Integrated application of HEC-RAS and GIS and RS for flood inundation mapping

Chapter 1
Introduction

Natural hazards are inevitable, unfortunate events resulting from combination of natural,
geological and anthropogenic disturbances. Flooding is one of the main natural hazards and occurs
frequently all over the world, especially in Asia and Africa than other countries. Every year,
flooding incurs loss of life, economy, environment and agriculture. Economic loss includes
damage to businesses, residential properties, roads, bridges, buildings and automobiles. As an
example, environmental damage includes contamination of water supplies and destruction to
natural habitats by chemical waste and oil spills from vehicles and industrial facilities, flooding is
mainly caused as a result of increased settlement along levees, unexpected high rainfall,
deforestation, sediment deposition and river channel changes. Due to heavy rainfall in a short
period of time, rivers are unable to transport the increased volume of water and other materials
along its course that results in over-bank flow causing inundation of neighboring lands. Once the
river water has overflowed or breached the levees, water races through the almost level flood plain
submerging the cultivated fields and villages along its way causing enormous damage to lives and
properties. In-depth exploration into the causes and effects of flooding would lead to improved
flood risk monitoring, prediction, mitigation and relief operations. However, poor disaster
management practices, limited financial resources and high population pressure are some common
characteristics of less developed countries such as Ethiopia, which affects the monitoring,
mitigation and relief operations.

Rivers are a significant of irrigation, hydropower, and drinking water supplies. Throughout history,
people developed habitats along rivers due to the easy availability of water for a multitude of
purposes. But, human populations that live on the banks of rivers are significantly vulnerable to
flooding in the river. In developing countries such as India, the inhabitants are particularly
vulnerable to devastation caused by floods. Another major reason for the huge devastation caused
by floods in the developing countries is the deficiency of rapid response infrastructure. The impact
of flooding on human life and infrastructure has become more profound in the wake of climate
change in the developing countries

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Flood occurs due to natural as well as man-made causes. Major causes of floods in India include
intense precipitation, inadequate capacity within riverbanks to contain high flows, and silting of
riverbeds. In addition, other factors are landslides leading to obstruction of flow and change in the
river course, poor natural drainage, cyclone and heavy rainstorms/cloud bursts, snowmelt and
glacial outbursts, and dam break flow. For minimizing the losses due to floods, various flood
control measures are adopted. The flood control measures which can be properly termed as “Flood
Management” can be planned either through structural engineering measures or non-structural
measures. Wise application of engineering science has afforded ways of mitigating the ravages
due to floods and providing reasonable measure of protection to life and property. Hence an
efficient Flood-Forecasting and advance warning systems are necessary in order to evacuate the
life and movable goods, to as much extent as possible owing to the non-structural measures. The
likely expenditure on developing such an efficient flood forecasting services automatically gets
justified in view of the large-scale savings generated by it.

Hydrological model, a simplified and conceptual representation of complex hydrological

processes, provides spatial and temporal changes over large areas and simplifies the complex
reality. It is very important to find accurate causes and effects and to understand the behavior of
flooding according to their specific locations. The complex processes of river and floodplain due
to combination of natural and anthropogenic activities have been assessed by several researchers.
However, hydrological modelling has some uncertainties either in input or model parameter, which
affects accuracy and efficiency of a model. The poor spatial distribution of basic input and model
parameters data in hydrological modelling such as precipitation, evapotranspiration, infiltration
and runoff can affect the model accuracy. For example, in complex topography, precipitation has
uncertainty in its spatial distribution due to uplifting air masses by the wind. Some studies
considered precipitation’s spatial discontinuity and used different occurrence/non-occurrence
estimation approach to improve the spatial distribution of precipitation. The spatial distribution of
input and model parameters also affects the accuracy of river’s geometry such as selection of their
spacing and channel shape.

Flood is an unusual high stage of a river overflowing its bank and inundating the marginal lands.
This is due to severe storm of unusual meteorological combination sometimes combined with
melting of snow on the catchment. This may also due to shifting of the course of the river, earth

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quake causing bank erosion, or blocking of river, or breaching of the river flood banks. Flooding
is one of the serious natural hazards in the world. The damages caused by flooding include loss of
life and property, displacement of people, disruption of socio-economic activities and loss of
valuable agricultural lands Floods have swept vast regions in India, particularly in the regions of
rivers Kosi, Brahmaputra, Godavari, Narmada and Tapti. Floods cause much loss of life and
property, disruption of communication, damage to crops, famine, epidemic diseases and other
indirect losses.

Design flood magnitude of floods are needed for the design of spillways, reservoirs, bridges
openings, drainage of cities and air ports, and construction of flood walls and levees (flood banks).
The damages due to the devastating floods can be minimized by various flood control measures.
Flood forecast information in a graphical format (flood inundation map) enables emergency
managers and disaster relief officials, to better prepare for potential flood conditions. So, it is
important to provide easy to read graphical information related to flood hazard to the public, city
planners and emergency managers. Flood inundation mapping is the process of defining the area
covered by water, a map during a flood event. It can be done by integrating geographic information
system with hydrologic and hydraulic models.

Inundation is result from flood and tsunami and even from the construction of hydraulic projects
that cuts off river to build a dam. Viewed as a distinct model of catastrophe, inundation usually
poses threats to mankind’s existence and development. United Nations Office for Disaster Risk
Reduction (UNDRR) reports that inundation accounts for over 30% of all-natural disaster losses.
Inundation appears to be a particular serious problem in crowded China due to its long-time span,
wide range, unexpected frequency, and tremendous devastation. Statistically, China has long been
harassed by inundations and is confronted with catastrophic flood every two years. Approximately
28% of arable land and 46% of the population nationwide are subject to the detrimental effect of
inundations, thus hampering the sustainable development of economy and society. In the process
of recovering from these damages, inundation estimation plays a significant role in analyzing the
affected extent and the loss amount. It forecasts inundation extent, marks the flood-stricken region,
and gives emergency response instructions.

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Chapter 2
Literature review

Baldassare, 2010 studied different methodologies for flood plain mapping and have analyzed,
discussed by comparing deterministic and probabilistic approaches using hydrodynamic numerical
solutions. At first map is prepared by applying an advanced deterministic approach: use of a fully
two-dimensional finite element model (TELEMAC-2D), calibrated against a historical flood
extent, to derive a 1 in 100-year flood inundation map. A second map is derived by using a
probabilistic approach: use of a simple raster-based inundation model (LISFLOOD-FP) to derive
an uncertain flood extent map predicting the 1 in 100-year event conditioned on the extent of the
2006 flood. Finally concluded, flood hazard as a probability seems to be a more correct
representation of the subject. In order to assist the diffusion of probabilistic approaches for
floodplain mapping, clear methods and applications need to be established.

Hazarika, 2007 studied capacity building in application of informatics in flood hazard mapping.
After conducting flood project studies in different nations such as Bangladesh, China, Cambodia,
and Nepal a detailed flood hazard map was prepared for Cambodia. Study reach was Nekong river
basin which covered 3 provinces, 26 districts, 305 communities with 245086 populations. The
main objective of the study was preparing hazard maps using depth map and the socio-economic
data. For this study RADERSAT satellite image was used for the period of September and October
2000. Discharge and water level data from 1991 to 2002 and manning's roughness coefficient of
river and floodplain from land use map were also used for this study. Flood depth map was
prepared using HEC-RAS and GIS and hence flood hazard map was also prepared. It was studied
for flood forecasting and early warning system in Bagmati river floodplain. Flood maps are
prepared and inundation areas were tabulated. The flood duration and crop damages were also
analyzed. Flood map was prepared for 6 return periods of 2, 5, 10, 20, 50, and 100 year and results
showed inundation area was ranging from 363 to 465 sq. km. which is 37% to 47% of the district
area.

Knebl, 2005 studied regional scale flood modeling using GIS, HEC-HMS, and HEC-RAS in San
Antario river basin summer 2002 storm event. They developed a framework for regional scale
flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-

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RAS/HMS). The San Atonio river basin (about 10000 sq. km.) in Central Texas, USA, was the
domain of the study because it was a region subject to frequent occurrences of severe flash
flooding. A major flood in the summer of 2002 was chosen as a case to examine the modeling
framework. The model consisted of a rainfall-runoff model (HEC-HMS) that converted
precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS)
that models unsteady state flow through the river channel network based on the HEC-HMS derived
hydrographs. HEC-HMS was simulated on a 4X4 km grid in the domain, a resolution consistent
with the resolution of NEXRAD rainfall taken from the local river authority. Watershed
parameters were calibrated manually to produce a good simulation of discharge at 12 sub basins.
With the calibrated discharge, HEC-RAS was capable of producing floodplain polygons that were
comparable to the satellite imagery. The modeling framework presented in this study incorporated
a portion of the recently developed GIS tool named ArcView that had been created on a local scale
and benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a
regional scale. While designed for the San Antonio river basin, this regional scale model might be
used as a prototype for model applications in other areas of the country.

Hicks and Peacock, 2005 studied the suitability of HEC-RAS for flood forecasting. They found
that most river flood forecasts were conducted using a two-step procedure. First, flood routing was
conducted, normally using hydrological models. The resulting flood peaks were then converted to
water level forecasts using a steady flow hydraulic model, such as HEC-RAS. The HEC-RAS
model had been extended to facilitate unsteady flow analysis, and while the numerical scheme was
not robust enough to handle dynamic events (such as ice jam release floods) or supercritical flows,
it did have the capability to route simple open water flows and produce water level forecasts at the
same time. Here, the viability of the HEC-RAS unsteady flow routine for flood forecasting was
examined through an application to the Peace River in Alberta and it is shown that accuracy
comparable to more sophisticated hydraulic models can be achieved. Since many agencies already
have HEC-RAS models established for floodplain delineation purposes, it would be simple matter
to extend them to the flood forecasting application. They thought an ancillary advantage would be
that flood forecasting accuracy could potentially be improved and simplified into a one-step
process, without necessitating a time consuming transition to unfamiliar models.

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Lant, 2013 prepared Flood-Inundation Maps for a 6.5 Mile Reach of the Kentucky River at
Frankfort, Kentucky. In this study, flood profiles were computed for the Kentucky River reach by
using HEC–RAS, a one-dimensional step-backwater model developed by the U.S. Army Corps of
Engineers. The hydraulic model was calibrated by using the most current (2013) stage-discharge
relation for the Kentucky River at Lock 4 at Frankfort, Kentucky, in combination with stream gage
and high-water-mark measurements collected for a flood event in May 2010. The calibrated model
was then used to calculate 26 water-surface profiles for a sequence of flood stages, at 1-foot
intervals, referenced to the stream gage datum and ranging from a stage near bank full to the
elevation that breached the levees protecting the City of Frankfort. To delineate the flooded area
at each interval flood stage, the simulated water surface profiles were combined with a digital
elevation model (DEM) of the study area by using geographic information system software.

Hatipoglu, 2007 studied on floodplain delineation in Mugla-Dalaman plain using GIS based river
analysis system. In his study, an application of HEC-RAS and HEC-GeoRAS model were applied
in Mugla-Dalaman floodplain. Due to its economic and touristic potential, Dalaman floodplain is
very important. They studied that area before and delineated the floodplain with traditional
methods and found the houses and lands of the local people prices were decreasing because of
being in the flood plain. For the determination of flood plain, first, HEC-RAS simulations were
performed to generate water surface profiles throughout the system. Floodplain zones for the
design storms were reproduced in three dimensions with HEC-GeoRAS by overlaying the
integrated terrain model for the region with the corresponding water surface TIN. As a result, it
was seen that the floodplain extend that was found by GIS based methods gives considerable better
results than by traditional methods.

Khan, 2009 has studied flood inundation mapping of Kaliganga-Dhaleswari river basin. In this
study, an application of HEC-RAS and HEC-GeoRAS model were applied for the Kaliganga-
Dhaleswari floodplain. Using discharge and water level data for the year 2004 floodplain maps
and the area of inundation within the floodplain during the post monsoon were prepared. It was
found that the downstream area of the basin is more vulnerable to flooding than the upstream area.
The area of inundation found from the flood map is about 3 to 4 km on both bank of the river.
From these maps it was shown that different thanas like Dhamrai, Savar and Saturia were
inundated by 11.81, 9.0 and 47.38% of the total area respectively. The inundation depth of different

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thanas was also showed.in this study the maximum and minimum inundation depth was found to
be 4.7 m in Savar thana and 0.3 m in Manikganj Sadar respectively. There was no calibration and
validation of hydrodynamic model and floodplain inundation was absent in this study.

Bhuiyan, 2014 has conducted a study to evaluate the flood hazard and vulnerability in a riverine
flood prone area using Remote Sensing (RS) and Geographic Information System (GIS) Flooding
was assessed for different flood magnitudes with the help of flood frequency analysis. Flood map
and hazard were prepared for 6 return periods 2.33, 5, 10, 25, 50 and 100 years. It was found that
the inundation areas range from 1250 to 2500 acres which is 23% to 50% of the total area. It was
also found in 100-yr return period 51.71 percent land became inundated, in which F0, F1, F2, F3
and F4 inundation areas were 6.47, 11.45, 5.78 and 16.14 acres respectively.

Alaghmand, 2012 studied GIS-based river basin flood modeling in Kayu Ara river basin. HEC-
HMS, MIKE11 and GIS were used in river basin flood modeling. The hydrologic model like HEC-
HMS was used to develop rainfall-runoff from a design rainfall or historic rainfall event. The
MIKE11, hydraulic model, was used to route the runoff through stream channels to determine
water surface profiles (including depth and velocity) at specific locations along the stream
network. Finally, ArcGIS was used to process and visualize the river flood extent map, river flood
depth distribution map, comparison map and river flood duration map. They were found increase
of rainfall event ARI (Average Recurrence Interval) from 20 year to 100 year causes 29% increase
in the river flood inundated area. They also were found increase of rainfall event ARI from 20 year
to 100 year, inundated area with 0 cm to 100 cm depth is increased from 10.36 hectares to 14.40
hectares.

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CHAPTER 3
FLOOD AND FLOOD MAPPING

3.1 introduction

Flood modeling and warning services needed for safety of life and protection of property in
vulnerable areas;

to evacuate the affected people to the safer places,

• it helps to regulate the floods through the barrages and reservoirs, so that the safety of these
structures can be taken care of against the higher return period floods.
• flood forecasts to safeguard and be used in the operation and maintenance of water
resources infrastructures such as dams and reservoirs;
Flood modeling to be used to optimize water resources conservation decisions in the operation of
dams and reservoirs by assisting in optimized scheduling of reservoir release operations.

The process of flood inundation mapping is an essential component of flood risk management
because flood inundation maps not only provide accurate geospatial information about the extent
of floods, but also, when coupled with a geographical information system, can help decision
makers extract other useful information to assess the risk related to floods such as human loss,
financial damages, and environmental degradation. For these reasons, flood maps have been
widely used in practice to assess the potential risk of floods.

The preparation of flood inundation mapping is needed for the following important purposes:

• flood inundation mapping is a vital component for appropriate land use planning in flood-
prone areas. It creates easily-readable, rapidly-accessible charts and maps which facilitate
the identification of areas at risk of flooding and also helps prioritize mitigation and
response efforts (Bapula and Sinha, 2005).

• flood hazard maps are designed to increase awareness of the likelihood of flooding among
the public, local authorities and other organizations. They also encourage people living and
working in flood-prone areas to find out more about the local flood risk and to take
appropriate action.

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• identification of flood risk areas is likely to help in the planning of a more effective
emergency response.

• the creation of flood inundation maps will therefore allow planners to locate these elements
in low risk areas so that they can continue to serve during an extreme event.

3.2 Types of floods

Floods are usually broken down in several types even if any classification it is somewhat arbitrary.
Let’s list and describe them separately:

• Fluvial flooding occurs when water levels in a channel, lake or reservoir rise so that water covers
nearby areas, which normally are dry land. A fluvial flood may be caused by heavy or persistent
rain, snowmelt or ice jam, sometimes also by debris jam, landslide or another blockage of the
channel. Flooding can be a regular feature of the yearly hydrological cycle, but rivers have
different patterns of flow and the severity of flooding varies. Forecasting fluvial floods is generally
easier than for other flood types.

• Pluvial flooding is caused by intense localized rainfall. Pluvial floods often cause damages in
urban environments in combination with overflowing sewers and high runoff in small catchments.
Urban pluvial floods often arise due to a combination of land sealing and insufficient capacities of
sewers and drainage systems. They are difficult to predict due to the difficulty in predicting local
rainfall patterns, lack of data on the actual hydrological status, and the short lead-times.

• Coastal flooding occurs when sea level exceeds normal levels due to storm surges, exceptional
tides or tsunamis. Forecasting is difficult, but risk analyses can be performed using models.

• Flash flooding is characterized by very rapid inundation. Some pluvial floods can be classified
as flash floods, particularly if heavy rain in the upper part of the catchment creates flood wave
surges downstream where it may not have rained at all. The forecasting of flash floods is often
extremely difficult due to the same factors as mentioned under pluvial flooding.

• Riverine flooding- riverine flooding is relatively high-water levels overtop the natural or
artificial banks of a stream or river. The nature of riverine flooding can vary significantly in terms
of cause, timing and depth between different locations. Coastal rivers with short, steep headwaters

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often have floods that rise and recede quickly. Inland floods with low gradients have floods that
move slowly down the river, sometimes lasting for several months.

• Dam failure - although dam failures are rare, their effects can be significant. In Victoria dam
safety is monitored, and warning arrangements are in place to warn downstream residents of
potential dam failure threats. Should dam failure occur, significant downstream flooding can
involve potentially swift flowing water and high amounts of debris.

• Storm surge - storm surge occurs when sea levels are elevated above the usual tidal limit due
to the action of intense low-pressure systems over the open ocean. The low pressure causes sea
level to rise as there is less air pressing down on the sea. Combined with gale force onshore winds,
this can lead to flooding of low-lying coastal land.

In order to predict flooding events as reliably as possible two main modelling approaches are
applied. Basically, river flow is estimated by hydrological modelling, while hydraulic modelling
is required to simulate water depths and velocities with the purpose of assessing the actual impacts
of a certain river flow either overland flow. Over the last decades, the performance of hydraulic
models has improved tremendously as a result of more powerful computers, while techniques such
as remote sensing and radar has improved the detail of the input data (e.g. rainfall, topography and
land use), therefore nowadays choosing to model it’s a standard in design and paramount for
decision-making processes establishing how and what kind of interventions has to be made for
every specific case study. Although it is a promising and innovative instrument, modelers still
tackle problems with lack of reliable data. In addition, current models are still far from being
capable to correctly represent the complexity of natural and urban environments. Relevant
improvements have to be done in performance and also in the features placed at the disposal of the
modeler, which sometimes has to spend a lot of time in setting up and running the model itself.

3.3 Benefits of flooding

• Soil Fertility

An excess of water can be damaging to any natural or manmade structure, but the replenishment
of essential nutrients in soil is possible with floodwater. Flooding can also water crops. The

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Ancient Egyptians utilised the annual flooding of the Nile to water their crops, replenish the soil
and provide food for their community.

Planting crops that can tolerate flooding could help areas of the modern world too. The habitats of
birds and fish, and thus the ecosystems containing these animals, can also benefit from flooding in
this way.

• Hydroelectricity

Building dams and other structures can make use of the powerful mass of water travelling along a
juncture of a river. This can electricity can power the computers used to improve techniques of
flood forecasting, prevention and protection, as well as warning the world through the internet of
flooding incidents. The remains of a town flooded to build a new hydroelectric dam in Zhejiang,
China, is pictured.

• Financial Input

The immediate aftermath of a flood may lead to the release of financial aid for a country from
other nations, but more often than not in the long-term the affected nation will spend money on
studying floods to design and implement new forms of flood protection.

This money will save money from being spent in the future, but more importantly lives will be
saved too. Flooding often causes home-owners to spend money fitting a home with domestic flood
products, such as those provided by Storm guard Flood plain, providing them with peace of mind.

Governments will usually release funds in the form of research grants and other funding for
scientists and engineers dedicated to creating flood solutions on a national scale in the period after
a devastating flood, which protects future generations from disaster.

• Awareness

Flooding awareness is a vital component of flood prevention and protection. If an individual in a

high risk area only becomes aware of the Environment Agency’s risk map and flood warning
facilities during a flooding crisis they may not be fully informed or prepared for the situation. With
foreknowledge of potential floods, those in affected areas can learn about the facilities and public

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services available to assist them, ensure their insurance policies cover flooding, learn First Aid
skills, and prepare their home for a possible incursion.

• Community

The people of Dresden in Germany truly embody this spirit. Hundreds of ordinary Germans worked
together to form sandbag embankments protecting their streets after insurance premiums have risen to
unreasonable costs for most in high risk areas. Flooding, as with many other forms of natural or
manmade disaster, can reveal individual heroes and a sense of community within an afflicted area.

3.4 How to tackle flood events?

There are essentially two cardinal ways to prevent high flood events:
• Structural mitigations
• Non-structural mitigations

First category embraces all those systems which act either by creating barriers for stopping the
water flows or diverting flows themselves further from most populated districts and in general
zones where are located significant industrial areas. Typical hydraulic structures used in flood risk
management are the dams, dikes, embankments, canalization and related works, flood diversion
channel or tunnels, storage ponds of flood attenuation. As it is generally known this kind of
mitigation measures were widely used over recent decades as primary protection method against
floods.

By developing new technologies, such as flood-mapping software and new hydraulic programs,
the approach in decision-making of how combat the climate change and therefore the escalation
of flood events, has completely changed. Within non-structural approach are included all those
mitigation’s actions such as: integrated river basin management (IRBM), preparation of guidelines
and design standard resettlement of population, flood forecasting and warning system.

Nevertheless, structural approaches remain important in solving flooding issues, studies have
shown that adequate undertaking of semi-structural and non-structural measures can considerably
decrease the costs of floods for households. Nowadays the market is populated by freeware capable
in simulations of floods, favoring even more the diffusion of non-structural methods employment.
Non-structural mitigation besides, allow to the designers and subsequently to the stakeholders

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involved in decision-making process, to better estimate the overall condition accounting for every
aspect including social partners, surrounding environment, further developments after the risk
assessment, to finally come to the decision of what measure should be taken to safeguard the area
threatened by floods or by hydraulic risks in general.

Concluding, the most effective fact with regards to non-structural methods is that they provide
reliable solutions, granting a major saving on the costs than structural approaches.

3.5 Causes of flood

River flows may rise to floods levels at different rates, from a few minutes to several weeks,
depending on the type of river and the source of the increased flow. Major issues in flood hazard
are the rapid development and urbanization along the riverbanks as well as some uncontrolled
natural phenomenon e.g. heavy intensity of rainfall, uneven rainfall. Flood also occurs because of
problem in Tal Areas, riverbed erosion, deforestation of upper catchment lead to increase the
silting load in river and this sediment block the river flow. It also occurs due to encroachment of
floodplain areas.

Flooding due to dam break is a mega disaster as it is associated with huge loss of life and property.
Flash floods, which causes damages because of suddenness. Sometimes reason of a flood is
snowmelt (if suddenly melting), accumulation of ground water in saturated ground. It also occurs
in river when the flow rate exceeds the capacity of river channel. Floods in Indian River basins are
also caused by cyclones, near to the coastal areas of Andhra Pradesh, Orissa, Tamil Nadu and West
Bengal experience heavy floods regularly. Urbanization has a great influence on rainfall runoff
and flood behavior. Localized flooding may be caused or exacerbated by drainage obstructions
such as landslides, ice, or debris

3.6 Effects due to flooding

• Economic

During floods (especially flash floods), roads, bridges, farms, houses and automobiles are
destroyed. People become homeless. Additionally, the government deploys firemen, police and
other emergency apparatuses to help the affected. All these come at a heavy cost to people and the
government. It usually takes years for affected communities to be re-built and business to come
back to normalcy.

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• Environment

The environment also suffers when floods happen. Chemicals and other hazardous substances end
up in the water and eventually contaminate the water bodies that floods end up in. In 2011, a huge
tsunami hit Japan, and sea water flooded a part of the coastline. The flooding caused massive
leakage in nuclear plants and has since caused high radiation in that area. Authorities in Japan fear
that Fukushima radiation levels are 18 times higher than even thought. Additionally, flooding
causes kills animals, and others insects are introduced to affected areas, distorting the natural
balance of the ecosystem.

• People

May people are killed in flash floods. Many more are injured and others made homeless. Water
supply and electricity are disrupted and people struggle and suffer as a result. In addition to this,
flooding brings a lot of diseases and infections including military fever, pneumonic plague,
dermatopathy and dysentery. Sometimes insects and snakes make their ways to the area and cause
a lot of havoc. The major effects of flooding are loss of life, loss of agriculture, damage to
infrastructures such as bridges, sewerage systems, roadways, and canals. Floods also frequently
damage power transmission and sometimes power generation. This includes loss of drinking water
treatment and water supply, which may result in loss of drinking water or severe water
contamination. It may also cause the loss of sewage disposal facilities. Lack of clean water
combined with human sewage in the flood waters raises the risk of waterborne diseases, which can
include Typhoid, Giardia, Cryptosporidium, Cholera and many other diseases depending upon the
location of the flood.

3.7 Flood control and Mitigation

It involves manage and control the floodwater movement; however, we cannot prevent all floods
but for controlling we can construct detention basins, levees, bunds, reservoirs, and weirs. It used
to prevent waterways from overflowing their banks. Some flood control measures reduce the
intensity of flood such as flood control reservoir for reducing the peak flood flow, land drainage,
channel modification for increase the flow capacity, soil treatment for increasing the infiltration
rate and delay time of flow concentration, provide river flushing system.

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3.8 Flooding zones of India

(a) Brahmaputra River Basin

(b)Ganga River Basin
(c) North-West Rivers Basin
(d) Central India and Deccan Rivers Basin.

(a) Brahmaputra River Basin

The basins of the rivers Brahmaputra and Barak with their tributaries belong to this zone. It covers
the States of Assam, Arunachal Pradesh, Meghalaya, and Mizoram, Northern parts of West
Bengal, Manipur, Sikkim, Tripura and Nagaland. The catchment of these rivers receives large
amount of rainfall. Due to of which, floods in this region take place very often and are severe by
nature.

(b) Ganga River Basin

The Ganga and its many tributaries such as the Yamuna, Ghaghra, Gandak, Kosi, Sone and the
Mahanadi comes under the second zone. This zone covers Uttaranchal, Uttar Pradesh, Bihar, south
and central parts of West Bengal, parts of Haryana, Himachal Pradesh, Rajasthan, Madhya Pradesh
and Delhi. The flooding and erosion problem is serious in Uttar Pradesh, Bihar and West Bengal.

(c ) North-West River Basins

This is the third zone and comprises of basins of North-West Rivers such as the Sutlej, Ravi, Beas,
Jhelum and Ghaggar. The flood problem in this zone is less as compared to above two zones.

(d)Central India and Deccan Rivers Basin

This zone comprises of basin Narmada, Tapi, Mahanadi, Godavari, Krishna and Cauvery. This
region covers all the southern States namely Andhra Pradesh, Chhattisgarh, Karnataka, Tamil
Nadu, Kerala, Orissa, Maharashtra, Gujarat and parts of Madhya Pradesh. The region does not
have very serious problems except for some of the rivers of Orissa (the Brahmani, the Baitarni,
and the Subarnarekha). The Delta areas of the Mahanadi, Godavari and the Krishna Rivers on the
east coast periodically face flood and drainage problems, in the wake of cyclonic storms. (Singh,
2014).

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CHAPTER 4
FLOOD MODELLING

4.1 Flood modelling

Modelling as a substitute for direct measurement and experimentation. It is the process of imitating
a real phenomenon or process with a set of mathematical formulas. Model analysis is actually an
experimental method of finding the solution of complex problem. It also refers to the process of
generating a model as a conceptual representation of some phenomenon. Models are typically used
when it is either impossible or impractical to create experimental conditions. The flood model
comprises a hydrological model and a hydraulic model. The hydrologic model determines the
runoff that occurs following a particular rainfall event. The primary output from the hydrologic
model is hydrographs at varying locations along the waterways to describe the quantity, rate and
timing of stream flow that results from rainfall events. These hydrographs then become a key input
into the hydraulic model. The hydraulic model simulates the movement of floodwaters through
waterway reaches, storage elements, and hydraulic structures. The hydraulic model calculates flood
levels, flow patterns, and models the complex effects of backwater, overtopping of embankments,
waterway confluences, bridge constrictions and other hydraulic structure behavior system was
adopted.

4.2 Methodology employed for river flood modelling

The various steps involved in flood forecasts are as follow:

• Observation and collection of hydrological and meteorological data.
• Analysis of data.
• Formulation of forecasts.
• Select suitable computing tool for modeling.
• Calibration of model.
• Testing of model.

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Fig 4.1 Methodology used in generating flood inundation map

4.3 Methods for river flood modelling

4.3.1 Statistical Methods

Methods base on statistical approach makes use of the statistical techniques to analyze the historical
data with an objective to develop methods for the formulation of flood forecasts. The methods thus
developed can be presented either in the form of graphical relations or mathematical equations. A
large number of data, covering a wide range conditions are analysed to derive the relationships
which inter-alia include gauge to gauge relationship with or without additional parameter and
rainfall peak stage relationships. These methods are more commonly used in India.

4.3.2 Numerical Methods

There are variety of numerical methods is available such as Boundary Element Method, Finite
Difference Method, Finite Element Method has been developed by engineers and scientists for the
solution of engineering problems. Selection of the methods depending upon the complexity of the
problem. The above methods are very useful for River flood modelling. The disadvantage of the
above methods that is, it’s required too much mathematical calculation.

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4.3.3 Soft Computing Techniques

It is the best alternative for complex problems. Soft Computing is an emerging collection of
methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to
achieve robustness, tractability and total low cost. It is a branch of computational intelligence
research that employs a variety of statistical, probabilistic and optimization tools to learn from past
examples and to then use that prior training to classify new data, identify new patterns or predict
novel trends.

4.3.4 Artificial neural network

Artificial neural network is inspired by the functioning of a human brain. Artificial Neural Networks
(ANNs) are computational models designed to mimic the learning abilities of a human brain. ANN
model is a system of interconnected computational neurons arranged in an organized fashion to
carry out an extensive computing to perform a mathematical mapping. The word network in the
term 'artificial neural network' refers to the inter–connections between the neurons in the different
layers of each system. An example system has three layers. The first layer has input neurons which
send data via synapses to the second layer of neurons, and then via more synapses to the third layer
of output neurons. More complex systems will have more layers of neurons with some having
increased layers of input neurons and output neurons. The synapses store parameters called
"weights" that manipulate the data in the calculations. The results were compared with statistical
regression and a simple conceptual model, to prove that ANN provides a more systematic approach
and reduces the time spent in calibration of the models.

An ANN is typically defined by three types of parameters:

1. The interconnection pattern between the different layers of neurons

2. The learning process for updating the weights of the interconnections

3. The activation function that converts a neuron's weighted input to its output activation.

Artificial Neural Networks are useful tools for modelling of nonlinear hydrologic processes. The
utility of artificial neural network models lies in the fact that they can be used to infer a function
from observations. This is particularly useful in applications where the complexity of the data or

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task makes the design of such a function by hand impractical. The ANN model has wide
applicability in Civil Engineering applications and many research papers have been published on
its application. Most of the application of neural networks in the field of water resources
engineering such as rainfall runoff modelling. Jeong and Kim used two types of ANNs, single
neural network and ensemble neural network to provide better rainfall-runoff simulation capability
than the existing models.

4.3.5 Fuzzy logic techniques

Fuzzy logic offers a more flexible, less assumption dependent and self-adaptive approach to
modelling flood processes, which by their nature are inherently complex, non-linear and dynamic.
Fuzzy Logic based model can be used to model process behavior even with incomplete information.
Fuzzy logic is widely regarded as a potentially effective approach for effectively handling
nonlinearity inherently present in the hydrological processes. The potential for improved
performance, faster model development and execution times and therefore reduced costs, the
capability to plug fuzzy logic directly into conventional models and the ability to provide a measure
of prediction certainty. Fuzzy Logic based procedures may be used, when conventional procedures
are getting rather complex and expensive or vague and imprecise information flows directly into
the modelling process. Quantitative and qualitative features can be combined directly in a fuzzy
model. This leads to a modelling process, which is often simpler, more easily manageable and closer
to the human way of thinking compared with conventional approaches.

The use of fuzzy logic in the field of hydrological forecasting is a relatively new area of research,
and the potential to enhance flood forecasting by incorporating other soft computing technologies
into a hybrid solution remains to be exploited. Recently use of fuzzy set theory has been introduced
to interrelate variables in hydrologic process calculations and modelling the aggregate behavior.

Further, the concept of fuzzy decision-making (Bellman and Zadeh, 1970) and fuzzy mathematical
programming have great potential of application in flood management models to provide
meaningful decisions in the face of conflicting objectives.

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4.3.6 Genetic algorithms

Genetic Algorithms (GAs) are a class of stochastic optimization technique, which work on the
principle of evolution. According to Koza [10] “The genetic algorithm is a highly parallel
mathematical algorithm that transforms a set (population of individual mathematical objectives
typically fixed–length character strings patterned after chromosome strings), each with an
associated fitness value, into a new population (i.e., the next generation) using operations patterned
after the Darwinian principle of reproduction and survival of the fittest and after naturally occurring
genetic operations (notably sexual recombination)”.

Genetic Algorithms is very useful in water resources engineering. Many research papers have been
published on its application. Nixon et al. examined the use of GA to identify water delivery
schedules for an open-channel irrigation system. The study showed the ability of GA techniques to
maximize the number of orders to be delivered at the requested time along with minimizing the
variation in the channel flow rate. Cheng et al. used GA to calibrate the conceptual rainfall runoff
model having 10 or more interdependent parameters. The current methodology showed
considerable reduction in overall optimization time and improvement in the solution quality. It
should be also applicable for River Flood Modelling. Hybridization of GA and ANN should be also
gives better result.

4.3.7. ARC-Hydro Tool

ARC-Hydro processes spatial and time data of water resources. This tool alone is cannot perform
simulation, but it makes the data ready for use in hydrologic simulation models. In this tool, the
phenomena are showed as Triangular Irregular Network (TIN) and Digital Elevation Models.

The access to position data, grants ARC GIS software the ability of managing location data, fast
processing and the ability to connect to other models in order to perform simulation. It is why we
use GIS in modeling basins. HEC - GEORAS tool, attaching GIS, simulates HEC – RAS model.
The HEC – GEORAS tool enables, correcting errors and quick decision making. In this approach,
the basin is divided into units with similar physical and rainfall-runoff properties. This method
reduces the complexity and the need for spatial data of distribution. In GIS system, the ARC Hydro
tool is used the processing of spatial data.

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4.3.8. HEC-GEORAS System

HEC-GEORAS system is a set of tools designed to use in GIS. This system is an interface between
HEC-RAS software and ARC GIS. This tool provides the data set to be used in RAS model and
processes the obtained results in GIS Process. Information processing in ARCGIS and
HECGEORAS makes it possible to create required geometrical files for RAS analysis. The
produced geometry files include information on stations, river, branches, cross-sectional cuts,
position of the coasts, beaches lengths in the left and right coasts, roughness coefficient, positions
of barriers in the path, data of

ports and bridges, and the surfaces influenced from river and reservoir areas. The results of the RAS
model simulation will be entered to GIS environment and further analyses will be performed using
HEC - GEORAS tool. The GIS data exchanged between RAS and ARC GIS are in sdf file format.
For proper implementation of GEORAS tool, the additional expansion tools of 3D Analyst and
spatial Analyst are required.

4.3.9 HEC-RAS Model

HEC-RAS model is a hydraulic model designed by Hydraulic Engineering Center of US military.

In 1964, the HEC-2 computer model was introduced in order to help Hydraulic engineers to in the
field of river channels and floodplains. The model has been rapidly developed as hydraulic analysis
program and was used in the analysis of bridges and ports. Although the HEC-2 model is designed
to use in central processor of large computers, but it can also be used in personal computers. In
1990, due to increased use of the Windows operating system, HEC-2 software was upgraded to be
used in this operating system and was named as River Analysis System (RAS). This graphical
software was developed in Visual Basic and benefits computational algorithms of FORTRAN.
HEC-RAS software, in addition to calculating one-dimensional profile of water surface in stable
rivers, it also is used for simulating unstable flows in rivers and calculating delivered sediment load.
Moreover, the system is also capable of modeling flows below and above critical conditions as well
as a combination of them for rivers composed from complete network of drainage channels,
dendritic branches, or single branches of the river. Model results are used to evaluate the impact of
flood and management of floodplains.

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4.3.10. Neurofuzzy

Another recently developed computing technique is the neurofuzzy approach, which is a

combination of a fuzzy computing approach and an artificial neural network technique. This
approach is becoming one of the major areas of interest because it gets the benefits of neural
networks as well as of fuzzy logic systems. And it removes the individual disadvantages by
combining them on the common features. A Fuzzy interface system can use human expertise by
storing its essentials components in a rule base, and perform fuzzy reasoning to infer the overall
output value. The derivation n of if-then rules and corresponding membership functions depends, a
lot, on the prior knowledge about the system. However, there is no systematic way to transform
experiences and knowledge of human experts to the knowledge base of a FIS. There is also a need
for adaptability or some learning algorithms to produce outputs within the required error rate. On
the other hand, ANN learning mechanism does not rely on human expertise. Due to the homogenous
structure of ANN, it is difficult to extract structured knowledge from either the weights or the
configuration of the ANN.FIS and ANN are complementary which induce the appearance of the
NFS that take advantage of the capacity that FIS have to store human expertise knowledge and the
capacity of learning of the ANN.

4.4 Benefits of hydraulic modelling for flood mapping

There are essentially two ways for estimating the flood risk in vulnerable areas to a flood disaster
and they are physical models and precisely the computational models.

A physical model is a physical copy of an object, often built at a smaller scale. Although fascinating,
physical models are costly (Gartner et al. 2016). Furthermore, our river system is not so complex
to require an additional implementation of physical modelling, therefore proceeding with a
computational model approach was the best and most suitable solution for our study.

Talking about mathematical modelling instead, we can point out two main types:

• Hydrologic modelling is used to determine the amount of flow discharge at a given location for a
given recurrence interval, and in this use it is one of several methods used in flow frequency
analysis.

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• Hydraulic modelling can be used to determine the elevation and lateral extent of water and other
hydraulic parameters at a given location for a given discharge.

The scales of these two types of modelling differ. Hydrologic modelling often has a basin-wide
view because the conditions throughout a contributing area can affect the amount of water delivered
to the location of interest while hydraulic modelling focuses on the reach scale (i.e., a given length
of a river or stream), often taking the amount of water delivered to a reach as a given input and then
simulating the hydraulic properties of the water and showing how it displaces on the river system
area.

FIG 4.2 Analysis of flood map

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Chapter 5
HEC-RAS MODELING

HEC-RAS is free and broadly used hydraulic program, secondly it is perfectly integrated to GIS
ecosystem due to the HEC Geo-RAS plug-in which enhances visualization of riverine systems in
concert with digital elevation models (DEMs) and remotely sensed imagery allowing interpolation
of model results between surveyed cross sections (Gartner et al. 2016). Outlining instead the
differences among the model types, underline that 1D are undoubtedly easy and fast to build and to
run, but the results of such models may have significant inaccuracies in the floodplains. However,
2D models can correct this problem but it needs much more time for the flow’s implementation and
simulations (Gharbi et al. 2016, 13). Concluding the choice has fallen on HEC-RAS since the US
Army Corps of Engineers has been developing a thorough experience in solving the toughest
engineering and environmental challenges.

5.1 Modelling overview and methodology

Hydraulic modelling potentials represent the core of this study, therefore now will be outlined how,
through modelling, have been performed the various steps to obtain our results.

HEC-RAS is can be used as a modelling tool to undertake hydraulic simulations and along with
ArcGIS, for the setting up of the geometry and the creation of all layers and sub-layers necessary
before exporting the edited map on HEC-RAS for the hydraulic computation. It comes clear that
the coupling between this two software, since HEC-RAS geometry editor functionalities falls short
to the necessities of the users of depicting accurately all the elements contained in the map to
achieve a reliable representation of the riverine system. It has to be mentioned that to model the
river’s scheme we exploited a HEC-RAS plug-in, namely HEC Geo-RAS, which allows to create
hydrological layers gathering all elevation data coming from the DEM terrain. Moreover, has to be
reported that in our terrain file the standard error is about 20 centimeters, which enables to maintain
a robust accuracy of data needed. Indeed, there are studies showing that significant time savings
are achieved throughout the modelling process and that filtering to four degrees can be performed
without compromising cross‐sectional geometry, hydraulic model results, or floodplain delineation
results.

Proceeding in order, were primary implemented all the geometry parts of the river system, as:

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• Main channel;
• Cross-sections;
• Overbanks;
• Bridge;
• Manning’s table containing the coefficients for each surface displayed in the map;
• Storage areas;
Then, after having created and drawn all the features, will be determined all the attributes
corresponding to each item edited. Subsequently, all the geometry file will be created through
ArcMap was exported to HEC-RAS by using a converted file made the HEC Geo-RAS plug-in
itself.

ArcGIS is a massive and really powerful software suite which offers an extensive variety of
functions and toolboxes which can likely perform any kind of user’s requests, and it is perfectly
integrated with HEC based software. Only downside is not a free-of-charge hence, freeware
solution can be QGIS with River GIS plug-in for example, which is also widely used worldwide,
providing manuals and tutorials for learning the main capabilities and tools.

Going backwards to description, the exported file containing all the necessary data for the hydraulic
computations was imported on HEC-RAS, so were started the hydraulic models by simulating the
1D, combined 1D/2D and at last only 2D. The core of investigation is to compare the overall
features supported by the application to analyze the current and further condition at river.

The effective calibration is to be performed exploiting a land use shapefile containing the contours
for each surface. The shapefile has been processed by HEC Geo-RAS and then included in the
exported file used for the hydraulic modelling. The calibration of roughness coefficients has played
a crucial role in the delineation of the floodplain mapping.

5.2 Estimation of Flood

• Conversion of Standard Project Storm (SPS) value into Hourly rainfall

• Point rainfall in to Areal rain fall –Apply areal distribution factor 24-hour rainfall
• clock hour correction
• Apply conversion ratio 12-hour rainfall
• Apply time distribution coefficients
• Find cumulative rainfall of each hours

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• Find incremental rainfall of each hour.

• Generate flood hydrograph Ordinates using SCS Curve method by HEC-HMS Software.

HMS computes runoff volume by computing the volume of water that is intercepted, stored,
evaporated or transpired and subtracting it from precipitation .Interception and storage are intended
to represent the surface storage of water by trees or grass, local depressions in the ground surface,
cracks and crevices in parking lots or roofs, or a surface area where water is not free to move as
overland flow. Infiltration represents the movement of water to areas beneath the land surface.
Interception, infiltration, storage, evaporation and transpiration collectively referred as losses in
HEC HMS. program considers that all land and water in a watershed can be categorized as either :
Directly connected impervious surface or pervious surface. Directly connected impervious surface
in a watershed is that portion of the watershed for which all contributing precipitation runs off, with
no infiltration, evaporation, or other volume losses. Precipitation on the pervious surfaces is subject
to losses.

Fig 5.1 Stepwise procedure of preparation of flood inundation mapping using HEC-RAS

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5.3 Hydraulic Analysis

HEC-RAS and its extension HEC-GeoRAS is used to prepare the flood inundation map of the
River. HEC-RAS will be used to compute water surface elevations from either a specified flow rate
(steady flow simulation) or from a discharge hydrograph (unsteady flow simulation). It requires
two basic inputs for flow analyses: geometric data and flow data. The geometric data file includes
all the information related to cross-sectional station and elevation data, reach lengths, bank stations,
energy-loss coefficients, stream junctions, and the geometry of hydraulic structures. The flow data
requires defining boundary conditions, initial conditions (unsteady), and either peak discharges or
discharge hydrographs depending on the simulation type (steady or unsteady, respectively). The
preprocessing of the geometric data (to extract the physical characteristics of the study region) and
the post-processing of the outputs (to visualize the flooding impact) that are required by the HEC-
RAS model processes are done using HEC Geo RAS. This interface (as an extension in Arc GIS)
allows the preparation of geometric data import into HEC-RAS and processes simulation results
exported from HEC-RAS in a geospatial environment. To create the geometric file, the DEM is
converted to a TIN (Triangulated Irregular Network) format.

5.4 Processing of Data

The TIN file of the watershed will be loaded in ArcMap. The next step is to do the preprocessing
of the data. For this we have to create the geometry file using HEC-GeoRAS, click on RAS
Geometry ->Create RAS layers. Selected attributes were Stream centerline, Bank lines, Flow path
Centerlines, XS Cutline in the study. A geodatabase was created by HEC-GeoRAS in the same
folder where the ArcMap document was saved. The river center line was then used to establish the
river network for the HEC-RAS. The river centreline was created using start editing attribute in the
editor toolbar in ArcMap. The river feature was selected in the create features window and line for
the construction tools. The river was digitized starting from the upper reach till the outlet. The lower
reach was started digitizing where there came a change in the elevation of contour. After the
digitization process the document was completed, the reaches were then named as the upper reach
and lower reach using the feature Assign River Code/ Reach code. Similarly using the editing
option, River Banks, Flow paths were also digitized. The next step is the creation of cross-sections
as they are the main input to HEC-RAS. It is used to extract the elevation data so as to create the

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ground profile across the channel. Cross sections were digitized by selecting the XS cut lines and
selecting line segment in the create feature window. cut lines were digitized by drawing line
perpendicular to the streamflow, spanning over the entire flood extent and digitized from left to
right. The final step before exporting the GIS data to HEC-RAS is to assign the Manning's n values.
This value will be assigned by clicking on RAS Geometry -> Manning's n Values -> Extract N
values. Constant value was assigned to each cross section using Table of Manning Values. In the
next step the GIS data was imported, by clicking on RAS Geometry -> Export RAS Data. By
exporting data, two files were created GIS2RAS.xml and GIS2RAS.RASImport.sdf. The pre-
processing steps are complete now.

5.5 Hydraulic Modelling

To perform hydraulic modelling, the Manning’s n roughness coefficient for the river channel ranged
from 0.020 to 0.080 m‐1/3s, while n for flood plain from 0.030 to 0.100 m‐1/3s (Chow 1959). In
order to test the sensitivity of the results to Manning’s n roughness coefficient, the models were run
using various n values within those ranges. In the absence of any knowledge of the prior distribution
of the model parameters, a random distribution was assumed to select 5000 sets of Manning’s n
roughness coefficients value within these ranges for each hydraulic model. Apart from the
Manning’s n roughness coefficients and cross‐section configurations, all other sources of data
including the boundary conditions were unchanged for all simulations.

5.6 Flood Inundation Mapping

Flood Inundation Mapping is an important tool for engineers, planners, and government agencies
used for municipal and urban growth planning, emergency action plans, flood insurance rates and
ecological studies. By understanding the extent of flooding and floodwater inundation, decision
makers are able to make choices about how to best allocate resources to prepare for emergencies
and to generally improve the quality of life. HEC-RAS and its extension HEC-GeoRAS was used
to prepare the flood inundation map of the Mangalam River. HEC-GeoRAS uses the functions
associated with Spatial Analyst and 3D Analyst extensions of ArcGIS. The only dataset required in
this mapping is the terrain data (TIN or DEM). The RAS Geometry menu contains functions for
pre-processing of GIS data for input to HEC-RAS. The RAS Mapping menu contains functions for
post-processing of HEC-RAS results to produce flood inundation map

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5.7 Types and content of flood map

In the past, government agencies implemented engineering solutions such as dams, levees, seawalls
and others in the attempt to reduce flood damage to the communities. However, these solutions
often did not reduce flood damage costs and property loss, nor discourage continued development
within the flood‐prone area. Now, there is an action to transform the management of flood from a
conventional flood defense solution to a flood risk management approach. In Europe, the European
Parliament has adopted a new Flood Directive with the main objective is to establish a framework
to assess and manage flood risk (EU, 2007). One of the directive tasks is to produce flood hazards
maps and risk maps in every state that will form the basis of a flood risk management plans in the
future. Thus, to achieve this directive, flood mapping has become a priority and an important aspect
for the EU members.

In the field of flood risk management, the confusion is not only arising in use of risk related
definition, but also in the naming of different flood maps (de Moel et al., 2009). For instance, Merz
et al. (2007), proposed four type of flood map namely as flood danger map, flood hazard map, flood
vulnerability map and flood damage risk map. In general, flood map can be defined as a map
presents the area prone to flooding at one or more floods with given return periods.

5.8 Use of flood maps

Flood maps are created by various institutions and used by many stakeholders. The main producers
of flood map either at local scale or basin scale are governmental institution and private company
particularly to insurance company or cooperation between the government and private company.
Van Alphen and Passchier (2007) highlighted that the use of flood maps serve at least one of the
three purposes of flood risk management as follow:

• Preventing the build‐up of new risks (planning and construction),

• Reducing existing risks, and
• Adapting to changes in risks factors

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Table 5.1 Proposal for systematic flood mapping at the local scale

Table 5.2 Flood map constituents and its uses

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Table 5.3 Flood map prepration purpose and specified users

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Chapter 6
Case study on flood inundation mapping using HEC-RAS
6.1 STUDY AREA

The study area selected is Kanchipuram which is badly affected by the November-December 2016
flood. The study district is located at the northeast part of Chennai city. Kanchipuram’s latitudinal
location is between 12˚20'N to 13˚00'N and longitudinal location is 79˚40'E to 80˚20'E.
Kanchipuram district occupies the total area of 4433 Sq. km. The normal temperature of ranges
from 26 to 35 Celsius in Kanchipuram district and it receives an average rainfall of 1200 mm.
Similarly, the average normal rainfall of Tiruvallur district is 1104 mm.

Fig 6.1 location map of study area

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6.2 Materials and Methods

For present work Geo-coded Indian Remote Sensing (Cartosate-1 SRTM) satel-lite image of April
2005 and topo sheets at 1:50,000 scales collected from Survey of India, used to prepared contour
and Drainage Map. Global positioning system (GPS) was used for carrying out field surveys were
conducted in Kanchiipuram District flood affected areas. Geological map collected from GSI, Soil
map pre-pared from Soil survey of India and the area rainfall collected in Public Work Department
(PWD) Chennai. All the data collect and prepare different thematic map using GIS software finally
integrated all maps and give the output for flood hazard zone.

6.3 Data Source

The principle supporting the data for this study was provided by the NASA which was satellite-
based estimation showing rainfall over the south-eastern In-dia on December 1 - 2, accumulating
in 30 minutes interval. The integrated Multi-satellite retrievals for GPM (IMERG), a product of
Global Precipitation Measurement mission is used to fetch the rainfall precipitation data. On the
maps (Figure 3) below represent rainfall total approaching 400 millimetres (16 inches) during the
48 hours period. These are the two regions in Tamil Nadu which received the heaviest rainfall and
experienced the maximum damages due to November-December 2015 flood. According to HAL
pierce, a scientist on the GPM team at NASA, areas just off the south-eastern coast received the
maxi-mum total rainfall that exceeded 500 mm (20 inches).

6.4 Geographical and Meteorological Reasons

Between the month of October and December of each year, an enormous area of south India,
including Tamil Nadu, receives up to 30percent of its annual rain-fall from the northeast monsoon.
The northeast monsoon is due to the annual gradual retreat of monsoonal rains from north-eastern
India. Unlike during the northeast monsoon, rainfall during the monsoon is sporadic, but the
rainfall during November-December 2015 was typically more than the normal rainfall by 90
percent.

6.5 Annual Rainfall

Chennai receives the average annual rainfall of about 140 cm (55 in). Starting from mid-October
to mid-December the city receives the heaviest rainfall from the northeast monsoon winds.
Sporadically the city experiences cyclones formed in the Bay of Bengal. The highest annual

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rainfall recorded is 257 cm (101 in) in 2005. The highest rainfall of 72.4 mm was recorded in
Tiruvelveli-Tamil Nadu. Tamil Nadu received an average annual rainfall of 1304.1 mm during the
year 2005-06. The rainfall was excess in 26 districts and normal in 4 districts as shown by the
rainfall records during 2005 and 2006 as compared to the normal annual rainfall. However, the
highest recorded rainfall of 2005-06 in Chennai had been beaten by the recent 2015 flood.

6.6 Drainage System

The City is drained by 2 rivers the Adyar and the Cooum Rivers, besides a number of major and
minor drains through Buckingham Canal into Sea via Ennore Creek and Kovalam Creek. Cooum
runs through the heart of the city whereas Adyar wends its way through the southern part of the
city before entering into the sea. Though the river Adyar can be traced to a point near Guduvancheri
vil-lage, it assumes the appearance of a stream only after it receives the surplus wa-ter from the
Chembarambakkam tank as wells as the drainage of the areas in the south-west of Chennai. Cooum
River starts from Kesavaram Anicut in Kesavaram village built across Kortaliyar River. The
surplus from Cooum tank joins this course at about 8 kms. lower down and this point are actually
the head of Cooum River which is located at 48 kms. west of Chennai.

As the water level crossed the normal limits in residential areas of Kanchipuram and Tiruvallur
districts, the drainage system measurably failed to pass the water. (Figure 4) shows the drainage
system of Chennai. The drainage system was blocked due to excessive dumping of garbage and as
well as the failure of administration to ensure periodic desilting. Hence water couldn’t find way to
flow. The failure of drainage system in Chennai and other parts of Tamil Nadu, especially
Kanchipuram and Tiruvallur, made the situation worst. Besides that, encroachment was seen on
Cooum River, Adyar River and Buckingham Canal, which serves as the main rain water drain for
the city. These encroachments were not slump dwellings but concrete directly affecting flow of
canal.

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Fig 6.2 Drainage map of the study area

6.7 Types of Soils

Chennai’s soil is mostly clay, sandstone and shale. Areas found along the coasts and the river
banks are sandy in nature and in these areas, run water percolates quickly thoroughly the soil. Few
parts of Chennai also comprise hard rock sur-face. The ground water table in Chennai is 4 - 5 m
below the ground surface. Among many soil test apparatuses few have been employed to test the
soil in Chennai, especially soil moisture test, triaxial soil testing instruments are the few among
many which determined the types of soils available in Chennai.

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Fig 6.3 Soil map of the study area

6.8 Geology of the Study Area

The geological formations are beach sands of quaternary and recent period, Cuddalore sandstone
of Mio-pliocene age, shals and sandstone of Upper Gond-wannas and charnockits of Archaean era.

6.9 Slope and Size of Watershed

There are numbers of watershed in Chennai alone. Some of the well known water shed are
Velachery, Adyar, Virugambakkam and few other canals. Watersheds range from 10.5 km to 50.99
km and it bears a slope of 1 in 20 which indicates that if the volume of water rises suddenly in the
river basin then there will be high chances of flood occurrence due to overflow.

6.10 Result and discussion

• Flood Risk Map

Evaluation of each unit based on ranking method was generated using the criterion maps combined
by logical operations and criterion values. the flood risk map created based on GIS and

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Multicriteria method. Using pair wise comparison, the normalized criterion weights were
calculated as 0.198, 0.387, 0.275 and 0.14 respectively for basin slope, annual rainfall, drainage
net-work of the river basin and soil type. The study carried out based on this method showed a
consistency ratio (CR) value of 0.0, which fell much below the threshold value of 0.1 which
indicates a high level of consistency. Therefore, the weights are acceptable.

Fig 6.4 Flood hazard map

• Conclusions

From the results it has been observed that the urbanization and encroachments of river banks,
marshy, low-lying areas especially Adyar River has aggravated the flooding problem. The narrow
and constrained river with no flood plains left could not carry the dis-charges and water simply
occupied the adjoining low-lying areas. A study carried out by Chandan et al. (2014) on analysis
of land use change pattern in past 4 decades shows that the total urban area has been increased by
more than 20 times mainly from the conversion of grazing, agricultural and open areas to urban
impervious surface. They have observed that Vegetation cover has dramatically decreased from

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70.47% in 1991 to 35.53% in 2013, whereas the non-vegetation i.e. built up, paved areas etc. have
increased 29.53% in 1991 to 64.47% in 2013. Management of reservoirs, revival of wetlands
which act as a “sponge” to absorb the excess water and resizing of the storm water drains keeping
in account the present and projected population should be taken as mitigative measures. Based on
2015 floods experience flood hazard plain zoning needs to be done for future expansion of the city
and assessing the threat to habitation living in various municipal zones for better preparedness.

• Recommendations

1) Better understanding of the weather: - It is always better to understand the weather condition
in advance and the data collected should be of great accuracy so that there won’t be any havoc
created afterwards. By doing so, people can act on the disastrous action easily.

2) Water wiring of cities:- Every small water bodies should be taken into consideration along
with the large and well-known rivers, reservoirs and canals. Only by doing so we would not miss
out any small bit of information and thus we could edify the problems.

3) Maintenance of watershed and drainage systems: - Watershed should be maintained very

well. Although it seems of less important, watershed plays a vital role in controlling the overflow
of water. Similarly, drainage system should be checked and kept clear of obstacles. All the
channels of drains should be connected properly and the outlet should be properly maintained.

4) Improvements of civil infrastructure and wetlands:- Most of the underlying ground soil of
Chennai is the refill of ponds and lakes. These soils act as a poor filter for the rainwater. Thus, all
the rain water gets collected on the surface which causes floods. This can be avoided by improving
the soil permeability and building proper civil infrastructure will allow the water to pass through
proper channels.

5) Human activities:- One of the factors which contributes to flood or any other natural disasters
is human’s selfish activities. Burning of harmful gases and fuels, raising the numbers of industries,
clearing the forest cover for constructions purposes etc. have dramatically changed the climatic
conditions over the decades. Therefore, hu-man needs to reduce their wants and try to live in
harmony using the nature wisely.

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Conclusion
The flood inundation map was prepared in ArcGIS by modeling the unsteady flow analysis in
HEC-RAS and then exporting the result back in GIS using the extension HEC-GeoRAS.
Combination of the results can aid in the flood mitigation plans, especially because the study area
is often experiencing flash floods. The Probable Maximum Flood occurring in the study area will
be found out using the Time distribution coefficient method. Estimation of this value helps in the
designing work of a civil structure in the study area, the flood hydrograph was developed, which
was further utilized in the flood plain mapping in HEC-RAS. By doing this analysis it was possible
to get an idea about how much flood can occur in the study area. Floodplain was also obtained by
performing the unsteady flow analysis in the HEC-RAS software using a GIS platform. This is in
concurrence with the flood inundation map prepared in the GIS. Since, no historical flood map
was available, the model results were validated using the cross-section data taken from the field.
Hence, Arc-GIS along with HEC-RAS and its GIS extension HEC-GeoRAS can aid in the
development of flood inundation maps. Flood mitigation measures were incorporated with the
analysis which gives a significant reduction in the flood inundation area of the river. The results
in the study can serve as an information guide for various activities like flood mitigation etc. The
spatial resolution of the DEM in the study area was not adequate, because of which the tributaries
of the river could not be considered in developing the flood inundation map. Study can be extended
up to the sea where the entire effect of flood will be neutralized.

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