ArticlePDF Available

Urban Flood Risk Assessment and Development of Urban Flood Resilient Spatial Plan for Bhubaneswar


Abstract and Figures

Urban flooding is growing as a serious development challenge for cities. Urbanization demands the conversion of pervious land to impervious land by pushing the transformation of water bodies, flood plains, wetlands and green spaces into built-up spaces. This affects the hydrological setting of the city’s geographic area. Bhubaneswar, one of the first planned cities of independent India, has expanded rapidly with an increase in the settlement land use cover from 41 km ² to 81 km ² in the last two decades. Non-consideration of disaster risk assessment in the land use plan has placed the city at high disaster risk. Hence, this article explores various avenues for making a flood resilient city through spatial planning. To understand the flood and its consequences, a flood hazard and vulnerability map was prepared by overlaying the existing social and infrastructure networks, and flood risk zones were generated through analytical spatial modelling in GIS. This accounts for the areas in which flood hazards are expected to occur, as well as the area whose socio-economic and infrastructure susceptibility to the disaster is more. The key outcome is to ensure urban development that can work concurrently with nature by integrating disaster risk reduction strategies into land use planning.
Content may be subject to copyright.
Urban Flood Risk Assessment and
Development of Urban Flood Resilient
Spatial Plan for Bhubaneswar
Alisa Sahu1, Tushar Bose2 and Dipak R. Samal2
Urban flooding is growing as a serious development challenge for cities. Urbanization demands the
conversion of pervious land to impervious land by pushing the transformation of water bodies, flood
plains, wetlands and green spaces into built-up spaces. This affects the hydrological setting of the city’s
geographic area. Bhubaneswar, one of the first planned cities of independent India, has expanded rapidly
with an increase in the settlement land use cover from 41 km2 to 81 km2 in the last two decades. Non-
consideration of disaster risk assessment in the land use plan has placed the city at high disaster risk.
Hence, this article explores various avenues for making a flood resilient city through spatial planning.
To understand the flood and its consequences, a flood hazard and vulnerability map was prepared by
overlaying the existing social and infrastructure networks, and flood risk zones were generated through
analytical spatial modelling in GIS. This accounts for the areas in which flood hazards are expected to
occur, as well as the area whose socio-economic and infrastructure susceptibility to the disaster is
more. The key outcome is to ensure urban development that can work concurrently with nature by
integrating disaster risk reduction strategies into land use planning.
Urban flood, resilient city, disaster risk assessment, land use planning, spatial planning
According to the United Nations report (2019), more than 50% of the world population lived in urban
areas by 2018. Half of the world’s population is contributed by only ve countries, including India. India
is estimated to contribute world’s maximum number of urban dweller of 416 million in the coming
1 Faculty of Planning, CEPT University, Ahmedabad, Gujarat, India.
2 Faculty of Technology, CEPT University, Ahmedabad, Gujarat, India.
Corresponding author:
Alisa Sahu, Faculty of Planning, CEPT University, Ahmedabad, Gujarat 380009, India.
Environment and Urbanization ASIA
12(2) 269–291, 2021
© 2021 National Institute
of Urban Affairs (NIUA)
Reprints and permissions:
DOI: 10.1177/09754253211042489
270 Environment and Urbanization ASIA 12(2)
30 years (United Nations, 2019). As per the 2011 census, 31% of India’s population was living in cities.
Today, more people are shifting to urban centres than ever before because cities offer better opportunities
and quality of life. While the total population of India has doubled in the last 50 years, its urban population
has grown by ve times (Taubenböck et al., 2009). This rapid urbanization has led to the unplanned
growth of the urban area, which has introduced complex ecological, economic and social changes
(DeFries & Pandey, 2010).
Globally, the implications of climate change in cities remain an urgent concern. Addressing this,
sustainable development goal number 11 (United Nations, 2015) concentrates on building sustainable
cities, which it specifically defines as cities that are resilient to disaster and adaptive to climate change.
The fifth assessment report of IPCC identifies urban areas with a ‘very high confidence’ level of risks
from increased storms and extreme precipitation, inland and coastal flooding, drought and water scarcity
(IPCC, 2014). Cities are the first responders in a crisis and are also the first to experience the threats.
Rapidly growing cities, along with their imminent threats, require an immediate intervention in terms of
redesigning the urban system, policies and governance for sustainable development. Sustainable and
inclusive growth is the key to unravel the true potential of urbanization. A resilient city considers both
the above-mentioned factors.
Flood is one of the most frequent and extensive natural disasters. About one-third of the world’s
population was affected by floods during 1985–2003 (World Bank, 2005). India leads the table among
the most flood-affected countries in the world (Winsemius & Ward, 2015). Urban areas are likely to
be more affected by floods because of the sheer number of resources and people exposed to them.
Among the various types of floods, urban flooding is a frequent disaster affecting many cities
worldwide. It is becoming more dangerous and costly to manage as the city grows (Jha et al., 2012).
As climate change, urbanization, increase in population growth and land use change takes place, it is
an urgent need to understand the urban flood risk and make flood-resilient cities (Cheng &
AghaKouchak, 2014).
Traditionally, urban flooding is assumed to be an infrastructural problem and thought to be solved
with engineering solutions. Sometimes such interventions cause more problems as opposed to resolving
them. Moreover, these are cost-intensive, especially for developing countries (Soz et al., 2016). However,
conventional engineering does not consider the dynamic of land use change. For long-term sustainability,
a good land use plan is required, which will consider the forthcoming disaster risks and balance both the
present and future needs. Land use planning is a powerful tool in disaster risk reduction, thereby
increasing the city’s resilience (Burby et al., 2000).
Bhubaneswar, one of the first planned cities of Independent India, has developed rapidly over the
decades, making the planning process clumsy (Das Chatterjee et al., 2015). Because of its topography,
Bhubaneswar is more prone to waterlogging when natural courses are disturbed. Rapid urbanization has
disrupted the hydrological environment of the city’s geographic area. This research article tries to find
the rift in the current practice of land use planning in Bhubaneswar and explores the plausibility of
formulating a flood resilient spatial plan. There are two objectives of this study. The first objective is to
analyse the existing natural drainage systems in the city with respect to its immediate land use and its
changes over the recent years of urbanization. The second objective is to assess the urban flood hazard,
risk and socio-economic vulnerability of the city. These two objectives would collectively help to
understand the lacuna in the present methodology and pave the path to plan a city that can take care of
flood risk.
Sahu et al. 271
Figure 1. Location of Study Area, Bhubaneswar
Source: Bhubaneswar Development Authority.
Study Area
Bhubaneswar is located in the lower catchment of Mahanadi River basin, on the coastal plain of Odisha
at 20°17’45’’N and 85°49’28’’E (Figure 1). The city is engulfed between Chandaka reserve forest to its
west, the river Khuakhai to its east and river Daya to its southeast. It falls under four micro water basins
with a prevalent slope running from the west towards east. Ten natural streams ow in the city, covering
71 km (Department of Architecture and Regional Planning, 2010). However, all the natural streams are
now converted into nallas that carry sewage and stormwater. Guangua nalla in the east acts as the main
drain that carries all wastewater, stormwater, industrial wastewater and pollutants and spills it into river
Daya in the south.
The city’s mean annual precipitation is 1,497mm, with a wet season from May to October. The
average rainy day by 2004 was 87 days, which has reduced to 75 days by 2011 (Department of Architecture
and Regional Planning, 2010). Also, Bhubaneswar is categorized as a ‘very high damage risk zone-B
(Vb = 50m/s)’, as per the wind and cyclone zone in Vulnerability Atlas of India, 2019 published by
BMTPC Government of India (BMTPC, 2019). Cyclones are generally accompanied by heavy rainfall,
making the post-cyclone phase also arduous to recover.
The first land use master plan for modern Bhubaneswar was formulated in the year 1968. Its population
has increased significantly from 16,512 in 1951 to 881,988 in 2011 (Census of India, 1951, 2011). During
1961–1971 the growth rate was 176.07%, which was the highest growth rate of the country during that
period (Department of Architecture and Regional Planning, 2010) (refer Table 1). By 2030, it is expected
to accommodate 2 million people (Department of Architecture and Regional Planning, 2010).
272 Environment and Urbanization ASIA 12(2)
Table 1. Decadal Population, Area and Density Growth
Population Growth and
Density Census Year Population
Decadal Population
Growth (%) Area (Sq. Km.) Density Per Sq. Km.
1951 16,512 25.90 638
1961 38,211 131.41 50.25 760
1971 105,491 176.07 65.03 1,622
1981 219,211 107.80 92.91 2,359
1991 411,542 87.74 124.74 3,299
2001 648,032 57.46 135.00 4,800
2011 8,43,402 30.1482 135.00 6,247
Source: Department of Architecture and Regional Planning, 2010.
Data and Methodology
This article explores how the city of Bhubaneswar can be made urban ood resilient by integrating
disaster risk assessment into its land use plan. The research is structured in 3 phases which are as follows.
In the first phase, changes in land use and land use policy are studied to understand the history of urbanization
patterns around the vicinity of water bodies. The assessment focuses on analysing how the city’s land use
master plan has addressed the water bodies, natural drainage and urban flood hazard. GIS and Remote sensing
(DEM-Landsat 8, Landsat 3,4) were used to prepare maps, and analyse land use and land cover (LULC)
changes over two decades through supervised classification. The LULC change pattern around the water body
is examined to understand the land use pattern in the riparian and floodplain area. All the master plan maps of
the city for research were collected from the Bhubaneswar Development Authority.
In the second phase, the article assesses the urban flood risk. To assess the flood risk, the flood hazard
and flood vulnerability assessment are carried out. The parameters of disaster hazard, vulnerability and
risk are very different. Therefore, a spatial multi-criteria index is prepared using ArcGIS to combine and
compare various natural, social and anthropogenic criteria. All parameters are processed within the GIS
using the overlay method (Heywood et al., 1993). GIS and Remote Sensing has always been an integral
component for risk assessment and mapping natural hazards like flooding (Lawal et al., 2014; Pradhan
et al., 2008). It is considered a complementary approach to flood modelling (Lecca et al., 2011). A wide
range of data from various sources is collected for creating spatial criterion layers using geospatial
techniques. For determining the relative importance of each factor of flood hazard and risk assessment,
an analytical hierarchy process (AHP) technique is adopted (Kazakis et al., 2015; Saaty, 1990). The
sections below describe the methodology in detail.
Flood Hazards
An assessment of ood hazards is necessary for understanding the area that can be affected by the
disaster. This gives a better rationale for managing the watershed area and consequently preparing for
forthcoming disasters. After an extensive literature review, Kazakis’s methodology for the assessment of
ood hazard (Kazakis et al., 2015) is adopted in this study. Five parameters are used to assess ood
hazards based on anthropogenic and natural factors: elevation, slope, ow accumulation, land use and
land cover, and distance from the channel (Stefanidis & Stathis, 2013). These parameters are extracted
from SRTM DEM and Landsat8 from USGS. The parameters are reclassied into ve classes from one
Sahu et al. 273
to ve, where one is the least prone to ood hazard and ve is the most prone to ood hazard. The
weights to these parameters are given by analytical hierarchy process (AHP). A pairwise matrix
comparison made in AHP is created by assigning a one to nine rating score to generate the weightage of
each factor (Table 2 and 3). Finally, various parameters with their weights are processed (Kazakis et al.,
2015). The ood hazard index was calculated using the following formula.
Ri is the rating of the parameter in each point
Wi is the weight of each parameter
n is the number of criteria.
The index is based on the specific geological and land use characteristics of the study area. Flow
accumulation has been considered as the most crucial parameter (Kazakis et al., 2015). Flow accumulation
increases with an increase in stream order and drainage density. It is an indirect way of measuring the
area of drainage (Schäuble et al., 2008). Likewise, flooding often occurs in low elevations (Botzen et al.,
2013) and near the drainage network. Therefore, distance from stream and elevation are given equal
importance. LULC influences the infiltration rate. Land use is considered the third most important
parameter. Areas with low slopes and low elevations have more chances of waterlogging than the areas
with high slopes. A steeper slope decreases the chance of infiltration and increases surface runoff (Hoque
et al., 2019; Lawal et. al., 2012). In urban areas, the slope value gets modified because of anthropogenic
intervention and has, thus, been assigned a lower importance value. Rainfall intensity is an important
factor of flood hazard and is considered within the literature study for the assessment. In this research,
the study area is comparatively small for rainfall intensity variation and data on this is not available.
Therefore, the rainfall intensity factor has not been considered. All the parameters, except LULC, were
classified using quantile classification (Table 4). LULC was classified based on the Kazakis et al.’s
(2015) study, which is similar to Pradhan (2009) and Forkuo (2010).
Tables 2 and 3 show the pairwise comparison of the criteria.
Social Vulnerability
United Nations International Strategy for Disaster Reduction (UNISDR) (United Nations Ofce for
Disaster Reduction, 2009) denes the term vulnerability as ‘the characteristics and circumstances of a
community, system or asset that make it susceptible to the damaging effects of a hazard’. It is an
interrelation of the exposure, susceptibility and coping capacity of a system. Vulnerability is a combination
Table 2. Parameter of Flood Hazard: AHP
Parameter Flow Accumulation Distance from Channel Elevation LULC Slope
Flow accumulation 1 2 2 3 5
Distance from channel 1/2 1 1 3 4
Elevation 1/2 1 1 2 4
LULC 1/3 1/3 1/2 1 2
Slope 1/5 1/4 1/4 1/2 1
Sum 2.533333 4.583333 4.75 9.5 16
Source: The authors.
274 Environment and Urbanization ASIA 12(2)
Table 3. Normalized Flood Hazard Parameters: AHP
Distance from
Channel Elevation LULC Slope Mean Weight
Flow accumulation 0.39 0.44 0.42 0.32 0.31 0.37 37
Distance from channel 0.20 0.22 0.21 0.32 0.25 0.24 24
Elevation 0.20 0.22 0.21 0.21 0.25 0.22 22
LULC 0.13 0.07 0.11 0.11 0.13 0.11 11
Slope 0.08 0.05 0.05 0.05 0.06 0.06 6
Source: The authors.
Note: The consistency ratio is 0.05.
Table 4. Ratings of Flood Hazard Parameter
Parameters Class Rating
Flow accumulation 0–400 1
1,200–410 2
2,800–1,300 3
8,800–2,900 4
>8,900 5
Elevation (in m) 56–110 1
40–55 2
26–39 3
21–25 4
8–20 5
Land use land cover High vegetation 1
Low vegetation 2
Barren land 3
Urban 4
Water bodies 5
Distance from the stream (in m) 0–30 5
31–50 4
51–100 3
101–200 2
>200 1
Slope (in degree) 2.9–3.2 1
2.1–2.8 2
1.5–2 3
0.9–1.4 4
0–0.89 5
Source: The authors.
of both social and physical processes (Brooks, 2003). Social vulnerability is the potential to be hampered
by natural hazards because of the lack of capacity to resist (United Nations Ofce for Disaster Reduction,
2009). Presently, there is no standard methodology to carry out a spatial vulnerability assessment
(Brooks, 2003; Kienberger et al., 2009, 2013; Villagrán de León et al., 2006), but some studies and
scientic literature are available. Social vulnerability comprises socio-economic and demographic
indicators, which are affected by oods. It represents the lack of resources to mitigate, resist or recover
from disasters. Based on literature studies, eight parameters have been selected for the present
Sahu et al. 275
study: population density, female population, rented house, schedule cast (SC) and schedule tribe (ST)
population, population less than 6 years old, house condition, illiterate population, unemployed
population (Armenakis, et al., 2017; Hoque, et al., 2019; Katic, 2017; Rufat et al., 2015; Sharma, et al.,
2018). All the data has been collected from the Census of India, 2011. These vulnerability indicators
represent an element’s susceptibility and resilience to disaster hazards (Birkmann, 2006). Armenakis and
Nirupama’s (2013) approach is followed in this study. All eight parameters were of different types. To
standardize it, the percentage of each of the parameters, except for population density, is calculated based
on the total population. Population density is directly used in the nal rating summation. The rating was
given according to the quantile method of classication. The total normalized weighted vulnerability for
each census ward is:
Where VPCT is total vulnerability per each census ward
Ci is category attribute
%Ci is category attribute percentage with respect to population in the census ward
P is total census population.
Evacuation activities before and after flood become complicated in a high-density location. Hence,
low population density is lesser vulnerable than the high population density area (Hoque et al., 2019).
Due to social and physical structures that already disadvantage women, for example, care responsibili-
ties at home, lower wages in the informal sector and specific employment, recovering from a catastrophe
becomes even more difficult for women (Cutter et al., 2003). Further, during pregnancy or menstruation,
mobility is limited and evacuation procedures become difficult. In a disaster, women are 14 times more
likely to die than men (Peterson, 2007). Similarly, Schedule caste (SC) and Schedule Tribe (ST) people
tend to be more vulnerable to disaster, as the chances to intrude into the floodplain area due to land una-
vailability is more for SC and ST people (Sharma et al., 2018). It is also difficult to evacuate children less
than 6 years old during an emergency as they are a dependent population (Sharma et al., 2018). Low
education levels can constrain one’s ability to understand the warning and recovery information.
It becomes a major hindrance in the preparedness, mitigation and recovery stage (Cutter et al., 2003).
Literacy is also linked with socio-economic status, unemployment, economic inequality and marginali-
zation (Sharma et al., 2018). Housing conditions reflect the potential financial losses, injuries and fatali-
ties from natural hazards. Dilapidated houses are the ones that get affected by the disaster first, and the
recovery becomes difficult after the disaster phase. Vulnerability is high when people live in a rented
house as opposed to their own house (Katic, 2017). Higher unemployment levels lead to an additional
number of existing unemployed workers in a community. Lastly, the pressure on the labour market in a
post-disaster period tends to increase (Katic, 2017), thereby contributing to a slower recovery from the
disaster (Cutter et al., 2003).
Infrastructure Vulnerability
Infrastructures whose presence or absence can leave people in a more vulnerable condition during or
after a disaster are drivers of socio-physical vulnerability. The following six parameters have been
selected to locate socio-physical vulnerability: water source, drinking water accessibility, availability of
latrine, pit latrine, night soil and presence of close drain. All the parameters are picked after a thorough
276 Environment and Urbanization ASIA 12(2)
study in the context of Bhubaneswar and urban ood disasters. Each parameter is standardized by giving
ratings using the quantile method (Table 5). Then equal weightage is given to all parameters. Physical
vulnerability map is obtained by using the following formula:
Where Ri is the rating of the parameter in each point
Wi is the weight of each parameter
The available data was at ward level, which is very coarse. The desired finer resolution of the grid data
set for socio-economic indicators is not available. An improved resolution is better for risk and vulnerability
assessment for spatial planning, preparedness and crisis management (Aubrecht et al., 2013).
Table 5. Infrastructural Vulnerability Parameter Rating
Sl. No. Factors Class Rating
1 Water supply 0.2–1.7 1
1.71–2.4 2
2.41–3.7 3
3.7–6.4 4
6.4–74.6 5
2 Drinking accessibility 0.4–11.9 1
11.9–18.3 2
18.3–26.1 3
26.1–36.7 4
36.7–93 5
3 Pit latrine 0.1–1 1
1.1–2.7 2
2.71–4 3
4.1–5.1 4
5.1–48.6 5
4 Night soil 0.01–0.1 1
0.11–0.3 2
0.31–1.31 3
1.31–2.10 4
2.1–12.7 5
5 No latrine 0.5–6 1
6.01–12.8 2
12.81–22 3
22.01–27.20 4
27.21–53.40 5
6 No close drain 9.3–33.7 1
33.71–41.90 2
41.91–57.4 3
57.4–73.2 4
73.21–97.2 5
Source: The authors.
Sahu et al. 277
Flood Risk Assessment
UNISDR (United Nations Ofce for Disaster Reduction, 2009) denes disaster risk as the potential loss
due to disaster hazards and people’s vulnerable conditions for coping with it. A risk assessment digs
beyond just the magnitude and potential loss. It reects the cause and impact of all the losses from a
disaster. The present article assesses the urban ood risk to evaluate the risk caused by the ood hazard
and the vulnerability of the people affected by it, with the understanding that it is a combination of both
(Apel et al., 2009; IPCC 2014; Vojinović & Abbott, 2012). For the ood risk map, all the three layers of
hazard, social vulnerability and socio-physical vulnerability are ranked, weighted using AHP (Table 6)
and overlapped in GIS. The ood risk map is veried by overlaying the past ooding point data collected
from Bhubaneswar Municipal Corporation (BMC).
R = HF × VP × VS
Where, R is the Disaster Risk,
HF is the Flood disaster hazard,
VP is Physical infrastructure vulnerability,
and VS is social vulnerability.
In the last phase, the article evaluates and integrates all the different layers of hazard, vulnerability,
and risk to prepare an urban flood resilient spatial plan to make the city a resilient city. Every bit of the
land use map is amplified with the risk map to formulate risk-sensitive land use planning.
Land Use Assessment
The rst land use master plan of Bhubaneswar had seven primary functional land use, and the master
plan was based on neighbourhood planning (Appendix A). The land use classication had no separate
category for water bodies. Instead, water bodies were included in the green belt zone, which also includes
vacant land and agricultural land. However, the water body was demarked in the land use plan and green
buffer zones were provided around it. To facilitate the rapid population growth and changing socio-
economic scenario of the city, the 1988 master plan came into action. In this plan, water bodies and
drainage channels were recognized as a separate land use zone and classied in the master plan.
Agriculture was permitted in the drainage channel zone and construction activities were restricted,
though the normal expansion of the existing settlements was permitted in this zone. In the latest 2010
Table 6. Parameter of Flood Risk: AHP
Parameter Hazard Social Vulnerability Physical Infrastructure Vulnerability
Hazard 1 2 4
Social vulnerability 1/2 1 1
Physical infrastructure vulnerability 1/4 1 1
Weightage 0.584 0.231 0.184
Source: The authors.
Note: The consistency ratio is 0.05.
278 Environment and Urbanization ASIA 12(2)
Table 7. Land use Zone Classification in Different Master Plan
1968 Master Plan 1988 Master Plan 2010 Master Plan
Residential use zone Residential use zone Residential use zone
Commercial use zone Commercial use zone Commercial use zone
Industrial use zone Industrial use zone Industrial use zone
Institutional and utilities Institutional and utilities Institutional and utilities
Administrative Administrative Administrative
Open space use zone Open space Open space
Green belt use zone Transport and communication use zone Transport and communication use zone
Green belt use zone Green belt use zone
Water bodies Archaeological garden use zone
Drainage channel Natural drainage channel use & water bodies
Environmentally sensitive zone
Source: Bhubaneswar Master Plan 1968, 1988 and 2010, Collected from Bhubaneswar Development Authority.
master plan, water bodies and environmentally sensitive zone are classied in the land use zone
(Table 7), but no green spaces around the water bodies are drafted. The riparian corridor was missing in
the land use map and building codes. The comprehensive development plan (Department of Architecture
and Regional Planning, 2010) ags the issue of drainage systems in the city. It quotes, ‘Earlier it used
to take 25 minutes after the shower for the rainwater to drain with these channels but nowadays the
water remains clogged in these areas for nearly 24 hours’ (Department of Architecture and Regional
Planning, 2010).
Land Use and Land Cover Change
The generated LULC map of Bhubaneswar (Figure 2) shows that urban areas have increased from
41 km2 to 81 km2 in two decades (Table 8). With the change of pervious surface to impervious, the
percolation of water into the ground decreases. When water does not percolate during rainfall, it becomes
overland water and ows downstream, leading to an increase in ood hazard. This change of impervious
surfaces that urbanization is associated with, is the primary driver of a catchment’s hydrological changes
Figure 2. (a) 1997 LULC Map; (b) 2009 LULC Map; (c) 2018 LULC Map
Source: The authors.
Sahu et al. 279
Table 8. Change of Land Use and Land Cover in Past Two Decades
Land Use 1997 2008 2018
Built up area 40.55 km2 65.48 km2 80.7 km2
Barren land 18.77 km2 23.32 km2 13.6 km2
High vegetation 31.71 km2 16.80 km2 12.6 km2
Low vegetation 51.97 km2 37.20 km2 38km2
Source: The authors.
(Shuster et al., 2005). It is observed that earlier settlements preferred to stay away from water bodies due
to frequent oods, giving room for water bodies to expand. However, with urbanization, the demand for
land increased with a decrease in affordable land availability, thereby compelling people to opt for land
near water bodies. The vicinity around the water streams is very fragile to ood and plays a vital role in
maintaining the balance of water through vegetation. In 1997, the paved area near the water body was
6.20 km2. By 2018, it increased to more than double to 15.03 km2 out of 37 km2 (Figure 3, Table 9).
Land Use Regulation and by Laws
The oor area ratio (FAR) of a building as per the existing bylaws depends on the road width, irrespective
of its location or disaster risk potential. The maximum permissible FAR for a residential building is 2.25
and for a commercial building is 2.75. In residential zones, a vast number of activities and amenities such
Figure 3. (a) 1997 LULC Map near the Stream
Figure 3. (b) 2009 LULC Map near the Stream
Figure 3. (c) 2019 LULC Map near the Stream
Source: The authors.
Table 9. Change of Land Use and Land Cover near the Waterbodies in Past Two Decades
Land Use 1997 2008 2018
Built up area 6.20 km2 11.85 km2 15.03 km2
Source: The authors.
280 Environment and Urbanization ASIA 12(2)
as banks, post ofces and health clinics are permitted in the master plan. Under certain permissions,
garages and godown are also permitted in the residential zone. In the open space zone, amenities such as
stadiums, maidans (public squares), holiday resorts, swimming pools, bus and railway terminals, public
utilities and the like are allowed. Places of entertainment and leisure, such as cinema, circus, public
assembly halls, restaurants and guest houses are allowed in this zone under specic permissions from the
competent authority. Some wetland in the municipal boundary is classied as open space in the land use
zone. Such areas can be developed legitimately into any of the above-specied structures with time,
thereby destroying the wetland. Even the basic conversion of wetland into multi-purpose maidans would
demand the clearing of low vegetation. In the water body land use zone, water-based resorts with special
bylaws and water theme parks are allowed under special permission from the competent authority. In the
building regulation, wetland, waterlogged and marshy areas are zoned as water bodies to avoid the
emergence of activities mentioned in other land use zones. However, the master plan classies only river,
canal, streams, ponds and lakes as water bodies, irrespective of the land use zone regulation. In
environmentally sensitive zone, activities like group housing, corporate type housing with special
bylaws, theme park, international convention centre, ve-star lake resort, hotel with special bylaws,
health institutions, research institution, and similar are permissible. Environmentally sensitive areas are
very fragile areas, and any form of intervention can destroy its natural setting. Some of the low-lying
areas near the river Khuakhai and Daya, and downstream of the Gangua nalla have been zoned as
environmentally sensitive. These areas are usually ooded every monsoon. Permissions for the structures
mentioned above in these areas under any circumstances through bylaws can accelerate the ooding in
Figure 4. (a) 2005 Google Image near Guangua Stream; (b) 2018 Google Image near Guangua Stream;
(c) Google Section Image; (d) PLU
Source: (a), (b) and (c)- Google maps; (d)- Department of Architecture and Regional Planning, 2010.
Sahu et al. 281
Bhubaneswar. Figure 4 shows that new residential areas are also coming up in the low-lying areas near
the natural stream. This means that during rainfall, residents may suffer from ooding since the areas
which could have acted as a natural sponge by allowing water to percolate, would be lost because of
residential development. These residential areas are legal as per the master plan.
The AMRUT report presented that 14 acres of land along the streams have been encroached in
Bhubaneswar; 436 slums are identied by BMC, out of which two slums are on existing forest land.
Most of the slums are located near the water bodies due to the free availability of land. Four unauthorized
slums are on water bodies and one in an environmentally sensitive zone. Generally, slums emerge during
the non-monsoon season when these lands are vacant and dry. Such patterns of settlement disrupt the
natural watercourse and habitation in such locations is disrupted by water bodies or upstream water
during monsoon (Anand & Deb, 2017). An overlap map of the waterlogged points with the slum location
in Bhubaneswar (Figure 5) reects that the low-lying slum areas are one of the drivers of urban ooding
and, at the same time, are the worst victims of it.
Figure 5. Location of Slum and Past Frequent Flooding Points
Source: Data collected from BMC, Map prepared by the authors.
282 Environment and Urbanization ASIA 12(2)
Figure 6. (a) Flood Hazard Map
Flood Risk Map
Figure 6a shows that the eastern part of Bhubaneswar is most susceptible to ood hazards due to its low
elevation and low slope. Areas along the streams in the city also have a high ood hazard potential.
Figure 6b shows that the social vulnerability of the city is very diverse. Wards that have more slums are
found to have a higher vulnerability to disaster than other wards. This map can help in preparing a plan
for disaster mitigation at the time of its occurrence. Figure 6c display the areas that need immediate
infrastructural intervention. The high-vulnerability areas are the ones with a comparatively lower
provision of infrastructure which causes more problems to people during or after a disaster.
Figure 7 represents the risk map that accounts for both hazard and vulnerability. It is found that most
of the very high-risk zones are near water bodies. High-risk zones are observed to be on the eastern side
of the city, which is at low elevation. The central and western part of the city is in a low-risk zone, where
further development can be proposed. This map can act as a base map for urban planners and the city
manager to develop disaster reduction strategies and increase disaster management effectiveness.
Proposed Master Plan
Land use planning offers several opportunities in managing oods in all the stages of the disaster risk
management cycle. The existing master plan of Bhubaneswar, Comprehensive development plan, 2010,
Sahu et al. 283
Figure 6. (c) Physical Vulnerability Map
Source: The authors.
Figure 6. (b) Social Vulnerability Map
284 Environment and Urbanization ASIA 12(2)
Figure 7. Flood Risk Map
Source: The authors.
has been closely studied and has been overlapped with ood hazards, vulnerability and risk map to nd
critical locations and the gap in the existing master plan that can result in loss of lives and economy in
case of disaster. Land use is studied along with the existing building codes and regulations for a more
thorough understanding of the scenario. It is found that disaster risk was never incorporated in the master
plan of the city. Any structure near the high ood risk zone follows the same rule as the low ood risk
zone. Therefore, an alternative proposed land use master plan (Figure 8a) is prepared in this study, where
areas under the ood risk zone have been critically examined and alternatives have been explored
through different lenses. All the risk-prone areas have been enlarged to examine the plots against the risk
map and individual parameters like slope and elevation, in order to propose an alternative master plan.
An additional land use zone called R2 residential zone is proposed, with a permissible maximum FSI
one, irrespective of its road width. The R2 residential zone is marked in the high flood risk zone to keep
density low. The R2 residential zone ground coverage will be a maximum of up to 40%, unlike the present
permissible limit of 50%. Wetland and marshy lands are added under the water body zone in the proposed
master plan. Furthermore, only fishery, boating and water sport are allowed under special permission.
Some of the very high flood risk zones that were under a particular kind of development zone have been
changed to environmentally sensitive zones, and only riverside green areas and existing village settlements
are permissible. Riverfront development, scenic value areas, camping, boating, picnic site are permitted
on application to the competent authority. Some of the very high and high flood risk zones have been
Sahu et al. 285
categorized as green space zones in the proposed master plan. Children’s parks, gardens, sports training
centres, green belts, zoos, nurseries, aquariums, natural reserves, water sports facilities, specialized parks/
maidans for multi-purpose use are permissible in this zone. Fisheries, boating, open air theatre, guest
house and nature-based theme parks are permissible on application to the competent authority (Table 10).
The maximum FSI permitted in this zone is 0.15. Green space zone has been allotted along the streams
where the immediate existing land use zones are pollutant oriented, like industries. In the draft proposed
master plan, green space is mandatory in every town planning (TP) scheme. Additionally, it is guided that
one green space zone should be near water bodies in every given TP scheme.
The low-lying eastern area is mainly proposed as an environmentally sensitive zone. Adjacent to it, a
low-density R2 zone is recommended and industrial and commercials are limited here to minimize
anthropogenic activities and their impacts. The open spaces near the water body downstream that get
waterlogged are demarcated as forest land to ensure no concretized development can come forward. One
of the significant wetlands in the middle of the city (Figure 8b and 8c) has been changed from a green zone
to a water body zone. The land near the wetland is proposed as a green zone where bird sanctuaries or parks
can be developed. The land surrounding the wetland and open space is given as an R2 residential zone.
By supporting the spatial integration of ‘grey’ conventional hard engineering with ‘green’ infrastructure
to manage water resources and protect against flooding, this proposed land use planning can help to
create balance in urban water resources. A risk-based approach to land use planning has been considered
to make it more resilient to floods. This proposed plan minimizes development in the flood-prone zone,
reduces water runoff through development controls for flood risk mitigation and designates routes and
open spaces for better response and recovery efforts. It also mitigates damage from an unavoidable flood
and accommodates urban growth expansion in the flood-safe areas. The proposed land use planning
encompasses the socio-vulnerability and incorporates the hazard zone to formulate a resilient urban
flood spatial plan.
Figure 8. (a) Proposed Master Plan; (b) Existing Master Plan; (c) Highlights of Changes in Existing Master Plan
Source: (a) The authors; (b) Department of Architecture and Regional Planning, 2010; (c) The authors.
286 Environment and Urbanization ASIA 12(2)
Table 10. Proposed Land Use
Use Zone Uses/Activities Permitted
Permissible on
Application to the
Competent Authority
Prohibited FSI
Residential use
zone (R2)
Residence plotted
(detached, semi-detached,
row housing), group
Place of worship All uses not
permitted in column
a and b
1.0 (irrespective of
its road width)
Hostel, boarding, lodging
Night shelter, guest house Government offices
Primary education building,
high school
Petrol filling station
Neighbourhood level social,
cultural and recreational
Restaurants, hotels
Marriage and community
Market for retail
Exhibition and art gallery Tourism related
Library and gymnasium Burial ground
Yoga centre and primary
health care
Services for household Bank and professional
Police posts and post
Bus stop, taxi stand
sensitive zone
River side green areas River front
All uses not
permitted in column
a and b
Existing Village settlements Scenic value areas
Camping, boating,
picnic site
Water bodies River, canal Fisheries All uses not
permitted in column
a and b
Stream, ponds, lake Boating, water sports Not specifically
related to water
bodies use, is not
permitted herein
Wetland, aqua culture pond
Waterlogged/marshy area
(Table 10 continued)
Sahu et al. 287
Use Zone Uses/Activities Permitted
Permissible on
Application to the
Competent Authority
Prohibited FSI
Open space use
Specialized parks/maidans
for multi-purpose use
Fisheries All uses not
permitted in column
a and b
Children’s park, garden Nature based theme
Sports training centre Open air theatre
Green belt Guest house
Zoo, nursery, aquarium,
natural reserve, boating,
water sports
Source: The authors.
Riparian Corridor
The transition between the water body and land is pivotal and plays a vital role in controlling or facilitating
the ood. The edge of the water body is crucial for a healthy freshwater system (Kenwick et al., 2009).
The riparian corridor around the water bodies helps to control the sedimentation, contamination and peak
ow runoff (Carter et al., 1979). It helps in reducing erosion by stabilizing the soil (Lee et al., 2000).
Considering all the riparian corridor benets, various urban development authorities of different cities
have proposed a buffer zone between water bodies and settlements. Depending on the stream order, the
buffer varies in some of the building codes. Assam has a regulation for 15m buffer around the river and
10m buffer for ponds and other notied water bodies. Nairobi, a city in Kenya, and Madhya Pradesh, a
state in India, have a 30m buffer from waterbodies for the riparian zone. Similarly, states like Maharashtra,
Andhra Pradesh, Telangana and Gujarat have a buffer zone around the water bodies as a no-development
zone in their state-building regulation norm. Cities further have changed or modied the law to a higher
buffer zone. For instance, Bangalore city in Karnataka state has assigned 50m, 35m and 25m buffer for
primary, secondary and tertiary water bodies, respectively. In comparison, the Karnataka state-building
regulation prescribes a buffer of 45m from the river and 25m from minor streams.
However, neither the Bhubaneswar Development Authority nor the Odisha State Urban Development
Authority has any such regulation in building codes, as a result of which, development happens just
beside the water bodies lawfully. Such development disrupts the watercourse at a very sensitive junction,
thereby causing flooding. The primary study observed that the structure immediately next to the water
body discharges solid waste and grey and black water directly into it.
Streams need their space to flow without disruption. Buffer network gives the ‘right-of-way’ to the
stream and as a whole function as an integral part of the stream ecosystem. Buffer is a standardization
system of giving space around the water bodies, but every topography is unique, and hence standardization
may not hold in all cases. Ideally, 25 years, 50 years and 100 years of flood line should be considered
before proposing a buffer zone. However, due to the unavailability of data for small water bodies, a
(Table 10 continued)
288 Environment and Urbanization ASIA 12(2)
standardized buffer of 15m, 20m, 25m, 30m and 50m from the streams is practiced world over (Beacon
Environmental Ltd., 2012; Jainer & Matto, 2017; McElfish et al., 2008; Schueler, 2000).
The following are the land uses that are proposed around the water bodies:
The 15m distance from the water bodies is very critical. Therefore, it is categorized as an eco-
sensitive zone, which is completely reserved for riparian use.
From 15m to 30m from the water bodies is categorized as a critical-risk zone, and open and green
space is allowed.
From 30m to 50m, the land is categorized as a high-risk zone and an R2 residential zone is
allowed. R2 zone has a lower FSI than the R1 residential zone. It is proposed for facilitating low-
density development in such areas.
From 50m to 100m, a medium risk zone, R2 residential zone is allowed. Under special conditions
and approval from the competent authority, high-rise buildings can also come near a minor
From 100m to 200m, all land use is permitted, with a suggestion to allow permission only after a
brief soil, slope and other basic hydrological studies of that particular area.
In this research, we have assessed the urban ood disaster risk and previous land use of the city with GIS
and remote sensing to explore the opportunity to formulate an urban ood risk-sensitive land use plan.
The research reects the nexus between land use planning and disaster risk management. The ood risk
obtained results were validated against data from past oods records.
The research article discussed the formulation of disaster risk reduction-based land use plans in
detail through zoning and building regulation codes. The risk-sensitive spatial planning process is
challenging, but ultimately, goes beyond providing an engineering solution with a short-term vision. It
considers disaster risk reduction as an integrated part of developing an approach for sustainable long-
term growth.
Urbanization is associated with concretization, which is one of the core reasons for urban flooding. A
risk-sensitive land use plan can change that perspective by prohibiting development in high-risk areas
and encouraging development in a low-risk area, thereby maintaining the natural environment and
providing sustainable infrastructural development. The proposed master plan will support the city in the
various phases of the disaster risk reduction cycle, starting from the pre-disaster prevention phase to the
mitigation and preparedness phase. It will also help city administrators in the post-disaster emergency
response and recovery phase. Such a resilience plan would make Bhubaneswar a better-prepared city for
the future. With better availability of data, the plan can be refined further.
Declaration of Conflicting Interests
The authors declared no potential conicts of interest with respect to the research, authorship and/or publication of
this article.
The authors received no nancial support for the research, authorship and/or publication of this article.
Sahu et al. 289
Appendix A. Maps of Bhubaneswar Master Plan
Figure 1. (a) 1968 Master Plan; (b) 1988 Master Plan; (c) 2010 Master Plan
Source: (a) Directorate of Town Planning Odisha.
(b) Bhubaneswar Development Authority.
(c) Department of Architecture and Regional Planning, 2010.
Anand, G., & Deb, A. (2017). Planning, ‘violations’, and urban inclusion: A study of Bhubaneswar. YUVA and IIHS.
Apel, H., Aronica, G. T., Kreibich, H., & Thieken, A. H. (2009). Flood risk analyses—How detailed do we need to
be? Natural Hazards, 49(1), 79–98.
Armenakis, C., & Nirupama, N. (2013). Estimating spatial disaster risk in urban environments. Geomatics, Natural
Hazards and Risk, 4(4), 289–298.
Armenakis, C., Du, E., Natesan, S., Persad, R., & Zhang, Y. (2017). Flood risk assessment in urban areas based on
spatial analytics and social factors. Geosciences, 7(4), 123.
Aubrecht, C., Özceylan, D., Klaus, S., & Freire, S. (2013). Multi-level geospatial modeling of human exposure
patterns and vulnerability indicators. Natural Hazards, 68, 147–163.
Beacon Environmental Ltd. (2012). Ecological buffer guideline review. Credit Valley Conservation.
Birkmann, J. (2006). Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and
definitions. Institute for Environment and Human Security Journal, 5, 7–54.
BMTPC. (2019, March 2). Vulnerability atlas of India.
Botzen, W. J. W., Aerts, J. C. J. H., & van den Bergh, J. C. J. M. (2013). Individual preferences for reducing flood
risk to near zero through elevation. Mitigation and Adaptation Strategies for Global Change, 18(2), 229–244.
Brooks, N. (2003). Vulnerability, risk and adaptation: A conceptual framework [Working Paper No 38]. Tyndall
Centre for Climate Change Research.
Burby, R., Deyle, R., Godschalk, D., & Olshansky, R. (2000). Creating hazard resilient communities through land-
use planning. Natural Hazards Review, 1(2).
Carter, V., Bedinger, M. S., Novitzki, R. P., & Wilen, W. O., (1979). Water resources and wetlands. In P. E. Greeson,
J. R. Clark, & J. E. Clark (eds.), Wetland functions and values: The state of our understanding (pp. 334–376).
American Water Resources Association.
Census of India. (1951). Census of India. Government of India.
Census of India. (2011). Census of India. Government of India.
Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for
infrastructure design in a changing climate [Scientific Reports 4].
290 Environment and Urbanization ASIA 12(2)
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social Vulnerability to Environmental Hazards. Social Science
Quarterly, 84(2), 242–261.
Das Chatterjee, N., Chatterjee, S., & Khan, A. (2015). Spatial modeling of urban sprawl around Greater Bhubaneswar
city, India. Modeling Earth Systems and Environment, 2, 1–21.
DeFries, R., & D. Pandey. (2010). Urbanization, the energy ladder and forest transitions in India’s emerging
economy. Land Use Policy, 27(2), 130–138.
Department of Architecture and Regional Planning. (2010). Comprehensive development plan for Bhubaneswar
development plan area. IIT Kharagpur.
Forkuo, E. K. (2010). Flood hazard mapping using Aster image data with GIS. International Journal of Geomatics
and Geosciences, 1, 932–950.
Heywood, I., Oliver, J., & Tomlinson, S., (1993). Building an exploratory multi-criteria modeling environment for
spatial decision support. In P. Fisher (Ed.), Innovations in GIS 2 (pp. 127–136). Taylor and Francis.
Hoque, M., Tasfia, S., Ahmed, N., & Pradhan, B. (2019). Assessing spatial flood vulnerability at Kalapara Upazila
in Bangladesh using an analytic hierarchy process. Sensors, 19(6).
IPCC. (2014). Climate change 2014: Synthesis report (p. 151).
Jainer, S., & Matto, M. (2017). Green infrastructure: A practitioner guide. Centre for Science and Environment.
Jha, A. K., Bloch, R., & Lamond, J. (2012). Cities and flooding: A guide to integrated urban flood risk management
for the 21st century. World Bank.
Katic, K. (2017). Social vulnerability assessment tools for climate change and DRR programming: A guide to
practitioners. United Nations Development Programme
Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-
based approach and analytical hierarchy process: Application in Rhodope–Evros region, Greece. Science of The
Total Environment, 538, 555–563.
Kenwick, R. A., Shammin, M., & Sullivan, W. (2009). Preferences for riparian buffers. Landscape and Urban
Planning, 91(2), 88–96.
Kienberger, S., Lang, S., & Zeil, P. (2009). Spatial vulnerability units—Expert-based spatial modelling of socio-
economic vulnerability in the Salzach catchment, Austria. Natural Hazards and Earth System Sciences, 9(3),
Kienberger, S., Blaschke, T., & Zaidi, R. Z. (2013). A framework for spatio-temporal scales and concepts from
different disciplines: The ‘vulnerability cube’. Natural Hazards, 68(3), 1343–1369.
Lawal, D. U., Matori, A. N., Hashim, A. M., Yusof, K. W., & Chandio, I. A. (2012). Detecting flood susceptible
areas using GIS-based analytic hierarchy process. 2012 International Conference on Future Environment and
Lawal, D., Matori, A., Yusuf, K., Hashim, A., & Balogun, A. (2014). Analysis of the flood extent extraction model and
the natural flood influencing factors: A GIS-based and remote sensing analysis. 8th International Symposium of
the Digital Earth (ISDE8). IOP Publishing.
Lecca, G., Petitdidier, M., Hluchy, L., Ivanovic, M., Kussul, N., Ray, N., & Thieron, V. (2011). Grid computing
technology for hydrological applications. Journal of Hydrology, 403(1–2), 186–199.
Lee, K. H., Isenhart, T. M., Schultz, R. C., & Mickelson, S. K. (2000). Multispecies riparian buffers trap sediment
and nutrients during rainfall simulations. Journal of Environmental Quality, 29(4), 1200–1205.
McElfish, J. M., Kihslinger, R. L., Nichols, S. S., & Environmental Law Institute. (2008). Planner’s guide to wetland
buffers for local governments [Working Paper No 857]. University of South Florida.
Peterson, K. (2007). Reaching out to women when disaster strikes. Soroptimist white paper, Soroptimist international
of the Americas, Philadelphia.
Pradhan, B. (2009). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote
sensing. Journal of Spatial Hydrology, 9, 1–18.
Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, N., & Khujaimah, Z. (2008). Utilization of optical
remote sensing data and geographic information system tools for regional landslide hazard analysis by using
binomial logistic regression model. Journal of Applied Remote Sensing, 2(1).
Rufat, S., Tate, E., Burton, C. G., & Maroof, A. S. (2015). Social vulnerability to floods: Review of case studies and
implications for measurement. International Journal of Disaster Risk Reduction, 14, 470–486.
Sahu et al. 291
Saaty, T. L. (1990). An exposition of the AHP in reply to the paper ‘remarks on the analytic hierarchy process’.
Management Science, 36(3), 259–268.
Schäuble, H., Marinoni, O., & Hinderer, M. (2008). A GIS-based method to calculate flow accumulation by
considering dams and their specific operation time. Computers & Geosciences, 34, 635–646.
Schueler, T. (2000). The architecture of urban stream buffers: The practice of watershed protection (pp. 225–233).
Centre for Watershed Protection, Ellicott City, MD.
Sharma, S. V. S., & Roy, P. S. (2018). Assessment of social vulnerability to the impact of flood hazard: A case study
of Kopili river basin, Assam, India. The International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences, 42(5), 455–460.
Shuster, W., Bonta, J., Thurston, H., Warnemuende, E., & Smith, D. (2005). Impacts of impervious surface on
watershed hydrology: A review. Urban Water Journal, 2, 263–275.
Soz, S. A., Kryspin-Watson, J., & Stanton-Geddes, Z. (2016). The role of green infrastructure solutions in urban
flood risk management. World Bank.
Stefanidis, S., & Stathis, D. (2013). Assessment of flood hazard based on natural and anthropogenic factors using
analytic hierarchy process (AHP). Natural Hazards, 68(2).
Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India—Spatiotemporal
analysis using remote sensing data. Computers, Environment and Urban Systems, 33(3), 179–188.
United Nations Office for Disaster Reduction (2009). UNISDR terminology on disaster risk reduction.
United Nations (2015). Transforming our world: The 2030 agenda for sustainable development.
United Nations, Department of Economic and Social Affairs, Population Division (2019). World urbanization
prospects: The 2018 revision (ST/ESA/SER.A/420).
Villagrán de León, J. C., United Nations University, & Institute for Environment and Human Security. (2006).
Vulnerability: A conceptual and methodological review. United Nations University, Institute for Environment
and Human Security.
Vojinović, Z., & Abbott, M. B. (2012). Flood risk and social justice: From quantitative to qualitative flood risk
assessment and mitigation. IWA Publishing.
Winsemius, H., & Ward, P. (2015). Aqueduct global flood risk country rankings. World Resources Institute.
World bank. (2005). Natural disaster hotspots: A global risk analysis.
... Using one dataset, the assessment results should only represent one physical condition in the study area. Therefore these results could not meet the need of local authorities to support flood adaptive spatial planning [27] and educate the community regarding the potential flood [28], [29]. This study fills this research gap by evaluating flood vulnerability based on time-series land cover data and predicting flood vulnerability using rainfall over various return periods. ...
... Consequently, well-informed local authorities, communities, and other stakeholders can take mitigation actions and prepare themselves and the people for flooding [15]. Flood adaptive land-use planning supported by adequate infrastructure is expected to reduce the flood risk that hampers community activities [15], [27], [34] and ultimately reduce losses due to flooding [43]. ...
Full-text available
Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used time-series land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r 2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy. Perubahan tata guna lahan berdampak pada meningkatnya kerawanan banjir fisik. Pendekatan Geographic Information System (GIS) dan Analytic Hierarchy Process (AHP) semakin banyak digunakan untuk penilaian kerentanan banjir. Namun, belum ada yang menggunakan data tutupan lahan deret waktu untuk evaluasi dan curah hujan selama berbagai periode ulang untuk prediksi secara bersamaan, terutama di Indonesia. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi dan memprediksi kerentanan banjir fisik menggunakan data tutupan lahan deret waktu dan data curah hujan pada berbagai periode ulang. Delapan kriteria dipertimbangkan dalam penilaian: elevasi, indeks kebasahan topografi, kemiringan, jarak ke sungai, jarak ke hilir, jenis tanah, curah hujan, dan tutupan lahan. Bobot kriteria ditentukan dengan metode AHP berdasarkan expert judgment. Model multikriteria dibangun dan divalidasi menggunakan data genangan banjir. Berdasarkan model yang telah divalidasi, pengaruh perubahan tutupan lahan terhadap kerentanan banjir dievaluasi. Perubahan kerawanan banjir juga diprediksi berdasarkan curah hujan pada berbagai periode ulang. Model evaluasi dan prediksi telah menunjukkan temuan yang dapat diandalkan. Kriteria elevasi dan jarak ke sungai berpengaruh nyata terhadap kerawanan banjir fisik sebesar 41% dan 20%. Model evaluasi menunjukkan korelasi yang kuat antara kawasan terbangun dan kawasan dengan kerawanan banjir tinggi (r2 = 0,96). Selain itu, model tersebut memperkirakan perluasan daerah genangan untuk curah hujan selama berbagai periode ulang. Penelitian lebih lanjut menggunakan data spasial dengan resolusi yang lebih tinggi dan teknik validasi yang lebih maju diperlukan untuk meningkatkan akurasi model.
... In many urban areas, installed drains are either undersized or nonexistent, primarily because stormwater drainage funding needs to be prioritized [62]. Additionally, these are expensive, particularly for developing nations [148]. In the past, storm sewers were constructed to handle 12 to 20 mm of rain per hour [149]. ...
Full-text available
Urban flooding is a frequent disaster in cities. With the increasing imperviousness caused by rapid urbanization and the rising frequency and severity of extreme events caused by climate change, the hydrological status of the urban area has changed, resulting in urban floods. This study aims to identify trends and gaps and highlight potential research prospects in the field of urban flooding in South Asia. Based on an extensive literature review, this paper reviewed urban flood hazard assessment methods using hydraulic/hydrological models and urban flood management practices in South Asia. With the advancement of technology and high-resolution topographic data, hydrologic/hydraulic models such as HEC-RAS/HMS, MIKE, SWMM, etc., are increasingly used for urban flood hazard assessment. Urban flood management practices vary among countries based on existing technologies and infrastructures. In order to control urban flooding, both conventional physical structures, including drainage and embankments, as well as new innovative techniques, such as low-impact development, are implemented. Non-structural flood mitigation measures, such as improved flood warning systems, have been developed and implemented in a few cities. The major challenge in using process-based hydraulic models was the lack of high-resolution DEM and short-duration rainfall data in the region, significantly affecting the model’s simulation results and the implementation of flood management measures. Risk-informed management must be implemented immediately to reduce the adverse effects of climate change and unplanned urbanization on urban flooding. Therefore, it is crucial to encourage emergency managers and local planning authorities to consider a nature-based solution in an integrated urban planning approach to enhances urban flood resilience.
... Bhubaneswar has a history that goes back over two thousand years; the city was a religious center, and gradually turned into the administrative capital of Odisha in 1948 after India's independence. The city grew sharply in the late 1990s and 2000s, with the rapid growth of public and private corporations and infrastructure projects [83,84]. This growth has been complemented by a rapid in-migration of population groups and a rapid growth in the local economy in the last two decades. ...
Full-text available
In this paper, we explore the complex entanglements between ongoing land conflicts and climate shocks, and their implications for risk governance paths and evolution. We focus on ways in which concepts of shock and conflict can be incorporated into social–ecological systems thinking and applied to risk governance practice in a southern cities context. Through a qualitative inquiry of two slum redevelopment projects in Bhubaneswar city in India, we trace the origin and evolution of conflict around land tenure and eviction in informal settlements, as well as its interaction with local manifestations of climate shocks. Climate policies, as responses to climate shock and intended to mitigate climate risk, are observed as constructed, interpreted, framed, and used strategically by formal actors to further urban development objectives, while the local knowledge systems, risk perceptions, and adaptations are ignored in practice. This study helps to re-think the complexities of climate risk governance in southern urban spaces where multiple risks overlap and interact within the diverse realities of informality and vulnerability. A singular focus on one type of risk, on the formal order to manage that risk, is likely to overlook other risks and opportunities. Hence, shocks are likely to produce more unanticipated effects, conflicts function as the unobserved middle term, and the formal policies and plans to mitigate climate risk contribute to the creation of new risk.
Full-text available
Floods are common natural disasters worldwide, frequently causing loss of lives and huge economic and environmental damages. A spatial vulnerability mapping approach incorporating multi-criteria at the local scale is essential for deriving detailed vulnerability information for supporting flood mitigation strategies. This study developed a spatial multi-criteria-integrated approach of flood vulnerability mapping by using geospatial techniques at the local scale. The developed approach was applied on Kalapara Upazila in Bangladesh. This study incorporated 16 relevant criteria under three vulnerability components: physical vulnerability, social vulnerability and coping capacity. Criteria were converted into spatial layers, weighted and standardised to support the analytic hierarchy process. Individual vulnerability component maps were created using a weighted overlay technique, and then final vulnerability maps were produced from them. The spatial extents and levels of vulnerability were successfully identified from the produced maps. Results showed that the areas located within the eastern and southwestern portions of the study area are highly vulnerable to floods due to low elevation, closeness to the active channel and more social components than other parts. However, with the integrated coping capacity, western and southwestern parts are highly vulnerable because the eastern part demonstrated particularly high coping capacity compared with other parts. The approach provided was validated by qualitative judgement acquired from the field. The findings suggested the capability of this approach to assess the spatial vulnerability of flood effects in flood-affected areas for developing effective mitigation plans and strategies.
Full-text available
In the present study, an attempt is made to understand the impact on Social Vulnerability of the Kopili basin due to various severities of flood hazard. The flood hazard is generated using multi-temporal historical satellite based analysis and integration of annual flood inundation layers. The census of India data of 2001 and 2011 is spatially joined with village database to study the impact at village level. Using 5 Census variables from both Census 2001 & 2011 as vulnerability indicators, the Social Vulnerability Index (SVI) is derived and classified into various vulnerable zones namely Low, Moderate and High Vulnerable zones. The findings of the study show that the number of villages falling in Low and High Vulnerable zones had decreased during Census 2011 when compared to 2001 and a rise of 6% in villages falling in moderate vulnerable zones during 2011 is observed. The spatial database generated is useful to understand the impact of floods on the Social Vulnerability status of the basin and can be a useful input to further study the Physical, Economic and Environmental Vulnerabilities of the basin.
Full-text available
Flood maps alone are not sufficient to determine and assess the risks to people, property, infrastructure, and services due to a flood event. Simply put, the risk is almost zero to minimum if the flooded region is “empty” (i.e., unpopulated, has not properties, no industry, no infrastructure, and no socio-economic activity). High spatial resolution Earth Observation (EO) data can contribute to the generation and updating of flood risk maps based on several aspects including population, economic development, and critical infrastructure, which can enhance a city’s flood mitigation and preparedness planning. In this case study for the Don River watershed, Toronto, the flood risk is determined and flood risk index maps are generated by implementing a methodology for estimating risk based on the geographic coverage of the flood hazard, vulnerability of people, and the exposure of large building structures to flood water. Specifically, the spatial flood risk index maps have been generated through analytical spatial modeling which takes into account the areas in which a flood hazard is expected to occur, the terrain’s morphological characteristics, socio-economic parameters based on demographic data, and the density of large building complexes. Generated flood risk maps are verified through visual inspection with 3D city flood maps. Findings illustrate that areas of higher flood risk coincide with areas of high flood hazard and social and building exposure vulnerability.
Full-text available
In developing countries, where urbanization rates are high, urban sprawl is a significant contributor of the land use change. However, characterizing sprawl has become a contentious issue with numerous arguments both for and against the phenomenon. Meanwhile, effective metrics to characterize sprawl in India are required to characterize this. We have attempted to capture urban sprawl over the landscape and hence adopt landscape metrics, entropy and principal component analysis for characterizing sprawling process. The measurement and monitoring of land-use changes in these areas are crucial to government officials and city planners who urgently need updated information for planning and management purposes. This paper examines the use of landscape metrics and entropy in the measurement and monitoring of urban sprawl by the integration of remote sensing and GIS techniques. The advantages of the entropy method are its simplicity and easy integration with GIS. The measurement of entropy is devised based on locational factors-distances from central business district and reveal spatial patterns of urban sprawl. The entropy space can be conveniently used to differentiate various kinds of urban growth patterns. The application of the method in the Bhubaneswar Metropolitan Area, one of the fastest growing and planned cities in India, has demonstrated that it is very useful and effective for the monitoring of urban sprawl. It provides a useful tool for the quantitative measurement that is much needed for rapidly growing regions in identifying the spatial dynamics, variations and changes of urban sprawl patterns.
Full-text available
A leading challenge in measuring social vulnerability to hazards is for output metrics to better reflect the context in which vulnerability occurs. Through a meta-analysis of 67 flood disaster case studies (1997–2013), this paper profiles the leading drivers of social vulnerability to floods. The results identify demographic characteristics, socioeconomic status, and health as the leading empirical drivers of social vulnerability to damaging flood events. However, risk perception and coping capacity also featured prominently in the case studies, yet these factors tend to be poorly reflected in many social vulnerability indicators. The influence of social vulnerability drivers varied considerably by disaster stage and national setting, highlighting the importance of context in understanding social vulnerability precursors, processes, and outcomes. To help tailor quantitative indicators of social vulnerability to flood contexts, the article concludes with recommendations concerning temporal context, measurability, and indicator interrelationships.
Full-text available
The present study introduces a multi-criteria index to assess flood hazard areas in a regional scale. Accordingly, a Flood Hazard Index (FHI) has been defined and a spatial analysis in a GIS environment has been applied for the estimation of its value. The developed methodology processes information of seven parameters namely flow accumulation, distance from the drainage network, elevation, land use, rainfall intensity and geology. The initials of these criteria gave the name to the developed method: “FIGUSED”. The relative importance of each parameter for the occurrence and severity of flood has been connected to weight values. These values are calculated following an “Analytical Hierarchy Process”, a method originally developed for the solution of Operational Research problems. According to their weight values, information of the different parameters is superimposed, resulting to flood hazard mapping. The accuracy of the method has been supported by a sensitivity analysis that examines a range for the weights' values and corresponding to alternative scenarios. The presented methodology has been applied to an area in north-eastern Greece, where recurring flood events have appeared. Initially FIGUSED method resulted to a Flood Hazard Index (FHI) and a corresponding flood map. A sensitivity analysis on the parameters' values revealed some interesting information on the relative importance of each criterion, presented and commented in the Discussion section. Moreover, the sensitivity analysis concluded to a revised index FHIS (methodology named FIGUSED-S) and flood mapping, supporting the robustness of FIGUSED methodology. A comparison of the outcome with records of historical flood events confirmed that the proposed methodology provides valid results.
Table of contents: Foreword by Prof J. Philip O'Kane Foreword by Prof Jean A. Cunge Introduction The Nature of Urban Flood Risk Urban areas and flooding, Tracing the roots of urban flood risk, The nature of risk, Adding Social and Ethical Aspects into Flood Risk Mitigation The technocratic way of thinking, Historical perspectives of social justice, Characterisations of social justice, Realising social justice in the context of flood risk mitigation, Leadership and social justice, On sociotechnology, Data-Information-Knowledge-Understanding-Wisdom, The role of hydroinformatics in active stakeholder participation, Scientific and Technical Aspects of Flooding Floods and drainage systems, Quantifying urban processes, Data collection for modelling, Rainfall data analysis and catchment delineation, Modelling wet weather and dry weather flows, Hydraulic modelling, Numerical solutions of equations, Modelling practice, Practical Aspects of Flood Risk Assessment and Mitigation Flood risk assessment, Flood mitigation measures, Production of plans