The advantages of the areas situated along watercourses have influenced over time the [627282]

Introduction
The advantages of the areas situated along watercourses have influenced over time the
development of civilizations in flood -prone areas. Even though these areas offer many resources,
being ideal for agriculture and development, river transportation and ene rgetic exploitation, the
settlements situated in floodplains are exposed to flood hazard. Over time, several flood protection
measures along rivers, such as hydrotechnical constructions, dams, embankments, regulation of
watercourse, stabilization of riverb eds, etc., have been developed and implemented in these areas.
However, the flood hazard and risk cannot be completely eliminated and a residual risk is still
present; furthermore, the rapid urbanization that took place in the past years has increased the
exposure of the population and economy, transforming flood events in catastrophes. On the other
hand, climate change increased the frequency of extreme flood events as well as their intensity,
producing great damages worldwide (de Moel, 2012; Samela et al. , 2017). In Europe, in the last
three decades floods were responsible for 39% of the total economic damages related to natural
hazards. In Romania the flood damages registered for this period represent 86% of the total damage
caused by natural hazards, Rom ania being one of the most affected country regarding floods
(CRED EM -DAT; Arghiuș et al., 2011). The extreme flood events are especially affecting the less
developed countries where higher losses were registered. The causes of these negative impacts on
less developed countries and with limited financial resources can be attributed to the lower living
standards, low awareness and level of education of the population regarding natural hazards as
well as the lack or pour implementation of proper protection an d prevention measures against
floods.
Hence, considering that the climate change will continue to increase the frequency and
intensity of the flood hazard and the socio -economic growth will increase the exposure to this
hazard, the improvement of flood ri sk assessment and management is becoming an important task
not only at local and national level but also at pan -European and global scale (Cirella et al., 2014;
Albano et al., 2015; Scorzini and Frank, 2015).
The flood risk assessment, that has been foc using traditionally on hazard analysis and
protection measures, has moved recently to a risk -based approach focusing not only on flood
control and reduction but also on the damage analysis, the risk being a combination between hazard
and its consequences; this change is supported by the adoption of Directive 2007/60/EC (Flood

Directive) of the European Parliament and Council (EU Directive 2007/60/CE, 2007). According
to the Directive 2007/60/EC, Art.1, “The purpose of this Directive is to establish a framew ork for
the assessment and management of flood risks, aiming at the reduction of the adverse
consequences for human health, the environment, cultural heritage and economic activity
associated with floods in the Community.” Furthermore, all member states mu st transpose this
Directive and must identify flood prone areas and develop hazard and risk hazard maps as well as
flood risk management plans (EU Directive 2007/60/CE, 2007; te Linde et al., 2011).
In this contex t, there is a need for a more comprehensive approach of flood risk
management that should properly evaluate and quantify all the aspects of flood risk assessment
such as hazard, exposure and vulnerability. These aspects play an important role in the decision –
making process, the identification of hi gh flood risk areas, the planning and the selection of
suitable objectives and design of the appropriate of flood management strategies and risk reduction
for flood risk management (Cirella et al., 2014; Scorzini and Frank, 2015). In this light, there is a n
emerging need for assessing flood consequences and for adopting different scenarios and courses
of action.
There are many ways to express the consequences, e.g. economic damages that can be
produced direct or indirect, loss of lives, environmental damag es or health impact. The ongoing
research studies on quantitative risk analysis are focusing mostly on the estimation of direct
economic damages because the latter are having the greatest impact on societies. Furthermore, the
ex-post estimation of these da mages offers important knowledge regarding the high -risk areas and
the potential damages that can occur to the stakeholders, such as the government, the insurance
industry and local authorities (Merz et al., 2010; Meyer et al., 2013).
The flood risk maps can be developed using two approaches: the qualitative approach,
which is based on the experts knowledge and experience from past flood events and the
quantitative approach which is using models in order to obtain damage and risk information that
can be mo netary quantified.
The general approach for quantitative flood risk assessment includes the determination of
hazard characteristics (water depth and velocity, flow), exposure and its vulnerability in order to
address the assessment of subsequent consequen ces for different probabilities of the hazard. The
results can be represented by hazard and risk maps, which are an important tool in the management
of floods and decision -making process (Albano et al., 2015). In particular, the increasing

development of F ree and Open -Source S oftware (FOSS), mos tly in the field of Geographic
Information S ystem (GIS) for flood hazard and flood consequence analysis has proved to be
particularly useful for flood hazard and risk analysis, being able to provide the necessary fea tures
for hazard and risk mapping, for spatial data analysis and visualisation of the results.
An important element that must be considered when a flood damage assessment is done is
the scale of the analysis. Each scale needs different input data, differen t methods, and is used for
different goals. There are three scales that are largely used: micro -, meso -, and macro -scale. The
micro -scale analysis is done at local level needing very detailed information, while the meso -scale
(regional/basin level) and mac ro-scale (national, international level) are using aggregated data
needing less detailed data (Messner and Meyer, 2005).
The hazard analysis is usually done by using hydraulic models combined with GIS tools in
order to estimate the characteristics of the flood. However, this approach is rather challenging
being time -consuming and needing detailed data that can limit the application in data scarce
regions and/or at large -scale. Therefore, the recent studies are developing new methods to identify
floodplain s in large and ungauged watersheds using publicly available data, such as the knowledge
of the geomorphology of the basin derived by public available DEM (Digital Elevation Mode l) by
using GIS systems (Nardi et al., 2013; Samela et al., 2017; Manfreda et a l., 2018). Moreover, many
tools and models were also developed worldwide for the damage estimation using the GIS
environments.
The estimation of total costs associated to flooding, considering also their distribution
within the territory, reflects context – and event -specific characteristics, such as extent and type of
flood event (e.g. flash flood, river flood, dam -break event, etc.), spatial variability and quantity of
exposed elements, resolution and type of input data, cultural or geographical differenc es, and so
on. Hence, increased effort s should be devoted to analyze the sensitivity of risk model parameters
in order to integrate and reduce uncertainties in decision -making processes in order to allow
decision -makers and stakeholders to make more inform ed and better decisions.
Current limitations in flood risk assessment
The current studies regarding flood risk assessment present several limitations such as: the
lack of data for extended areas, the analysis being restricted and not complete; the use of c oarse
resolution data that can affect the analysis results in small areas; the use of high -computational

models that are costly and time -consuming, and cannot be applied over large areas or in data –
scarce environments.
The hazard analysis requires a wide r ange of data that may lack in many regions or they
are not consistent over large areas. The unavailability of data, particularly in ungauged basins,
limits the analysis to certain areas, this fact leading at the same time to the development of
heterogeneou s datasets. This situation represents an issue for the large -scale analysis, where the
use of hydraulic models in order to provide the necessary information may prove challenging.
Therefore, the existing hazard and risk maps have a limited extend, the reso urces needed to conduct
this analysis in data -scarce environments being unaffordable and time -consuming (Samela et al.,
2018)
In the past years, the collection of exposure data has improved due to the increasing
availability of Earth Observation (EO) datas ets, which triggered the interest for high -resolution
data development. However the current approaches in the field still work with coarse resolution
data which may be further improved.
Moreover, different approaches (qualitative and/or quantitative) are used for the
development of flood risk maps. The qualitative approach presents the information in a more
simplistic way, being useful for fast risk communication and identification and prioritisation for
future in -depth analysis of high -risk areas. However , it cannot be used for cost -benefit analysis,
not offering information about the amount of damage that may occur and about the uncertainties
related to the flood risk assessment process. On the other hand, the use of a quantitative approach
has a higher a ccuracy. It can provide information that can be monetarily quantified for cost -benefit
analysis of the mitigation measures and it can assess and communicate the uncertainties of damage
calculations (Albano et al., 2017c).
The hazard and risk maps are done using different models, the diversity of free and open
tools increasing over the past years. However their transferability from one area to another can
induce uncertainties in the results, the application of these models therefore needing thorough
validati on and sensitivity analyses. Therefore, the uncertainties estimation must be considered in
the interpretation of the results and must be included in the flood risk management process having
an important role in the decision -making process and for cost -bene fit analysis (Scorzini and Frank,
2015).

Therefore, the present work offers some solutions in order to overcome a part of these
limitations by developing a cost -effective approach for flood risk assessment in data -scarce
environments. This is done by usin g high -resolution data and free tools that are easy to apply in
different areas and at different scales.
Scope and objectives of the thesis
The main goal of this thesis is to quantitatively analyze the flood damage and its related
uncertainties in data -scarce environments. For this purpose, a methodology that can offer cost –
effective (instead of expensive and time -consuming approach) and repeatable (transparent)
solution for the flood area delineation and damage calculation is proposed. This approach could
support different stakeholders in flood hazard delineation, damage estimation and flood risk
analysis in different territorial and socio -economic contexts. Therefore, the presented approach
could represent a starting point that can be useful for knowledge transfer from the scientific
community to stakeholders (e.g. practitioners and decision makers).
In this context, the questions addressed in this thesis refer to: how the free and open -source
GIS features potential could be used in quantitative flood dama ge assessment; which are the
uncertainties related to flood damage assessment and how they can be analyzed; how to assess the
flood damages in data -scarce environments; how the accuracy of large -scale application could be
improved using publicly available datasets and EO data;
This thesis is focused on the damage assessment in data -scarce environments, applying a
method that is able to use the existing data in small areas and extend it over areas where the
information is missing. In this way, useful knowle dge can be provided in order to conduct a
complete flood damage assessment, including all the flood affected areas. Moreover, the
assessments from this thesis were done using high -resolution data (EO) that improve the accuracy
and reliability of the result s.
The main objectives of this thesis are:
– To present a theoretical backgroung regarding the flood risk and damage assessment
and the current tools and methods used in the literature;
– The quantitative estimation of the economic damages at large -scale and for data-scarce
environments, using pan -European depth -damage functions;

– The application of a GIS -based model for water -depth calculation at large -scale and for
data-scarce environment s, using high -resolution data;
– The use of the machine learning techniques and Landsat 8 images in order to provide
high-resolution land use data over large areas with limited availability of data;
– The use of free and open source tools for flood damage qua ntification;
– To conduct an uncertainty and sensitivity analysis regarding the flood damage
assessment.
Therefore, in this thesis two case studies were conducted. The aim of the first case study
was to conduct a flood damage assessment and to quantify the uncertainty and sensitivity regarding
the damage functions that are used for damage calculation. Romania is one of the countries in
which the damage functions are lacking, therefore the application of damage functions from other
regions can induce great u ncertainties in the results. The hazard analysis was done using HEC
RAS hydraulic model and the exposed elemen ts were obtained from the CORINE Land Cover
database. The flood damage was calculated using the functions proposed by Huizinga, 2007 (JRC
function s) for different European countries and the free and open source tool named FloodRisk.
Given the fact that the function transferability is a common practice in areas for which they have
not yet been developed, the uncertainty analysis represents an importa nt part of flood risk
assessment. It offers a better knowledge on how the model choice influences the results and the
reliability of the results, this information being used in the decision -making process.
For the second case study, a large -scale flood dam age analysis (for a return period of 100
years) was conducted for the entire Romanian territory. The aim was to develop a preliminary
simplified methodology for flood risk assessment that can be applied at large -scale and in data-
scarce environments, using high-resolution data and GIS tools. First, for the hazard analysis a
geomorphic method was applied, using the G eomorphic Flood Area GIS tool (GFA), for rapid and
cost-effective flood extent and water -depth mapping over the entire territory of Romania.
Furthermore, a DEM with a resolution of 30 m was used. The resolution of current analysis in the
field are ranging from 100 m to 1 km, therefore one aim of this study was to provide a hazard
method that is offering high -resolution hazard maps. Furthermore, th is approach generates an
extended flood map, covering the entire territory including secondary and minor rivers which
usually are not considered in large scale analyses due to the lack of available data.

For the exposure data, a new land use map was devel oped by using machine learning
techniques and Landsat 8 multispectral satellite images with a resolution of 30 m. The available
land use data for large scales (such as the CORINE Land Cover database) has a low resolution
(100 m) containing limited land use classes, thus decreasing the accuracy of the results (Albano et
al., 2015). In the last years the growth availability of high -resolution satellite imagery (e.g. Landsat
8, ERS, ASAR and TerraSAR -X) represents an important source of information in data -scarce
environments, allowing the automated detection of different land use classes and the development
of high -resolution land use maps. Therefore, the proposed method can be used for large -scale
detection of elements -at-risk (represented by land use maps) i n areas with limited availability of
data. Finally, the economic damages were calculated using the pan -European JRC depth -damage
curves and the FloodRisk QGIS plugin.
In this thesis, free and open source tools were used for flood hazard and damage analysi s,
which allow the exchange of information in the scientific community, offering the possibility of
further development and improvement of knowledge through a transparent and collaborative
approach. Therefore, the scientific findings become available to th e large public in order to be used
for better practices and implementation of flood risk management. Moreover, the input data that
have been used (e.g. DEM, CORINE L and cover, Urban Atlas, Landsat 8) have a free availability,
being products of EU projects. The proposed methodology was applied for the case study of
Romania; however, due to the use of free tools and data that are required as input, it can be easily
applied in other areas with limited availability of data.
In conclusion, the proposed methodolo gy is using harmonised and homogeneous data in
order to provide reliable and comparable results in data -scarce environments. The damage and
uncertainty analysis done in the first study is offering a better understanding of damage functions
applicability an d their impact on the results. The large -scale approach is using high -resolution data
in order to extract the necessary information for hazard and exposure mapping in areas with limited
availability of data. Therefore, this thesis brings new improvements t o the current methodologies
in the field, offering high -resolution and comparable results using free and open source GIS tools
and information which is easily available worldwide.

Outline of the thesis
The thesis is structured as follows:
Chapter 1 pre sents the main concepts related to floods, flood risk and damage, as well as
the situation in Europe and Romania regarding flood hazard. In addition, Chapter 1 presents the
flood risk governance in Romania, showing the legal framework of flood risk assessm ent.
Chapter 2 describes the methodological approach regarding flood damage assessment. The
available methodologies are described, highlighting the current gaps in the field and how these
gaps can be overcome. The main components of flood damage assessme nt are described as well
as a review of the tools, metho ds and datasets that are used.
Chapter 3 presents a quantitative flood damage assessment approach and an uncertainty
analysis for a basin in Romania. The approach is using free and open source tools and damage
functions for the damage estimation. This study is focusing on the applicability and t ransferability
of depth -damage functions in areas with limited availability of data that don’t allow the
development of site specific functions. An uncertainty analysis was conducted in order to underline
the influence that these functions have on the dama ge results.
Chapter 4 presents a large -scale flood damage assessment methodology for data -scarce
environments, combining EO high -resolution data (30 m) and free and open -source GIS tools. A
case study for the entire Romanian territory was conducted. A GIS-based model (GFA tool) that
is using a geomorphic method was applied in order to develop an extended and high -resolution
water depth map for the study area. A machine learning technique was used to perform a
supervised classification, in order to obtain the land -use map from Landsat 8 images over the entire
territory of Romania potentia lly exposed to flood hazard. The flood damages were calculated using
a GIS tool, combining the previous estimated hazard and exposure data and the damage functions.
This ch apter introduces modelling as a crucial tool for flood risk and damage assessment, and more
important, reveals the advantages of using modelling combined with free available datasets in
order to develop hazard and risk maps in data -scarce environments.
Chapter 5 and 6 bring toghether the conclusions from the case studies and put forward
recommendations and potential applications for stakeholders and outline future potential research
topics derived from the present thesis.

List of figures

No. Title of the figure Page no.
1. Factors that contribute to flooding
2. The frequency of natural hazards in Europe in the last 30 years (1988 – 2018)
3. The economic damages caused by natural hazards in Europe in the last 30
years (1988 – 2018)
4. The frequency of natural hazards in Romania in the last 30 years (1988 –
2018)
5. The economic damages caused by natural hazards in Romania in the last 30
years (1988 – 2018)
6. Schematic representation of the flood damage assessment process
7. HEC RAS data processing
8. Exposed elements identification
9. CORINE Land Cover 2012 in Europe
10. Urban Atlas 2012 in Europe
11. Residential land use comparison in Cluj Napoca city, Romania; a. CLC
residential class b. Urban Atlas residential classes
12. Rhine Atlas depth -damage functions
13. FloodRisk plugin components
14. Localization of the study area; a. Romania – georgaphical map; b. Ilișua
Catchment
15. Schematic representation of performed flood damage assessment
16. Representation of data analysis in HEC RAS
17. Flood hazard map for Ilișua Basin developed in this study
18. Identification of the exposed elements for the city of Căianu Mic
19. The improved CORINE land use map developed for Ilișua basin
20. The JRC functions for different countries, introduced in the FloodRisk plugin
21. Graphic representation of the results of damage calculation using different
JRC depth -damage functions and the reported damages
22. Differences in the shape of damage functions (Netherlands -JRC and UK –
JRC)
23. Study area; a. Danube River – Europe location; b. The Lower Danube –
Romania
24. Schematic representation of the applied methodology
25. Schematic representation of the hazard analysis methodology
26. Water depth ma p developed in this study (GFI hazard map)
27. Comparison between the JRC hazard map and the GFI hazard map
obtained in this study
28. Landsat 8 satellite image, band 1
29. QA band processing
30. Satellite image, band 1, with no clouds covered areas
31. Urban Atlas land use of Cluj -Napoca city; a. Vector file of Urban Atlas land
use; b. Raster file of Urban Atlas land use

32. Input and output data for t he validation of the algorithm. Sofia city, Bulgaria
(accuracy 0.76)
33. Land use map developed for the flood prone area using Landsat 8 satellite
imagery
34. Graphic representation of damage results for the next scenario s: a. JRC
water -depth and CORINE land use; b. GFI water -depth and Landsat 8
landuse
35. Graphic representation of dam age results for the next scenarios: c. GFI
water -depth and CORINE land use; d. JRC water -depth and Landsat 8
landuse
36. Damage value comparison between the f our scenarios
37. Comparison between urban damages
38. Comparison between industrial damages
A1. Flow direction
A2. Flow accumulation
A3. The pan -European flood hazard map (JRC map)
A4. Lansat8 Band 1 – Romania
A5. Lansat8 Band 2 – Romania
A6. Lansat8 Band 3 – Romania
A7. Lansat8 Band 4 – Romania
A8. Lansat8 Band 5 – Romania
A9. Lansat8 Band 6 – Romania
A10. Lansat8 Band 7 – Romania
A11. Lansat8 Band 8 – Romania
A12. Lansat8 Band 9 – Romania
A13. Lansat8 Band 10 – Romania
A14. Lansat8 Band 11 – Romania

List of tables

No. Title of the table Page no.
1. Damage types and examples
2. Impact of flood events in Central -Eastern Europe countries
3. The CORINE Land Cover database 2012 nomenclature
4. The Urban Atlas database 2012 nomenclature
5. Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor
(TIRS)
6. Comparative description of the damage models
7. Land use classes reclassification and site -specific assets value
for the selected case study
8. Correspondences between JRC depth damage curves and
CORINE land-use classes
9. Results of damage calculation using different JRC depth -damage
functions and the reported damages
10. Relative error and function uncertainty determination
11. Values of the optimal thresholds and relative performance measures
obtained in calibrating the classifier over the investigated basins
12. Examples of pixels values and their meaning for QA band (Landsat 8)
13. Reclassification of Urban Atlas classes
14. Reclassification of CORINE land use classes
15. Corresponden ce between Landsat 8 land use classes and Urban Atlas
codes
16. Assets value for the study area
17. Damage results for the next scenarios: a. JRC wa ter-depth and CORINE
land use; b. GFI water -depth and Landsat 8 landuse
18. Damage results for the next scenarios: c. GFI w ater-depth and CORINE
land use; d. JRC water -depth and Landsat 8 landuse
19. Results of confusion matrix between CORINE Land Cover and Urban
Atlas

Abbreviations

ASAR Advanced Synthetic Aperture Radar
CLC CORINE Land Cover
CONHAZ Costs of Natural Hazards
DEM Digital Elevation Model
DSM Damage Scanner Model
EEA European Environment Agency
EM-DAT Emergency Events Database
EO Earth Observation
ERS European Remote -Sensing
EU European Union
FLEMO Flood Loos Estimation MOdel
FLORA FLOod and Roughness Analysis
FOSS Free and Open -Source Software
FSO Feature Space Optimisation
GDP Gross Domestic Product
GFA Geomorphic Flood Area
GFI Geomorphic Flood Index
GIS Geographic Information S ystem
GLCM Gray -Level Co-occurrence M atrix
GRASS Geographic Resources Analysis Support System Software
HIS-SSM High -water Information System – Damage and Casualty Module
HEC -RAS Hydrologic Engineering Center's – River Analysis System
JRC Joint Research Centre
MCM Multi -Coloured Manual
NUTS (fr.) Nomenclature of Territorial Units for Statistics
OCSVM One-Class Support Vector M achine
OLI Operational Land Imager
QA Quality Assessment
QRAS Quantitative Risk Assessment Software
RAM Rhine Atlas Model
RT Random Tree
SVM Support Vector Machine
TIRS Thermal Infrared Sensor
TUFLOW Two-dimensional Unsteady FLOW
VHR Very -High -Resolution
WD Water D epth

Part I – Theoretical consideration s regarding flood risk assessment

1. Theoretical concepts and statistical data regarding the flood risk
1.1. Floods
Floods are hydrodynamic phenomena characterized by “temporary covering of land by
water outside its normal confines” (Gouldby et al., 2005) as a result of an increase in water
level and water flow (Arghius, 2008). There are a couple of criteria regarding f lood
classification, in the following being presented the most important and relevant for this study.
Regarding their geographical distribution, the floods can be divided in (Johnson et al.,
2016; Blöschl et al., 2015):
– Coastal floods, which occur when th e coast is flooded by the sea and are caused by
strong winds, high tides, storm surge;
– Fluvial floods, which occur along rivers and are caused by rainfall and/or snowmelt.
Regarding the flood genesis, they can be divided into three categories:
– Natural flo ods, caused by weather conditions and their natural evolution; this type
of flood can be caused by rainfalls, snowmelt, ice break forming ice jams;
– Anthropogenic floods, that are a result of human activities, can be caused by dam
failures, failures of spil lway structures, bad management of reservoirs,
deforestation;
– Natural -anthropogenic (mixed) floods, caused both by human activities and natural
phenomena; for example, extreme rainfall that can cause dam failures (Arghius,
2008; Istomina et al., 2005)
Depe nding on the manifestation of the floods (temporal and spatial scale), they can be
divided into two categories:
– Slow on -set floods . This type of flood usually occurs in large catchments as a result
of prolonged rainfall. The water level increases gradually and can be forecasted,
therefore the authorities and the population have the possibility to prepare and take
protection measures. Usually they can last days or even weeks (Arghius, 2008;
Johnson et al., 2016).
– Flash floods. This type of flood occurs sudde nly (usually in small catchments) and
lasts a short period of time, the water level having a rapid increase, therefore the
time for warning the exposed population is limited. The manifestation is violent,
occurring with high velocity, having the potential to cause great economic damages

as well as loss of lives (Arg hius, 2008). They can be caused by very intense rainfall
and/or dam failures.
The genesis of a natural flood is dependent on a number of factors (Fig. 1) that
characterize the catchment. For ex ample, the climate influences the local weather conditions
which can cause floods due to heavy rainfall or snowmelt. The geology and the land use of the
area are two other important factors, along with the soil texture, influencing the runoff
conditions an d the infiltration characteristics. The slops influence the water velocity, the flow
accumulation represents the low areas where the water can accumulate and produce floods. The
distance from the drainage channel indicates the flood potential for the close areas and the
density of drainage networks indicates the flow accumulation potential (Jhonson et al., 2016;
Kabenge al., 2017).

Fig. 1. Factors that contribute to flooding (adapted after Jhonson et al., 2016
and Kabenge et al., 2017)
Flood hazard is c haracterized by the magnitude and probability of occurrence. The
magnitude refers to the intensity of the flood and depends on the flood parameters such as water
depth, water velocity, duration of the flood, maximum discharges, etc. The probability indicat es
the recurrence period of a flood with a certain magnitude (Olfert and Schanze, 2007).

1.2. Flood risk terminology
In literature, the terminology regarding flood risk assessment can be different and in
some cases it’s used with different meanings, which can lead to wrong interpretations and can
produce confusion regarding these terms among different research groups (Arghiuș, 2008;
Gouldby et al., 2005). This variation in the interpretation of one term can be caused by the fact
that this terminology is used in different scientific disciplines, being therefore adapted to
respond to different needs. Therefore, researchers from different fields may understand
differently the risk assessment process. This problem has been approached by some European
proje cts, such as FLOODsite and CONHAZ in order to adopt a unitary and common
terminology (Albano et al., 2015a). In the FLOODsite project two definitions regarding the
term “risk” are described which are widely used and accepted in the scientific community:
R = P x C (1)
R (Risk); P (Probability); C (Consequence)

R = H x E x V (2)
R (Risk); H (Hazard); E (Exposure); V (Vulnerability)

In the first definition the term „risk” is defined as the product between the probability
of occurrence of the event and the co nsequences of this event on the population, economy or
environment (Ozunu and Anghel, 2007; Klijn, 2009; Scorzini et al., 2015; Botezan et al., 2015).
When referring to flood risk assessment the term „frequency” of occurrence is also used, which
must not b e confused with the term „probability”. Therefore, the probability is defined as „the
chance of occurrence of one event compared to the population of all events” (Samuels and
Gouldby, 2009). It can be expressed as a number from 0 to 1 or, as a percent from 0 to 100%,
and usually refers to a specific time frame (for examp le annual exceedance probability)
(Samuels and Gouldby, 2009; Arghius, 2008). The frequency is defined as „the expected
number of occurrences of an event within a specific number of events, often related to a time
frame” (Samuels and Gouldby, 2009), for ex ample annual occurrence frequency.
The consequences represent the impact of a certain event and refer to economic
damages, loss of lives, number of people affected, environmental damages, etc. (Gouldby et al.,
2005).

In the second definition the risk depe nds on three factors: hazard, exposure and
vulnerability.
The hazard refers to the characteristics and frequency of the flood; the characteristics
of the flood provide information regarding the magnitude of the event and are represented by
factors such as water depth, water velocity and flooded area (Albano.et al., 2015a; Winsemius
et al., 2013). The frequency of the flood can provide information about the areas that will be
flooded, helping in the process of identifying the potential exposed elements. For example, a
flood with a return period of 1000 years will affect a larger area then a flood with a return period
of 10 or 100 years. However, a hazard doesn’t always result in a disaster, this depending on
the exposed factors and their vulnerability (Goul dby et al., 2005).
The exposure refers to the population and the economic assets situated in the flooded
area, being in this way exposed to the hazard. If in the flood affected area there are no socio –
economic elements that are exposed, we cannot speak of a hazardous outcome or risk.
The vulnerability refers to the susceptibility of the population and the potential assets
affected by a flood. It is a very complex component that can change in space and time (de Moel,
2012; Kaźmierczak and Cavan, 2011; Barr edo et al., 2006; Ștefănescu et al., 2018 ). The
consequences of an event depend on the vulnerability of this elements at risk, in other words it
depends on the way they will behave and on their inherent characteristics that make them to
resistant or not to flood impact (Messner and Meyer, 2005).

1.3. Flood damage terms and concepts
In the flood risk assessment process, the hazard characteristics (water depth and
velocity, flow), the exposure, the vulnerability and subsequently the consequences are
calcul ated for different probabilities. Most commonly, there are used three probabilities: 0.1
(10 years return period), 0.01 (100 years return period) and 0.001 (1000 years return period).
Therefore, by associating the consequences with the corresponding probab ility, the damage –
probability curve can be developed. The area under this curve represents the expected annual
flood damage (Messner et al., 2007). On the other hand, the flood damage assessment involves
the quantification of consequences for one flood eve nt with a certain probability.
1.3.1. Type of damages
Floods affect the exposed elements (such as population, economic assets, the
environment), producing damages. In general, the potential damages produced by floods are

classified in four categories: dir ect tangible damage, direct intangible damage, indirect tangible
damage and indirect intangible damage (Scorzini and Frank, 2015). Direct damage occurs
immediately after the hazard, as a result of the direct contact between the hazard and the
exposed eleme nts. These damages are further classified in tangible damage, when they can be
assessed in monetary terms, and intangible damage, when the quantification in monetary terms
is not possible or is difficult. Indirect damages usually refer to long term effects , and can affect
also the sectors outside the flood plain area and can be caused by direct damage or production
interruption, which involves additional costs after the occurrence of the event. These damages
are further classified in tangible and intangible damage (Jongman et al., 2012; Meyer et al.,
2013; Merz et al., 2010) . In Table 1 examples of damages for each category described above
are presented.
Table 1. Damage types and examples (Meyer et al., 2013)
Tangible damage Intangible damage
Direct damage Destruction of buildings,
infrastructure, goods and crops Loss of lives, injuries
Indirect damage Production loss, cost of traffic
disruption, disruption of public
services Trauma, increased vulnerability of
the survivors, negative effects on
the envir onment

Even though for a complex and comprehensive flood damage assessment, all the
damages described above must be considered and analysed, Meyer et al., 2013 and Merz et al.,
2010 highlighted the fact that most of the research in the field focuses on direct tangible damage.
This can be explained by the fact that more data are available for direct tangible damage
assessment as well as more mo dels and methods, this type of damage being therefore easier to
quantify. Furthermore, the stakeholders, such as decision makers or the insurance industry, may
be more interested in gathering this type of damage data.
1.3.2. Factors that influence flood d amage
The flood damage magnitude is determined by the flood impact and the exposed
element’s resistance. Therefore, the damage influencing factors are divided into impact and
resistance parameters (Merz et al., 2010, Sterna, 2012, Thieken et al., 2005).
The impact parameters are represented by the flood characteristics which influence the
flood impact and action:
– The area of inundation is used to identify the affected elements at risk;

– The water depth is the most used and important parameter, the amount of damage
produced increasing with the water level;
– The duration of flooding influences especially the damages to buildings and
agriculture. The longer the water persists in these areas, the buildings w ill deteriorate
and more crops will fail and therefore the recovery costs will increase;
– The velocity of the water can lead to physical destruction of structures, increasing
the probability of structural failure. Furthermore, this parameter can influence t he
loss of lives increasing the number of deaths. The velocity is important especially in
the case of flash floods and/or dam failures;
– The time of occurrence influences the damage to agriculture, this damage being
determined by the season in which the flo od occurs. The time of day is also
important, the floods occurring during night having the potential to produce more
damage due to the fact that the warning process is more difficult and the people can
be more confused about the preparedness measures;
– The contamination of water , as a results of Natech events, can increase the damage
(Messner et al., 2007; Thieken et al., 2005; Merz et al., 2010).
The resistance parameters refer to the characteristics of the exposed elements, such as
type of the construction , building material, etc. These characteristics can indicate the capability
of the structural elements to resist to the flood impact and determine their vulnerability (Merz
et al., 2010).

1.4. Flood hazard in Europe and Romania
1.4.1. Flood hazard in Europe
Over time, floods have caused great socio -economic losses in Europe, in particular in
the countries crossed by large rivers such as the Elbe, Danube and Rhine. In the last three
decades (1988 – 2018) the natural hazards with the highest frequency in Europe are represented
by floods. The CRED EM-DAT database recorded a number of 506 events since 1988,
representing 37% of the most important natural hazard events that occurred in this period. They
are followed by storms with 27% and extreme temperatur e with 16%. However, regarding the
mortality caused by natural hazards, floods caused a number of 2770 deaths, lower than extreme

temperatures (143.954 deaths) or earthquakes (28.532 deaths) (Fig. 2). Moreover, 39% of the
total economic damages related to natural hazards are are caused by floods (CRED EM -DAT)
(Fig.3).

Fig. 2. The frequency of natural hazards in Europe in the last 30 years (1988 – 2018)
(data from CRED EM -DAT, 2018 )

Fig. 3. The economic damages caused by natural hazards in Europe in the last 30 years
(1988 – 2018) (data from CRED EM -DAT, 2018)

Among the most catastrophic flood events, the following can be included: the flood
from 1970 on the Danube and Tisza rivers a ffecting Romania and Hungary and producing
damages of 585 million US$; the floods from 1997, affecting Poland, Germany and Czech
Republic and producing total damages of 5.900 million US$; in 2000 major floods occurred in
UK, Italy and France producing tota l damages of around 10.000 million US$; the floods from
2002 produced damages of around 27.000 million US$ affecting many countries (Barredo,
2006; Kundzewicz et al., 2013). In the past three decades Central -Eastern Europe has been the
most affected part of Europe, the flood events having a great impact on population and economy
(Table 2).
Table 2. Impact of flood events in Central -Eastern Europe countries (adapted after Raška,
2015)
Country Major floods between 1990 and 2014
Casualties Damage (M US$) Worst events
Czech Republic 100 5.744 1997, 2002, 2013
Slovakia 65 306 1998
Slovenia 1 270 2012
Hungary 10 881 1999, 2010
Romania 426 3.047 1991, 2005, 2006,
2010
Bulgaria 74 510 2005, 2014
Poland 104 7.380 1997, 2001, 2010
Lithuania 4 0 2005, 2010
The adoption of Directive 2007/60/EC (Flood Directive) had an important role in the
flood risk management, requiring a uniform and harmonized approach between countries, this
Directive being implemented in each EU member state. The implementation of the Flood
Directive requires three steps: flood risk assessment, flood hazard and risk mapping and flood
risk management planning (Müller, 2013). The aim of the Flood Directive is to reduce the flood
risk and consequences by the implementation of a common fram ework, offering also a better
coordination of national flood risk management with the joint EU policy (Priest et al., 2016).
The traditional approaches focus more on the hazard and exposure components of the risk, and
less on the vulnerability and thus on consequences, mainly due to limitations in available data
and knowledge on damage processes and influencing factors. Therefore, the Flood Directive
came to fill this gap by requiring a holistic approach of flood risk management, including all
components of the flood risk (Albano et al., 2017a).

1.4.2. Flood hazard in Romania
In Romania, floods are the natural hazard with the highest frequency, Romania being
one of the most affected countries in Europe (Arghiuș et al., 2011). Moreover, “ Romania is also
the second country in Europe exposed to this type of natural disaster in terms of mortality rate
(42.6%)” (Kovacs et al., 2017). According to CRED EM-DAT, in the last 30 years, the
economic damages caused by natural hazards are related to floods (86%) and dr oughts (14%)
(Fig. 4, Fig. 5). Regarding the loss of lives, the natural hazards that caused the highest number
of deaths are extreme temperatures (529 deaths) and floods (436 deaths). In the past years
several flood events had major consequences on the pop ulation and economy: the flood from
April 2000 in the Crișul Alb basin in west of the country produced total damages of 5,5 million
euro; the flood from August 2002 on Slanic river, Buzau County with precipitation exceeding
100 l/m2 caused damages of 292.500 euro; the floods from July 2008 on the Suceava, Moldova
and Bistrița rivers from the Siret basin caused great damages, on the Suceava river the historical
rates were exceeded, 107 cities were affected, the reported damages being around 200 million
euro. Also the floods from 2010 with damages of 800 million euro affected 37 counties in
Romania (Albano et al., 2017a).
In this context the development and implementation of improved flood risk management
approaches and policies has bec ome a priority. To serve this purpose the legislation in the field
was constantly improved. In the next section the flood risk governance in Romania is described.

Fig. 4. The frequency of natural hazards in Romania in the last 30 years (1988 – 2018)
(data from CRED EM -DAT, 2018)

Fig. 5. The economic damages caused by natural hazards in Romania in the last 30 years
(1988 – 2018) (data from CRED EM -DAT, 2018)
1.5. Flood risk legislation in Romania
The flood risk management involves the implementation of different protection and
mitigation measures, policies and procedures regarding flood risk identification and
assessment. The legislation in the field is an important tool that supports the implementation of
all these measures and procedures with the a im of reducing the impact of floods. The most
important legislation that regulates the flood hazard in Romania is presented bellow.
The Water Law 107/1996 had as a main purpose the protection of water resources and
aquatic ecosystems against pollution as w ell as flood defense. However, regarding flood
defense, the document was lacking details, pointing out just the need for structural defense
measures, prevention and intervention measures, as well as the need for the elaboration of
certain strategies ( The Water Law 107/1996, art.67; Albano et al., 2017a).
The Law 310/2004 was adopted for modifying and completing the Water Law. It
addressed issues such as protection measures against floods, highlighting the need for an
efficient flood risk reduction. This law states that warning and protection actions must be
undertaken for the entire national territory, on stages of development and must be available to
the public authorities. Furthermore, it forbids the placement of buildings, economic and social
objectives i n the flood prone areas. The construction of protective structures against floods must
be maintained and repaired under the above mentioned law ( The Law 310/2004, art. 44, 49, 69
and 85).

The Water Law was also completed by the Law 112/2006 that requires the development
of the National Strategy for Flood Risk Management. It also highlights the importance of flood
protection plans and flood risk identification ( The Law 112/2006, art. 43).
The Law 146/2010, which further modifies the Water law, has the aim t o transpose the
Directive 2000/60/CE of the European Parliament and Council regarding the flood risk
assessment and management. It includes the concept of flood risk management with the aim of
reducing the negative impact of floods. For each river basin th e following information must be
provided: a preliminary flood risk assessment, flood hazard and risk maps, flood risk
management plans. The management of emergency situations regarding floods is also taken
into consideration ( The Law 146/2010).
Furthermore , the flood events that took place in the past years highlighted the need of a
comprehensive and holistic approach regarding the flood risk management, that could support
decision makers to prioritize intervention and to select alternative risk mitigation options. In
this context the Directive 2007/60/EC regarding flood risk assessment and management was
transposed in 2010 at national level by the G.O. 846/2010 , regarding the National Strategy for
Flood Risk Management for medium and long term. The main aim of this Strategy was the
mitigation of damages and life loss prevention. It includes prevention, protection and
preparedness activities, emergency situation activities and activities that take place after the
flood event (G.O. 846/2010).
The implementation process has three stages: preliminary flood risk assessment,
development of hazard maps and flood risk maps, and the final stage regarding the development
of flood risk management plans. All these stages were accomplished and as a result The
National Plan for prevention, protection and mitigation of flood effects was developed
(N.I.H.W.M ., 2016).
However, all three of flood risk components are acknowledged ( hazard, exposure,
vulnerability) and should be analyzed in order to provide objective results and a rational
procedure for quantitative risk and cost -benefit analysis, as prescribed by the Flood Directive:
according to Art. 7 of the Directive 2007/60/EC, flood reduction management plans have to
include measures for flood risk reduction, taking also into account “relevant aspects such as
costs and benefits”. Hence, it is evident that only traditional qualitative risk assessments,
commonly used in Romania, ar e not alone sufficient to comply with these prescriptions and that
more sophisticated quantitative methods should be adopted (Albano et al., 2017a).
Romania is also a member of the International Commission for the Protection of the
Danube River (I.C.P.D.R. ) since 1995 when the Law 14/1995 regarding the collaboration for

the protection and sustainable usage of the Danube River was adopted. According to the Law
14/1995, art. 2: “Water management cooperation shall be oriented towards sustainable water
manageme nt, relying on criteria for a stable, environmentally sound development, which are at
the same time directed towards: maintaining the overall quality of live; maintaining continuous
access to natural resources; avoiding long lasting environmental damage an d protect
ecosystems; exerciseing preventive approach”.

2. General methodological approach regarding flood damages assessment
The classical approach for flood damage assessment (Fig. 6) has the following three
steps: hazard analysis, exposure analysis and vulnerability analysis (de Moel et al., 2015;
Bubeck and Kreibich, 2011; Scorzini and Frank, 2015).

Fig. 6. Schematic representation of the flood damage assessment process

Before starting a flood damage assessment it’s very important to establish t he aim and
scale of the study as well as the available data and resources. Depending on these factors, the
appropriate approach for the study can be sel ected (Messner et al., 2007).
The flood damage assessment plays an important role in the decision making process,
nowadays the focus being on the development of improved methods and tolls that will help the
stakeholders in the flood management process and in the implementation of the Flood Directive.
Therefore, European projects such as FLOODsite, Danube Flo odrisk or CONHAZ were
developed, providing information regarding best practices in flood damage assessment
(Messner et al., 2007; Eleuterio, 2012).

2.1. Review of the current methodologies in the field of flood damage assessment
The deterministic method for flood damage assessment presented above represents the
state of the art in the field and has been applied in several research studies. Bubeck et al., 2011
analysed the future changes in flood damage for the Rhine River basin, using two damage
models. The land use data were represented by the CORINE Land C over (CLC) database and
for the future land use projection the Land Use Scanner model was used. The estimations of
relative flood damage differ by a factor of 1.4, while the land use projections provid e damage
results that differ by a factor of 3. Jongman et al., 2012 conducted a comparative flood damage
assessment for two case studies using seven flood damage models. Also a sensitivity analysis
was done in order to determine the uncertainties induced b y depth -damage functions and
maximum damage values. The results showed that the modelling approaches have a great
variation, the depth -damage functions being the main source of uncertainties. Also, the study
suggests that attention must be paid when aggreg ated land use data (such as CLC database) are
used, the generalisation of this data inducing over or underestimation of flood damage
(Jongman et al., 2012). Escuder -Bueno et al., 2012 proposed a flood risk methodology that
includes social data and analyse s the impact and efficiency of non -structural measures on risk
reduction.
The accuracy of the results depends mostly from the resolution of the input data,
therefore the use and development of high resolution datasets at large scale has become an
importan t goal of the studies in the field. Furthermore, the focus is to develop simplified
methodologies that can be applied over large areas offering in this way harmonised and
consistent results regarding the flood prone areas as well as the flood damage.
Sever al studies conducted by the Joint Research Centre of European Commission (JRC)
developed large scale methodologies in order to respond to the above identified need. Barredo
and De Roo, 2007 proposed a methodology of flood risk based on the standard definit ion of the
flood risk which states that the risk is the product between hazard, exposure and vulnerability.
For this large scale approach, homogenous data at European level are required. For the hazard
analysis, the flood hazard map of Europe, developed by De Roo et al., 2006, was used. The map
has a 1 km grid size, being developed using a DEM with a resolution of 1 km. For the exposure
and vulnerability analysis the CLC 2000 and the GDP per capita at NUTS 3 level were used.
This study represents a first at tempt of a harmonised approach of flood risk assessment at large
scale providing a standardised risk index map. However, it is a coarse qualitative methodology
that uses low resolution data and simplified methods and therefore offers low accuracy results

(Barredo and De Roo, 2007). Winsemius et al., 2013 proposed a framework for flood risk
mapping at global scale using one cascade of models and data with a resolution of 1 km. Lugeri
et al., 2010 re -processed the map developed by De Roo et al., 2007 in orde r to use it for a pan –
European quantitative flood risk assessment. The data were downscaled to a resolution of 100
m, and depth -damage functions and maximum damage values were used in order to calculate
the flood damages (Lugeri et al., 2010). The same met hod was used by Feyen et al., 2012 in a
study that assess the impact of climate change for future flood damage in Europe. In this study,
in order to obtain the hazard map, topographic information and a 100 m DEM were used. The
analysis was done for differe nt return periods and the available data were downscaled to a
resolution of 100 m. For the economic damages the depth -damage functions provided by
Huizinga, 2007 and the CLC 2000 database were used. Furthermore, te Linde et al., 2011
estimated the future f lood risk for the entire Rhine basin taking into account climate change and
by applying the same general methodology. The damage estimations were done with a
resolution of 100 m using the Damage scanner model.
Using the cascading model approach proposed in Barredo et al., 2007 study, Alfieri et
al., 2014, developed a pan -European hazard map for a return period of 100 years, downscaling
the available data to a resolution of 100 m. The pan -European hydrological model Lisflood was
calibrated and used for hyd rological simulations and flood hydrographs development. The
hydraulic simulation was done for 5 km sections using the 2D hydraulic model Lisflood – ACC
and subsequently were put together, representing in this way the pan -European hazard map.
More informat ion regarding the applied methodology are available in Alfieri et al., 2014. The
limitation of this approach is represented by the relatively low resolution of the input data that
affects the analysis in small river areas and the overall accuracy of the re sults. However, this
approach represents a first attempt to improve the existing methodologies of flood hazard
assessment at large scale, offering guidelines for future analysis (Alfieri et al., 2014).
Furthermore, in order to carry out a comprehensive f lood risk assessment, this
methodology was applied for different return periods and the consequences were calculated in
a study by Alfieri et al., 2016. The damages were calculated overlapping the 100 m resolution
hazard map with the population density map (in order to determine the affected population) and
with la land use map (in order to determine the economic damages). For the economic damages
the depth -damage functions provided by Huizinga, 2007 and a refined version of CLC database
were used.

An atte mpt to improve the previous approaches and offer more reliable results with a
better resolution, Sampson et al., 2015 proposed a model that provides hazard maps at a
resolution of 90 m and includes a detailed river basin network.
As illustrated above, even though there is an increased interest to improve the
methodologies and the quality of the data applied at large scale there are still gaps that make
the process difficult. Many studies focused on improving the hazard maps, developing methods
that offer be tter resolution results. However, the limited availability of data over large areas still
represents a problem, therefore simplified methods are used. Furthermore, less attention is
given to quality and resolution of land use data, which represents an impo rtant factor in the
exposure analysis and subsequently in the flood damage analysis.

2.2. GIS tools and methods applied in the field of flood damage assessment
In the past years the use of free and open source GIS (Geographical Information
Systems) softwa re in the process of flood damage assessment has increased due to the fact that
the GIS tools are able to provide adequate spatial data processing, analysis and mapping. These
features are particularly useful for the development of flood hazard and flood r isk maps as well
as for the visualisation of the results. Furthermore, the free availability of data lead to an
increase in the development of tools and models that can be used for flood hazard and flood
consequence analysis (Steiniger and Bocher, 2009; Ch ingombe et al., 2015; Albano et. al,
2015). These tools can process data and provide information that are further used for hydraulic
modelling with the aim of obtaining the flood characteristics such as flooded area, water depth
and water velocity. For exa mple, the GIS tools can provide georeferenced input data (stream
network, cross sections) for the hydraulic HEC RAS model, based on the DEM (Digital
Elevation Model) (Gogoașe Nistorean et al., 2016). The QRAS tool from the QGIS software
(QGIS, 2017)c can be used for the preparation of the geometry data needed by the HEC RAS
hydraulic model. This approach is often used for areas with ungauged basins where the
information is limited and therefore the GIS tools offer an alternative to obtain the geometry
data t hat are needed. The results of the hydraulic models can be used also as an input data in
the GIS for further analysis such as flood damage analysis. On the other hand, more hazard
tools and models were developed on the GIS platform with the aim of offeri ng a more user –
friendly environment for data analysis, reducing also the computational time and the
uncertainties induced by the using of different combined models. Such tools are developed in
the existing GIS software QGIS and GRASS (Geographic Resources Analysis Support System

Software). For example the GRASS tools ( r.hazard.flood , r.inund.fluv , r.damflood ) use the
geomorphologic characteristics of an area in order to provide flood prone area maps (Albano
et. al, 2015; Marzocchi et al., 2014). Furthermore, the GFA (Geomorphic Flood Area) plugin
developed in QGIS can provide flood prone areas based on the ge omorphic information using
the DEM of an areas. This plugin can be used for mapping the flood prone area and the water
depth in areas with limited availability of data. It can be applied for preliminary flood risk
assessment over large areas for a complete analysis that includes all the streams of the basin
(Samela et al., 2018). Another QGIS plugin is FloodRisk, which can calculate the damage
produced by floods based on the distribution and value of elements at risk combined with the
water depth and water velocity (Albano et al., 2017b).
In flood damage assessment the distribution of elements at risk is an important factor
that can influence the magnitude of the consequences. The exposure and the vulnerability of
the assets can be easily represented in the GIS environment using different tools that can
provide exposure and vulnerability maps. Therefore, GIS -based land use databases were
developed (such as CLC ) using satellite images that can be processed using the GIS tools. In
order to obtain the assets th at are actually exposed to the hazard, the hazard data are combined
with the vulnerability data and the GIS environment is ideal to perform this type of spatial
overlay analysis (Eleuterio, 2012).

2.3. Hazard analysis . Tools and methods
The hazard analysis implies the determination of the flood probability and inte nsity . The
flood intensity can be represented by many parameters such as water depth, water velocity,
duration and extent of the flood, however the most important and commonly u sed are the water
depth and water extent (Sole et al., 2013). These parameters can be calculated using 1 –
dimens ional or 2 -dimens ional hydrodynamic models. (de Moel, 2012; Messner et al., 2012).
However, there are ungauged locations where the data necessary for detailed modelling are not
available, or the resources and the time are limited and therefore more simplified methods are
needed. Such a methodology for the flood area estimation was proposed by Samela et al., 2017.
This method is using the basin’s ge omorphology in order to obtain a large scale analysis of the
flooded area in scarce -data locations.
The hazard is represented by flood maps, illustrating the characteristics of the hazard
(flood extent, water depth) for different return periods or for a s pecific historical event (de Moel,

2012). Usually, these maps are plotted using the interface of different software such as GIS,
which can provide adequate spatial visualization of results.

2.3.1. Hydraulic models
The most common method to perform flood hazard analysis is to use one -dimensional
(1D) or two -dimensional (2D) hydraulic models which provide flood maps containing the
flooded area, water depth and water velocity. Usually the 2D models are applied for small areas,
being time -consuming and the co mputationally expensive, while the 1D models can be applied
for larger areas and don't require as many resources. There is a wide range of hydrological –
hydraulic models that are used worldwide for flood simulations, such as HEC RAS, FLO -2D,
MIKE FLOOD, Riv erFlow 2D and FLORA 2D (Patro et al., 2009; Manfreda et al., 2015; Banks
et al., 2014).
HEC -RAS (Hydrologic Engineering Centers’ River Analysis System) (Fig. 7) is the
most common free hydraulic model which can be used for river flood as well as dam flood
simulations (HEC -RAS, 2017) . The input data refers to the geometric features of the basin that
can be obtained with GIS tools using a high resolution DEM. In this situation the accuracy of
the obtained data depends mostly on the DEM resolution. The geometr y data offers information
about the river and the floodplain. Other data that are needed are the discharge and the flow
conditions. The model contains a viewer for the visualisation of the results as well as a tool
called RAS Mapper that can transform the results in a 2D grid and a raster file. This output of
the tool can be imported in a GIS environment for further analysis such as exposure and
consequence analysis (Kumar Sharma et al., 2017; Banks et al., 2014; Albano et al., 2017b). In
fact, many studies in the flood risk field (Khattak et al., 2016; Mosquera -Machado and Ahmad,
2007; Ullah et al., 2016) combine the HEC RAS model together with GIS tools. The model has
a stable platform and the computational time is reduced, however it has limitations regar ding
its applicability in flat basin areas which can provide less reliable results (Manfreda et al., 2015).

Fig. 7. HEC RAS data processing