The FLO -2D is a commercial hydrological -hydraulic model that can perform [627278]

The FLO -2D is a commercial hydrological -hydraulic model that can perform
hydrodynamic analysis using the topographic features of an area. It is used for floodplain
delineation as well as for debris flow simulations. (Banks et al., 2014; Stancanelli and Fori, 2015).
The MIKE FLOOD model, developed at the Danish Hydraulic Institute, can be used for
river flood, urban flood and dam break flood simulations. It’s a flexible and user friendly model
that contains several packages such as MIKE 1 1, MIKE 21 and MIKE urban. Mike 11 is a 1D tool
that can simulate the flow in the channel section, MIKE 21 is a 2D tool used for free surface flow
while MIKE URBAN is used for collection systems. It can be applied at any scale for flood risk
analysis and m apping (Patro et al., 2009; MIKE FLOOD, 2017).
RiverFlow 2D is an advanced commercial hydrological -hydraulic model based on a
flexible non -structured mesh and it can simulate river, urban and dam break flooding as well as
sediment and pollutant transport a nd mud -debris flow. It offers accurate results, being extensively
validated and it has efficient computational times. ( RiverFlow 2D, 2017 ).
FLORA 2D (FLOod and Roughness Analysis) is a hydraulic model that simulates the flood
propagation in flat areas tak ing into account the effect of vegetation. It is based on the “shallow
water equations” (Manfreda et al., 2015; Cantisani et al., 2014).
TUFLOW is a commercial model that performs 1D, 2D and 3D modelling and is also using
GIS software for data processing ( TUFLOW, 2017 ).

2.3.2. GIS tools and plugins
The hydraulic models are constantly developing and improving, advancing with the IT
technology, resulting more detailed models which offer a better accuracy of the results. However,
in order to use detaied hydra ulic models a consistent and detailed data set is needed, but most often
such data are not available. Moreover, the use of these models involves high costs and their
application in large areas is difficult and time -consuming, currently representing an impor tant
problem (Freni et al., 2010; Samela et al., 2017). Furthermore, a study done by Freni et al., 2010,
highlighted the fact that the use of detailed hydraulic models in the process of damage flood
assessment is not justified most of the time, for reasons such as: additional costs and resources;
the advantages of their use are partially reduced by the fact that the use of depth -damage functions
leads to significant uncertainties, the results mostly depend on the type of depth -damage function
used and not o n the hydraulic model. The final results of the damage assessment when depth –

damage functions are used, shows little differences between using a simplified approach and using
complex hydraulic models (Freni et al., 2010).
Therefore, in order to determine the flooded areas in large watersheds with scarce -data, or
in watersheds with rivers where there are no hydrological stations in order to obtain the stream
flow, it is necessary to apply a simplistic approach which doesn’t need complex and detailed data
sets. In this context the geomorphic features of an area can be a useful factor in the process of
flood prone area delineation. Lastra et al., 2008 demonstrated that the geomorphological method
is more consistent than the hydrological -hydraulic method, when it is applied over large areas and
in ungauged basins. Furthermore, the use of a digital elevation model (DEM) has become a
common practice in the developing of new and improved tools and methods for flood area
delineation. Manfreda et al., 2015 and 2018 used DEM information and a linear binary classifier
to analyse different morphological descriptors and their potential to identify flood -prone areas.
These studies revealed that two morphological features showed particularly good performance: the
distance from the nearest stream (D), which offers better results for flat areas; and the Geomorphic
Flood Index (GFI), which compares water depth (h r) with the elevation difference (H), ln(h r/H).
The GFI index exhibits good performance when applied in large areas with limited data availability
(Manfreda et al., 2018).
A simplified methodology for the flooded area estimation was proposed in a study by
Samela et al., 2017 and is based on the Geomorphic Flood Index (GFI). This method uses the
basin’s geomorphology in order to obtain a large scale analysis of the flooded areas, which contains
all the rivers, including the small ones which usually are not considered in large scale analysis due
to the lack of sufficient data. The analysis was conducted on the Ohio basin, U.S., and the results
showed a good performance of this approach based on the GFI index, offering accurate flood maps
for large areas with scarce -data (Samela et al., 2017).
Based on this methodology the GFI (Geomorphic Flood Area) tool was developed as a
QGIS plugin. All the input data can be obtained based on a DEM: DEM, filled DEM, flow direction
and flow accumulation. The tool runs a terrain analysis and subsequently the delineation of flood
prone areas is performed. The result is a binary map containin g the flood area.

2.4. Exposure analysis and land use data
The elements at risk are usually represented by population, buildings, or type of land use
such as industrial area or agricultural areas. These information, which are represented by maps
indicati ng the characteristic of the elements at risk and their location, are overlapped with the
information regarding the hazard (flood maps) and in this way the exposed elements are obtained
(Fig. 8) (de Moel et al., 2015; Albano et al., 2015).

Fig. 8. Exposed elements identification

2.4.1. Elements at risk
The elements at risk represent the assets that can be affected by the flood. In order to be
able to assess them, they are classified into groups or classes based on their characteristics and the
damage assessment will be done for these classes. This is necessary because of the lack of data
that would be necessary for a detailed assessment, this being also time consuming and very difficult
to accomplish (Merz et al., 2010; Bubeck and Kreibich, 2011 ).
Usually, the elements at risk are classified into economic sectors (urban, industry,
agriculture), individual elements of each sector having the same characteristics. The reason to use
this type of classifications is the fact that the economic data reg arding these elements are also
available in an aggregated form for each sector and not for each object (Bubeck and Kreibich,
2011).
Based on these sectors, land use maps were developed with different resolutions and
different number of land use classes. T hese maps are used for the representation of the elements

at risk. The most common database is CLC which is a pan -European database with a resolution of
100 m x 100 m. It provides data for most of the countries, containing datasets for the years 2000,
2006 and 2012 (see chapter 2.4.2. Land use data sets for more details).
The exposed elements are identified by overlap ping the land use data with the flood data.
After they are identified, ther vulnerability is determined using depth -damage functions. The
functions will calculate the potential damage based on the economic value of the assets and the
water depth (de Moel and Aerts, 2010).
Therefore, in order to perform a quantitative damage assessment, the assets’ value must be
determined. There are two types of values that are currently used in literature: the depreciated
value , which is the actual value of an asset at the moment of the event; the replacement value ,
which is the value needed to replace the asset that was damaged during the flood (Merz et al.,
2010; Jongman et al., 2012). However, Merz et al., 2010, highlighted the fact that using
replacement values can lead to an overestimation of the potential damages.

2.4.2. Land use datasets
The land use is represented by maps at different scales and with different resolutions in
which the areas with the same characteristics are aggregated in homogeneous zones.
CORINE Land C over (CLC) database
CLC offers a pan -European land use data at a sca le of 1:100 000. The project coordinated
by European Environment Agency (EEA) provided the first datasets for the reference year 1990
with the aim of offering standardized land use data. These data were subsequently updated for the
years 2000, 2006 and 201 2. It has a coarse resolution and the number of classes regarding built -up
areas are limited. Even though it contains 44 land use classes, just two of them are referring to
residential buildings. These 44 land use classes are organized on three levels: at the first level the
land use is divided in five main categories, while at the second and third levels the main classes
are divided into more detailed categories (Table 3). CLC is the most used database given its large
and free availability. Furthermore, it is the only harmonized European land use database. The CLC
database is available on the Copernicus website (Fig. 9) ( Copernicus, 2018 ; Prastacos and
Chrysoulakis, 2011; Feranec et al., 2007; Kuntz et al., 2014; Büttner, 2014).

Table 3. The CORINE Land C over database 2012 nomenclature (adapted after Yılmaz, 2010)
Class 1 Class 2 Class 3 Code
Artificial surfaces Urban fabric Continuous urban fabric 111
Discontinuous urban fabric 112
Industrial,
commercial, and
transport units Industrial or commercial units 121
Road and rail networks and associated land 122
Port areas 123
Airports 124
Mine, dump, and
constructions sites Mineral extraction sites 131
Dump sites 132
Construction sites 133
Artificial,
nonagricultural
vegetated areas Green urban areas 141
Sport and leisure facilities 142
Agricultural areas Arable land Nonirrigated arable land 211
Permanently irrigated land 211
Rice fields 213
Permanent crops Vineyards 221
Fruit trees and berry plantations 222
Olive groves 223
Pastures Pastures 231
Heterogeneous
agricultural areas Annual crops associated with permanent crops 241
Complex cultivation patterns 242
Land principally occupied by agriculture, with
significant areas of natural vegetation 243
Agro -forestry areas 244
Forest and semi –
natural areas Forests Broad -leaved forests 311
Coniferous forests 312
Mixed forests 313
Scrub and/or
herbaceous
vegetation
associations Natural grasslands 321
Moors and heathland 322
Sclerophyllous vegetation 323
Transitional woodland -scrub 324
Open spaces with
little or no
vegetation Beaches, dunes, sands 331
Bare rocks 332
Sparsely vegetated areas 333
Burnt areas 334
Glaciers and perpetual snow 335
Wetlands Inland wetlands Inland marshes 411
Peat bogs 412
Maritime wetlands Salt marshes 421
Salines 422
Intertidal flats 423
Water bodies Inland waters Water courses 511
Water bodies 512
Marine waters Coastal lagoons 521
Estuaries 522
Sea and ocean 523

Fig. 9. CORINE Land Cover 2012 in E urope (Copernicus, 2018 )

Urban Atlas database
The Urban Atlas database provides high -resolution land use maps for large urban areas
having a resolution of 10 m (Fig. 10). It contains data for the reference years 2006 (for 305
European cities) and 2012 (for 697 European cities). The updated version contains 27 urban land
use classes from which 6 are referring to residential buildings (Table 4). It has a high accuracy
which makes it suitable for quality verification of other land use datasets such as CLC (Pazúr et
al., 2017; Copernicus, 2018 ). Compared with the CLC database the focus of Urban Atlas is more
on the built -up areas, providing a more detailed classification regarding residential areas (Fig. 11).
The aim of this project was to provide pan -European comparable land use data with a high
resolution (Montero et al., 2014).

Fig. 10. Urban Atlas 2012 in Europe
(Copernicus, 2018a )

Table 4. The Urban Atlas database 2012 nomenclature
(Copernicus, 2018a)
Code Land use class
11100 Continuous Urban Fabric (S.L. > 80%)
11210 Discontinuous Dense Urban Fabric (S.L. : 50% – 80%)
11220 Discontinuous Medium Density Urban Fabric (S.L. : 30% – 50%)
11230 Discontinuous Low Density Urban Fabric (S.L. : 10% – 30%)
11240 Discontinuous Very Low Density Urban Fabric (S.L. < 10%)
11300 Isolated Structures
12100 Industrial commercial public military and private units
12210 Fast transit roads and associated land
12220 Other roads and associated land
12230 Railways and associated land
12300 Port areas
12400 Airports
13100 Mineral extraction and dump sites
13300 Construction sites
13400 Land without current use

14100 Green urban areas
14200 Sports and leisure facilities
21000 Arable land (annual crops)
22000 Permanent crops
23000 Pastures
24000 Complex and mixed cultivation patterns
25000 Orchards at the fringe of urban classes
31000 Forests
32000 Herbaceous vegetation associations
33000 Open spaces with little or no vegetation
40000 Wetlands
50000 Water

Fig. 11. Residential land use comparison in Cluj Napoca city, Romania;
a. CLC residential class b. Urban Atlas residential classes

2.4.3. Remote sensing data
The information regarding land use has a significant role in the process of flood damage
assessment. The use of land use data can be a major source of uncertainties in the results of the
flood damage assessment due to their accuracy, the level of detail, t heir availability and their
actualization frequency. Due to the rapid urbanization from the last years, there is a lack of up to
date land use data, especially in developing countries (Wieland and Pittore, 2016). Furthermore,
most of the available maps hav e a low resolution, containing limited land use classes, thus
decreasing the accuracy of the results (Albano et al., 2015).
Remote sensing techniques allow the acquisition of information regarding land use
characteristics with high spatial, temporal and sp ectral resolution. Various remote sensing
platforms can serve this purpose, the most common being airborne and satellite sensors. In the case
of satellite remote sensing, the data acquisition is usually done by Earth -orbiting satellites, which
use on -board sensors to record the electromagnetic energy reflected by the Earth at different
wavelengths. The collected data, also referred to as satellite imagery, are analysed in order to
extract different features that characterize the Earth’s surface (spectral, t extural, geometrical and
elevation features). For pattern recognition object -oriented approaches are commonly used which
analyse segments (also referred to as objects) instead of pixels. In order to achieve this purpose
certain image segmentation procedure s are applied (Frohn et al., 2009).
Therefore, in the last years there is an increased interest in using the satellite imagery with
the purpose of obtaining land use data. The current availability of high -resolution satellite imagery
allows the automated d etection of different land use classes and different settlement types. This
trend led to the development of different approaches for land use classification based on satellite
imagery (Ok, 2013). Biro et al., 2013, applied an object -oriented approach to ob tain land use
classification from the TerraSAR -X satellite images. This method is based on three steps: image
segmentation, feature -based description based on training data, and classification. This approach
effectively identified eight land use classes, u sing the Feature Space Optimisation (FSO) to
calculate the best features to separate the classes (Biro et al., 2013). Ok, 2013, proposed a new
method for building identification from very -high-resolution (VHR) multispectral satellite images.
This method is based on information provided by the buildings’ shadows and it can be applied to
buildings with different characteristics. Liu et al., 2014 used the Landsat TM images with a
resolution of 30 m to detect terrestrial land use conditions, showing the distrib ution of land use

changes and their impact on flood exposure. Wieland and Pittore, 2014 used four machine learning
algorithms to identify urban patterns from multispectral satellite images. The association of these
algorithms with certain selected features (spectral, textural and geomorphic) provided good
performance and flexibility for the method, the best performance being exhibited by the Support
Vector Machine (SVM) and Random Tree (RT) algorithms. The study highlighted the potential of
the method to be applied over large scale areas. Zhang et al., 2014 proposed a method that
combines Landsat spectral data with multivariate texture information in order to detect the urban
built-up areas. The method is using the one -class support vector machine (OCSVM) c lassifier
which requires training data from one class. The image -based approach proposed Dornaika et al.,
2016 is using image segmentation and descriptor classification to detect buildings from optical
aerial images. Wieland and Pittore, 2016 proposed a la rge scale application, combining object –
based approach with machine learning techniques to identify settlement patterns from Landsat 8.
The SVM classifier was used, offering a good performance and indicating a good transferability.

Landsat 8 images
The Landsat Program offers multispectral data of the Earth’s surface, with a medium
resolution, the data being kept in a national archive and made available to the public. This data can
be used in fields such as agriculture, geology, land use planning, etc . The latest available data –
obtained by Landsat 8 satellite, launched on February 11, 2013 – are high quality, thus improving
and updating the existing database. The system is composed of two instruments: the Operational
Land Imager (OLI) sensor and the Thermal Infrared Sensor (TIRS); these instruments provide
images (each of them containing 11 spectral bands) with a resolution of 30 meters (for the visible
spectrum, near infrared spectrum and shortwave infrared spectrum), 100 meters (for thermal
infrare d spectrum) and 15 meters for the panchromatic band (Table 5) (USGS, 2018; NASA,
2018 ).

Table 5. Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)
(USGS, 2018 )
Landsat 8
Operational
Land Imager
(OLI)
and
Thermal
Infrared
Sens or
(TIRS)
Bands Wavelength
(micrometers) Resolution
(meters)
Band 1 – Ultra Blue (coastal/aerosol) 0.435 – 0.451 30
Band 2 – Blue 0.452 – 0.512 30
Band 3 – Green 0.533 – 0.590 30
Band 4 – Red 0.636 – 0.673 30
Band 5 – Near Infrared (NIR) 0.851 – 0.879 30
Band 6 – Shortwave Infrared (SWIR) 1 1.566 – 1.651 30
Band 7 – Shortwave Infrared (SWIR) 2 2.107 – 2.294 30
Band 8 – Panchromatic 0.503 – 0.676 15
Band 9 – Cirrus 1.363 – 1.384 30
Band 10 – Thermal Infrared (TIRS) 1 10.60 – 11.19 100 * (30)
Band 11 – Thermal Infrared (TIRS) 2 11.50 – 12.51 100 * (30)
* TIRS bands are acquired at 100 -meter resolution, but are resampled to 30 meters in the delivered data
product.

2.5. Damage analysis
The elements within the flooded area can be affected to a different extent, depending on
their proximity to the river, the depth and velocity of the water in their locations as well as their
characteristics (type of land use, building material in the case of constructions). The degree of the
damage that can occur can be determined using damage functions. As mentioned before, the most
common used are the depth damage functions which determine the susceptibility of a certain
exposed element depending to the water depth (Jongman et al., 2012; Cammerer e t al., 2013).
These functions are considered a standa rd approach and they are used by different damage
models and can be applied at different scales. Nowadays a wide range of damage functions have
been developed in many countries in Europe and around the world. However, they are specific to
the areas and the context for which they were created and therefore their use in other areas can
lead to uncertainties in the results, the validation process being very important in these cases
(Scorzini and Frank, 2015 ; Cammere et al., 2013). A first attempt to develop pan -European
harmonized damage functions was made by the European Commission Joint Research Center
(JRC) (Huizinga, 2007). They developed functions for nine different European countries as well
as an aver age function that can be used for the countries were no specific functions exist (Huizinga,
2007; Jongman et al., 2012). Furthermore, JRC developed a comprehensive global database

containing depth -damage functions which can be used for global scale analyse s (Huizinga et al.,
2017).
2.5.1. Damage functions
The most common and used method for the estimation of the expected damage caused by
floods are damage functions. It must be specified that these functions are used to calculate the
direct tangible damages , this ones producing the highest costs. Moreover, as mentioned before,
more attention and interest is given to the economic damage, usually the risk analysis being limited
to the estimation of this type of damage (Merz et al., 2010; Messner and Meyer, 200 5). These
functions calculate the damage of a certain element or class of elements exposed to the flood
considering the type and characteristics of this elements and the impact parameters of the flood
(Molinari et al., 2014). The water depth is considered the most important parameter in determining
the potential damage. Therefore, the most used functions are the depth damage functions, which
calculate the expected damage in function of the water depth (Jongman et al., 2012). In Fig. 12,
the Rhine Atlas functions are presented which determine the total damage for different land use
categories.

Fig. 12. Rhine Atlas depth -damage function s (de Moel and Aerts, 2010)

Depending on the data that are used to develop the damage functions, there are two
approaches that can be used:
– The empirical approach uses real data regarding the affected assets collected after a
flood event. Having the data about the type of assets, the water depth and the damage
of each asset or class of assets, depth damage functions can be developed for different
classes of assets. However, these type of data are specific for a certain area and a certain
hazard event and the use of the resulting functions in other situations and areas may
induce great uncertainties in the results (Messner et al., 2007; Sterna, 2012).
– The synthetic approach focuses on developing functions based on standardised types
of assets, for example l and use classes, using “what if” questions. First, the typology
of the classes or constructions (in the case of micro -scale analysis) are established based
on their similar characteristics, the monetary value of the assets is determined and their
susceptib ility to certain water levels is assessed by experts. For example, it is
determined which will be the expected damage for a certain type of building having a
certain water depth (Merz et al., 2010; Messner et al., 2007).
The depth damage functions can be d ivided into two categories:
– Relative functions , calculating the damage as a percentage of the total value of the
affected asset;
– Absolute functions, calculating the damage in monetary terms based on the water depth
(Jongman et al., 2012; Messner et al., 2 007).
Therefore, in order for the damage functions to be able to calculate the damage , the asset
value must be predetermined (Thieken et al., 2005).

2.5.2. Tools for consequence analysis – Damage models.
The development and the use of damage models in the past years has increased, becoming
an important tool for flood damage estimation and subsequently flood risk assessment. The
diversity of these models is given by the way in which they were developed as w ell as the goal for
which they were developed. A first differentiation can be made depending on the type of estimated
damage. Most of these models are focusing on direct economic damages using damage functions.
However, there are also models that are also considering the loss of lives in their assessment.
Another factor that must be considered is the scale of application. Each scale needs different

damage assessment approaches, for example at micro -scale, the analysis is done at object level,
while for meso and macro -scale, economic sectors and land use classes are used. Moreover,
depending on the scale, some models may need different input data with a different detail levels.
There are however models that can be applied for different scales and allow the us er to choose the
level of detail of the input data, but in this situation attention must be given in order to correlate
the correct approach with the analysis scale that is used (H. Kreibich et al., 2011; Messner and
Meyer, 2005). In the following a descri ption of the most common damage models used in the
literature are presented along with a comparative review (Table 6).
FloodRisk
FloodRisk (Fig. 13) is a QGIS plugin that calculates the direct economic damages and the
loss of lives caused by floods and can be applied in analyses at different scales. It is a free and
flexible plugin based on a transparent and collaborative approach. The main input data are referring
to the hazard characteristics (water depth, water velocity), exposure characteristics (land u se map,
maximum damage value of the assets) and vulnerability (depth -damage functions). For the
susceptibility analysis, the model is using depth damage functions. The model provides a set of
depth -damage functions that were collected from the most commonl y used models in this research
field: Hazus MH, Standard method (HIS – SSM model), Rhine Atlas model, Flemish model,
Damage Scanner model, JRC model, Multi -Coloured Model, FLEMO model. These models are
further described in this section. The provided funct ions are associated with land use classes’
codes of the Urban Atlas database. Therefore, if the user uses a different database, a reclassification
of the land use classes’ codes must be done. However, the model also allows the user to input and
use other functions. For the estimation of loss of lives, the model takes into consideration factors
such as the preparedness level of population and the existence of warning systems. The provided
results regarding economic flood damage assessment, contain quantitat ive damage values for each
land use class as well as damage and vulnerability maps. The results for the loss of lives analysis
contain the number of losses for different water depth ranges and maps representing the population
at risk and loss of lives ( Ma ncusi et al., 2015; Albano et al., 2017b). The model is available at:
https://github.com/FloodRiskGroup/floodrisk .

Fig. 13. FloodRisk plugin components (Albano et al., 2017b)

FLEMO model
The FLEMO model (Flood Loos Estimation MOdel) was developed by the German
Research Centre for Geosciences and it calculates the damages for the private sectors (FLEMOps)
as well as the commercial sectors (FLEMOcs). The FLEMOps estimates direct tangible dam age
to residential buildings, while FLEMOcs is used for the estimation of direct tangible damage to
buildings, equipment and goods of the companies. The models were developed using empirical
damage data from the German regions affected by floods in 2002, 2 005 and 2006. The damages
are calculated considering the water level, building type and quality of the buildings. Furthermore,
the effect of private precaution measures and the flood water contamination are taken into
consideration offering in this way mor e accurate results (Thieken et al., 2008; Jongman et al.,
2012; H. Kreibich et al., 2010).
The model can be applied at micro -scale (building level) as well as to meso -scale (land use
class level), showing good results for large areas, especially for the e conomic sector. However, it
tends to overestimate the damages and large uncertainties are registered for the residential sector

at very high water levels. It also presents uncertainties related to the transferability to other regions.
(Stena, 2012; Thieken et al., 2008; Seifert et al., 2010).

HAZUS MH
The HAZUS Multi -Hazard software is estimating the potential damages caused by natural
hazards such as riverine and coastal flooding, hurricane winds and earthquakes. It can be applied
at micro and meso -scale. It has a high level of detail, in this way the user can choose to use an
individual object approach or a surface area approach; also, regarding the vulnerability analysis
two options can be used: replacement values or depreciated values. The model uses re lative
functions and both empirical and synthetic data. The hydrological characteristics are: depth,
duration, velocity, debris, rate of rise, and timing (Jongman et al., 2012; Ding et al., 2008).
The model can be applied at three levels: Level 1 requires minimal data and the analyses
are quick, however the results are less accurate compared to the other levels. It can be used
however for a preliminary analysis in order to determine the flood prone areas. Using Level 2 more
detailed analyses can be perform ed, offering more reliable damage results. The Level 3 analysis is
the most accurate, however more resources and information are needed (Ding et al., 2008). The
uncertainties of this model are related to the spatial distribution of the asset values, the mo del
considering a uniform distribution (Sterna, 2012).

HIS-SSM (the Standard Method)
The Standard Method was developed in Netherlands for the estimation of flood damages
(economic damage and the loss of human lives). The software developed based on this m ethod is
called HIS -SSM (HIS = High -water Information System; SSM = Damage and Casualty Module)
(Egorova et al., 2008). The damage categories considered in the Standard Method are the direct
tangible and intangible damages as well as the indirect tangible damages (Kok et al., 2004). The
following hazard parameters are taken into account: velocity, rise rate and water depth. The main
inputs for this model reffer to flood characteristics, land use data, maximum damage value and
damage functions. The maximum d amage value of the assets is determined using statistics and
insurance values. The damage functions are based on the water depth, however, for the residential
land use class water velocity is also considered. This model can be applied at meso and macro –
scale, providing good results. The uncertainties of this model are represented by the fact that the

direct and indirect damages are calculated using the same depth -damage functions (Meyer and
Messner, 2005; Sterna, 2012).

Damage scanner (DSM)
The Damage Scanner was developed based on the HIS -SSM module and can also be
applied at meso -scale. Given the limited availability of land use data for large areas, one advantage
of the model is the fact that it doesn’t require detailed data regarding land use, these data being
aggregated. The model is using synthetic data, replacement values and relative functions for the
calculation of direct tangible damages (Jongman et al., 2012; Kellermann et al., 2015). The general
input data are referring to water depth and lan d use data. The limitations of the model are
represented by the fact that it doesn’t have empirical validation and it can’t be use for loss of lives
estimation (te Linde et al., 2011).

Flamish model
The model was developed in Belgium and is used for the e stimation of flood damages at
meso and macro -scale. The model is calculating the direct and indirect tangible damages and the
loos of lives using relative damage functions, synthetic data and replacement values. The input
data refers to water depth and lan d use data, however for the estimation of loss of lives, the water
velocity is also taken into consideration. It is using homogeneous land use areas extracted from
the CLC database (Jongman et al., 2012; Vanneuville et al., 2006).

Multi -Colo ured Manual ( MCM)
The MCM is a method for flood damage calculation that was developed in UK and it can
be applied for different spatial scale analyses. It calculates the direct and indirect economic
damages as well as the loss of lives, having a high level of detail. A large number of damage
categories are considered, but there are no damage functions developed for infrastructure damage
estimation, these damages being calculated based on observed traffic volumes. Regarding the
hazard characteristics, beside the water de pth the model also takes into consideration the duration
of the flood. The MCM estimates damages using absolute depth –damage functions and synthetic
data. By using the absolute approach for the development of depth -damage functions, a frequent

update of fu nctions must be done due to the change in the value of properties over time (Jongman
et al., 2012; Bubeck and Kreibich, 2011; Meyer and Messner, 2005).

Rhine Atlas (RAM)
The RAM model is used for the direct flood damage estimation and can be applied at micro
and meso -scale. It doesn’t have a very detailed land use classification, containing only five land
use classes. The model is using both empirical and synthetic data fo r the development of depth –
damage curves. The required input data are: water depth, assets characteristics, land use data,
warning time, season (Jongman et al., 2012; Bubeck and Kreibich, 2011; de Moel and Aerts,
2010). The model offers low damage values for different land use classes, underestimating the
potential flood damage, as demonstrated in several surveys (de Moel and Aerts, 2010; Jongman et
al., 2012).

JRC (Joint Research Centre)
This model is a pan -European model developed by the European Comm ission’s Joint
Research Centre. The model can be applied at meso and macro -scale and uses both empirical and
synthetic data. Depth -damage functions for nine counties were developed in order to calculate the
damages for five land use classes. Using these fu nctions an average function was developed, for
application in countries with limited data availability. The method is also providing maximum
damage values of each European country. In this way a harmonized approach for large scale
applications was develope d (Jongman et al., 2012; Huizinga, 2007).
Some of the described models (FLEMO, DSM, Flemish model, HAZUS -MH, MCM, RAM
and JRC model) were used by Jongman et al., 2012 to estimate flood damage, taking into
consideration two case studies. The results show th at JRC, MCM and the Hazaus MH models had
the best performance, while the RAM model underestimated the damages and DSM and Flemish
models tend to overestimate the residential damages (Jongman et al., 2012).
In another study by Scorzini and Frank, 2015 a c omparative analysis regarding the
applicability of depth -damage functions was conducted. In this study, each function of JRC was
applied separately. In this case the greatest relative errors were represented by the results of the
JRC-UK and JRC -Danmark fun ctions, while the Standard Method and JRC -Switzerland showed
a good performance.

De Moel and Aerts, 2011 analyzed the flood damage modeling uncertainties, comparing
three models: RAM, the Flemish model and DSM. The results showed that while the Flemish
model and DSM have similar results, the RAM tends to underestimate the results.
The difference seen in the results when using different models may also be attributed to
the different approaches that are used to develop these models. For instance, some facto rs that can
influence the results are: the shapes of the damage functions, the maximum damage values that are
used, the type of damages that are taken into consideration, and the scale of application.

Table 6. Comparative description of the d amage models

FloodRisk
FLEMO
Hazus -MH HIS-SSM
(Standard
Method)
DSM Flemish
model
MCM
RAM
JRC

Country
Italy
Germany
USA
Netherlands
Netherlands
Belgium
UK
Australia
Europe
Damage
function
Relative
Relative
Relative
Relative
Relative
Relative
Absolute
Relative
Relative
Data
development
approach
Synthetic
Empirical Empirical –
synthetic
Empirical –
Synthetic
Synthetic
Synthetic
Synthetic Empirical –
synthetic Empirical –
synthetic
Empirical
validation
No
Yes
Yes
No
No
No
Limited
No
No
Maximum
damage value
Replacement
values
Replacement
values Replacement
values
Depreciated
values Replacement
values
Depreciated
values
Replacement
values
Replacement
values
Depreciated
values
Depreciated
values Replacement
values
Depreciated
values

Scale of
application
All scales Micro -scale
(for each
building)
Meso -scale
(land -use
units)
Micro -scale
Meso -scale
Meso -scale
Macro -scale
Meso -scale
Meso -scale
Macro -scale
All scales
Miscro -scale
Meso -scale

Meso -scale
Macro -scale
Units of
analysis Individual
objects
Surface area
Surface area Individual
objects
Surface area
Individual
objects

Surface area
Surface area Individual
objects
Surface area
Surface area

Input data
Hazard
characteristics
(water depth,
flood
velocity), land
use data,
building type,
warning time,
population
census, Hazard
characteristics
(water depth,
contamination
of flood
water), value
of exposed
assets,
building type
and quality,
private
precaution Hazard
characteristics
(water depth,
flood velocity,
intensity and
timing of the
flood,
duration, rate
of rise,
debris), object
type, land use
data. Hazard
characteristics
(water depth,
flood duration
and velocity),
building type,
age, social
class,
occupants. Hazard
characteristics
(water depth),
land use data Hazard
characteristics
(water depth ),
Land use data Hazard
characteristics
(water depth,
flood
duration),
value of
exposed
assets,
building type,
age, social
class of the
occupants Hazard
characteristics
(water depth),
land use data,
object
characteristic,
season Hazard
characteristics
(water depth)

Type of
damage
calculated Direct
tangible and
intangible

Direct
tangible Direct
tangible
Indirect
tangible Direct tangible
and intangible
Indirect
tangible Direct tangible
Direct
tangible and
intangible
Indirect
tangible
Direct
tangible and
intangible
Indirect
tangible Direct tangible Direct tangible

Uncertainties Does not
consider the
protection
measures in
the analysis Limited
transferability
in other
regions; tend
to
overestimate
the damages. The model
assumes a
uniform
distribution of
buildings. Requires
highly
detailed data
on
individual
buildings,
industries and
infrastructure.
The model
consider just
the depth
water factor.
There is only
one
infrastructure
and one
“industry”
(industry plus
commerce)
class The omission
of direct
damages
regarding
traffic
infrastructure
and cars. The
classification
system is not
very detailed;
Has only one
residential class;
The model
strongly
underestimates
the damages. Aggregated and
generalized data
Advantages The tool is
flexible and
can process
data of
different types
depending
of those that
are actually
available It provides
good results in
large areas for
the economic
sector
High level of
detail of the
model Easy to apply
and provides
comparable
results. It can be used
for the
estimation of
future flood
risk under
climate and
land use
changes. Flexibility of
the model; fast
processing Very
advanced
method for
flood damage
estimation Doesn’t require
detail data,
being easy to
apply in scarce –
data areas Can be applied for
pan-European
flood risk
assessments;
harmonized
approach.
References L. Mancusi et
al., 2015;
Albano et al.,
2017b Thieken et al.,
2008;
Jongman et
al., 2012;
Kreibich et
al., 2011;
Seifert et al.,
2010;
Stena, 2012 Jongman et
al., 2012
Bubeck,
Kreibich,
2011
Sterna, 2012
Ding et al.,
2008 Jongman et al.,
2012, Meyer
and
Messner, 2005 Jongman et al.,
2012
te Linde et al.,
2011 Jongman et
al., 2012
Vann euville et
al., 2006
de Moel, and
Aerts, 2010 Meyer and
Messner, 2005
Jongman et
al., 2012
Bubeck and
Kreibich,
2011 de Moel, and.
Aerts, 2011
Jongman et al.,
2012
te Linde et al.,
2011
Bubeck and
Kreibich, 2011 Jongman et al.,
2012
Huizinga, 2007.

2.6. Flood damage uncertainties
The results of the flood damage assessment are affected by different uncertainties which
can be induced by the input data, the modelling process as well as the spatial and temporal changes
in the information that are used (de Moel and Aerts, 2011). The ana lysis of these uncertainties is
important for a better understanding of the flood risk process, highlighting the parameters that
induce the greatest errors in the results. In this way the data and the methods that are used can be
improved, making the resul ts more reliable and accurate (de Moel et al., 2012).
In the study by de Moel et al., 2015 the uncertainties related to different spatial scales are
described, showing the gaps and the needs for improvement at each scale. The uncertainties related
to mac ro-scale analyses are represented by the fact that the flood protection and defence measures
may not be considered or by the fact that this information has a low accuracy. Furthermore, unitary
input data may not be available for very large areas and theref ore the available databases must be
harmonised in order to be used in the analysis. At meso -scale the uncertainties refer to the
probability of the hazard and the damage functions that are used, while at micro -scale they refer
to the modelling process and lack of detailed data (de Moel et al., 2015).
Another way to approach the estimation of the general uncertainties is to analyse the
uncertainties induced by each component of the flood damage assessment process. For example in
the study by de Moel and Ae rts, 2011 the uncertainties related to the flood depth, land use and
damage models are analysed. This study highlighted the fact that the greatest sources of
uncertainties are represented by the assets value and by the depth -damage functions.
In general the uncertainties related to the hydrological component are caused by the input
data and the choice of the hydraulic model. The use of a complex hydraulic model will need
detailed data for the study area which may not be available and therefore assumptions are made or
general data are used in order to perform the analysis (de Moel and Aerts, 2011). Moreover, if a
simplified method or model is used, less accurate results are obtained due to the fact that not all of
the geomorphologic characteristics of the a rea will be considered, the input data being less detailed.
In the exposure analysis the uncertainties can be attributed to the land use data resolution
and level of detail. In many cases the land use data has a low resolution and a reduced number of
land use classes, the data being aggregated (de Moel and Aerts, 2011). The continuous growth of
economy and human settlements lead to changes in the land use, therefore attention must be paid
when using this input data, the use of outdated information leading to less accurate and reliable

results. Furthermore, as mentioned above, the value of the assets is an important source of
uncertainties. These values are variable in time but also depend on their location, type and
characteristics of the asset, being ther efore hard to assess and maintain an updated database. The
use of general averaged values may not reflect the reality in the field for certain areas.
The depth -damage functions represent another important source of uncertainties, this
problem being appro ached by many studies in the past years (Scorzini and Frank, 2015; Cammerer
et al., 2013; Egorova et al., 2008; de Moel and Aerts, 2011; Jongman et al., 2012). The biggest
issue regarding the depth -damage functions is their transferability in space. These functions have
been developed for a particular region or country, and their use in other areas is limited, leading to
uncertainties and unreliable results (Jongman et al., 2012). Such a study was perform by Scorzini
and Frank, 2015 in order to analyse the uncertainties related to different depth -damage functions.
The results showed that for the meso -scale analysis the relative difference factor for function
uncertainty ranges between 1.1 and 14.8 and for micro -scale analyses this factor ranges between
1.1 a nd 2.2. Another study by de Moel and Aerts, 2011 in which the contributions of different
components to the general uncertainties are analysed, showed that the depth -damage functions
together with the value of elements at risk have a variation factor of 4, while the land use and
hydraulic components have a small effect on the uncertainties. Cammerer et al., 2013 highlighted
the importance of model validation, showing that the use of depth -damage functions without
validation in regions other than those for wh ich they were created can cause the results to differ
by a factor of 18, compared with the results of validated functions which will only differ by a
factor of 2.3. This study also suggests that the selection of functions from similar geographical
regions will improve the accuracy of the results.
Other uncertainties related to the modelling process can be related to the fact that the
existing methods are mainly focusing on direct damages, while the indirect and intangible damages
are not taken into consideration even though they can be accountable for a great part of the total
flood damage (Meyer et al., 2013; Albano et al., 2014; Penning -Rowsell et al., 2010). Also, even
though the water depth is the most significant factor that influences the amoun t of damage that can
occur, other factors such as the water velocity, flood duration and the presence of dangerous
substances in the water can contribute and increase the damages caused by floods. However, few
models take also this factors into considerati on in their analysis, the majority of them using just
the water depth factor (Messner and Meyer, 2005).

Conclusions
Floods are natural hazards with a great negative impact on societies, in the past years
triggering an increased interest among the scientif ic community and stakeholders regarding the
assessment, mitigation and management of this type of hazard. The flood risk management is
focusing on a more comprehensive approach, including in the analysis all the components of the
flood risk: hazard, exposu re, vulnerability . The consequences are represented by the economic
damages and the loss of life. The flood damage assessment methodologies are focussing on the
economic damage estimation, these providing important knowledge on the flood risk. The flood
damage estimation includes information regarding: the hazard (water depth and velocity, flooded
area), the exposure (land use data) and the vulnerability (depth -damage functions and maximum
damage value).
The acknowledgement of the importance of flood damag e assessment increased the need
for more effective and adequate tools and methods. Particularly the use of GIS tools has a cru cial
role in spatial analysis. Furthermore, with the development and availability of new tools and
datasets more attention is give n to the development of standardised and harmonised methods that
can be applied over large areas.
However, the flood damage assessment process is affected by uncertainties induced by the
lack of data, the resolution and accuracy of the input data and the modelling process. The most
important factor in the modelling process that still presents shortcomings are the depth -damage
functions, this factor inducing the greatest uncertainties in the results. Another factor that can
influence the results is the land use data that most of the time are available in aggregated sectors.
Moreover, the large scales analyses are struggling with a lack of consistent data and it’s
low resolution, which induce large uncertainties in the results. Therefore, the current scientif ic
research field is focusing on developing simplistic approaches for macro -scale flood damage
assessment using high -resolution data, improving the accuracy of the results.
The flood damage assessment and subsequently the flood risk assessment process pla y an
important role in the development of flood management strategies and policies, in the
implementation of adequate protection and reduction measures, and in the cost -benefit analysis.

Part II. Flood damage assessment and its uncertainties in data -scarce environments .
Applications and results

In this part of the thesis two applications regarding the estimation of flood damages and
the related uncertainties using free and open source GIS tools and data that are publicly available
are presented . The a im is to provide a framework methodology that can be applied for different
scale analysis and in different area, and particularly in data -scarec environments. Two case study
were selected for the analyses. In the first case study the damages for the 2006 f lood event from
Ilișua basin, Romania were estimated and an uncertainty analysis was conducted. This flood event
represents one of the greates flood disaster in the country, regarding the loss of lives in material
losses in a reduced area. Moreover, in the Ilișua basin the implementation of protection measures
and the land use planning is poor, this aspects increasing the flood damages that may occur.
The second study presents a national flood damage assessment methodology that was
applied for the entire Romanian territory. Romania is one of the most affected country in Europe
regarding flood, 97% of its terri tory being situated in the Danub e River Basin (Zaharia and
Toroimac, 2018). Due to the location in the continental region, many areas of the Romania territory
are exposed to floods (Ozunu et al., 2011). In this context the flood risk mitigation become a major
concern, the national flood damage analyses being necessary for an efficient flood management.

3. Case study 1. Flood damage as sessment and uncertainties analysis for the 2006 flood
event in Ilișua basin in Romania
3.1. Introduction
In the past years the flood risk assessment approaches started to give more attention to
the consequences produced by floods, since they represent an important factor in the flood risk
management. This trend has been supported and encouraged by the impleme ntation of the Flood
Directive that requires the development of comprehensive approaches for flood risk
management. This approaches must include all the flood risk factors, such as hazard, exposure
and vulnerability. Furthermore, the Flood Directive highli ghted the importance of developing
tools that can bring improvements in the flood risk quantification process.
In order to quantify the damages, different damage models were developed across
Europe, most of them using depth -damage functions in order to co rrelate the water depth with
the amount of damages that may occur.
In this chapter a quantitative flood damage assessment approach is implemented. For
this purpose, the FloodRisk plugin, developed in QGIS software was used. In particular, the
depth -damage functions collected and harmonized by the European Joint Research Centre
(JRC), (Huizinga, 2007) have been used for a comparative assessment showing that the
outcomes are strongly influenced by the shape of the depth -damage functions. Furthermore, an
unce rtainty analysis was performed comparing the assessed damage obtained through the use
of JRC damage functions and real, surveyed damage of the proposed case study in North –
Western Romania, i.e. Ilișua Basin, regarding the 2006 flood event.
The proposed methodology has been applied at meso -scale, considering the Ilișua basin
in North -Western Romania. The meso -scale analyses are done at regional or basin scale having
the aim to support flood protection and mitigation strategies, flood risk management as we ll as
to provide hazard and risk maps. This analysis can also be helpful to insurance companies and
decision -makers (de Moel et al., 2015; Albano et al., 2015). The land use data are aggregated
in classes that represent economic sectors. The asset values a re specific, referring to the region
or basin that is analysed and they can be obtained from official statistics (Messner et al., 2007).
Complex models are used that need more detailed data, such as 2D hydraulic models, although
they require more time and effort. Even though the meso -scale analysis offers accurate results
and detailed information, it still presents uncertainties regarding the accuracy of absolute
damage results (de Moel et al., 2015).

3.2. Study area
For this case study the flood that occur red in the Ilișua Basin in June, 2006 was
considered. This catchment, (Fig. 14), is located in North -Western Romania, has a surface of
353 km2 and a mean altitude of 493 m. The main river is Ilișua with a total length of 52 km
which is the right tributary of the Someșul Mare River. The average slope of the Basin has a
value of 15%, varying from 28% up -stream to 4% down -stream. The flood that occurred on
June 21st 2006 was characterized by a peak flow of Qmax = 280 m3/s (calculated in a section
located in th e middle of the basin). This corresponds to an occurrence probability of 0.7 – 0.8%
(125 – 140 years return period). The main cause of this flood was the extreme rainfall that
exceeded 100 l/m2 (Sofronie et al., 2013; Albano et al., 2017a).

Fig. 14. Loca lization of the study area; a. Romania – georgaphical map; b. Ilișua
Catchment

The effects were severe, with 13 deaths and significant structural damages (Sofronie et
al., 2013). For this analysis six main affected villages were considered: Cristeștii Ci ceului,
Ilișua, Căian, Lunca Borlesei, Spermezeu, and Borleasa. The real, surveyed damage data was
presented in the Coverage and Risk Assessment Plan of Bistrița -Năsăud County, (2006) of the
County Committee for Emergency Situations Bistrița -Năsăud. The to tal registered damages for

the analysed area was 1.1 million euro, as following: for buildings/urban, 194.000 euro; for
roads, 687.000 euro; for agriculture, 127.000 euro (Albano et al., 2017a).

3.3. Methodology
In order to quantify the potential damages that may occur in the study area, the approach
is following the next steps: hazard analysis, exposure and vulnerability analysis, and damage
analysis. The main steps of this analysis are presented in Fig. 15 and described in the following
sub-sections.

Fig. 15. Schematic representation of performed flood damage assessment (Albano et al.,
2017a)
In the hazard analysis, the water depth map is developed using the HEC RAS hydraulic
model and a DEM with a resolution of 5 m. The exposure is represented by the assets at risk,
which are classified usually based on economic sectors (i.e. buildings, infrastructure and
agriculture). For this study, the CLC database was used. The susceptibility of the assets at risk
relates the damages of the assets at risk to flood characteristics, using damage functions (Albano

et al., 2017a). For the present study the JRC depth -damage functions (Huizinga, 2007) were
introduced in the FloodRisk model in order to calculate the damages.

3.3.1. Hazard analysis
In the hazard analysis p rocess, the flood extent and water depth are calculated. For this
purpose, the QGIS and HEC RAS software were used. The QGIS applications are ideal for the
processing and preparation of data that are subsequently used for hydraulic simulations (i.e.
HEC RA S modelling). The QGIS contains specific tools that are able to process and analyse
the DEM of an area in order to extract the information that are needed for the development of
the hazard maps. The input data (i.e. the topographic map, the cross -section o f the river, the
land use map etc.) were provided by the Romanian Waters National Administration of Someș –
Tisa Water Branch. By using the 1:5000 topographic map as a background, the contour lines,
the elevation points and the river centreline were digitize d. The elevations of the river thalweg
were calculated by interpolating the data between two subsequent contour lines along the river
centreline. Next, using the "Raster – Interpolation" QGIS plugin, these data were used to create
a DEM through the triangu lar interpolation method (Cellsize X and Y of 5 m). Given the
reduced number of river cross sections available (9), synthetic cross -sections were created
based on the available DEM. The resulting data were exported in HEC RAS using the Q -RAS
tool of QGIS. Giving the fact that the preparation of geometry data in HEC RAS is difficult and
time-consuming, QGIS could be used for this process, Q -RAS being a useful tool that can
export the results in the hydraulic model.
The hydraulic modelling process is essential for the development of the hazard maps.
The hazard maps are important tools, constituting the base of flood control, land -use policies,
emergency situation management, etc. For the hydraulic simulations, the 1D hydraulic model
HEC RAS (Fig. 16) w as used and the flow profile was developed. The initial and boundary
conditions that were used are: Q = 280 m3/s, flow conditions – critical depth upstream and
normal depth downstream, mixed flow regime. The discharge (flow) of 280 m3/s represents the
estimated value for the June 21st 2006 event at a stream gauge in the middle of the basin.

Fig. 16. Representation of data analysis in HEC RAS

Fig. 17. Flood hazard map for Ilișua Basin developed in this study

By using the RAS Mapper tool from HEC RAS, the outputted data were used to construct a
2D grid and a raster file containing gridded values of the maximum water depth for every
flooded