GIS FLOODING PURI

1. Abstract:
Floods in coastal regions present significant challenges to communities, economies, and
ecosystems. As climate change exacerbates the frequency and intensity of such events,
identifying effective mitigation measures becomes imperative. This thesis investigates the
application of Geographic Information Systems (GIS) and remote sensing technologies to
identify and implement mitigation strategies in flood-prone coastal regions, with a focus on
Puri, India. By analyzing spatial data, including topography, land use/land cover, and
hydrological parameters, this study aims to assess vulnerability, understand flood dynamics,
and propose tailored mitigation measures. Through a combination of field surveys, satellite
imagery analysis, and GIS modeling, the research seeks to provide actionable insights for
policymakers and planners to enhance resilience and reduce flood risk in coastal areas.
Identifying areas susceptible to urban flash floods is crucial for developing effective plans to
mitigate the impacts of this natural hazard. Therefore, this study focuses on identifying
flood-prone areas within the Puri urban agglomeration region (PUAR) using the Analytic
Hierarchy Process (AHP) method. Initially, twelve factors influencing flash floods were
selected, followed by a multicollinearity test to assess their intercorrelation. The importance
of each factor was then evaluated using the AHP method to assign weightage. Results
indicated that Land Use and Land Cover (LULC) (26.20%), distance to river (19.00%),
Normalized Difference Vegetation Index (NDVI) (14.20%), and distance to road (11.00%)
were the most significant influencing factors. Subsequently, a flood susceptibility map was
generated, categorizing study areas into very high (12.23%), high (15.42%), medium
(19.14%), low (37.51%), and very low (15.70%) susceptibility zones. Validation of the flood
susceptibility mapping using receiver operating characteristic (ROC) and Area Under Curve
(AUC) analysis yielded a high AUC value of 0.85, indicating the reliability of the AHP method.
These findings will assist hydrologists, planners, and water resource managers in effectively
managing highly flood-prone areas in Puri and mitigating potential damages.
Area Of Study
The study area of Puri encompasses the coastal city of Puri and its surrounding region in the
eastern state of Odisha, India. Puri is situated along the Bay of Bengal and is known for its
rich cultural heritage, including the famous Jagannath Temple. However, the region is also
prone to natural hazards, particularly cyclones and floods, due to its coastal location and
low-lying topography.

2. Key
characteristics of the study area include:
1. Geographic Location: Puri is located on the eastern coast of India, bordering the Bay of
Bengal. Its coordinates are approximately 19.8133° N latitude and 85.8315° E longitude.
2. Topography: The topography of Puri is characterized by low-lying coastal plains,
interspersed with rivers, creeks, and wetlands. The city itself is situated close to the coast,
making it vulnerable to storm surges and inundation during cyclonic events.
3. Hydrology: Puri experiences a tropical monsoon climate, with heavy rainfall during the
monsoon season (June to September). The region is drained by several rivers, including the
Bhargavi, Daya, and Kushabhadra rivers, which discharge into the Bay of Bengal.
4. Land Use/Land Cover: The land use/land cover in the study area is diverse, comprising
urban areas, agricultural land, forested areas, wetlands, and coastal ecosystems. Rapid
urbanization and encroachment into natural habitats have altered the landscape and
exacerbated flood risk in certain areas.
5. Socio-economic Profile: Puri is home to a diverse population, including urban residents,
rural communities, and fishermen living in coastal villages. The region is a popular tourist
destination, attracting pilgrims, tourists, and visitors throughout the year. Socio-economic

3. factors, such as population density, income levels, and access to infrastructure, influence
vulnerability to floods and the capacity to cope with disaster events.
Understanding the geographical, environmental, and socio-economic characteristics of the
study area is crucial for assessing flood risk, identifying vulnerable populations and assets,
and developing effective mitigation strategies using GIS and remote sensing technologies.
By focusing on Puri as a case study, this research aims to contribute to the field of flood
management and resilience-building in coastal regions facing similar challenges worldwide.
Flood studies with Geospatial Technology – Current status
Nature of Floods in India
The major flood prone regions of India are the Ganges and the Brahmaputra flood plains.
These rivers originate from the Himalayan mountains and cause heavy floods in plains of
Uttar Pradesh, Bihar, West Bengal and Assam due to heavy high discharges concentration
during monsoon months (June to September) and large volumes of sediment in flood plains.
The heavy sediment load causes reduction in channel capacity, which results in extensive
over-bank spilling and consequent inundation. The other flood prone areas of country are
Mahanadi delta region, some portions of Mahi, Narmada and Sabarmati river in Gujarat and
occasional heavy rainfall floods in Andhra Pradesh, Rajasthan and Maharashtra. The coastal

4. floods are mainly due to Cyclone/Tsunami’s along eastern/western ghats in Tamilnadu,
Orissa, and Gujarat. 2.2 Role of Remote sensing and GIS in Floods Space and Air based
observations of earth provide a unique vantage point for monitoring and assessing the
floods and other disasters. The traditional floods mapping and studies were based on
conventional surveys and historical flood records. In this regard, space technology has made
substantial contribution in every aspect of flood disaster management such as
preparedness, prevention and relief (Rao 1994, Rao et al 1998). The Indian and other global
remote sensing satellite are being used for obtaining the information about floods
inundation areas and flood damage assessment. The major Indian remote sensing and
foreign remote sensing satellites used for flood studies are given in table 1 and 2.
Flood studies have been greatly improved with the geospatial technology mainly in three
phases of floods. i.e., a) before floods (preparedness phase), b) during floods (monitoring
phase) c) after floods (damage assessment and mitigation phase). Table 3 and 4 list the
major flood related themes, their utilization, spatial and temporal resolution requirements.
Steps taken by gov for Flood Management
Total control of flood is not practicable from economic considerations & therefore flood
management is essential. So far, a number of structural and non-structural measures have
been taken to minimize flood. As a part of structural measures, reservoirs namely Hirakud
on the Mahanadi river, Rengali on the Brahmani river, Upper Kolab in the Kolab river &
Upper Indravati in the Indravati river have been constructed. Chandil dam & Ichha dam
(under construction) in Jharkhand will control flood to some extent in Subernarekha delta.
Similarly, Kanpur dam under construction in Keonjhar will also moderate flood in Baitarani
delta. Rivers namely Rushikulya, Vansadhara, Nagavali, Bahuda and Budhabalanga
For long term solution of flood problems, construction of reservoir with adequate flood
cushion is required. Under the present circumstances, construction of flood control
reservoirs are difficult due to large scale submergence and other environmental and
ecological aspects. Other measures such as construction of cascade reservoirs and to
reframe the rule curve so of existing reservoirs has been planned for flood moderation.
Besides, raising and strengthening of flood protective embankments,
do not have flood control reservoirs. In the deltaic area, floods are being controlled by flood
protection embankments constructed on both sides of the rivers. Total 7473.206 Kms of
flood protective embankments, (Capital Embankment-1717.288 Km, Other Agricultural
Embankment-2496.151 Km, Test Relief Embankment-1623.82 Km, Saline Embankment-
1635.945 Km) have been constructed particularly in the deltaic areas to control the flood
and saline ingress which is given in the table

5. clearance of river mouths, inter basin transfer of water within the state, Flood plain
regulation / Flood plain zoning & Flood forecasting & warning systems have also been
planned for effective flood management. At present, flood protection and river training
works are in progress in Mahanadi, Brahmani, Kharasuan, Rushikulya, Devi, Kelua,
Vamsadhara, Bahuda, Ramnadi, Ghodahado, Genguti, Daya, Luna, Nagabali, Dahuka, Suktel,
Kuakhai, Duanto, Chitrotpala, Kathjodi, Baitarani rivers etc.
Schemes 1.
Rural Infrastructure Development Fund (RIDF) During 2003-04, NABARD has agreed to
provide loan for flood control & drainage works. Sofar, flood control projects and
embankment road improvement works have been sanctioned. 547 flood protection works
with an estimated cost of `3851.45 Crore have sofar been taken up under RIDF. Outof which,
371 flood control projects have been completed by end of March 2020 with an expenditure
of ` 2911.37 Crore. Apart from that, 24 road projects on different flood embankment have
been takenup under RIDF, outof which 20 projects have been completed as of March 2020.
During 2020-21, a sum of ` 760.00 Crore (RIDF ` 450.00 crore, State Fund- ` 310.00 crore)
has been allocated in the budget, outof which ` 10.00 crore has been earmarked for Pre-
flood preparedness work. The tranche-wise expenditure incurred and disbursement made
by NABARD up to end of March 2020 of these projects are given in the following table

6. Flood Damage Restoration Works
Restoration of damaged assets normally requires resources well beyond those available
with the Department. These works are taken up through CRF/NCCF/NFCR grant. The
allotments received under different schemes from 2004-05 to 2019-20 are given in the table

7. Flood Forecast and Warning
Non-structural measures like flood forecasting and warning of incoming floods have also
played a significant role in reducing the loss of life and property apart from alerting the civil
and engineering authorities in-charge of various works to take appropriate advance action
to fight the on slaught of floods. There are eleven flood forecasting stations managed by
CWC located in our state at Naraj, Alipingal, Nimapada in Mahanadi Basin, Jenapur in
Brahmani Basin, Anandapur & Akuapada in Baitarani Basin, NH5 (Gobindapur) in
Budhabalanga Basin, Rajghat in Subernarekha Basin, Purusottampur in Rushikulya Basin and
Gunupur, Kashinagar in Vansadhara Basin. Apart from that one inflow forecasting station is
functioning at Burla in Mahanadi Basin. In Upper Mahanadi Basin, modern technique such
astelemetry system was installed for flood forecasting. CWC collects daily readings of river
gauges, discharge and rainfall etc. of various water bodies in all basins. They also collect
daily hydro meteorological data from State Departments, IMD
and other agencies. CWC maintains wireless communication network between their gauge
stations in Odisha. Basingon the field information and IMD forecast, they prepare the
forecast message and warnings and communicate them to different departments including
Water Resources Department. This message is immediately communicated to the field
functionaries/ Collectors including Revenue Department to take precautionary measures.
Real Time Forecast System
Travel times of flood in different rivers have been worked out so that advance warning of
flood in the delta region can be given. The basin-wise list of travel time from control
structures/from important gauge station to station is given in the following page. A Flood
Management Information Cell (FMIC) was established during 2007, in the office of the
Engineer-in-Chief, Water Resources which is providing real time information on early flood
warning, possible flood in undation and its impact by using advanced space technology
(Remote Sensing & Geographical Information system).

8. Drainage
The natural topographical factor (flat terrain) is the primary cause of drainage congestion in
coastal belts of Odisha. Therefore, disposal of run-off resulting from rainfall takes
considerable time. Further, the problem gets aggravated due to formation of sand bars
across the river mouths and Tidal lockage. The drainage congestion affects crop yield. It has
been estimated that 30% of the CCA in deltaic area about 2.17 lakh ha suffers from poor
drainage and water logging problems. To harness the potential for increased agricultural
growth, a Masterplan to reclaim by development of above command area of 17 Doabs 1.90
lakh ha. of water logged area has been prepared. The doab-wise abstract of Master Plan is
given in the table

9. RIDF: During 2004-05, NABARD has agreed to provide loan for drainage works. So far, 101
drainage works have been takenup, out of which 94 works have been completed. Tranche-
wise physical and financial status is given in the table
Drainage Improvement Programme (DIP)
To address drainage congestion in the irrigated, un-irrigated command and in urban areas,
State Government have launched “Drainage Improvement Programme (DIP)” with an outlay
of `1000.00 crores to be implemented over a period of five years i.e. from 2014-15. The
Scheme will be operational in the seventeen Doabs facing drainage congestion where a part
of agricultural land is mostly water logged due to poor drainage, selected urban areas and
low lying area around wetlands where flooding for a longer period possess threat to the life
and property of the affected people. The main objectives of the scheme areas follow
Retrieval of about 1,79,000 ha. of cultivable area.
Increase in crop productivity by an average of 10% in the area of influence. Arresting saline
ingress in around 10,000 ha. of Gross Cultivable Area (GCA). Reduction of inundation time
through improvement of carrying capacity by way of removal of shoals and islands etc. from
rivers and drainge channels and river mouth clearance. Improved natural draiange facilities

10. in select urban areas and areas facing acute drainage problem around wetlands through
gravity or by pumping arrangement
Renovation and de-silting of select urban water bodies.
8.3 Materials and Methods
Data Sources In the present study, twelve flash flood influencing factors, namely, elevation,
slope, curvature, slope, LULC, NDVI, distance to road, distance to the river, soil map,
drainage density and TWI, were considered for the flash flood susceptibility mapping. It is
worth noting that we created the DEM file from Google Earth. When creating the DEM, we
selected about two million points in Google Earth for the study area. After which, a very
high-quality DEM with a spatial resolution of 5 5mwas obtained. A detailed data sources of
these factors is given in Table
Flood Susceptibility Map
Creating a flood inventory map serves as a crucial initial step in assessing flood
susceptibility. Typically, historical flood records obtained from such maps are instrumental in
analysing flood risks in a specific area In the case of Puri, a flood inventory map for the Puri
urban agglomeration region (PUAR) was developed using data from 122 flood event points
gathered through a comprehensive field survey. This survey involved collecting primary data
through interviews with residents and capturing photographs of various localities during the
monsoon period from Jan 2023 to September 2024. Two types of points, flash flood and
non-flash flood points, were identified in the study region based on the data collected from
respondents. Subsequently, the flash flood points were processed and validated using
Google Earth.
Methodology

11. Selection of Flash Flood Influencing Factors
Elevation
Elevation is considered as a key factor in flash flood susceptibility mapping. . It is defined as
the difference between highest and lowest point in a particular region. Generally, the
surface water flows from higher to lower areas. As a consequence, the lower region is
subjected to flash floods due to the constant f low of surface water (Pham et al. 2020). In
this study, the elevation map was classified into five categories: very high (37.60–40), high
(40.01 45), moderate (45.01–50), low (50.01–55), and very low (55.01–60 m) susceptible
areas.

12. Aspect
The direction of slope in earth surface is known as slope aspect. It greatly controls the
conver gence and direction of surface water flow. Hence, it can be identified as a key factor
in developing the flood susceptibility map. In this study, we classified the slope aspect map
into two categories: high (360) and low (0)
Slope
In general, surface slope has a significant impact on a variety of hydrological processes,
particularly on infiltration process and surface water f low (runoff) Therefore, it can be
regarded as a significant factor in flash flood susceptibility mapping. Infiltration process is
comparatively lower on steep gradient slopes, while speed of surface water (runoff)
becomes greater on steeper slopes. This excessive surface water flow (runoff) on flat
regions causes flash flood condition. As a consequence, flat regions nearer to higher slopes
are frequently subjected to flash flood events We classified the slope map into five classes:
very low (0–1.29°), low (1.3–3.36°), moderate (3.37–6.47°), high (6.48–14.2°), and very high
(14.3–66°)

13. LULC
Landscapes in a particular location are mostly modified as a result of changing the LULC
map The LULC map is a key component in determining flood-prone areas since it has a
significant role in numerous hydrological processes such as evaporation, runoff,
evapotranspiration, and infiltration. Thematic maps of LULC usually include buildings, roads,
bare lands, vegetation cover, agricultural areas, and water bodies, etc. We created a LULC
map for the present study, which contained five fea tures: water bodies, plant cover, built-
up area, agricultural land, and open areas.

14. NDVI
The Normalized Difference Vegetation Index (NDVI) is a useful graphical index that depicts
the distribution of healthy vegetation in a particular region. Besides, infiltration capacity and
surface water flow are also dependent on the vegetation cover. Hence, it can also be
adopted as a significant factor in determining flood susceptibility areas. This index was
usually derived from satellite image. The NDVI is computed by the following equation:
where, PNIR represents reflectance in the form of infrared portion and PR represents
reflectance in the for red. The NDVI value is ranged between −1 and +1. In this study, the
obtained NDVI map was classified into five categories: very low (−0.03–0.00), low (0.00–
0.28), moderate (0.29 0.35), high (0.36–0.43), and very high (0.44 0.61) susceptible areas

15. Distance to Road
The distance of any location from the road is another important factor in determining flash f
lood susceptible places. Roads, in fact, slow down the infiltration process by preventing
water from entering the ground. As a result, areas with a high density of roadways are
inundated by light rain and hence, creating a flash flood situation. Furthermore, regions
closer to the road are more prone to flash floods due to less infiltration and a faster runoff
process. In this study, we divided the spatial map of distance to road into five classes: very
high (0–36.31 m), high (36.32 86.74 m), moderate (86.75–155.33 m), low (155.34–215.15
m), and very low (250.16 514.42 m)

16. Distance to River
The severity and extent of a flood in a region are primarily determined by the distance from
the river to that location. Generally, distance from the river is widely considered to be
disproportionately connected with the flash flood occurrence. Hence, flash flood is a
common phenomenon in areas that are close to rivers. Contrarily, places far away from river
are less vulnerable in terms of flood damages (Souissi et al. 2020). We divided the spatial
map of distance to river into five classes: very high (0 2103 m), high (2103–2287m),
moderate (501 775 m), low (776–1000 m), and very low (1001 1617 m).

17. TWI
TWI is a prominent terrain-derived parameter that assesses topographic effects on some hy
drological processes, especially flood events. In general, a higher TWI score implies a
greater susceptibility of a specific region to flash flood events. For the present study, we
developed a TWI map and classified it into five classes as very low (0.00–35), low (0.36–
1.46), moderate (1.47–2.47), high (2.88–4.35), and very high (4.36–7.99) susceptible groups.

18. Analytical Hierarchy Process (AHP) AHP
is a useful and widely used multicriteria decision method, which is first introduced by Saaty
in 1980. It is typically used to rank factors in order to determine the most dominant factor
based on Expert opinion (Das 2017). In this study, a total of twelve flash flood influencing
factors were incorporated for developing the f lash flood susceptible areas
Pairwise Comparison Matrix
Based on the selected flash flood influencing factors, we created a pairwise comparison
matrix table. Following that, each factor was assigned a specific weight based on the
Expert’s opinion. As stated in Table , the relative importance or value was assigned using the
scale relative importance. t is pertinent to mention that the length of the comparison matrix
table is equiva lent to the number of factors selected in the specific study. In the
comparison matrix table, factors weight, class weight, and the CR value were computed, as
shown in Table 8.6. For the computation of CR value following expression is used (Saaty
1980, 2000):
Consistency Index (CI)

19. For testing the consistency of results, the rule of transitivity is commonly used. The value of
kmax is computed by the following expression
The obtained CR value determines the con sistency of the matrix. If CR = 0, the matrix will
be consistent, however, a value of >0 indicates that the matrix
is inconsistent. To eliminate type II error, Saaty (1980) proposed
the consistency of the matrix (CR.10). In the present study, the
computed CR value was 0.089. In most cases, max does not
equal to n. As a result, we evaluated CI to see if the transitivity
criteria were violated or not. The following equation was used to calculate