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Floods are the most widespread hazard globally and have a significant impact on local communities in terms of material and loss of life. Flood risk analysis is a complex process that needs to be addressed both physically and socially. The study provides a method for identifying the risk using Goegraphical Informational Systems techniques. Each indicator taken into account was analyzed, standardized and weighted to obtain the final results. The risk values have been divided into five classes: very low, low, medium, high and very high. The case study was represented by the River Gilort (a tributary to Jiu River), in a hilly area (Getic Sub Carpathians), between Bălcești and Bolbocești). Thus, the last two risk classes listed characterize the localities with the highest population density and which are situated near the river proximity. The results can also be used by the competent local authorities to effectively manage flood risk.
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Flood risk identication using multicriteria spatial
analysis. Case study: Gilort River between Bălcești
and Bolbocești.
Alexandru-Andrei Giurea ( )
University of Bucharest: Universitatea din Bucuresti
University of Bucharest: Universitatea din Bucuresti
Robert DOBRE
University of Bucharest: Universitatea din Bucuresti
Alexandru NEDELEA
University of Bucharest: Universitatea din Bucuresti
Ioana-Alexandra MIREA
University of Bucharest: Universitatea din Bucuresti
Research Article
Keywords: oods, risk, multi-criteria analysis, physical vulnerability, social vulnerability, Gilort, Romania
Posted Date: May 20th, 2022
License: This work is licensed under a Creative Commons Attribution 4.0 International License. 
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Flood risk identification using multicriteria spatial analysis. Case study: 1
Gilort River between Bălcești and Bolbocești. 2
Authors: Alexandru GIUREA1, Laura COMĂNESCU1, Robert DOBRE1, Alexandru 3
NEDELEA1, Alexandra MIREA1 4
1 University of Bucharest, Faculty of Geography, Bucharest, Romania 5
Abstract: Floods are the most widespread hazard globally and have a significant impact on local communities in 6
terms of material and loss of life. Flood risk analysis is a complex process that needs to be addressed both physically 7
and socially. The study provides a method for identifying the risk using Goegraphical Informational Systems 8
techniques. Each indicator taken into account was analyzed, standardized and weighted to obtain the final results. 9
The risk values have been divided into five classes: very low, low, medium, high and very high. The case study was 10
represented by the River Gilort (a tributary to Jiu River), in a hilly area (Getic Sub Carpathians), between Bălcești 11
and Bolbocești). Thus, the last two risk classes listed characterize the localities with the highest population density 12
and which are situated near the river proximity. The results can also be used by the competent local authorities to 13
effectively manage flood risk. 14
Keywords: floods, risk, multi-criteria analysis, physical vulnerability, social vulnerability, Gilort, Romania 15
1. Introduction 16
Floods are a widely-spread hydrological hazard at global level, with significant economic and 17
social impact on local communities. It is very important to analyze and map flood risks with a 18
view to sustainable territorial planning and the appropriate development of infrastructure. 19
Such an analysis may have several types of approaches. The first of them uses qualitative analyzes 20
based on expert judgment. Another approach includes quantitative methods based on the numerical 21
relationship between the affected elements and the hazard itself. 22
The present study is a combination of the two methods and can thus be considered a semi-23
quantitative analysis. The results of these analyzes are partly subjective and largely based on expert 24
knowledge (Wang, 2011). However, they are used because they are simple analyzes, capable of 25
integrating large sets of data (Zhan et al. 2003; Zhang et al. 2005; Furdada et al. 2008) and have 26
proven to be effective for regional studies (Zhou et al. 2000; he et al. 2004; Tang and Zhu 2005; 27
Dewan et al. 2007). In Romania, the subject of flood risk has been addressed in various studies 28
and from different perspectives (Armaș et al. 2015, Armaș and Avram 2009, Țîncu et al 2020, 29
Arseni et al 2020). 30
The main purpose of this work is to identify the flood risk in floodplain of the river Gilort, between 31
the localities of Bălcești and Bolbocești, using multi-territorial spatial analysis. This analysis is a 32
useful method to incorporate large spatial data sets and generate an efficient risk estimation. There 33
are many works worldwide on the multi-criteria analysis (Zimmermann and Gutsche 1991; Munda 34
1995; Belton and Stewart 2002). It was first presented by van Herwijnen (1999), and there are a 35
series of works in which the multi-criteria analysis is applied to identify the flood risk (Tkach and 36
Simonovic 1997; Simonovic and Nirupama 2005; Thinh and Vogel 2006; Rijaaamakers et al. 37
2008; Wang 2011, Prawiranegara 2014). 38
2. Study area 39
The study area is located in southwestern Romania (Fig.1), in the Gorj Sub Carpathians unit. The 40
length of Gilort river in this area, measured on the thalweg, is approximately 5 km. 41
Fig 1 - Location of the study area 43
The altitude range is between 227 and 503 m. Lower altitudes characterize depression areas and 45
valley corridors, and the higher altitudes characterze hill areas, with the Gorj Subcarpathians being 46
a highly fragmented relief unit. The slope of the terrain varies between (quasi-horizontal 47
surfaces, located mainly in river floodplain, respectively on interfluves) and 45° (specific to the 48
slopes). Geological composition is exclusively made of sedimentary rocks with different 49
characteristics. There are sands, gravels and loessoid deposits (alluvial deposits), as well as marls, 50
clays, gypsum, salt and quartz conglomerates (Ielenicz et al. 2003). From a pedological point of 51
view, there are two distinct groups of soils: specific floodplain soils (alluvial and alluvial 52
prototypes) and soils specific to the topography and geographical position (especially brown 53
argillaceous soils, brown eu-mesobasic soils and rendzine). 54
The process of gleization on these soils is null or very small (soil map of Romania, 1973). The 55
average flow of the Gilort River in the study area is about 10 m3/s. 56
Recent history of the study area has seen a number of floods with different socio-economic 57
implications: Year 2007 maximum recorded flow rate of 158 mc/s, year 2013 maximum 58
recorded rate 145.4 mc/s, year 2014 maximum recorded rate 174 mc/s, year 2016 maximum 59
recorded rate 118 mc/s, data recorded at the Târgu Cărbunesti Station (downstream of the study 60
area). 61
Regarding the human component, the localities located in the the study area have a total population 62
of less than 3,000 inhabitants (the maximum value recorded in Bengești 2723 inhabitants). The 63
age groups of interest for the analysis are those under 14 and those over 65 years old. The highest 64
population values for these categories are also recorded in the town of Bengești (595 inhabitants 65
over 65 years and 388 inhabitants under 14 years). 66
3. Methodology 67
This work is based on multi-criterial analysis, which includes a number of indicators relevant to 68
the determination of flood risk. The general risk calculation formula is RISK = HAZARD * 69
VULNERABILITY (Wang 2011), so for the accuracy of the results the two elements must be 70
calculated and analyzed individually. For the type of hazard (flooding with a recurrence period of 71
10 years), the following data sets were used: The topographic map, scale 1:25000 (1982) for 72
extracting the level curves, making the digital elevation model (DEM, re-interpolated to 5 m) and 73
slope, Romania's geological map 1:200000 (1968), Romania's soil map 1:200000 (1973), 74
statistical data sets obtained from the National Institute of Statistics, the configuration of the 75
intravillan space from the National Agency of Cadastre and Real Estate Advertising, the flood 76
band of the Gilort River obtained from the Jiu Water Basin Administration. 77
The vulnerability of the area to this type of hazard must be treated in a dual perspective: the 78
physical vulnerability of the area (the way in which the shape of the relief and the natural elements 79
are affected) and the social vulnerability (the degree of damage to the population and socio-80
economic activities). In order to summarize the concept of vulnerability, the following schema has 81
been achieved. 82
Vulnerability concept - synthesis elements (after Wang, with modifications) 83
The contorus extracted from the Romanian topographic map (1:25000), re-interpolated at 5 m., 85
were used to make the digital elevation model. The lower the altitude, the greater the vulnerability 86
of the terrain as the possibility of flooding is higher at lower altitudes. The digital elevation model 87
Digital elevation
Shore erosion
Land use
depth (degree of
The population
Population over
65 years
Population under
14 years
Density of
was used to calculate the slope gradient. The lower the slope (measured in degrees), the lower the 88
flash flood movement speed, which means that the water stagnates for a long period of time, 89
increasing the vulnerability of the area. Geology elements are important in terms of the degree of 90
compaction of the rock, in the sense that the greater the porosity of the rock, the faster water can 91
flow into it, leading to a decrease in the vulnerability of the territory. The riparian vegetation 92
patches present natural barriers to the propagation of the flood wave. Bank erosion, or the Bank 93
Erosion Hazard Index (BEHI), is an indicator that provides information on the bank potential for 94
erosion according to their specific morphometric and geological characteristics. This indicator was 95
considered necessary because, the higher the BEHI, the more the banks are at risk of "breaking" 96
in the event of a flash-flood. This would create the possibility of the flood spreading radially. This 97
indicator has been calculated in the field. Land use, although essentially an indicator created by 98
man-made intervention, is treated as a physical element. This indicator shows maximum 99
vulnerability within the perimeter of built spaces. The degree of gleization has been taken into 100
account in conjunction with the depth at which the groundwater is found. Thus, the higher the 101
degree of gleization the more the groundwater is near the surface, so the saturation level is higher 102
and therefore decreases the soil retention capacity, increasing the vulnerability of the terrain. 103
Social vulnerability refers to the population of the affected area, expressed in terms of population 104
density. Social vulnerability increases in proportion to population density values. Populations over 105
65 years and under 14 years respectively are the most vulnerable social groups due to age, health 106
problems and reduced mobility (over 65 years) or inability to raise awareness of the danger (under 107
14 years). The density of houses can be seen in the light of the potential material damage that can 108
occur as a result of hazard occurence. 109
For input data, being both text and numeric, values were required to be assigned and normalized. 110
The attribution of values was made on the basis of the impact the indicator has on the analysis 111
(cost or benefit) and the normalization of data was done using the formula: 112
𝑁_𝑠𝑐𝑜𝑟𝑒 = 𝑠𝑐𝑜𝑟𝑒 𝑙𝑜𝑤𝑒𝑠𝑡 𝑠𝑐𝑜𝑟𝑒
𝑖𝑔𝑒𝑠𝑡 𝑠𝑐𝑜𝑟𝑒 𝑙𝑜𝑤𝑒𝑠𝑡 𝑠𝑐𝑜𝑟𝑒 113
Given that the indicators do not have an equal impact on vulnerability of any type, weighting was 114
required. This was done by using the ILWIS 3.4 software, with the Spatial Multi-criteria analysis 115
tool where a multicriteria graph was created that includes all the above-mentioned indicators. 116
Weighting was performed by the Pairwise type, where the indicators were compared in pairs, and 117
depending on the result, the program automatically generated weights. These are as follows: 118
Physical vulnerability: DEM 0.33, slopes 0.22, bank erosion 0.05, land use 0.19, geology 119
0.04, riparian vegetation 0.13, soils 0.04; 120
Social vulnerability: population density 0.32, population under 14 years 0.22, population 121
over 65 years 0.38, density of houses 0.08 122
After the two indicators were calculated, they were combined, using equal weights, to achieve the 123
total vulnerability of the area. It was added to the hazard (the Gilort flood band) using equal 124
weights, and the risk of flooding was obtained. 125
The flood band in a vector format represents the spatial distribution of floods with a 10-year 126
recovery period. 127
The resulting flood risk values range from 0 to 1, thus divided into 5 equal classes (very low, low, 128
medium, high and very high), using a range of 0.2. 129
The methodology for identifying the physical, social and total vulnerability as well as the risk of 130
flooding in the study area was based on multiplication of raster datasets and processing the 131
statistical values generated. 132
4. Results and discussions 133
Physical vulnerability 134
For the calculation of the physical vulnerability of the study area in the event of a flood, the 135
parameters mentioned above (DEM, slope, bank erosion, land use, geology, riparian vegetation, 136
soils) have been processed, the input data has been normalized and the entire database has been 137
rasterized (Fig.2) 138
Fig.2 - Database, normalized and rasterized 140
For the DEM, high and very high vulnerabilities have been identified in the floodplain areas (which 141
represents most of the study area and have the highest potential for the flood wave to spread) and 142
along the watercourses, permanent or temporary, where the altitude of the terrain is reduced. Low 143
and very low vulnerability has been identified on the interfluves in the eastern half of the study 144
region where altitudes are above 400m. 145
Looking at the slopes, it is noted that the flat or quasi-horizontal surfaces (slopes below 5°), which 146
characterize the valley corridors, have high and very high vulnerability, while high slopes are less 147
vulnerable to flooding. The land use has medium, high and very high vulnerability in the proximity 148
of the river, for two reasons: the concentration in this area of the localities and the population (the 149
municipalities of Bălcești, Bengesti, Albeni, Mirosloveni, Bolbocesti) and most of the land along 150
the river is exploited from an agricultural point of view, thus, the economic impact of a flood 151
would be high (the above mentioned localities have a agricultural profile, so that land degradation 152
or set-aside would affect the local economy). In the geological composition, sands and gravel 153
(alluvial deposits) predominate (Ielenicz et al. 2003), with distribution along minor channel and 154
floodplain. They offer the area a low vulnerability, as they are non-cohesive rocks , which allow 155
water to be quickly infiltrated into the groundwater. The intensity of the gleization process is very 156
low in the study area, so the vulnerability of the region from this point of view is also low. 157
The physical vulnerability of the area has been achieved through the multiplication of rasters. The 158
result is shown in Fig.3. 159
Fig.3 - Physical vulnerability of the study area 176
Thus, it turned out that the highest physical vulnerability is present in the area of the floodplain, 178
where settlements exist, or where agricultural land predominates, which are the main factors 179
affected in the event of floods. Vulnerability decreases in the eastern part of the study area. It 180
should be noted that the physical vulnerability of the area is lower in the 181
minor riverbed and in its immediate proximity, as there is a relatively uniform distribution of the 182
riparian vegetation patches along the river course. By extracting the area data, a percentage 183
distribution of vulnerability has been achieved (divided into 5 classes “very low” class did not 184
register values) (Table 1, Fig.4). 185
Table 1 weight of ranges with different physical vulnerability classes 186
Very high
Fig.4 - Distribution of physical vulnerability by class 189
Social vulnerability 191
The parameters were used to calculate social vulnerability in the event of a flood (population 192
density, population under 14, population over 65 years, density of houses) have been processed, 193
the input data has been normalized and the entire database has been rasterized (Fig.5). The 194
statistical data used in the analysis are given in Table 2. 195
Low Medium High Very High
Fig. 5 - Database, normalized and rasterized 197
For all the parameters considered, the highest values are recorded in the Bengesti town, which is 198
also the most important settlement in the study area. It is followed by Albeni town, as well as 199
values of population and importance in the area. 200
Table 2 - Population statistics (source: INS, 2020) 201
under 14 yo
above 65 yo
No. of houses
House density
The social vulnerability of the region is shown in the Fig. 6 thus, the highest values of social 203
vulnerability are recorded in the two mentioned localities. Average vulnerability is recorded in the 204
village of Bălcești (located in the north of the study region). The area not including permanent 205
settlements has been considered to have zero social vulnerability. 206
Average values of social vulnerability have been recorded in the southern part of the village of 207
Mirosloveni and the lowest values have been recorded in 208
the village of Bolbocesti, as a result of the distribution of 209
demographic parameters considered relevant for this 210
analysis. 211
The two types of vulnerability were combined (using equal 212
weights) to achieve the overall vulnerability of the study 213
area (Fig.8). It is noted that the highest values of 214
vulnerability are recorded in the proximity of human 215
settlements, where both types of calculated vulnerabilities 216
are present. The floodplain area have medium 217
vulnerability, while the hill areas with higher altitudes and 218
higher pitch slopes are less vulnerable. The statistical 219
distribution of area distribution data according to the 220
vulnerability of the area is shown in Fig.7. More than 90% 221
of the study area has low and very low vulnerability and 222
the highest values characterize the built spaces. 223
Fig 6 - The social vulnerability of the region
Fig 7 Percentage distribution of total vulnerability 232
The flood risk distribution (Fig 9) has been based on the risk calculation formula, with the two 234
components: hazard and vulnerability. Thus, the hazard was considered the flood band of the Gilort 235
River. Raster data sets representing hazard and vulnerability were aggregated using equal weights 236
and the result was the spatial distribution of flood risk. 5 risk classes (very low to very high) have 237
been identified in the study area and their spatial and percentage distribution is as follows: very 238
low risk: 63.53 km2 (88.90%), low risk 3.27 km2 (4.57%), medium risk 1.23 km2 (1.72%), high 239
risk 3.37 km2 (4.71%) and very high risk 0.07 km2 (0.1%). It is noted that the highest flood risk 240
figures are recorded where the flood band intersects the area of development of the localities (in 241
the case of the Bengești and Albeni), and all conditions for material and human damage are met. 242
The remaining locations in the study area identify medium, low and very low values, with a number 243
of upstream exceptions (Bălcești) where high risk values are present. According to fig 10, over 244
75% of the existing settlements area has a low and very low risk, 20% medium risk and only 3.29% 245
high and very high risk. High risk values are also identified in the Gilort River floodplain, 246
throughout the flood band. 247
3.39 1.34
Very low Low Medium High Very High
Fig 9 - Risk of flooding in the study area
Fig. 8 Total vulnerability in the study area
Fig 10 - The percentage distribution of risk data within the localities 251
5. Conclusions 253
Flooding is a hydrological hazard with a significant impact on population, constructions and 254
economic activities. Identifying the hazard is not sufficient and the economic impact of the hazard 255
must be taken into account. Such analysis may be carried out using GIS techniques, taking into 256
account also elements of physical and social vulnerability. For the study area, the risk of flooding 257
is high where the flood band intersects the built area, the most affected localities being Bengesti 258
and Albeni. 259
The limitations of the study relate in particular to the accuracy of the data available at the time. 260
Such analysis may be used as a decision-making tool for risk management and may be extended 261
to all types of natural hazard. 262
The study can continue with the development of effective flood risk management measures based 263
on the results achieved. 264
1.93 1.36
Very low Low Medium High Very High
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7. Statements & Declarations 320
Funding. 321
The authors declare that no funds, grants, or other support were received during the preparation 322
of this manuscript. 323
Competing Interests. 324
The authors have no relevant financial or non-financial interests to disclose. 325
Author Contributions. 326
All authors contributed to the study conception and design. Material preparation, data collection 327
and analysis were performed by Alexandru-Andrei GIUREA, Laura COMĂNESCU, Robert 328
DOBRE, Alexandru NEDELEA and Ioana-Alexandra MIREA. The first draft of the manuscript 329
was written by Alexandru-Andrei GIUREA and Ioana-Alexandra MIREA and all authors 330
commented on previous versions of the manuscript. All authors read and approved the final 331
manuscript. 332
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For exposed and vulnerable communities, the perception of natural risk is an essential link in the analysis of man–environment coping relationship and also an important parameter in the quantification of complex vulnerability as a central predictive variable in the risk equation. The topic of flood risk in related perception is of considerable interest, as some recently published papers have proven (Messner and Meyer 2005, 2006; Raaijmakers et al. 2008). The aim of the current study is to reveal the conscious and unconscious attitudes towards the flood risk for the inhabitants of the Danube Delta/Romania. These attitudes, defined by different degrees of psychological vulnerability, represent the background for a series of psycho-behavioural patterns that generate certain adjustment mechanisms and strategies. Application of a specially designed questionnaire and the statistical analysis of the results revealed two psychological factors as essential in establishing the psychosocial vulnerability degree of the interviewed subjects: (i) an internal control factor and (ii) an external control factor. The persons characterized by inner control have a significantly reduced general anxiety level in comparison to individuals with the control factor placed externally. As confidence diminishes, it increases the tendency of the individual to rely on the external factors for support and security. The lack of resources (indicating lower resilience) and mistrust in the support given emphasizes non-adaptive behaviours.
Although many studies have made assessment of flood/waterlogging disaster risk now, they have not developed an integrated index. This study presents a methodology for risk assessment and zoning of flood/waterlogging in middle and lower reaches of Liaohe River of Northeast China based on geographical information systems (GIS) and the technology of natural disaster risk assessment from the viewpoints of climatology, geography, disaster science, environmental science and so on.
This paper explores the methodology for compiling the torrent hazard and risk zonation map by means of GIS technique for the Red River basin in Yunnan province of China, where is vulnerable to torrent floods. Based on a 1:250,000 scale digital map, six factors including the number slope angle, rainstorm days, buffer of river channels, maximum runoff discharge of standard area, debris flow distribution density and flood disaster history were analyzed and superimposed to create the torrent hazard risk evaluation map. Population density, farmland percentage, house property, and GDP as indexes accounting for torrent hazards were analyzed in terms of vulnerability mapping. Torrent risk zonation by means of GIS automatically was overlaid on the two data layers of hazard and vulnerability. Then each grid unit with a resolution of 500 m × 500 m was divided into four categories of the risk: extremely high, high, medium and low. Finally the same level risk was combined into a confirmed zone, which represents torrent risk of the study area. The risk evaluation result in the upper Red River basin shows that the extremely high risk area takes up 17.9% of the total inundated area of 13 150 km2, the high risk area is 45.9% of 33 783 km2, the medium is 25.2% of 18 563 km2 and the low risk is 11.0% of 8115 km2.
Flood risk can, in general terms, be defined as probability time consequence. It consists of flood hazard analysis, vulnerability analysis and damage evaluation. A variety of methods have been developed and applied. Among them, Quantitative Risk Analysis (QRA) is a method of quantifying risk through systematic examination of the factors contributing to the flood hazard and affecting the severity of flood consequence, their interaction and relative contribution to the occurrence of the flood. The QRA technique is well established in many fields such as chemical engineering and hazardous materials processing. The application of QRA to flood risk is relatively new and still under development. While the basic risk assessment concepts and tools can be used, the methodologies need to be adapted. Category - based model for flood risk analysis is used to assign a value to each driven - factor such as triggering factor of rainfall, dam break, ground surface conditions of topography, land cover, and others. The keys to the model are to synthesis the spatial - referenced data and create the risk zone. The diffusion of Geographical Information Systems (GIS) technology opens up a range of new possibilities for hazard mitigation and disaster management. Microzonation is greatly facilitated by the kind of automation that GIS offers, especially as it involves comparison, indices and overlays in much the same way that GIS does. In this article, ArcInfo GIS has been chosen to quantitatively represent the influencing factors, spatialize the data into the uniform grid system, and transfer all the data item into the effect degrees on the probability of flooding. At last, with the support of Arc/Info GRID model, a categorical model for flood risk zonation has been put forward. The approach has been applied to the Liaohe river basin, the north-eastern of China, flood disaster risk zonation. The results show that the flood risk of the lower reaches of the Liaohe river is more serious than other places, which accord with the fact. The case study showes that the GIS - based category model is effective in flood risk zonation.
Hazard Maps for the Lower Course of the Siret River
Hazard Maps for the Lower Course of the Siret River, Romania. Sustainability, 272