in the range of −0.6°C to 0.1°C. On regional scales, the confidence in
model capability to simulate key climate variables remains lower than for the larger scales.
However, there is high confidence that simulation of regional-scale surface temperature has
significantly improved now than at the time of the AR4. The new versions of Earth System
Models reproduce better some important circulation features modulating the climate
anomalies. There is high confidence that the key circulation features controlling the Asian
monsoon and El Niño-Southern Oscillation (ENSO) based on multi-model simulations have
improved since AR4 (IPCC, 2013)7.
A spatial distribution of rise in annual mean maximum and minimum surface air
temperatures in Bhubaneswar City of Odisha, as downscaled from outputs from one of the
5
Lal, M. 2003: Global climate change: India’s monsoon and its variability, Jr. Environmental Studies &
Policy, 6, 1-34.
6
Rajiv Kumar Chaturvedi, Jaideep Joshi, Mathangi Jayaraman, G. Bala and N. H. Ravindranath, 2012:
Multi-model climate change projections for India under representative concentration pathways, Current
Science, VOL. 103, NO. 7, 12 pp.
7
IPCC, 2013: Summary for Policymakers, in: Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.;
Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. (Eds.) Climate Change 2013: The Physical
Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY,
USA.
state-of-the-art global climate model used in CMIP5 simulations (Taylor et al., 2012) 8for the
5th scientific assessment report of IPCC under RCP 6.0 scenario, is presented here. The
projected rise in maximum (day-time) and minimum (night-time) surface air temperatures in
Bhubaneswar city of Odisha state at two time slices, namely, 2040s and 2080s are
illustrated in Figure 2-32 and Figure 2-33 respectively. The plausible changes in annual
mean and monsoon season rainfall over Bhubaneswar City of Odisha for two time slices,
namely, 2040s and 2080s are depicted in Figure 2-34 and Figure 2-35 respectively. The
model used here for the purpose is HadGEM2-ES model developed by Met Office Hadley
Center (UK). The emission scenario RCP 6.0 used for these projections is identified as
modest future scenario, bracketing plausible future climate change without stringent
mitigation policies. In our previous research and analysis of model validation, this model has
demonstrated reasonable degree of skill in simulating the baseline climatology over the
Indian sub-continent (HadGEM2-ES is found to be the best-performing individual model in
simulating the annual and seasonal Indian climatological characteristics, followed closely by
a few others).
hot summer months for 2040s in Khordha District of Odisha (Bhubaneswar city is marked
with black boundary here)
Further, it is evident from Figure 2-32 that the mean maximum day-time surface air
temperatures during hot summer months in the city of Bhubaneswar is likely to rise on an
average by about 0.8°C around the middle of this century while the rise in mean night-time
minimum surface air temperature during the hot summer months could exceed 1.1°C by the
middle of this century. This illustration further suggests that the diurnal temperature range
would reduce in future in Bhubaneswar city of Odisha State. During 2080s, the maximum
day-time and minimum night-time surface air temperatures in the city of Bhubaneswar on hot
summer months mean basis are expected to rise in excess of 2.1°C and 2.3°C respectively
(Figure 2-33). These projections of rise in surface air temperatures in future suggest that the
intensity of heat waves in the city of Bhubaneswar should become stronger with time during
peak summer months and record high temperatures could be experienced here more often
in future.
8
Taylor, Karl E., Ronald J. Stouffer, Gerald A. Meehl, 2012: An Overview of CMIP5 and the Experiment
Design. Bull. Amer. Meteor. Soc., 93, 485–498.
hot summer months for 2080s in Khordha District of Odisha (Bhubaneswar city is marked
with black boundary here)
An examination of the change in rainfall patterns as depicted in Figure 2-34 suggests that
the annual mean and monsoon season rainfall is projected to increase by about 0.46 mm /
day and by about 1.12 mm / day respectively (a total of about 170 mm in a year) over
Bhubaneswar city by the middle of this century. Figure 2-35 reveals that the seasonal
monsoon rainfall could increase by about 2.27 mm / day (a total of about 270 mm in the
season) over Bhubaneswar city by the end of this century. On annual basis, the rainfall
would increase over Bhubaneswar city by around 0.81 mm / day (a total of about 295 mm in
a year) by the end of this century. It is evident from Figure 2-34 and Figure 2-35 that, on an
average, Bhubaneswar city is likely to experience a significant increase in monsoon rainfall
only in the latter part of this century.
here)

2080s in Khordha District of Odisha (Bhubaneswar city is marked with red boundary here)
While an enhanced focus has been placed in recent years on short-term climate change
projections (say 2040s), it must be acknowledged that there remain many uncertainties
regarding future climate change on local scales. This is because the future level of global
greenhouse-gas emissions is uncertain, and the available knowledge about the climate-earth
ocean system is still rather inadequate for reliably forecasting the local climate change. The
climate information and projections provided in this study should, therefore, be considered
only as indicative, not predictive.
According to a recent World Bank Report 9, a four degrees Celsius world would bring about
unprecedented heat waves, severe drought, and major floods in many regions, with serious
impacts on ecosystems and associated services. In a summary for policy makers report of
the Working Group 2 of the IPCC (March 2014) 10, this has been reasserted with a high
degree of confidence that globally, the impacts from recent climate-related extremes, such
as heat waves, droughts, floods, cyclones, and wildfires, reveal significant vulnerability and
exposure of some ecosystems and many human systems to current climate variability.
Impacts of such climate-related extremes include alteration of ecosystems, disruption of food
production and water supply, damage to infrastructure and settlements, morbidity and
mortality, and consequences for mental health and human well-being. For countries at all
levels of development, these impacts are consistent with a significant lack of preparedness
for current climate variability in some sectors. In such a scenario, the frequency and duration
of heat waves and extremes in daily rainfall in some States of India including Bhubaneswar
city is also likely to increase substantially, taking a toll on disruption to city life and human
health. Agriculture too would be adversely affected by the thermal stress due to rise in
temperature although part of the loss in soil evapotranspiration due to higher temperature
could be compensated by increase in rainfall. Intense rainfall spells could lead to loss of top
soils in farmlands and cause sedimentation concerns in river basins and deltas. A warming
of the global surface temperature by 4°C could lead to an associated sea level rise of one
9
World Bank, 2012: Turn Down the Heat: Why a 4°C Warmer World Must be Avoided, World Bank, Washington,
D.C.
10
IPCC, 2014: Summary for Policy Makers, in: Christopher B. Field (USA) et al., Climate Change 2014: Impacts,
Adaptation, and Vulnerability. Contribution of the Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA.

meter or more11. Many cities of coastal Odisha are vulnerable to rising sea levels as is the
Mahanadi delta.
2.6.5 APPLICATION OF CLIMATE CHANGE STUDIES IN CITY DEVELOPMENT
With increasing demands on resources such as water and energy, expansion of informal
settlements and overall expansion of the city of Bhubaneswar is posing enormous
challenges to its urban planners. In order to mainstream climate change impact in
development planning tailored to the varying physical, socio-economic, and hazard contexts,
it is important to understand the climate change trends and its influence on various climate
related hazards in urban settlement. The climate change impacts may overwhelm the
infrastructure that ensures movement of people, goods, and services; security and health to
city residents, and assures livelihood opportunities and economic benefits to increasing
numbers of urban migrants. It will have disproportionate impacts on poor and vulnerable
urban populations. This necessitates a comprehensive vulnerability assessment for
designing medium and long terms adaptation and mitigation measures for continued
development in a sustainable pathway. It may be stated here that heat waves and droughts
can also cause massive damage on rural (particularly peri-urban surroundings)
agricultural/horticulture areas vital to providing food staples to urban population centers.
Reservoirs and aquifers quickly dry up due to increased demand on water for drinking,
industrial and agricultural purposes. The result can be shortages and price spikes for food
and with increasing frequency, shortages of drinking water as observed with increasing
severity seasonally in Bhubaneswar and throughout most of the large urban centers in India.
From an agricultural standpoint, farmers could be required to plant more heat and drought
resistant crops. Agricultural practices would also need to be streamlined to higher levels of
hydrological efficiency. Reservoirs should be expanded and new reservoirs and water towers
should be constructed in areas facing critical water shortages. In the short term, the city of
Bhubaneswar needs to conduct in-depth “sector studies” for deeper analysis on priority
issues facing the city and testing small pilot activities to explore specific vulnerability needs
of small areas in which they can begin making changes and monitoring the outcomes.
Armed with knowledge of key vulnerabilities and existing adaptive capacities, and drawing
on both international and local experience, the city needs to develop climate proof and local
resilience strategies and action plans that will enable it to better prepare for the challenges of
current and future climate variability. This includes a process of identifying, assessing, and
prioritizing actions that will effectively build climate resilience of the city’s systems and
resilience of its poorest and most vulnerable populations. These resilience strategies and
action plans should be integrated with the city’s development planning processes.
Climate change is expected to lead to warmer temperatures particularly in urban
environment due to heat island effect (summer day-time temperatures can reach up to 6°C
hotter in Bhubaneswar city than in surrounding rural areas and between 3–4°C warmer at
night), resulting in greater variability in local conditions and are likely to increase the
frequency, intensity, and duration of such extreme events in unpredictable ways. Greening
urban towers and structural spaces is among the most frequently mentioned strategies to
address urban heat island effects. The idea is to increase the amount of natural cover within
the city. This cover can be made up of grasses, bushes, trees, vines, water, rock gardens;
any natural material. Covering as much surface as possible with green space will both
reduce the total quantity of thermally absorbent artificial material, but the shading effect will
reduce the amount of light, heat and electromagnetic radiation that reaches the concrete and
asphalt that cannot be replaced by greenery. Trees are among the most effective greening
11
IPCC, 2013: Summary for Policymakers, in: Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.; Boschung, J.;
Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. (Eds.) Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA.

tool within urban environments because of their coverage/footprint ratio. Trees require a very
small physical area for planting, but when mature, they provide a much larger coverage
area. This both absorbs solar energy for photosynthesis (improving air quality and mitigating
global warming), reducing the amount of energy being trapped and held within artificial
surfaces, but also casts much-needed shade on the city and its inhabitants.
Climate change projections, at current level of scientific understanding, are uncertain at least
at local levels for many reasons – not the least of which being that we do not know what kind
of energy and lifestyle choices will be made in the future. While climate projections cannot
provide exact estimates of how much rise in maximum temperature could be expected in the
following week or month or how much monsoon precipitation will fall in the city of
Bhubaneswar by the middle of this century, the projections provide very useful information
on the changes in trends and ranges of climate conditions here. Uncertainty in climate
change projections is challenging and city planners need to explore innovative actions and
“no regret” strategies that are capable of being resilient against a wide range of climate
conditions, rather than relying on traditional approaches to planning and engineering that
assume the future climate, including extreme events, are simply predictable reflections of
historical trends.
The integration of urban systems, climate change and vulnerability to test resilience
strategies would need to be based on considering both direct and indirect impacts of climate
change. This would be required to develop adaptation strategies that enable the responsible
agencies to confront the complexities of climate change and introduce actions that
mainstream climate resilience into city planning and operations and develop strategies that
support the most vulnerable groups within the city to respond effectively to climate change
induced risks.
The city of Bhubaneswar needs to explore what existing capacities can enhance their ability
to adapt and be more resilient to climate change. These assessments result in vital
information that feeds into the resilience planning processes. They help ensure that
resilience strategies, actions, and interventions will target the most vulnerable populations,
address vulnerabilities of urban sectors and systems, and build on existing capacities.
Climate vulnerability assessment entails:
∙ An understanding of projected climate scenarios and potential impacts and the limitations
of the projections;
∙ Identification of who/what are the most vulnerable groups, areas, sectors, and urban
systems and how they may be affected;
∙ Identification of the range of factors that systematically combine to make them vulnerable,
including both direct (e.g. exposure to hazards) and indirect (e.g. regional or
international food security) factors; and
∙ Assessment of existing capacities to adapt

3 Development of Exposure Database for
Bhubaneswar City
Exposure is a critical component of any risk assessment study. Exposure constitutes
population, the built environment, systems that support infrastructure and livelihood
functions, or other elements present in the hazard zones that are thereby subject to potential
losses. Quantifying building exposure includes the "bottom-up" approach that includes
classifying the different types of buildings into different categories, estimating the number of
buildings under each category, combining building counts with per unit built-up floor area,
and applying costing information relevant to the conditions. In this regard, modeling
vulnerability of a system to natural hazards involves establishing a relationship between the
potential damageability of critical exposure elements and different levels of local hazard
intensity for the hazard of interest. Thus, damage susceptibility associated with a given level
of hazard is measured in terms of a Mean Damage Ratio (MDR) defined as the expected
proportion of the monetary value of repair needed to bring back the facility to pre-event
condition, over the replacement value of the facility, as a consequence of the hazard for
various exposure elements.
In the present study, the objective of this exposure database development is to create a
ward-level database of various assets. Data for various exposure elements like demography,
households, public health, education, housing, amenities, transportation etc. were collected
from different sources and finally exposure database was created at ward-level.
3.1 Data sources
The data used for the analysis includes:
4. Demographic and housing data of 2011 vintage from Census of India
5. Administrative boundary and major landmarks from BMC
6. Transportation and river network data from BMC
7. Building cluster data developed from LISS-4 satellite images
8. Structural information and unit replacement costs of different housing types and
infrastructural elements from the field survey and literature review
9. Cultural heritage sites from internet sources
3.2 Inventory of vulnerable demographics, buildings and
infrastructure
Inventory of vulnerable buildings, infrastructure, demographics and any other asset elements
present in hazard zones and thereby subject to potential losses constitute critical exposure
elements that are considered for risk assessment. Figure 3-1 shows the broad categories of
exposure elements considered in this study.

Figure 3-1: Broad categories of exposure elements considered in the study
3.3 Methodology Adopted for Exposure Database Development

The team quantified exposure using the "bottom-up” approach. This includes classifying the
different types of houses and infrastructure elements into different categories, estimating
their count under each category, combining building counts with per unit built-up floor area in
case of buildings or other infrastructure characteristics, and applying per unit costing
information relevant to the category. The output of exposure is the total monetary value by
asset category. The overall process of developing the exposure database is illustrated in
Figure 3-2.
One of the important aspects of exposure data development was to categorize the exposure
elements into ‘aggregate’ or ‘site specific’, to analyze the impact of hazards. Aggregate data
were those where area and count are summed up at a suitable administrative unit level (for
instance at Municipal Ward-level), while the site-specific data were represented by
geographic locations (coordinates). A general rule for categorizing data as aggregated or
site specific was based on the level at which the location information is available.
The first step of developing exposure database involved collection of data from concerned
agencies. In the present study, demographic data is collected from Census of India and field
survey conducted by the project team, while various site-specific data is collected from BMC
and other government agencies. During data processing, the tabular and GIS data collected
were processed into usable format for the defined exposure elements and brought at the
ward level with required attribute information associated with them. The processed data was
then analyzed for data gaps and the team identified alternate data sources to fill these gaps.
After processing, these data sets are appended with other exposure data processed earlier.
In the next step, the spatial location of the site-specific exposure data is validated using
higher resolution satellite images/ Google Earth. For example, the spatial distribution of
houses in this study was determined by using similar methods. To get an idea of
construction practices (construction materials, structural types, architecture, and unit costs)

for residential, commercial, industrial buildings, as well as for religious and infrastructural
facilities such as roads, bridges, etc., the team undertook sample surveys. During these
surveys, inputs from various government and non-government agencies are collected and
existing information are validated through personal interviews, photographs, and documents.
Figure 3-2: Approach to exposure development

The verified and processed data was then used as inputs to the relevant steps (count
estimation, area/length estimation, unit cost estimation) from which the final total exposure
value was calculated.

3.4 Analysis of Exposure Elements
3.4.1 POPULATION
The key sources of demographic data in the present study are Census of India (Census
2011) and BMC, and these data are available at municipal ward level for Bhubaneswar city.
This includes population distribution by age, gender, education, and occupation. In addition,
details about the other exposure elements such as access to electricity, potable water and
sewerage etc. are analyzed.
As per Census 2011, the total population of the city is 840,834, which is distributed in 67
municipal wards. Out of city’s total population, about 53% are male while remaining 47% are
female (Figure 3-3). It is observed that Ward Number 1 has the highest percentage of male
population (about 63% of ward’s total population) while Ward Number 6 has higher
percentage of female population (over 50% of ward’s total population). The ward-wise
distribution of population is presented in Table 9-1 of Annexure 2: Detailed Exposure Data of
this report.
Figure 3-3: Distribution of population by gender and SC/ST (caste)

In Bhubaneswar city, about 13% of city’s total population falls in Schedule Caste/ Schedule
Tribe (SC/ST) category. Again, it is observed that out of the total SC/ST population of the
city, about 62% falls in SC category and remaining 38% falls in ST category (Figure 3-3).
The SC population of the city comprises of 52% male and 48% female while the ST
population comprises of 54% male and 46% female.
The city has a higher literacy rate and about 83% of its total population is literate. Moreover,
among the total literate population of the city, 55% are male and 45% are female. In terms of
ward-wise distribution of literacy rates, it is observed that most of the wards have identical
share of literacy rates, which is about 83% of the ward’s total population. Details of ward
wise distribution of literacy rates are presented in Table 9-2 of Annexure 2: Detailed
Exposure Data.

Figure 3-4: Distribution of population by literacy
The city has about 63% of non-working population residing across various wards. The non
working population comprises of about 62% female and about 38% male (Figure 3-5). It is
also observed that ward number 4 has the highest number of non-working male and female
population while compared to other municipal wards.
If we consider the percentage child population (<6 Years) with respect to city’s total
population, it is observed that Bhubaneswar city has about 9.7% (81,625 children) of child
population located in it (Figure 3-5). This child population (< 6 Years) comprises of about
53% male child and 47% female child. Table 9-1 of Annexure 2: Detailed Exposure Data
presents the ward-wise distribution of child population (< 6 Years).
Figure 3-5: Distribution of non-working population and child population (<6 Years)
Figure 3-6 presents the density of population by municipal wards. It is observed that ward
number 21 has the highest population density of more than 54,969 persons/ sq. km.
whereas ward number 15 is the least densely populated (about 1,136 persons/ sq. km.)
amongst all the wards.

Figure 3-6: Ward-wise population density
3.4.2 HOUSING
In the present study, building classification is carried out primarily based on occupancy types
and structure types. The occupancy-based classification differentiates housing into four
occupancy classes viz., residential, commercial, industrial, and others. The class named as
others comprises of occupancy classes such as schools and colleges, hospitals and
dispensaries, government offices, places of worship etc. Structure based classification,
categorizes the houses based on the construction materials used (roof and wall materials)
as per Census 2011, their architecture, and height. The analysis also considered classifying
houses into occupied and vacant (Figure 3-8). The different classifications represent
elements that are distinctly vulnerable to the same level of hazard. The determined exposure
value is used as an input to the vulnerability and risk assessment.
As mentioned above, the source of housing data is the Census of India (2011), which
provides city-level data based on the usage and construction materials. Further, the data
generated from the field survey conducted by the team and information gathered from local
builders is used for filling the data gaps and validation of information. In this process, the
city-level data is brought to the ward level using the household numbers available at ward
level in the demographic data table of Census. These statistics are then correlated with the
housing data received from BMC and finally ward-wise distribution of houses based on their
occupancy and structural class are determined. Figure 3-7 presents the building footprints
delineated from LISS IV satellite images for Bhubaneswar city. The classifications are done
based on the tone, texture, shape, size, pattern, and associations between buildings as
present on satellite images. For example, residential strips generally have a uniform size and
spacing between structures with linear driveways and lawn areas. Commercial strips are
more likely to have buildings of different sizes with non-uniform spacing between them. They
are also characterized by the larger driveways and parking areas associated with them.
Commercial areas are often abutted by residential, agricultural, or other contrasting uses
that help in defining them. Similarly, Industrial areas are generally situated at some distance
from urban centers. The ward-wise distribution of houses based on their occupancy and
structure is given in

Table 9-4 of Annexure 2: Detailed Exposure Data.
Figure 3-7: Distribution of built-up area by occupancy and major building use
It is observed that about 89% of the total houses in Bhubaneswar city are occupied and the
remaining 11% are vacant (Figure 3-8). The occupancy based distribution of housing shows
that about 75% of the houses are used for residential purposes, about 14% are used for
commercial or residential-cum-commercial use, about 2.5% houses are used for industrial
use, and remaining 8.5% houses are used for other use that include social and public uses,
viz., schools, colleges, hospitals, dispensaries, place of worship etc. Figure 3-8 provides the
distribution of houses based on their occupancy for Bhubaneswar city.

Figure 3-8: Percentage distribution of occupied and vacant house (left), Distribution of the
Census houses based on usage in Bhubaneswar city (right)
3.4.2.1 Structural Types

From vulnerability and risks to hazard point of view, structural classification of buildings is a
critical component of exposure data development. The vulnerability of houses to various
hazards depends largely upon the construction materials used, structural types, and height
the houses, which are categorized into different structural types based on these different
characteristics. The different categorizations represent elements that are distinctly
vulnerable to the same level of hazard. As the available data lacked structural details, the
teams undertook a sample field survey and collated that information with data from other
sources to categorize the buildings into different structural types. From structural
vulnerability point of view, based on materials used for construction of walls and roofs, and
building structure, the residential houses of Bhubaneswar city is classified into 90 distinct
combinations that are further grouped into seven distinct structural categories (Table 3-1).
Table 3-1: Building Categories by construction materials and Structural Types

S.No. Building Category Structural Types (combination of major wall and roof
materials)
1 Grass/ thatch/ bamboo/ Grass/ thatch/ bamboo/ wood/ plastic/ polythene etc. used
wood/ plastic/ polythene in combination for wall and roof materials
etc.
2 Mud/ Unburnt Brick/ Stone Mud/Un-burnt brick/stone without mortar as wall materials
without mortar and grass/thatch/bamboo/ Plastic/ polythene/handmade
tiles/ machine-made tiles etc as roof materials
3 Light Metal G.I./metal/asbestos sheets as wall materials and

grass/thatch/bamboo/ Plastic/ polythene/tiles/
G.I./metal/asbestos sheets as roof materials
4 Burnt Brick/ Stone with Burnt brick/ Stone packed with mortar as wall materials
mortar with Temporary and temporary roof (tiles, wood, GI, slate, etc.)
Roof
5 Reinforced Masonry Burnt brick walls and RCC roof

Buildings
6 Reinforced Concrete Reinforced Concrete Frame buildings with brick-in-fills

Frame (RCF) with brick walls and RCC roof
S.No. Building Category Structural Types (combination of major wall and roof
materials)
infill
7 Reinforced Cement Combination of concrete and steel to build a structure

Concrete (RCC)
3.4.2.2 Residential
The residential houses of Bhubaneswar city are broadly classified into four categories viz.,
Villas, Apartments, Row Houses and Huts based on construction materials and structures.
Sample photographs of each category are provided in Figure 3-9. Though, in some cases,
one category of house is often found mixed up with other categories, however, in some
cases the distinct clusters can be identified. For demarcation of ward-wise residential house
clusters, interpretation of LISS IV satellite image, landmark locations received from BMC and
Census 2011 housing statistics are considered. The results obtained are then correlated and
validated against the field survey data collected by the survey team at sample wards.
Villa
Apartment

Row Houses
Huts
Figure 3-9: Different types of residential houses in Bhubaneswar

From vulnerability and risk point of view, the residential houses are further categorized into
various classes based on condition and structural parameters. In terms of residential building
use, the houses of the city are classified into residence (about 98%) and residence-cum other
use (about 2%) Figure 3-10. Amongst the houses used exclusively for residential purposes,
60% are in good condition, 35% are in livable condition and about 5% are in deteriorating
condition. For Residence-cum-other use, about 47% and 48% of the houses are in good and
livable condition, respectively. The ward-wise distribution of the Census houses based on
the condition of the houses is presented in Figure 3-10.
Figure 3-10: Distribution of residential houses by use (left), Condition of residential houses in
Bhubaneswar city (right)
Reinforced concrete frame (RCF) with brick infill is the dominant structural class that
comprises of about 72% of the residential houses (Table 3-2). Burnt brick (with mortar) with
temporary roof and masonry buildings are the next dominant classes that are present for
10% and 8% residential houses, respectively (Figure 3-11). The latter two types of structures

are more common in older areas and suburban areas of the city, and in isolated pockets
inhabited by economically weaker sections of population. Houses made up of temporary
materials like mud/ unburnt bricks/ stone without mortar are more commonly found in areas
of unauthorized encroachments and slums. About 4% residential houses of Bhubaneswar
are pure Reinforced Cement Concrete (RCC) buildings and majority of them are observed in
planned areas of the city. Among these RCC buildings, about 77% are apartments and
remaining 23% are villas. The ward-wise distribution of residential houses by structure is
presented in of Annexure 2.
Table 3-2: Residential built-up area by structural types

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Total 0.37 3.26 0.05 6.44 5.51 35.35 12.88 0.59 2.02 0.12
Built-up
Area
(sq. km.)
Figure 3-11: Distribution of residential houses by building construction materials and

structure
3.4.2.3 Commercial
The data regarding the number and location of commercial buildings in Bhubaneswar city is
collected from the Census of India 2011, BMC and other internet sources. The GIS data with

detailed landmarks received from BMC is collated with the records obtained from Census of
India at ward level. In the next step, based on Census records, the commercial buildings are
classified into shops and offices, hotels, lodges, guest-houses, residence-cum other uses,
and other non residential houses. All these categories of data are combined together to get
the ward-wise aggregated number of commercial buildings in the city. Sample photographs
of some of the commercial centers are provided in Figure 3-12.
It should be noted that the data on commercial buildings do not include schools and
colleges, hospitals and dispensaries, places of worship, and industries.
Figure 3-12: Different types of commercial buildings/ centers in Bhubaneswar

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United Nations Development Program

Enhancing Institutional and Community Resilience to

Hazard Risk and Vulnerability Analysis (HRVA) of

Emergency Analyst and Officer-in-

Final Report Confidential Page 2 of 174

Version Date Author(s) Brief Description of

1.0 Oct 2014 Sushil Gupta, Draft

2.0 Nov 2014 Sushil Gupta, Final

Final Report Confidential Page 3 of 174

Final Report Confidential Page 4 of 174

Final Report Confidential Page 5 of 174

Final Report Confidential Page 6 of 174

Final Report Confidential Page 7 of 174

Final Report Confidential Page 8 of 174

Final Report Confidential Page 9 of 174

∙ City administration should develop a ward-level plan to check on vulnerable populations

∙ Public awareness for improving hygiene and sanitation.

∙ Desilting drains to avoid water logging during rainy season

Final Report Confidential Page 10 of 174

IT and database development measures

Mainstreaming integrated DRR in city development planning

Final Report Confidential Page 12 of 174

Final Report Confidential Page 13 of 174

Final Report Confidential Page 14 of 174

Final Report Confidential Page 15 of 174

Final Report Confidential Page 19 of 174

AAL Average Annual Loss

ADD Acute Diarrheal Disorders

AR4 Fourth Assessment Report

BDA Bhubaneswar Development Authority

BMC Bhubaneswar Municipal Corporation

CDMP City Disaster Management Plan

CDMP Community Disaster Management Plan

DRR Disaster Risk Reduction

DRM Disaster Risk Management

DRR Disaster Risk Reduction

EWS Early warning System

GAA Gopabandhu Academy of Administration

GIS Geographic Information Systems

GOI Government of India

HRVA Hazard Risk Vulnerability Assessment

INR Indian Rupees

IPCC The Intergovernmental Panel on Climate Change

IRS Incidence Response System

LEC Loss Exceedance Curve

LISS Linear Imaging Self-Scanner

MDR Mean Damage Ratio

NDMA National Disaster Management Authority

NDRF National Disaster Response Force

NGOs Non Government Organization

NHAI National Highways Authority of India

NIDM National Institute of Disaster Management

NRHM National Rural Health Mission

ODRAF Odisha Disaster Rapid Action Force

OSDMA Odisha State Disaster Management Authority

PML Probable Maximum Loss

PPP Public- Private- Partnership

PWD Public Works Department

RCC Reinforced Cement Concrete