Flood Susceptibility Mapping in Erythropotamos River Basin with the Aid of Remote Sensing And

Original Article 149

Flood susceptibility mapping in Erythropotamos river basin with the aid of Remote Sensing and GIS Christos Domakinis1,*, Antonios Mouratidis1, Kostas Voudouris1, Theodore Astaras1, Maria Chara Karypidou2

1 Aristotle University of Thessaloniki, Department of Environmental and Physical Geography, Greece 2 Aristotle University of Thessaloniki, Department of Meteorology and Climatology, Greece * Corresponding author: [email protected]

ABSTRACT Erythropotamos is a tributary of river Evros and during the last decade its drainage basin flooded many times, causing extensive damage on properties. In order to assess flood susceptibility in the aforementioned study area, the inundated areas of floods that occurred in 2010, 2017 and 2018 were initially delineated with the aid of SAR (Synthetic Aperture Radar) imagery by applying an established flood delineation methodology. Subsequently, flood susceptibility mapping was conducted for the study area by applying the Analytical Hierarchy Process (AHP). Topographical, hydrological and meteorological factors were used and each one of them was classified into three (3) flood susceptibility categories (low, medium and high). The determination of the importance for each factor over the others, which is the main objective of this research, was decided according to the proportion of the 2010 inundated area, captured by ENVISAT/ASAR imagery, which intersected with each factor’s high susceptibility class. Finally, the resulting flood susceptibility map was validated according with the inundated areas of the 2017 and 2018 flood events, captured by SENTINEL – 1 A/B imagery, indicating that approximately 60% of both of these areas intersected with the map’s high susceptibility zone.

KEYWORDS GIS; Susceptibility mapping; Analytical Hierarchy Process (AHP); floods; remote sensing

Received: 15 May 2019 Accepted: 7 May 2020 Published online: 31 July 2020

Domakinis, C., Mouratidis, A., Voudouris, K., Astaras, T., Karypidou, M. C. (2020): Flood susceptibility mapping in Erythropotamos river basin with the aid of Remote Sensing and GIS. AUC Geographica 55(2), 149–164 https://doi.org/10.14712/23361980.2020.11 © 2020 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0). 150 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

1. Introduction to become more effective. Inundation and susceptibility mapping are among the main procedures that Floods can potentially cause fatalities, displacement of people and damage to the environment, to severely its goals. compromise economic development and to under- floodRegarding hazard assessmentinundation follows,mapping, in SARorder systems to achieve are mine the economic activities of every community that particularly suitable, thanks to the synoptic view, the suffers the effects of these destructive environmental capability to operate in almost all-weather conditions hazards (Patrikaki et al. 2018; Zhong et al. 2018; Birk- and during both day-time and night-time, as well as holz et al. 2014; Mouratidis and Sarti 2013; Yésou et the sensitivity of the microwave radiation to water al. 2013; Astaras et al. 2011). During the last decade, (Pierdicca et al. 2013). Furthermore, various methods such phenomena have also plagued Greece, with their majority occurring in the eastern part of the region of water from SAR data. Change detection highlights the Thrace (Kazakis et al. 2015; Nikolaidou et al. 2015; temporalhave been changesused within in theland literature cover by to comparing delineate flood the Mouratidis 2011; Mouratidis et al. 2011). Most such cases are attributed to the river Evros, which is the Psomiadis 2016; Schlaffer et al. 2015). The differ- natural borderline between Greece and Turkey, and, enceflood between scene to thea previous images candry be image combined (Li et withal. 2018; oth- along with its tributaries, has burst its banks on sev- er image segmentation techniques to identify areas eral occasions during the aforementioned time peri- producing an unusually low backscatter response, od. Erythropotamos is one of Evros’ tributaries and when compared to the single image methodologies been observed within its river basin, there is a lack (Matgenimproving et al. the 2011). reliability of the flood delineation although, in many occasions, flood phenomena have - catchment. ing to the current literature, the contemporary of floodDuring hazard the last assessment few decades, studies advances referring in remote to this trendConcerning involves flood mostly susceptibility the creation mapping, of ensemble accord models, which are based on the combination of sensing and GIS have helped flood hazard assessment

Fig. 1 a) Location of the study area (drainage basin of Erythropotamos), b) Geological formations within the drainage basin of Erythropotamos river (CoG 1989, I.G.M.E. 2002), c) Spatial distribution of elevation within the catchment of Erythropotamos river (E.E.A. 2017) and d) Spatial distribution of slope angle values within the drainage basin of Erythropotamos river. Flood susceptibility mapping in Erythropotamos river basin 151 different data-driven (machine learning), statistical or Tab. 1 Distribution of elevation into categories according to Dikau’s multi-criteria methods. This approach aims in achiev- classification (Dikau 1989). ing higher accuracy of the delineated susceptibility Elevation Description Area (km2) Percent (%) <150 Lowland 429.6 26.543 mapping methodologies that employ a single meth- odzones, or model in comparison (Costache etwith al. 2020;the flood Kanani-Sadat susceptibility et al. 150–600 Hilly 969.1 59.876 2019; Wang et al. 2019; Khosravi et al. 2016). How- 600–900 Semi-mountainous 197.3 12.190 ever, there is a plethora of methodologies that can be 900> Mountainous 22.5 1.390 - tistical and data-driven approaches (Ettinger et al. 2016;used in Nandi flood et susceptibility al. 2016; Tehrany assessment, et al. 2015; such Tehrany as sta Tab. 2 Slope angle categorization within the study area according et al. 2014; Pulvirenti et al. 2011). Among them, the to Demek’s classification (Demek 1972). analytic hierarchy process (AHP) (Kazakis et al. 2015; Slope Angle Description Area (km2) Percent (%) Stefanidis and Stathis 2013) is considered as the most (°) widely used and, because of its simplicity, continues 0–2 Plain to slightly sloping 147.5 9.11 Seejata et al. 2018; Tang et al. 2018). Additionally, this 2–5 Gently inclined 390.3 24.11 methodologyto be popular haseven proved in recent many works times (Lyu that et it al.can 2018; han- 5–15 Strongly inclined 864.9 53.44 dle sparse or poor quality data and that it can operate 15–35 Steep 215.7 13.33

Wang et al. 2011). >35 Precipitous 0.1 0.01 efficientlyThe main in regionalaim of this studies research (Chen iset toal. 2015,introduce 2013; a methodology that deals with the subjectivity that involves the determination of the hierarchy of factors 2. Location of the study area

To this end, the proposed methodology employed the Erythropotamos is a tributary of Evros River, which resultsin flood of susceptibility SAR-based inundation mapping with mapping the use that of delin AHP.- is the longest river that runs solely in the interior

- of 1,618.5 km2. The largest part of its river basin eatedtors was the determined flood extent according of the 2010 to flood.the proportion Specifically, of belongsof the Balkans, to Greece and and its particularly catchment tocovers the geographic an extent the aforementionedhierarchy between inundated the flood areas susceptibility that intersected fac region of Thrace in Northern Greece, while the rest with each factor’s high susceptibility class. of its drainage basin belongs to Bulgaria (Figure 1a).

Fig. 2 Spatial distribution of land cover in the catchment of Erythropotamos river (Copernicus 2017). 152 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

Fig. 3 General flowchart of the applied methodology.

Regarding administrative distribution within nation- - al borders, the Greek part of Erythropotamos’ river nicus 2020) data layer, is chronologically closer to the basin belongs to the Prefectures of Evros and Rhodopi. which, instead of the Corine Land Cover 2018 (Coper- The drainage basin of Erythropotamos River potamos River is dominated by forests and semi nat- belongs to both the Circum – Rhodope geotectonic uralgauged areas 2010 (Figure flood 2).event, Along the with catchment agricultural of Erythro areas, zone and the Rhodope massif. The geological for- mation that covers the largest part of the study area consists of orthogneissess and augen gneisses (Fig- these two land cover categories occupy approximately ure 1b). the 98% of the total extent of the study area (Table 3). Elevation in the drainage basin of Erythropotamos 3. Materials and methodology river ranges from 16 m to about 1,258 m above mean The materials that were used and the methodology area can be described as hilly according to Dikau’s that was followed in order to achieve the aims of this sea level (M.S.L.), and the largest part of the study Additionally, the spatial distribution of slope angle inundation mapping with the use of SAR images, while valuesclassification within (Dikauthe study 1989) area, (Figure indicates 1c and that Table most 1). of study can be divided into two parts. The first involves its terrain belongs to the strongly inclined category isthe given second in Figure part is 3. concerned with flood susceptibility angles (Demek 1972) (Figure 1d and Table 2). mapping with the use of AHP. The general flowchart (5°–15°)Finally, according based on toinformation Demek’s classification provided by theof slope data 3.1 Inundation mapping with SAR imagery

Eleven ENVISAT/ASAR and twenty seven SENTI- Tab.layer 3 Distributionof Corine Landof Land Cover within 2012 the (Copernicus river basin of 2017), - Erythropotamos according to Corine Land Cover 2012 (Copernicus 2017). NEL – 1 A/B images of VV (Vertical transmit – Verti cal receive) polarization were used to map the flood Land Cover Area (km2) Percent (%) - isticsextents appear of the on February Table 4. 2010, April 2017 and March Artificial surfaces 18.4 1.137 2018The flood aforementioned events. Their SARdetailed images product were character pre-pro- Agricultural areas 586.2 36.219 cessed with the aid of ESA’s SAR satellite image anal- Forest and semi natural areas 1007.9 62.274 ysis software SNAP (Sentinel Application Platform). Wetlands 0.1 0.006 - Water bodies 5.9 0.365 Initially, they were calibrated to σ° backscatter coef ficient values and despeckled using a 3 × 3 Gamma map filter. Regarding the co-registration step, the SAR Flood susceptibility mapping in Erythropotamos river basin 153

Tab. 4 Product information of ENVISAT/ASAR and SENTINEL – 1 A/B imagery.

Satellite ENVISAT SENTINEL – 1 andcategorizes non-water as flooded land cover only classes. areas that On arethe temporarilyother hand, Flood image: Flood images: 18/4/2017 NDFVIcovered was by water,used in excluding order to permanent detect shallow water water bodies in 16/2/2010 (1) & 26/3/2018 (2) low vegetation. Dates Reference images: Reference images: According to Cian et al. (2018) NDFI values that 5/8/2008 to 8/10/2016 to are greater than 0.7 and NDFVI values that are great- 27/4/2010 (10) 28/9/2018 (25) er than 0.75 can be used to delineate inundated areas Spatial 11.1 m × 11.1 m 8.8 m × 8.8 m in open land and in low vegetation respectively. How- Resolution - Pass Ascending Descending cessing according to the following criteria: Mode N/A IW ever, the resulting flooded areas require further pro Type N/A GRD 1. becauseFlooded theyareas can with be extent considered smaller as thanspurious the size (Cian of Level 1 1 et10 al. pixels 2018). in NDFI and NDFVI images were excluded Polarization VV VV Relative Orbit 14 109 < 0.015), which correspond to permanent water 2. Pixels with σο(mean) values less valuesthan 0.015 greater (σο(mean) than - images were co-registered with the use of EU-DEM, sistentlybodies, and decrease pixels with their σ ο(min)backscatter during the which is the Digital Surface Model (DSM) of European 0.03 (σο(min) > 0.03) that represent pixels that con Environment Agency (EEA) member and cooperating - - flood, indicating that something happened, but not- nated by the sensors. It is a hybrid product based on enoughdation maps to reach (Cian a σ etο(min) al. 2018). value typical of water pix countriesSRTM and that ASTER represents GDEM datathe first fused surface by a asweighted illumi Moreover,els, have to the be filteredadverse out weather from theconditions resulting during inun averaging approach (EEA 2017). Its horizontal spa- optical imagery and aerial vehicles from capturing the while its absolute and relative vertical accuracy are the 2010, 2017 and 2018 floods prevented satellite- 3.6tial mresolution and 5.3 m, is respectively1 arc second (Mouratidis (approximately et al. 2019).25 m), tion of their SAR-based inundation mapping results The Change Detection And Thresholding (CDAT) wasextents not offeasible. the corresponding floods and thus valida methodology by Cian et al. (2018), based on the work - 3.2 Flood susceptibility mapping with the use of AHP and with the aid of GIS and satellite imagery events.of Long This et al. procedure (2014), was involved applied the in calculation order to delinof the eate the inundated areas of the aforementioned flood The compilation of the susceptibility map can be achieved by conducting multi-criteria analysis (MCA), (NDFVI),Normalized which Difference are based Flood on the Index multi-temporal (NDFI) and stathe- which involves the selection of criteria whose weights tisticalNormalized analysis Difference of two setsFlood of inimages, low Vegetation one containing Index will be determined via the AHP. In this process, the only the images before or after the event, and anoth- selection of criteria is very important. A plethora of er one containing images both before or after the event and during the occurrence of the event. NDFI criteria has been used in previous research on flood susceptibility mapping (Hong et al. 2018; Lyu et al.

Tab. 5 Details regarding the data from which each factor was compiled.

Original Map scale or spatial Primary input data Source Derived map format resolution EU-DEM Raster 25 m × 25 m EEA Elevation EU-DEM Raster 25 m × 25 m EEA Slope Angle Corine Land Cover 2012 Vector Better than 100 m Copernicus Land Cover EU-DEM Raster 25 m × 25 m EEA Drainage Density EU-DEM Raster 25 m × 25 m EEA TWI 1) Geologic Map of SE 1) 1:200,000 Institute of Geology and 1) 1:200,000 Rhodope – Thrace Raster Mineral Exploration (IGME) of Greece Geology 2) 1:50,000 2) Geologic map of Bulgaria 2) 1:50,000 Committee of Geology (CoG) WorldClim Raster 825 m × 825 m Fick et al. 2017 Rainfall Distance from EU-DEM Raster 25 m × 25 m EEA Streams 154 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

2018; Seejata et al. 2018; Tang et al. 2018; Xiao et 3.2.2 Hydrological indicators al. 2018; Zhao et al. 2018; Kazakis et al. 2015). Their main characteristics being that they should be con- 3.2.2.1 Topographic Wetness Index (TWI) - tion mechanism, they can be measured or quickly cal- the indices of soil erosion, since it is used to relate the nectedculated with for the the whole physical study process area andof the that flood they genera ought effectsThis index of runoff according with togeomorphometry. Miliaresis (2011) It belongs is used toin to have simple interpretability (Papaioannou et al. 2015). Beven and Kirkby (1979) equation: According to Xiao et al. (2018) and Zhao et al. order to assess soil moisture and it is defined by the = ln (2) tan( ) availability, three types of indicators were considered in(2018), the present the factor’s research, effect i.e.on thetopographical, flood hazard hydrolog and data- � � area draining through a certain point per unit con- indicators (Xiao et al. 2018) provide information of In equation (2), α stands for the local upslope ical and meteorological. Specifically, topographical High values of TWI indicate areas more susceptible to the impact of the terrain. In the current study they tour length and tan(β) is the local slope in radians. consistthe flow of or elevation, stagnating slope of the angle water and on drainage the ground densi due- ty. On the other hand, hydrological indicators (Xiao et to3.2.2.2 flooding. Distance from streams - The drainage network of the drainage basin of Eryth- tration and accumulation of the water and the river ropotamos river has been produced by the EU-DEM al. 2018) provide information of the intercept, infil data layer with the use of raster-processing routines (TWI), distance from streams, land cover and geolo- (Voudouris et al. 2007). Furthermore, the distance gy.network. Finally, They the meteorologicalconsist of Topographic indicators Wetness (Zhao Index et al. from the streams of the drainage network data lay- 2018) provide information on the spatial distribution er was compiled through the use of geoprocessing of precipitation in the study area and were represent- - ed by the annual total rainfall. ceptibility mapping, because areas that are closer to The input data, their original format, the source buffer routines. This factor is crucial to flood sus from which the input data originated and their map event. scale or spatial resolution for each factor are synopti- streams are more likely to be inundated during a flood cally presented in the following table (Table 5). 3.2.2.3 Geology The synoptic geologic map of SE Rhodope – Thrace 3.2.1 Topographical indicators - tion (IGME) of Greece, at a scale of 1:200,000 (I.G.M.E. 3.2.1.1 Elevation 2002),from the was Institute used in of order Geology to produce and Mineral the part Explora of the Elevation is considered as an important factor for data layer that belongs to Greece. Accordingly, the geologic map of Bulgaria from the Department of drainage basin areas with low elevation values. The Geophysical Prospecting and Geological Mapping datafloods, layer because of elevation flood-prone was derived areas from tend EU-DEM. to occupy of the Committee of Geology (CoG 1989), at a scale of 1:50,000, was used in order to produce the part of the 3.2.1.2 Slope Angle data layer that belongs to Bulgaria. The slope angle data layer was produced by the - EU-DEM data layer with the aid of raster-processing - routines. Slope angle is also an important factor when calGeology formations is considered favor surface a significant runoff. factorOn the in other determin hand, ing flood-prone areas, because impermeable geologi it comes to discerning flood-prone areas, because 3.2.2.4permeable Land geological Cover formations favor infiltration. surfaceareas in terrain. a river basin that occupy flat terrain surfaces tend to flood more easily than areas with more steep 2017) was used to determine the land cover classes 3.2.1.3 Drainage Density withinThe data the layer limits of Corine of the Landstudy Cover area. 2012 It is worth(Copernicus men- length per unit area, which can be calculated as shown inThe the drainage following density equation is defined(1) (Zhou as et the al. total2014): stream ittioning depicts that more Corine closely Land the Cover surface 2012 relief’s was chosen land cover over Corine Land Cover 2018 (Copernicus 2020), because 1 = (1) by the station on Didymoteicho’s bridge. Addition- ally,conditions regarding during the the catchment 2010 flood, of which Erythropotamos was gauged DD stands∑ for drainage density, while S represents River, the differences between the aforementioned S the area of the grid and Li represents the length of river i within the grid. Areas with high drainage den- 2 - lateddata layersland cover are insignificant classes with sinceManning’s they covern roughness a total extent of approximately 2 km . Vieux (2004) corre sity indicate high flood susceptibility. Flood susceptibility mapping in Erythropotamos river basin 155

annual precipitation sums were considered as more formula: coefficient1 (Table2 1 6), which participates in Manning’s = 3 2 (3) prone3.3 Analytical to flooding. Hierarchy Process (AHP)

In equation (3), V 3/s), 3.3.1 Determination of flood susceptibility classes n A is the “wetted” for each factor cross-sectional area stands (m2), rfor stands discharge/flow for the hydraulic (m In order to apply the AHP methodology, which was radius is Manning’s and S is roughnessthe slope of coefficient, hydraulic grade or the lin- introduced by Saaty (1980), the data layer of each ear head loss (m/m). Moreover, Manning’s n rough- which means that low Manning’s n values correspond factor was classified into three classes according to toness high coefficient discharge is inverselyvalues. In proportional that way, areas to discharge, suscepti- assignedhow prone a ratingeach one of three of these (3), classes while those was to that flooding. are of n values. mediumClasses that susceptibility are highly were susceptible assigned to aflooding rating of were two (2) and those of low susceptibility were assigned a 3.2.3ble to Meteorological floods can be related indicators to low Manning’s rating of one (1).

3.2.3.1 Rainfall 3.3.2 Determination of the hierarchy between The annual total rainfall layer was derived using raw the flood susceptibility factors with the aid data that were retrieved from the WorldClim data- of the results of SAR-based inundation mapping base (Fick et al. 2017). The raw data involve monthly Having to deal with the subjectivity that often accom- precipitation totals, which refer to the climatological panies this step of AHP, the importance of each factor - was determined according to the proportion of the byperiod 824 1970–2000m) grid (Fick and et are al. 2017).available The as totalan approxi annual of 6.84 km2 for both open water and shallow water precipitationmately 30 seconds layer by was 30 constructedseconds (approximately by summing 824 all ininundated low vegetation) areas of that the intersected2010 flood withevent each (total factor’s area 12 monthly precipitation totals with the aid of map high susceptibility class (Figure 4 and Table 7). This algebra. Subsequently, the aforementioned rainfall concept was based on the idea that a SAR image that

water is concentrated. Moreover, the factors or indi- oflayer 25 wasm, was converted derived. to The a point downscaling shapefile, of from the whichorigi- is taken during a flood indicates the areas where flood- the final rainfall data layer, with a spatial resolution to the layer that was eventually used in the current cators of flood susceptibility all coexist in these are nal WorldClim layer (824 × 824 m grid resolution) as and it is known how each factor influences floods. by employing the universal kriging spatial interpola- For example it is known that, regarding e.g. slope analysis (25 × 25 m grid resolution), was performed whereangle, flatthe areasmost tendfavourable to flood conditions more easily. for Thus,most facthe- were the total annual precipitation values obtained at areas where flood water is concentrating are those tion method (Li et al. 2014). The interpolated values- for most factors or indicators intersect. Subsequent- iary variables used were elevation, slope, aspect and ly,tors the coexist, more i.e.a high where susceptibility the high susceptibility class of a factor classes or distanceeach point from of the the original sea. The WorldClim elevation grid.data Theused auxil was indicator is encountered in inundated areas, the more

Monitoring Services data portal (EEA 2017) and is susceptibility. providedthe EU-DEM on a obtained 25 by 25 mfrom grid. the Slope COPERNICUS and aspect Landwere influential this factor or indicator is in terms of flood derived from the EU-DEM using the available raster-processing routines. Distance from the sea was - Tab. 7 Proportion of the total inundated area of the 2010 flood tines, at a spatial resolution of 25 m. Areas with high event that intersects with each factor’s high susceptibility class. also computed, by applying proximity analysis rou Extent of inundated Percent Factor area (km2) ratio (%) Tab. 6 Manning’s n roughness coefficients for certain land cover Land Cover 0.15 2.19 types according to Vieux (2004). TWI 0.12 1.75 Land Cover Manning’s n coefficient Geology 0.39 5.70 Artificial surfaces 0.015 Distance from streams 3.44 50.29 Agricultural areas 0.035 Rainfall 0.01 0.15 Forest and semi natural areas 0.100 Slope Angle 5.59 81.73 Wetlands 0.700 Drainage Density 0.99 14.47 Water bodies 0.030 Elevation 6.66 97.37 156 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

Fig. 4 The data layer of the inundated areas of the 2010 flood event has been superimposed upon the flood susceptibility classes of the factors’ data layers: a) Elevation, b) Slope angle, c) TWI, d) Distance from streams, e) Geology, f) Land cover, g) Rainfall and h) Drainage density. Flood susceptibility mapping in Erythropotamos river basin 157

Tab. 8 Pairwise comparison of the factors that affect flood susceptibility.

Distance Slope Drainage Land Elevation from Geology TWI Rainfall angle Density Cover streams Elevation 1 2 3 4 5 6 7 8 Slope angle 1/2 1 2 3 4 5 6 7 Distance from streams 1/3 1/2 1 2 3 4 5 6 Drainage Density 1/4 1/3 1/2 1 2 3 4 5 Geology 1/5 1/4 1/3 1/2 1 2 3 4 Land Cover 1/6 1/5 1/4 1/3 1/2 1 2 3 TWI 1/7 1/6 1/5 1/4 1/3 1/2 1 2 Rainfall 1/8 1/7 1/6 1/5 1/4 1/3 1/2 1 Total 2.718 4.593 7.5 11.28 16.08 21.83 28.5 36

Tab. 9 Calculation of the factor weights with the use of the arithmetic mean method.

Distance Slope Drainage Land Elevation from Geology TWI Rainfall Mean angle Density Cover streams Elevation 0.368 0.435 0.403 0.355 0.311 0.275 0.246 0.222 0.327 (32.7%) Slope angle 0.184 0.218 0.268 0.266 0.249 0.229 0.211 0.194 0.227 (22.7%) Distance 0.123 0.109 0.134 0.177 0.187 0.183 0.175 0.167 0.157 (15.7%) from streams Drainage 0.092 0.073 0.067 0.089 0.124 0.137 0.140 0.139 0.108 (10.8%) Density Geology 0.074 0.054 0.045 0.044 0.062 0.092 0.105 0.111 0.073 (7.32%) Land Cover 0.061 0.044 0.034 0.030 0.031 0.046 0.070 0.083 0.050 (5%) TWI 0.053 0.036 0.027 0.022 0.021 0.023 0.035 0.056 0.034 (3.4%) Rainfall 0.046 0.031 0.022 0.018 0.016 0.015 0.018 0.028 0.024 (2.4%)

- because the measurements from the gauging station The 2010 flood extent was chosen for that purpose, theresulting corresponding matrix table, column an arithmetic factor with value which of 8 it indi has beencates compared,that a row whilefactor an is mucharithmetic more value significant of 1 means than wason Didymoteicho’s taken on 16/2/2010, bridge confirmed Erythropotamos that during indeed the date and time that the ENVISAT/ASAR’s flood image completion of Table 8, the arithmetic mean method lower spatial resolution when compared with SENTI- hasthat been both applied factors to are its equally results significant. and the weights After thefor flooded. Additionally, ENVISAT/ASAR’s imagery has- each factor were calculated (Table 9). ing station went out of order in 2012, the only way To sum up, Table 10 presents synoptically the fac- NEL-1 A/B imagery. Since the aforementioned gaug events was to rely on statements from members of the Departmentto collect information of Civil Protection for the 2017of the and region 2018 of Evrosflood classtors, theand classes the weight of flood that susceptibility was calculated into for which each they fac- (C. Papapostolou, Department of Civil Protection of torwere via classified, the application the rating of AHP that methodologywas assigned (Kazakisfor each the region of Evros, personal communication, 2018). et al. 2015).

3.3.3 Pairwise comparison between the flood 3.3.4 Consistency ratio susceptibility factors and determination In order to check the consistency of the eigenvector of their weights with the use of the arithmetic mean method according to the following formula: The factors were paired with each other and follow- matrix of AHP, the consistency ratio was calculated ing that, each factor was given an arithmetic value = (4) agreement with Table 7, when compared to the other In mathematic formula (4), CR stands for consis- factor,between with 1 which and 8, it according formed the to pair its (Table significance, 8). In the in tency ratio, CI RI

stands for consistency index, and 158 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

Tab. 10 Synoptic table presenting the factors, their flood the weigh for each factor and Xi are the rating values susceptibility classes, the rating that was assigned for each class and the weight for each factor that was assigned through AHP methodology. referred. forThe each resulting pixel according susceptibility to the map factor was to validated which it by is Factor Class Rating Weight calculating, with the aid of geoprocessing routines, >600 1 the proportion of the inundated areas of the April Elevation 150–600 2 0.327 (m) 0–150 3 with its high susceptibility areas. This procedure indi- cated2017 thatand 59%March and 2018 58% flood of the events inundated that intersectedareas of the >15 1 Slope angle 2–5 2 0.227 (°) - 0–2 3 ing2018 map. and 2017 floods respectively coincided spatially Distance >500 1 with the high flood susceptibility zones of the result from 200–500 2 0.157 streams (m) 0–200 3 4. Results

Drainage 2.15–9.68 1 density 9.68–17.21 2 0.108 - 2 (m/m ) 17.21–24.74 3 According to the results that were produced by flood Permeable formations 1 event,extent covermapping, a total the of inundated 6.84 km2 , areaswhile withinthe inundated Erytro Geology Semi – permeable formations 2 0.073 potamos’ drainage basin, regarding the 2010 flood 2 and Impermeable formations 3 areas20.60 ofkm the2 respectively. flood events The that proportions occurred on of April the inun2017- Forests and wetlands 1 and March 2018 cover a total extent of 18.23 km Land Agricultural areas and water bodies 2 0.050 Cover areas and as shallow water in low vegetation areas Artificial surfaces 3 aredated presented areas that in morewere detaildetected in Table in open-land 11. flooded Regarding susceptibility mapping, the resulting −0.22–6.06 1 - TWI 6.06–12.34 2 0.034 tained areas of high, medium, and low susceptibility 12.34–18.62 3 (Figuremap was 5). classified Moreover, into the three application categories, of AHP which method con- 579.34–623.25 1 ology produced the criteria weight for each indicator. Rainfall 623.25–667.18 2 0.024 According to these results elevation was considered (mm) as the most important indicator with the weight value 667.18–711.10 3 of 0.327, followed by the slope angle with the weight value of 0.227. Distance from stream and drainage density are respectively considered as the third and RI depends on the number fourth most important criteria, and their weight val- of factor that are used to perform AHP and in our case, ues are 0.157 and 0.108, respectively. The weights of stands for random index.RI= 1.41 (Saaty 1980), while RI the remaining indicators are below 0.1, which indi- can be calculated by the following equation: cates that they present less important than aforemen- for an 8 by 8 matrix, tioned four indicators. The criteria weight value of = max (5) geology, land cover, TWI and rainfall are 0.073, 0.05, 1 − 0.034 and 0.024, respectively (Tables 8 and 9). Finally, by superimposing the delineated areas of − - the April 2017 and March 2018 inundation mapping In equation (5), λmax is the = maximum 8.41 and n eigenvalue = 8, there- onto the resulting susceptibility map, with the aid of offore the CR comparison = 0.042. According matrix andto Saaty n is the(1980), number if CR ofis lessfac geoprocessing routines, calculations indicated that tors. In the current study, λmax

3.3.5than 0.1,Calculation then the weights’of flood consistencysusceptibilityand is affirmed. the largest part of the aforementioned estimated flood validation of the results Tab. 11 Flood extents of the inundated areas for February 2010, Finally, the data layers for each factor were added April 2017 and March 2018 flood events. together in accordance with the mathematical equa- NDFI based NDFVI based Total tion (6): Flood event inundated area inundated area inundated area (km2) (km2) (km2) = (6) 2010 February 6.49 0.35 6.84 =1 2017 April 17.52 0.71 18.23 � ∗ 2018 March 19.28 1.32 20.60 i is In equation (6), S is the value for each pixel of the final flood susceptibility map of the study area, w Flood susceptibility mapping in Erythropotamos river basin 159

Fig. 5 Flood susceptibility map upon which are superimposed: a) the data layer of the April 2017 inundated areas (both NDFI and NDFVI based), and b) the data layer of the March 2018 inundated areas (both NDFI and NDFVI based).

class, while high and medium susceptibility classes susceptibility of the resulting map (Figure 5). Regard- intersected with 19.73 km2 (95.8%) of the resulting extents coincided with the areas of high and medium2 of the total inundated area in both open-land and in inglow thevegetation, April 2017 10.6 flood, km2 from(58.17%) the total intersected of 18.23 withkm flood extent (Table 12). 5. Discussion and medium susceptibility classes intersected with 17.54the high km flood2 susceptibility class. Additionally, high Correspondingly, from the total of 20.60 km2 of the appeared that the inundated areas of 2010 are consid- March 2018 (96.22%) inundated of area,the resulting 12.22 km flood2 (59.33%) extent. erablyBy comparing smaller thethan flood the inundatedextents of theareas flood of 2018events, and it 2, were included within the high flood susceptibility 2017. The 2010 flood covered an extent of 6.84 km 160 Christos Domakinis, Antonios Mouratidis, Kostas Voudouris, Theodore Astaras, Maria Chara Karypidou

Tab. 12 Area extent and percentage of the part of the March such as TWI (Arabameri et al. 2019; Das 2018; Tang 2018 and April 2017 inundated areas, which intersect with high to medium classes of the susceptibility map. Das 2018; Mahmoud et al. 2018; Kazakis et al. 2015), Inundated area Percentage Flood susceptibility classes drainageet al. 2018), density flow (Arabameriaccumulation et (Vojtekal. 2019; et Souissi al. 2019; et (km2) (%) al. 2019; Vojtek et al. 2019; Das 2018; Mahmoud 2018 March flood et al. 2018; Seejata et al. 2018) and rainfall (Souis- High 12.22 59.33 si et al. 2019; Mahmoud et al. 2018; Seejata et al. High and medium 19.73 95.80 2018; Tang et al. 2018; Kazakis et al. 2015) appear in most works. On the contrary, curvature (Arabameri 2017 April flood et al. 2019; Das 2018), NDVI (Normalized Difference High 10.60 58.17 High and medium 17.54 96.22 (Curve Numbers) (Vojtek et al. 2019; Mahmoud et al. Vegetation Index) (Arabameri et al. 2019), runoff/CN 2019) and groundwater depth (Souissi et al. 2019) 2018), SPI (Stream Power Index) (Arabameri et al. in comparison with the inundated areas of the 2017 Moreover, the number of the factors that are used in appear rarely on flood susceptibility mapping works. km2 and 20.60 km2 respectively; however, as it was et al. 2018; Rahmati et al. 2015) with the most com- andmentioned 2018 flood earlier, events, validation which of covered these results areas ofwas 18.23 not monflood numbersusceptibility of factors mapping ranging varies from greatly seven (Mahmoud to nine. feasible due to unfavourable weather conditions. At This paper tries to employ the most common and - first place, these differences in flood extents might- included,important since factors its datathat layercan be was used indirectly in flood employed suscep thermore,indicate that it is theworth 2010 mentioning flood had that probably according a lowerto the tibility mapping. Thus, flow accumulation was not gaugingreturn period station than on Didymoteicho’sthe other two flood bridge, events. the 2010 Fur 3/s, utilizedby the TWIbecause, factor. according Likewise, to Miliaresis Stream Power(2006), Indexthese while, on the other hand, such gauges were not avail- are,(SPI) like and TWI, Sediment indices Transport of soil erosion Index with(STI) very were simi not- flood reached a peak discharge of 1,255.05 m similar results. Curvature is considered to have a inundationable for the mapping2017 and with 2018 the floods. use of Remote Sensing lar mathematical expressions and thus they produce andRegarding especially theSAR, uncertainties it has to be mentioned that exist that in floodsuch and therefore it was not included in the factors that techniques and methodologies suffer mostly from minor impact on the occurrence of a flood (Das 2018)-

- were utilized in the assessment of flood susceptibili rentlyspeckle there and fromis no undermethodology or over-detection that can overcome of flood ty. Additionally, Topographic position index (TPI) and- extents especially in urban and vegetated areas. Cur lyTopographic appear to be roughness more important index (TRI) than theare factorsrarely usedwith mapping is still considered appropriate for validation whichin flood they susceptibility are compared, mapping so they and were even too more omitted. rare these difficulties entirely. However, flood inundation- Finally, the curve numbers (CN) data layer was not ping. (Giustarini et al. 2015a; Giustarini et al. 2015b; feasible to be compiled since there were no available Schumannin cases of floodet al. 2015)susceptibility and flood hazard map maps depicting the spatial distribution of the hydrological soil groups. wide variety of works that utilize the AHP methodol- The determination of the importance between the ogyConcerning for its implementation. flood susceptibility The main mapping, differences there and is a similarities of these works with the present research susceptibility mapping can be achieved by applying focus on the following points: 1) the factors that are variousfactors thatprocedures. are used Many in AHP researches when conducting use sensitivity flood employed by the research, 2) the determination of the analysis in order to overcome the subjectivity of AHP importance between the factors that are used by the (Souissi et al. 2019; Mahmoud et al. 2018; Tang et al. 2018), while weight linear combination is also a pop- factors of the study area and 4) the validation of the ular approach (Vojtek et al. 2019; Kazakis et al. 2015). AHP procedure, 3) the dominant flood susceptibility There is also a great number of works that employ The number and type of factors that are used in resulting flood susceptibility map. (Das 2018; Seejata et al. 2018; Rahmati et al. 2015). susceptibility, with the use of AHP, depend heavily on However,expert opinion the current in dealing trend with involves the hierarchy the use of of factors train- dataorder availability to determine (Xiao the et spatial al. 2018; distribution Zhao et al. of 2018). flood ing algorithms over a part of the elements that will be However, it can be observed that certain factors such used for the validation of the resulting susceptibility as elevation, slope angle, land cover, lithology and map, which usually involves a database of historical distance from streams are used in the vast majority of works due to being easily produced via Digital The present paper is introducing the use of the results Elevation Models (DEMs), geological maps and the points where floods occurred (Arabameri et al. 2019).

of SAR-based inundation mapping, of a confirmed various Corine Land Cover data layers. Other factors via gauges flood event, in the determination of the Flood susceptibility mapping in Erythropotamos river basin 161

- data layer of rainfall had to be downscaled in order to reach the spatial analysis of the EU-DEM data lay- importance of the factors that affect flood susceptibil er. However, the aforementioned spatial variations of ity in AHP. Specifically, the aforementioned hierarchy- edwas with determined each factor’s according highest to susceptibility the part of the class, extent thus of effect on the resulting susceptibility map since they overcomingthe inundated the area subjectivity of the 2010 of AHP. flood that intersect these data layers do not appear to have a significant- When it comes to the determination of the most thewere study ranked area among involved the least a considerably important flood large suscep drain- results of AHP in various researches indicate that agetibility basin, factors which, (Table in terms 10). Additionally,of size, allowed the the extent use of thereimportant is no factorfactor into floodhave clearsusceptibility dominance mapping, over other the small scale data, which are widely used in likewise factors. Many papers indicate slope angle as the most - important factor (Arabameri et al. 2019; Vojtek et al. 2015).cases according to the existing literature regard elevation (Souissi et al. 2019) or even rainfall (Seejata ing AHP flood susceptibility mapping (Kazakis et al. et2019), al. 2018) while have flow accumulationbeen determined (Kazakis as the et dominant al. 2015), 6. Conclusions flood susceptibility factors in certain regions and by The present research paper introduced the idea to susceptibilitycertain methodologies. factor, but Likewise it can be the observed present researchthat the resultsdetermined depend elevation heavily as on the both most the important procedure flood that SAR imagery in order to determine the importance is employed in the determination of the hierarchy of use the extent of a flood that has been captured by factors and the conditions that lie within the studied with the subjectivity that involves the determination region. ofbetween the hierarchy flood susceptibility of factors in AHP.factors The and larger thus the dealing part of the inundated area that intersects with the factor’s susceptibility mapping with the use of AHP, the vast high susceptibility zone, the more important the fac- majorityRegarding of works the validationinvolves the of compilationthe results of aflood his- tor is considered over the others. torical database that includes, in the form of points, According to the results of the applied methodolo- sites where according to eye-witnesses or Remote susceptibility factor in the catchment of Erythropota- 2019; Souissi et al. 2019; Vojtek et al. 2019; Mahmoud mos.gy, elevation However, was this found has to to be be the further most dominantascertained flood by etSensing al. 2018). techniques The present floods work occurred handles (Arabameri this matter et byal. utilizing the results of SAR-based inundation map- and by taking advantage of the current and prospec- considering more future flood events in the same area- over, the resulting susceptibility map appeared to be wereping fornot specific involved flood in the occurrences. determination To ofthis the end, impor the- intive consistency availability with of SENTINEL-1 the, April 2017 imagery and Marchdata. More 2018 tanceinundated between areas the of factors the 2017 in AHP,and 2018were floods,used in whichorder - - bilityflood classextents, of the since resulting the aforementioned map. inundated tibilityto provide zones the of proportions the resulting of map. their Furthermore, respective flood the areasIt appears coincided that mostly the suggested with the methodology, high flood suscepti regard- extents that intersected with the high flood suscep- - ed that the areas of high susceptibility are located on ceptibility factors, via the results of SAR-based inun- resulting flood susceptibility map (Figure 5), indicat dationing the mapping,determination in AHP of producedthe hierarchy some of interesting flood sus results. Nevertheless, more thorough testing of this the basineastern mouth. part of the study area, specifically in the proposed methodology is required, while it also firstMoreover, half of main the streamscores thatand wereappear achieved increased by thetoward val- remains to be seen if its application on other drainage idation of the susceptibility map were quite high. In basins shall indicate each time another factor as more

- withparticular, the high approximately susceptibility 60% zones of theof the inundated map. The areas per- tibilityprevalent are in unique flood susceptibility,for each catchment. thus maintaining the from the April 2017 and March 2018 floods intersect argument that the conditions that affect flood suscep the aforementioned inundated areas intersect with thecentage map’s rises high to to approximately moderate susceptibility 96% in the zones. case that References Finally, it is worth mentioning that the source data - Arabameri, A., Rezaei, K., Cerdà, A., Conoscenti, C., bility mapping, in terms of scale and spatial resolu- Kalantari, Z. (2019): A comparison of statistical methods tion,layers were of the quite factors consistent that were since used the in majority flood suscepti of them were derived from EU-DEM that has a spatial resolu- susceptibility in Northern Iran. Science of the Total tion of 25 m. The data layers of geology and Corine Environmentand multi-criteria 660, decision443–458, making https://doi.org/10.1016 to map flood hazard /j.scitotenv.2019.01.021.

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